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"13298af00e2443a3a1b311078bd175cd": {
"model_module": "@jupyter-widgets/controls",
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},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"
"
]
},
{
"cell_type": "markdown",
"source": [
"# Introduction\n",
"This tutorial dives into the intersection of financial data and AI, demonstrating how you can harness the power of Python libraries like yfinance and NeuralForecasting to transform raw stock market data into actionable forecasts.\n",
"\n",
"What We'll Cover:\n",
"\n",
"* We'll start by utilizing yfinance, a powerful tool for downloading historical stock prices and fundamental financial data directly from Yahoo Finance.\n",
"* You'll learn how to clean, transform, and structure your financial data to make it AI-ready, setting the stage for model training.\n",
"* We'll introduce you to the intuitive NeuralForecasting library, a specialized toolkit designed to streamline the creation of time series forecasting models using neural networks.\n",
"* We'll guide you through building and training a neural network model to predict stock prices, showcasing the synergy between financial domain knowledge and AI techniques.\n"
],
"metadata": {
"id": "rALNi6cWmwJQ"
}
},
{
"cell_type": "markdown",
"source": [
"# Prerequisites\n",
"But before we start, we need to install the prerequisites."
],
"metadata": {
"id": "7Zj640MWnGrn"
}
},
{
"cell_type": "code",
"source": [
"!pip install yfinance neuralforecast"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "uALPxJB9jK5K",
"outputId": "af830814-c5fe-41bd-e2f9-ee02424da237",
"collapsed": true
},
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Requirement already satisfied: yfinance in /usr/local/lib/python3.10/dist-packages (0.2.50)\n",
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]
}
]
},
{
"cell_type": "markdown",
"source": [
"# Financial data with Yahoo Finance"
],
"metadata": {
"id": "h6LqZpGunW7B"
}
},
{
"cell_type": "code",
"source": [
"import yfinance as yf\n",
"data = yf.download(\"AAPL\", start=\"2015-01-01\", end=\"2023-06-30\")\n",
"data"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 504
},
"id": "qkENtVnirRSF",
"outputId": "c42286e7-51d1-4b6e-c043-0ae3cb2221dd",
"collapsed": true
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"\r[*********************100%***********************] 1 of 1 completed\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Price Adj Close Close High Low Open \\\n",
"Ticker AAPL AAPL AAPL AAPL AAPL \n",
"Date \n",
"2015-01-02 24.347172 27.332500 27.860001 26.837500 27.847500 \n",
"2015-01-05 23.661272 26.562500 27.162500 26.352501 27.072500 \n",
"2015-01-06 23.663498 26.565001 26.857500 26.157499 26.635000 \n",
"2015-01-07 23.995317 26.937500 27.049999 26.674999 26.799999 \n",
"2015-01-08 24.917269 27.972500 28.037500 27.174999 27.307501 \n",
"... ... ... ... ... ... \n",
"2023-06-23 185.275299 186.679993 187.559998 185.009995 185.550003 \n",
"2023-06-26 183.875916 185.270004 188.050003 185.229996 186.830002 \n",
"2023-06-27 186.644897 188.059998 188.389999 185.669998 185.889999 \n",
"2023-06-28 187.825958 189.250000 189.899994 187.600006 187.929993 \n",
"2023-06-29 188.163391 189.589996 190.070007 188.940002 189.080002 \n",
"\n",
"Price Volume \n",
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"metadata": {},
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]
},
{
"cell_type": "code",
"source": [
"import yfinance as yf\n",
"aapl = yf.Ticker('AAPL')\n",
"aapl.balance_sheet"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 424
},
"id": "3I3X18avsD2_",
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}
},
"metadata": {},
"execution_count": 23
}
]
},
{
"cell_type": "code",
"source": [
"import yfinance as yf\n",
"import pandas as pd\n",
"\n",
"\n",
"# Fetch data for multiple tickers\n",
"tickers = [\"AAPL\", \"GOOG\", \"MSFT\"]\n",
"data = yf.download(tickers, start=\"2015-01-01\", end=\"2023-06-30\")\n",
"\n",
"# Reshape the data\n",
"df = data['Close'] # No need to unstack here\n",
"\n",
"# Convert the Series to a DataFrame if it's not already (optional but recommended)\n",
"if isinstance(df, pd.Series):\n",
" df = df.to_frame()\n",
"\n",
"# Melt the dataframe to long format\n",
"hist = df.melt(ignore_index=False, var_name='Ticker', value_name='Close')\n",
"hist.reset_index(inplace=True)\n",
"\n",
"\n",
"print(hist)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "zmjkyylFnVvv",
"outputId": "3beb0ba9-f42b-4204-da2f-350d5910612c",
"collapsed": true
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"[*********************100%***********************] 3 of 3 completed"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" Date Ticker Close\n",
"0 2015-01-02 AAPL 27.332500\n",
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"4 2015-01-08 AAPL 27.972500\n",
"... ... ... ...\n",
"6406 2023-06-23 MSFT 335.019989\n",
"6407 2023-06-26 MSFT 328.600006\n",
"6408 2023-06-27 MSFT 334.570007\n",
"6409 2023-06-28 MSFT 335.850006\n",
"6410 2023-06-29 MSFT 335.049988\n",
"\n",
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},
{
"output_type": "stream",
"name": "stderr",
"text": [
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]
}
]
},
{
"cell_type": "markdown",
"source": [
"# Preparing the time-series data for NHITS training\n"
],
"metadata": {
"id": "Nok1wRTondPT"
}
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "UWUotxtamp9g"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"\n",
"from neuralforecast import NeuralForecast\n",
"from neuralforecast.models import NHITS"
],
"metadata": {
"id": "40IRPX9tutr-",
"colab": {
"base_uri": "https://localhost:8080/"
},
"collapsed": true,
"outputId": "f4c02c69-7680-458e-de40-664c4f96f0c7"
},
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/dask/dataframe/__init__.py:42: FutureWarning: \n",
"Dask dataframe query planning is disabled because dask-expr is not installed.\n",
"\n",
"You can install it with `pip install dask[dataframe]` or `conda install dask`.\n",
"This will raise in a future version.\n",
"\n",
" warnings.warn(msg, FutureWarning)\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"hist.rename(columns={'Date': 'ds', 'Ticker': 'unique_id', 'Close':'y'}, inplace=True)\n",
"hist.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
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"id": "6HY4bw_f1Hjj",
"outputId": "3db8df78-9ca8-4745-d280-3804756529cb",
"collapsed": true
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
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"1 2015-01-05 AAPL 26.562500\n",
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},
"metadata": {},
"execution_count": 26
}
]
},
{
"cell_type": "markdown",
"source": [
"# NHITS Training & Prediction"
],
"metadata": {
"id": "DqIhi59UH74m"
}
},
{
"cell_type": "code",
"source": [
"horizon = 12\n",
"\n",
"# Try different hyperparmeters to improve accuracy.\n",
"models = [NHITS(h=horizon, # Forecast horizon\n",
" input_size=2 * horizon, # Length of input sequence\n",
" max_steps=1000, # Number of steps to train\n",
" n_freq_downsample=[2, 1, 1], # Downsampling factors for each stack output\n",
" mlp_units = 3 * [[1024, 1024]]) # Number of units in each block.\n",
" ]\n",
"nf = NeuralForecast(models=models, freq='M')\n",
"nf.fit(df=hist, val_size=horizon)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 406,
"referenced_widgets": [
"c9d9026041c74dfe837712e480673f5a",
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" | Name | Type | Params | Mode \n",
"-------------------------------------------------------\n",
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"source": [
"Y_hat_insample = nf.predict_insample(step_size=horizon)"
],
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"text": [
"/usr/local/lib/python3.10/dist-packages/utilsforecast/processing.py:384: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
" freq = pd.tseries.frequencies.to_offset(freq)\n",
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" unique_id ds cutoff NHITS y\n",
"0 AAPL 2015-01-05 2014-12-31 -0.121156 26.562500\n",
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"summary": "{\n \"name\": \"Y_hat_insample\",\n \"rows\": 6408,\n \"fields\": [\n {\n \"column\": \"unique_id\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"AAPL\",\n \"GOOG\",\n \"MSFT\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ds\",\n \"properties\": {\n \"dtype\": \"date\",\n \"min\": \"2015-01-05 00:00:00\",\n \"max\": \"2023-06-29 00:00:00\",\n \"num_unique_values\": 2136,\n \"samples\": [\n \"2020-12-31 00:00:00\",\n \"2015-12-07 00:00:00\",\n \"2016-09-29 00:00:00\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"cutoff\",\n \"properties\": {\n \"dtype\": \"date\",\n \"min\": \"2014-12-31 00:00:00\",\n \"max\": \"2023-05-31 00:00:00\",\n \"num_unique_values\": 102,\n \"samples\": [\n \"2017-06-30 00:00:00\",\n \"2020-07-31 00:00:00\",\n \"2020-02-29 00:00:00\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"NHITS\",\n \"properties\": {\n \"dtype\": \"float32\",\n \"num_unique_values\": 6384,\n \"samples\": [\n 52.217674255371094,\n 101.85413360595703,\n 205.29917907714844\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"y\",\n \"properties\": {\n \"dtype\": \"float32\",\n \"num_unique_values\": 5997,\n \"samples\": [\n 31.252500534057617,\n 236.9600067138672,\n 47.147499084472656\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 29
}
]
},
{
"cell_type": "markdown",
"source": [
"# Darstellungs-Versuche mit Matplotlib und verschiedenen Skalierungen"
],
"metadata": {
"id": "Slu11frPHO4F"
}
},
{
"cell_type": "code",
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"plt.figure(figsize=(10, 5))\n",
"\n",
"# Iterate through each unique stock using the index\n",
"for unique_id in Y_hat_insample.index.unique():\n",
" stock_data = Y_hat_insample.loc[unique_id] # Index-based selection\n",
"\n",
" # Plot true values and forecast for the current stock\n",
" plt.plot(stock_data['ds'], stock_data['y'], label=f'True ({unique_id})')\n",
" plt.plot(stock_data['ds'], stock_data['NHITS'], label=f'Forecast ({unique_id})', linestyle='--') # Dashed line for forecast\n",
"\n",
" # Mark the train-test split for this stock (if applicable)\n",
" # Assuming the split point is the same for all stocks\n",
" if len(stock_data) > 12:\n",
" plt.axvline(stock_data['ds'].iloc[-12], color='black', linestyle='dotted', alpha=0.7) # Dotted line for split\n",
"\n",
"# General plot formatting\n",
"plt.xlabel('Timestamp [t]')\n",
"plt.ylabel('Stock value')\n",
"plt.title('True vs. Forecast Values per Stock')\n",
"plt.grid(alpha=0.4) # Less obtrusive grid\n",
"\n",
"# Adjust legend to fit better\n",
"plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "FnTz3mI-39z2",
"outputId": "450620b5-efee-4359-c1b9-845e601b8800",
"collapsed": true
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
":27: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations.\n",
" plt.tight_layout()\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"\n",
"# 1) If Y_hat_insample has a MultiIndex, flatten it for easier filtering:\n",
"if isinstance(Y_hat_insample.index, pd.MultiIndex):\n",
" # Reset index -> now \"unique_id\" and \"ds\" become columns\n",
" Y_hat_insample = Y_hat_insample.reset_index()\n",
"\n",
"# 2) Ensure we do have columns named exactly [\"unique_id\", \"ds\", \"y\", \"NHITS\", ...]\n",
"# Adjust these next lines if your columns differ.\n",
"required_cols = {'unique_id', 'ds', 'y', 'NHITS'}\n",
"missing = required_cols - set(Y_hat_insample.columns)\n",
"if missing:\n",
" raise ValueError(f\"Missing columns {missing} in Y_hat_insample!\")\n",
"\n",
"# 3) For convenience, find all unique stocks:\n",
"unique_ids = Y_hat_insample['unique_id'].unique()\n",
"\n",
"plt.figure(figsize=(100, 50))\n",
"\n",
"for uid in unique_ids:\n",
" # Filter rows for this stock\n",
" stock_data = Y_hat_insample.loc[Y_hat_insample['unique_id'] == uid].copy()\n",
"\n",
" # Sort by ds so the lines plot in correct temporal order\n",
" stock_data.sort_values('ds', inplace=True)\n",
"\n",
" # Plot the true values\n",
" plt.plot(stock_data['ds'], stock_data['y'], label=f'True ({uid})')\n",
"\n",
" # Plot the forecast\n",
" plt.plot(stock_data['ds'], stock_data['NHITS'],\n",
" label=f'Forecast ({uid})', linestyle='--')\n",
"\n",
" # Mark the train-test split by the last 12 points, if you want:\n",
" if len(stock_data) > 12:\n",
" test_start = stock_data['ds'].iloc[-12] # The first test date\n",
" plt.axvline(test_start, color='black', linestyle='dotted', alpha=0.7)\n",
"\n",
"plt.xlabel('Timestamp')\n",
"plt.ylabel('Stock Value')\n",
"plt.title('True vs. Forecasted Values per Stock')\n",
"plt.grid(alpha=0.4)\n",
"\n",
"# This will show a combined legend of all lines off to the right\n",
"plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
"plt.tight_layout()\n",
"plt.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 615
},
"id": "JpispdhsB7Wh",
"outputId": "02d2e4f9-3be3-49fd-fa0a-7e15509620cf",
"collapsed": true
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"# If you have many stocks, plotting them all in one figure can exceed the pixel limit.\n",
"# This approach creates subplots, one per stock, so each subplot remains a reasonable size.\n",
"\n",
"unique_ids = Y_hat_insample.index.unique()\n",
"n_stocks = len(unique_ids)\n",
"\n",
"# If you still have too many stocks, consider slicing: unique_ids = unique_ids[:10]\n",
"# or you can group them in bigger subplots (e.g. 2x2 or 3x3) instead of one row.\n",
"\n",
"fig, axes = plt.subplots(n_stocks, 1, figsize=(10, 4 * n_stocks), sharex=True)\n",
"\n",
"# If there's only 1 stock, axes won't be a list; make it a list for uniformity\n",
"if n_stocks == 1:\n",
" axes = [axes]\n",
"\n",
"for ax, uid in zip(axes, unique_ids):\n",
" stock_data = Y_hat_insample.loc[uid]\n",
" ax.plot(stock_data['ds'], stock_data['y'], label=f'True ({uid})')\n",
" ax.plot(stock_data['ds'], stock_data['NHITS'], label=f'Forecast ({uid})', linestyle='--')\n",
" # Train-test split marker\n",
" if len(stock_data) > 12:\n",
" ax.axvline(stock_data['ds'].iloc[-12], color='black', linestyle='dotted', alpha=0.7)\n",
" ax.set_ylabel('Stock value')\n",
" ax.grid(alpha=0.4)\n",
" ax.legend(loc='upper left')\n",
"\n",
"axes[-1].set_xlabel('Timestamp [t]')\n",
"fig.suptitle('True vs. Forecast per Stock', y=0.97)\n",
"fig.tight_layout()\n",
"\n",
"plt.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "yVi2kBxo_Dkd",
"outputId": "86b168b7-645c-4d5a-a787-be2d1133f1ac",
"collapsed": true
},
"execution_count": null,
"outputs": [
{
"output_type": "error",
"ename": "KeyboardInterrupt",
"evalue": "",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m_set_lim\u001b[0;34m(self, v0, v1, emit, auto)\u001b[0m\n\u001b[1;32m 1243\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_view_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1244\u001b[0m \u001b[0;31m# Mark viewlims as no longer stale without triggering an autoscale.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1245\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0max\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_shared_axes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1246\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stale_viewlims\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1247\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mauto\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m_get_shared_axes\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 735\u001b[0m \u001b[0;34m\"\"\"Return Grouper of shared axes for current axis.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 736\u001b[0m return self.axes._shared_axes[\n\u001b[0;32m--> 737\u001b[0;31m self._get_axis_name()].get_siblings(self.axes)\n\u001b[0m\u001b[1;32m 738\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 739\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_get_shared_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/cbook.py\u001b[0m in \u001b[0;36mget_siblings\u001b[0;34m(self, a)\u001b[0m\n\u001b[1;32m 910\u001b[0m \u001b[0;34m\"\"\"Return all of the items joined with *a*, including itself.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 911\u001b[0m \u001b[0msiblings\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mapping\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 912\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msiblings\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 913\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 914\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/cbook.py\u001b[0m in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 910\u001b[0m \u001b[0;34m\"\"\"Return all of the items joined with *a*, including itself.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 911\u001b[0m \u001b[0msiblings\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mapping\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 912\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msiblings\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 913\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 914\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.10/_weakrefset.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0m_IterationGuard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 65\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mitemref\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m \u001b[0mitem\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mitemref\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 67\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[0;31m# Caveat: the iterator will keep a strong reference to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Error in callback (for post_execute):\n"
]
},
{
"output_type": "error",
"ename": "ValueError",
"evalue": "Image size of 120x307584 pixels is too large. It must be less than 2^16 in each direction.",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36m_draw_all_if_interactive\u001b[0;34m()\u001b[0m\n\u001b[1;32m 195\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_draw_all_if_interactive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 196\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_interactive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 197\u001b[0;31m \u001b[0mdraw_all\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 198\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 199\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, width, height, dpi)\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwidth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_renderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_RendererAgg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mheight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 71\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filter_renderers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: Image size of 120x307584 pixels is too large. It must be less than 2^16 in each direction."
]
},
{
"output_type": "error",
"ename": "ValueError",
"evalue": "Image size of 120x307584 pixels is too large. It must be less than 2^16 in each direction.",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[1;32m 149\u001b[0m \u001b[0mFigureCanvasBase\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 151\u001b[0;31m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 152\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 153\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m 2148\u001b[0m \u001b[0;31m# CL works. \"tight\" also needs a draw to get the right\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2149\u001b[0m \u001b[0;31m# locations:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2150\u001b[0;31m renderer = _get_renderer(\n\u001b[0m\u001b[1;32m 2151\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2152\u001b[0m functools.partial(\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36m_get_renderer\u001b[0;34m(figure, print_method)\u001b[0m\n\u001b[1;32m 1640\u001b[0m figure.canvas._switch_canvas_and_return_print_method(fmt))\n\u001b[1;32m 1641\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1642\u001b[0;31m \u001b[0mprint_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1643\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mDone\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1644\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36m\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 2041\u001b[0m \"bbox_inches_restore\"}\n\u001b[1;32m 2042\u001b[0m \u001b[0mskip\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptional_kws\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minspect\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmeth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2043\u001b[0;31m print_method = functools.wraps(meth)(lambda *args, **kwargs: meth(\n\u001b[0m\u001b[1;32m 2044\u001b[0m *args, **{k: v for k, v in kwargs.items() if k not in skip}))\n\u001b[1;32m 2045\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# Let third-parties do as they see fit.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, metadata, pil_kwargs)\u001b[0m\n\u001b[1;32m 495\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mincluding\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdefault\u001b[0m \u001b[0;34m'Software'\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 496\u001b[0m \"\"\"\n\u001b[0;32m--> 497\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_print_pil\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"png\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpil_kwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 498\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 499\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mprint_to_buffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36m_print_pil\u001b[0;34m(self, filename_or_obj, fmt, pil_kwargs, metadata)\u001b[0m\n\u001b[1;32m 443\u001b[0m *pil_kwargs* and *metadata* are forwarded).\n\u001b[1;32m 444\u001b[0m \"\"\"\n\u001b[0;32m--> 445\u001b[0;31m \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 446\u001b[0m mpl.image.imsave(\n\u001b[1;32m 447\u001b[0m \u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuffer_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfmt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morigin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"upper\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 381\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 382\u001b[0m \u001b[0;31m# docstring inherited\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 383\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 384\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 385\u001b[0m \u001b[0;31m# Acquire a lock on the shared font cache.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mget_renderer\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 396\u001b[0m \u001b[0mreuse_renderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lastKey\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 397\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mreuse_renderer\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 398\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRendererAgg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 399\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lastKey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 400\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, width, height, dpi)\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwidth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_renderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_RendererAgg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mheight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 71\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filter_renderers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: Image size of 120x307584 pixels is too large. It must be less than 2^16 in each direction."
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
]
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"# Autoformer Training & Prediction\n"
],
"metadata": {
"id": "FSd8l7hFqRKB"
}
},
{
"cell_type": "code",
"source": [
"from neuralforecast.models import Autoformer\n",
"\n",
"\n",
"# Try different hyperparmeters to improve accuracy.\n",
"models = [ Autoformer(h=horizon,\n",
" input_size=horizon,\n",
" max_steps=1000,\n",
" val_check_steps=100,\n",
" early_stop_patience_steps=3)\n",
" ]\n",
"\n",
"nf = NeuralForecast(models=models, freq='M')\n",
"nf.fit(df=hist, val_size=horizon)\n",
"Y_hat_insample = nf.predict_insample(step_size=horizon)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 478,
"referenced_widgets": [
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"INFO:lightning_fabric.utilities.seed:Seed set to 1\n",
"INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
"INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
"INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
"INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
"INFO:pytorch_lightning.callbacks.model_summary:\n",
" | Name | Type | Params | Mode \n",
"--------------------------------------------------------\n",
"0 | loss | MAE | 0 | train\n",
"1 | padder_train | ConstantPad1d | 0 | train\n",
"2 | scaler | TemporalNorm | 0 | train\n",
"3 | decomp | SeriesDecomp | 0 | train\n",
"4 | enc_embedding | DataEmbedding | 384 | train\n",
"5 | dec_embedding | DataEmbedding | 384 | train\n",
"6 | encoder | Encoder | 148 K | train\n",
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"--------------------------------------------------------\n",
"290 K Trainable params\n",
"0 Non-trainable params\n",
"290 K Total params\n",
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},
{
"cell_type": "markdown",
"source": [
"# Visualization attempts"
],
"metadata": {
"id": "gHa_BfCCIe0v"
}
},
{
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"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"plt.figure(figsize=(10, 5))\n",
"\n",
"# Iterate through each unique stock using the index\n",
"for unique_id in Y_hat_insample.index.unique():\n",
" stock_data = Y_hat_insample.loc[unique_id] # Index-based selection\n",
"\n",
" # Plot true values and forecast for the current stock\n",
" plt.plot(stock_data['ds'], stock_data['y'], label=f'True ({unique_id})')\n",
" plt.plot(stock_data['ds'], stock_data['Autoformer'], label=f'Forecast ({unique_id})', linestyle='--') # Dashed line for forecast\n",
"\n",
" # Mark the train-test split for this stock (if applicable)\n",
" # Assuming the split point is the same for all stocks\n",
" if len(stock_data) > 12:\n",
" plt.axvline(stock_data['ds'].iloc[-12], color='black', linestyle='dotted', alpha=0.7) # Dotted line for split\n",
"\n",
"# General plot formatting\n",
"plt.xlabel('Timestamp [t]')\n",
"plt.ylabel('Stock value')\n",
"plt.title('True vs. Forecast Values per Stock with Autoformer')\n",
"plt.grid(alpha=0.4) # Less obtrusive grid\n",
"\n",
"# Adjust legend to fit better\n",
"plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
],
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"height": 1000,
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"outputs": [
{
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"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/utilsforecast/processing.py:384: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
" freq = pd.tseries.frequencies.to_offset(freq)\n",
"INFO:pytorch_lightning.utilities.rank_zero:Trainer already configured with model summary callbacks: []. Skipping setting a default `ModelSummary` callback.\n",
"INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
"INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
"INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
"INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
]
},
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"text": [
":28: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations.\n",
" plt.tight_layout()\n"
]
},
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""
],
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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# ↓ Lower the DPI to drastically reduce the pixel dimension\n",
"# so the figure remains within Matplotlib's allowed range.\n",
"plt.figure(figsize=(10, 5), dpi=50)\n",
"\n",
"# Iterate through each unique stock using the index\n",
"for unique_id in Y_hat_insample.index.unique():\n",
" stock_data = Y_hat_insample.loc[unique_id]\n",
"\n",
" # Plot true values and forecast for the current stock\n",
" plt.plot(\n",
" stock_data['ds'],\n",
" stock_data['y'],\n",
" label=f'True ({unique_id})'\n",
" )\n",
" plt.plot(\n",
" stock_data['ds'],\n",
" stock_data['Autoformer'],\n",
" label=f'Forecast ({unique_id})',\n",
" linestyle='--'\n",
" )\n",
"\n",
" # Optional: Mark the train-test split for this stock\n",
" if len(stock_data) > 12:\n",
" plt.axvline(\n",
" x=stock_data['ds'].iloc[-12],\n",
" color='black',\n",
" linestyle='dotted',\n",
" alpha=0.7\n",
" )\n",
"\n",
"# General plot formatting\n",
"plt.xlabel('Timestamp [t]')\n",
"plt.ylabel('Stock value')\n",
"plt.title('True vs. Forecast Values per Stock with Autoformer')\n",
"plt.grid(alpha=0.4)\n",
"\n",
"# Adjust legend to fit better\n",
"plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 595,
"referenced_widgets": [
"64dba13e67a040c899d1abae75a3d1fd",
"adf11709c9384d6da3c11b1b6e7cadaa",
"a0ac0e838233474c99184e10ee7fda82",
"3ce81d64b2e84235b57f65237b41bd30",
"d17435352a634e1cbe7f722fb86b13e7",
"91e991a684c14800a7689b0d3ce0612f",
"1c1c1be332b74c44993f7c0c1a2d7d51",
"97e32d6b7ef6471284c192769d3e5ec8",
"d0bcac2569b94e158540bf4a16daaf78",
"d1ba7c7a79d24fa9a9359b8fa818b9c8",
"5326493209354dc2a9f3128ab62e5f67"
]
},
"id": "qpVZfaSfHsfD",
"outputId": "e28016b8-13e7-4d25-b6aa-1f4d4883d79f",
"collapsed": true
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/utilsforecast/processing.py:384: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
" freq = pd.tseries.frequencies.to_offset(freq)\n",
"INFO:pytorch_lightning.utilities.rank_zero:Trainer already configured with model summary callbacks: []. Skipping setting a default `ModelSummary` callback.\n",
"INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
"INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
"INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
"INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Predicting: | | 0/? [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "64dba13e67a040c899d1abae75a3d1fd"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
":44: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations.\n",
" plt.tight_layout()\n"
]
},
{
"output_type": "error",
"ename": "ValueError",
"evalue": "Image size of 527x127830 pixels is too large. It must be less than 2^16 in each direction.",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m 339\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 340\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 341\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 342\u001b[0m \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[1;32m 149\u001b[0m \u001b[0mFigureCanvasBase\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 151\u001b[0;31m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 152\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 153\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m 2185\u001b[0m \u001b[0;31m# force the figure dpi to 72), so we need to set it again here.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2186\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setattr_cm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2187\u001b[0;31m result = print_method(\n\u001b[0m\u001b[1;32m 2188\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2189\u001b[0m \u001b[0mfacecolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfacecolor\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36m\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 2041\u001b[0m \"bbox_inches_restore\"}\n\u001b[1;32m 2042\u001b[0m \u001b[0mskip\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptional_kws\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minspect\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmeth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2043\u001b[0;31m print_method = functools.wraps(meth)(lambda *args, **kwargs: meth(\n\u001b[0m\u001b[1;32m 2044\u001b[0m *args, **{k: v for k, v in kwargs.items() if k not in skip}))\n\u001b[1;32m 2045\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# Let third-parties do as they see fit.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, metadata, pil_kwargs)\u001b[0m\n\u001b[1;32m 495\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mincluding\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdefault\u001b[0m \u001b[0;34m'Software'\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 496\u001b[0m \"\"\"\n\u001b[0;32m--> 497\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_print_pil\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"png\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpil_kwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 498\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 499\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mprint_to_buffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36m_print_pil\u001b[0;34m(self, filename_or_obj, fmt, pil_kwargs, metadata)\u001b[0m\n\u001b[1;32m 443\u001b[0m *pil_kwargs* and *metadata* are forwarded).\n\u001b[1;32m 444\u001b[0m \"\"\"\n\u001b[0;32m--> 445\u001b[0;31m \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 446\u001b[0m mpl.image.imsave(\n\u001b[1;32m 447\u001b[0m \u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuffer_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfmt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morigin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"upper\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 381\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 382\u001b[0m \u001b[0;31m# docstring inherited\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 383\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 384\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 385\u001b[0m \u001b[0;31m# Acquire a lock on the shared font cache.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mget_renderer\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 396\u001b[0m \u001b[0mreuse_renderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lastKey\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 397\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mreuse_renderer\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 398\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRendererAgg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 399\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lastKey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 400\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, width, height, dpi)\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwidth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_renderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_RendererAgg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mheight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 71\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filter_renderers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: Image size of 527x127830 pixels is too large. It must be less than 2^16 in each direction."
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
]
},
"metadata": {}
}
]
},
{
"source": [
"\n",
"# Change index to column so that the plotting functions work\n",
"Y_hat_insample = Y_hat_insample.reset_index()\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# ↓ Lower the DPI to drastically reduce the pixel dimension\n",
"# so the figure remains within Matplotlib's allowed range.\n",
"plt.figure(figsize=(10, 5), dpi=50)\n",
"\n",
"# Limit the number of unique ids to plot.\n",
"unique_ids = Y_hat_insample['unique_id'].unique()[:3]\n",
"\n",
"# Iterate through each unique stock\n",
"for unique_id in unique_ids:\n",
" stock_data = Y_hat_insample.loc[Y_hat_insample['unique_id'] == unique_id]\n",
"\n",
" # Plot true values and forecast for the current stock\n",
" plt.plot(\n",
" stock_data['ds'],\n",
" stock_data['y'],\n",
" label=f'True ({unique_id})'\n",
" )\n",
" plt.plot(\n",
" stock_data['ds'],\n",
" stock_data['Autoformer'],\n",
" label=f'Forecast ({unique_id})',\n",
" linestyle='--'\n",
" )\n",
"\n",
" # Optional: Mark the train-test split for this stock\n",
" if len(stock_data) > 12:\n",
" plt.axvline(\n",
" x=stock_data['ds'].iloc[-12],\n",
" color='black',\n",
" linestyle='dotted',\n",
" alpha=0.7\n",
" )\n",
"\n",
"# General plot formatting\n",
"plt.xlabel('Timestamp [t]')\n",
"plt.ylabel('Stock value')\n",
"plt.title('True vs. Forecast Values per Stock with Autoformer')\n",
"plt.grid(alpha=0.4)\n",
"\n",
"# Adjust legend to fit better\n",
"plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
],
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 438,
"referenced_widgets": [
"e16a87085f8846bdab8bf98727f96bc9",
"239f875367824d76bbc7e24bb70d5eb7",
"306b5dfc80484e7780b20af9736f1f57",
"22028d56a8494cb8b01927565ca1fe6e",
"ec2910eb7eee44a3960b29037e06a4c9",
"7337c22377ce49f68ff44fcb7396bbc8",
"3ebabd4fca984caeb5b58f3fd45d35e4",
"fd7005b4dcaa48f2ab67b06b41939b4e",
"44572ecc8c14412f8fdb2d3b56e1f3b7",
"6539461d304e4d678b3fbb75d4ba485d",
"8a8bad290434450799b100d34dd94fbb"
]
},
"id": "FM5TOPCiJCKg",
"outputId": "72e61b4d-9013-418d-c912-91b86d7ae70b",
"collapsed": true
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/utilsforecast/processing.py:384: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
" freq = pd.tseries.frequencies.to_offset(freq)\n",
"INFO:pytorch_lightning.utilities.rank_zero:Trainer already configured with model summary callbacks: []. Skipping setting a default `ModelSummary` callback.\n",
"INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
"INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
"INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
"INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Predicting: | | 0/? [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "e16a87085f8846bdab8bf98727f96bc9"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"# Automatic hyperparameter tuning with Auto models (AutoNHITS & AutoAutoFormer)"
],
"metadata": {
"id": "EByr_3tX35xW"
}
},
{
"cell_type": "code",
"source": [
"!pip install statsforecast ray\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "IgVF7CgbyJhN",
"outputId": "db60dbe7-2c08-4945-bc8b-2b334785c9ba",
"collapsed": true
},
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting statsforecast\n",
" Downloading statsforecast-2.0.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (28 kB)\n",
"Requirement already satisfied: ray in /usr/local/lib/python3.10/dist-packages (2.40.0)\n",
"Requirement already satisfied: cloudpickle in /usr/local/lib/python3.10/dist-packages (from statsforecast) (3.1.0)\n",
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"Requirement already satisfied: numpy>=1.21.6 in /usr/local/lib/python3.10/dist-packages (from statsforecast) (1.26.4)\n",
"Requirement already satisfied: pandas>=1.3.5 in /usr/local/lib/python3.10/dist-packages (from statsforecast) (2.2.2)\n",
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"Requirement already satisfied: statsmodels>=0.13.2 in /usr/local/lib/python3.10/dist-packages (from statsforecast) (0.14.4)\n",
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"Collecting fugue>=0.8.1 (from statsforecast)\n",
" Downloading fugue-0.9.1-py3-none-any.whl.metadata (18 kB)\n",
"Requirement already satisfied: utilsforecast>=0.1.4 in /usr/local/lib/python3.10/dist-packages (from statsforecast) (0.2.10)\n",
"Requirement already satisfied: threadpoolctl>=3 in /usr/local/lib/python3.10/dist-packages (from statsforecast) (3.5.0)\n",
"Requirement already satisfied: click>=7.0 in /usr/local/lib/python3.10/dist-packages (from ray) (8.1.7)\n",
"Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from ray) (3.16.1)\n",
"Requirement already satisfied: jsonschema in /usr/local/lib/python3.10/dist-packages (from ray) (4.23.0)\n",
"Requirement already satisfied: msgpack<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from ray) (1.1.0)\n",
"Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from ray) (24.2)\n",
"Requirement already satisfied: protobuf!=3.19.5,>=3.15.3 in /usr/local/lib/python3.10/dist-packages (from ray) (4.25.5)\n",
"Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from ray) (6.0.2)\n",
"Requirement already satisfied: aiosignal in /usr/local/lib/python3.10/dist-packages (from ray) (1.3.2)\n",
"Requirement already satisfied: frozenlist in /usr/local/lib/python3.10/dist-packages (from ray) (1.5.0)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from ray) (2.32.3)\n",
"Collecting triad>=0.9.7 (from fugue>=0.8.1->statsforecast)\n",
" Downloading triad-0.9.8-py3-none-any.whl.metadata (6.3 kB)\n",
"Collecting adagio>=0.2.4 (from fugue>=0.8.1->statsforecast)\n",
" Downloading adagio-0.2.6-py3-none-any.whl.metadata (1.8 kB)\n",
"Requirement already satisfied: llvmlite<0.44,>=0.43.0dev0 in /usr/local/lib/python3.10/dist-packages (from numba>=0.55.0->statsforecast) (0.43.0)\n",
"Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.3.5->statsforecast) (2.8.2)\n",
"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.3.5->statsforecast) (2024.2)\n",
"Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.3.5->statsforecast) (2024.2)\n",
"Requirement already satisfied: patsy>=0.5.6 in /usr/local/lib/python3.10/dist-packages (from statsmodels>=0.13.2->statsforecast) (1.0.1)\n",
"Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema->ray) (24.3.0)\n",
"Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema->ray) (2024.10.1)\n",
"Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema->ray) (0.35.1)\n",
"Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema->ray) (0.22.3)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->ray) (3.4.0)\n",
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->ray) (3.10)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->ray) (2.2.3)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->ray) (2024.12.14)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas>=1.3.5->statsforecast) (1.17.0)\n",
"Requirement already satisfied: pyarrow>=6.0.1 in /usr/local/lib/python3.10/dist-packages (from triad>=0.9.7->fugue>=0.8.1->statsforecast) (17.0.0)\n",
"Requirement already satisfied: fsspec>=2022.5.0 in /usr/local/lib/python3.10/dist-packages (from triad>=0.9.7->fugue>=0.8.1->statsforecast) (2024.10.0)\n",
"Collecting fs (from triad>=0.9.7->fugue>=0.8.1->statsforecast)\n",
" Downloading fs-2.4.16-py2.py3-none-any.whl.metadata (6.3 kB)\n",
"Collecting appdirs~=1.4.3 (from fs->triad>=0.9.7->fugue>=0.8.1->statsforecast)\n",
" Downloading appdirs-1.4.4-py2.py3-none-any.whl.metadata (9.0 kB)\n",
"Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from fs->triad>=0.9.7->fugue>=0.8.1->statsforecast) (75.1.0)\n",
"Downloading statsforecast-2.0.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (319 kB)\n",
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"\u001b[?25hDownloading fugue-0.9.1-py3-none-any.whl (278 kB)\n",
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"\u001b[?25hDownloading adagio-0.2.6-py3-none-any.whl (19 kB)\n",
"Downloading triad-0.9.8-py3-none-any.whl (62 kB)\n",
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"\u001b[?25hDownloading appdirs-1.4.4-py2.py3-none-any.whl (9.6 kB)\n",
"Installing collected packages: appdirs, fs, triad, adagio, fugue, statsforecast\n",
"Successfully installed adagio-0.2.6 appdirs-1.4.4 fs-2.4.16 fugue-0.9.1 statsforecast-2.0.0 triad-0.9.8\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from neuralforecast.auto import AutoNHITS, AutoAutoformer\n",
"from neuralforecast.losses.pytorch import MQLoss\n",
"from ray import tune\n"
],
"metadata": {
"id": "xiH3Ulg2zyRG"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"config_nhits = {\n",
" \"input_size\": tune.choice([48, 48*2, 48*3]), # Length of input window\n",
" \"start_padding_enabled\": True,\n",
" \"n_blocks\": 5*[1], # Length of input window\n",
" \"mlp_units\": 5 * [[64, 64]], # Length of input window\n",
" \"n_pool_kernel_size\": tune.choice([5*[1], 5*[2], 5*[4],\n",
" [8, 4, 2, 1, 1]]), # MaxPooling Kernel size\n",
" \"n_freq_downsample\": tune.choice([[8, 4, 2, 1, 1],\n",
" [1, 1, 1, 1, 1]]), # Interpolation expressivity ratios\n",
" \"learning_rate\": tune.loguniform(1e-4, 1e-2), # Initial Learning rate\n",
" \"scaler_type\": tune.choice([None]), # Scaler type\n",
" \"max_steps\": tune.choice([1000]), # Max number of training iterations\n",
" \"batch_size\": tune.choice([1, 4, 10]), # Number of series in batch\n",
" \"windows_batch_size\": tune.choice([128, 256, 512]), # Number of windows in batch\n",
" \"random_seed\": tune.randint(1, 20), # Random seed\n",
"}\n",
"Autoformer(h=horizon,\n",
" input_size=horizon,\n",
" max_steps=1000,\n",
" val_check_steps=100,\n",
" early_stop_patience_steps=3)\n",
"config_autoformer = {\n",
" \"input_size\": tune.choice([48, 48*2, 48*3]), # Length of input window\n",
" \"encoder_layers\": tune.choice([2,4]), # Number of layers in Autoformer\n",
" \"learning_rate\": tune.loguniform(1e-4, 1e-2), # Initial Learning rate\n",
" \"scaler_type\": tune.choice(['robust']), # Scaler type\n",
" \"max_steps\": tune.choice([500, 1000]), # Max number of training iterations\n",
" \"batch_size\": tune.choice([1, 4]), # Number of series in batch\n",
" \"random_seed\": tune.randint(1, 20), # Random seed\n",
"}"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "idiphs0Dz6Tt",
"outputId": "b493af0d-476f-4d80-d57e-a37cec625df8"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"INFO:lightning_fabric.utilities.seed:Seed set to 1\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"nf = NeuralForecast(\n",
" models=[\n",
" AutoAutoformer(h=48, config=config_autoformer, loss=MQLoss(), num_samples=2),\n",
" AutoNHITS(h=48, config=config_nhits, loss=MQLoss(), num_samples=5),\n",
" ],\n",
" freq='H' # hourly ?!\n",
")"
],
"metadata": {
"id": "0XOzGrg9zsIo"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"nf.fit(df=hist)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000,
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"51e55cfa341a464f9dbc7db9d62ea4df",
"58d11b586e0d48acad0771ffb170af3a",
"378633f0f3924073ab7d083fdb33ee57",
"0ce996b58598448cbd67557acf7cdf85",
"9e97d8fa2eac42ffb0267015ab4af02b"
]
},
"id": "WWiMyEvOzuDX",
"outputId": "f22869f4-b54b-4595-ff24-ef11b51b0328",
"collapsed": true
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"2025-01-07 14:22:18,767\tINFO worker.py:1821 -- Started a local Ray instance.\n",
"2025-01-07 14:22:21,079\tINFO tune.py:253 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call `ray.init(...)` before `Tuner(...)`.\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"+--------------------------------------------------------------------+\n",
"| Configuration for experiment _train_tune_2025-01-07_14-22-15 |\n",
"+--------------------------------------------------------------------+\n",
"| Search algorithm BasicVariantGenerator |\n",
"| Scheduler FIFOScheduler |\n",
"| Number of trials 2 |\n",
"+--------------------------------------------------------------------+\n",
"\n",
"View detailed results here: /root/ray_results/_train_tune_2025-01-07_14-22-15\n",
"To visualize your results with TensorBoard, run: `tensorboard --logdir /tmp/ray/session_2025-01-07_14-22-15_419131_253/artifacts/2025-01-07_14-22-21/_train_tune_2025-01-07_14-22-15/driver_artifacts`\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\u001b[36m(pid=23277)\u001b[0m /usr/local/lib/python3.10/dist-packages/dask/dataframe/__init__.py:42: FutureWarning: \n",
"\u001b[36m(pid=23277)\u001b[0m Dask dataframe query planning is disabled because dask-expr is not installed.\n",
"\u001b[36m(pid=23277)\u001b[0m \n",
"\u001b[36m(pid=23277)\u001b[0m You can install it with `pip install dask[dataframe]` or `conda install dask`.\n",
"\u001b[36m(pid=23277)\u001b[0m This will raise in a future version.\n",
"\u001b[36m(pid=23277)\u001b[0m \n",
"\u001b[36m(pid=23277)\u001b[0m warnings.warn(msg, FutureWarning)\n",
"\u001b[36m(_train_tune pid=23277)\u001b[0m /usr/local/lib/python3.10/dist-packages/ray/tune/integration/pytorch_lightning.py:198: `ray.tune.integration.pytorch_lightning.TuneReportCallback` is deprecated. Use `ray.tune.integration.pytorch_lightning.TuneReportCheckpointCallback` instead.\n",
"\u001b[36m(_train_tune pid=23277)\u001b[0m Seed set to 12\n",
"\u001b[36m(_train_tune pid=23277)\u001b[0m GPU available: True (cuda), used: True\n",
"\u001b[36m(_train_tune pid=23277)\u001b[0m TPU available: False, using: 0 TPU cores\n",
"\u001b[36m(_train_tune pid=23277)\u001b[0m HPU available: False, using: 0 HPUs\n",
"\u001b[36m(_train_tune pid=23277)\u001b[0m 2025-01-07 14:22:29.335760: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"\u001b[36m(_train_tune pid=23277)\u001b[0m 2025-01-07 14:22:29.361361: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"\u001b[36m(_train_tune pid=23277)\u001b[0m 2025-01-07 14:22:29.368985: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
"\u001b[36m(_train_tune pid=23277)\u001b[0m 2025-01-07 14:22:30.534029: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
"\u001b[36m(_train_tune pid=23277)\u001b[0m LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
"\u001b[36m(_train_tune pid=23277)\u001b[0m \n",
"\u001b[36m(_train_tune pid=23277)\u001b[0m | Name | Type | Params | Mode \n",
"\u001b[36m(_train_tune pid=23277)\u001b[0m --------------------------------------------------------\n",
"\u001b[36m(_train_tune pid=23277)\u001b[0m 0 | loss | MQLoss | 5 | train\n",
"\u001b[36m(_train_tune pid=23277)\u001b[0m 1 | padder_train | ConstantPad1d | 0 | train\n",
"\u001b[36m(_train_tune pid=23277)\u001b[0m 2 | scaler | TemporalNorm | 0 | train\n",
"\u001b[36m(_train_tune pid=23277)\u001b[0m 3 | decomp | SeriesDecomp | 0 | train\n",
"\u001b[36m(_train_tune pid=23277)\u001b[0m 4 | enc_embedding | DataEmbedding | 384 | train\n",
"\u001b[36m(_train_tune pid=23277)\u001b[0m 5 | dec_embedding | DataEmbedding | 384 | train\n",
"\u001b[36m(_train_tune pid=23277)\u001b[0m 6 | encoder | Encoder | 297 K | train\n",
"\u001b[36m(_train_tune pid=23277)\u001b[0m 7 | decoder | Decoder | 143 K | train\n",
"\u001b[36m(_train_tune pid=23277)\u001b[0m --------------------------------------------------------\n",
"\u001b[36m(_train_tune pid=23277)\u001b[0m 441 K Trainable params\n",
"\u001b[36m(_train_tune pid=23277)\u001b[0m 5 Non-trainable params\n",
"\u001b[36m(_train_tune pid=23277)\u001b[0m 441 K Total params\n",
"\u001b[36m(_train_tune pid=23277)\u001b[0m 1.764 Total estimated model params size (MB)\n",
"\u001b[36m(_train_tune pid=23277)\u001b[0m 119 Modules in train mode\n",
"\u001b[36m(_train_tune pid=23277)\u001b[0m 0 Modules in eval mode\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Sanity Checking DataLoader 0: 0%| | 0/1 [00:00, ?it/s]\n",
"Epoch 0: 0%| | 0/1 [00:00, ?it/s] \n",
"Epoch 1: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.520, train_loss_epoch=1.520]\n",
"Epoch 1: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=1.520, train_loss_epoch=1.520]\n",
"Epoch 2: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.410, train_loss_epoch=1.410]\n",
"Epoch 2: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=1.410, train_loss_epoch=1.410]\n",
"Epoch 3: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.360, train_loss_epoch=1.360]\n",
"Epoch 3: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=1.360, train_loss_epoch=1.360]\n",
"Epoch 4: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.240, train_loss_epoch=1.240]\n",
"Epoch 4: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=1.240, train_loss_epoch=1.240]\n",
"Epoch 5: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.150, train_loss_epoch=1.150]\n",
"Epoch 5: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=1.150, train_loss_epoch=1.150]\n",
"Epoch 6: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.948, train_loss_epoch=0.948]\n",
"Epoch 6: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.948, train_loss_epoch=0.948]\n",
"Epoch 7: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.804, train_loss_epoch=0.804]\n",
"Epoch 7: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.804, train_loss_epoch=0.804]\n",
"Epoch 8: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.730, train_loss_epoch=0.730]\n",
"Epoch 8: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.730, train_loss_epoch=0.730]\n",
"Epoch 9: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.739, train_loss_epoch=0.739]\n",
"Epoch 9: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.739, train_loss_epoch=0.739]\n",
"Epoch 10: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.693, train_loss_epoch=0.693]\n",
"Epoch 10: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.693, train_loss_epoch=0.693]\n",
"Epoch 11: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.669, train_loss_epoch=0.669]\n",
"Epoch 11: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.669, train_loss_epoch=0.669]\n",
"Epoch 12: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.646, train_loss_epoch=0.646]\n",
"Epoch 12: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.646, train_loss_epoch=0.646]\n",
"Epoch 13: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.689, train_loss_epoch=0.689]\n",
"Epoch 13: 100%|██████████| 1/1 [00:00<00:00, 1.68it/s, v_num=0, train_loss_step=0.689, train_loss_epoch=0.689]\n",
"Epoch 14: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.692, train_loss_epoch=0.692]\n",
"Epoch 14: 100%|██████████| 1/1 [00:00<00:00, 1.68it/s, v_num=0, train_loss_step=0.692, train_loss_epoch=0.692]\n",
"Epoch 15: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.669, train_loss_epoch=0.669]\n",
"Epoch 15: 100%|██████████| 1/1 [00:00<00:00, 1.67it/s, v_num=0, train_loss_step=0.669, train_loss_epoch=0.669]\n",
"Epoch 16: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.691, train_loss_epoch=0.691]\n",
"Epoch 16: 100%|██████████| 1/1 [00:00<00:00, 1.68it/s, v_num=0, train_loss_step=0.691, train_loss_epoch=0.691]\n",
"Epoch 17: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.670, train_loss_epoch=0.670]\n",
"Epoch 17: 100%|██████████| 1/1 [00:00<00:00, 1.68it/s, v_num=0, train_loss_step=0.670, train_loss_epoch=0.670]\n",
"Epoch 18: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.672, train_loss_epoch=0.672]\n",
"Epoch 18: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.672, train_loss_epoch=0.672]\n",
"Epoch 19: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.657, train_loss_epoch=0.657]\n",
"Epoch 19: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.657, train_loss_epoch=0.657]\n",
"Epoch 20: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.670, train_loss_epoch=0.670]\n",
"Epoch 20: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.670, train_loss_epoch=0.670]\n",
"Epoch 21: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.660, train_loss_epoch=0.660]\n",
"Epoch 21: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.660, train_loss_epoch=0.660]\n",
"Epoch 21: 100%|██████████| 1/1 [00:00<00:00, 1.14it/s, v_num=0, train_loss_step=0.670, train_loss_epoch=0.660]\n",
"Epoch 22: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.670, train_loss_epoch=0.670]\n",
"Epoch 23: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.647, train_loss_epoch=0.647]\n",
"Epoch 23: 100%|██████████| 1/1 [00:00<00:00, 1.24it/s, v_num=0, train_loss_step=0.647, train_loss_epoch=0.647]\n",
"Epoch 24: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.624, train_loss_epoch=0.624]\n",
"Epoch 24: 100%|██████████| 1/1 [00:00<00:00, 1.45it/s, v_num=0, train_loss_step=0.624, train_loss_epoch=0.624]\n",
"Epoch 25: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.665, train_loss_epoch=0.665]\n",
"Epoch 25: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.665, train_loss_epoch=0.665]\n",
"Epoch 26: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.657, train_loss_epoch=0.657]\n",
"Epoch 26: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.657, train_loss_epoch=0.657]\n",
"Epoch 27: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.662, train_loss_epoch=0.662]\n",
"Epoch 27: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.662, train_loss_epoch=0.662]\n",
"Epoch 28: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.651, train_loss_epoch=0.651]\n",
"Epoch 28: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.651, train_loss_epoch=0.651]\n",
"Epoch 28: 100%|██████████| 1/1 [00:00<00:00, 1.15it/s, v_num=0, train_loss_step=0.663, train_loss_epoch=0.663]\n",
"Epoch 29: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.663, train_loss_epoch=0.663]\n",
"Epoch 29: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.663, train_loss_epoch=0.663]\n",
"Epoch 30: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.650, train_loss_epoch=0.650]\n",
"Epoch 30: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.650, train_loss_epoch=0.650]\n",
"Epoch 31: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.651, train_loss_epoch=0.651]\n",
"Epoch 31: 100%|██████████| 1/1 [00:00<00:00, 1.67it/s, v_num=0, train_loss_step=0.651, train_loss_epoch=0.651]\n",
"Epoch 32: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.668, train_loss_epoch=0.668]\n",
"Epoch 32: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.668, train_loss_epoch=0.668]\n",
"Epoch 33: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.662, train_loss_epoch=0.662]\n",
"Epoch 33: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.662, train_loss_epoch=0.662]\n",
"Epoch 34: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.646, train_loss_epoch=0.646]\n",
"Epoch 34: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.646, train_loss_epoch=0.646]\n",
"Epoch 35: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.650, train_loss_epoch=0.650]\n",
"Epoch 35: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.650, train_loss_epoch=0.650]\n",
"Epoch 35: 100%|██████████| 1/1 [00:00<00:00, 1.16it/s, v_num=0, train_loss_step=0.662, train_loss_epoch=0.662]\n",
"Epoch 36: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.662, train_loss_epoch=0.662]\n",
"Epoch 36: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.662, train_loss_epoch=0.662]\n",
"Epoch 37: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.662, train_loss_epoch=0.662]\n",
"Epoch 37: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.662, train_loss_epoch=0.662]\n",
"Epoch 38: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.658, train_loss_epoch=0.658]\n",
"Epoch 38: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.658, train_loss_epoch=0.658]\n",
"Epoch 39: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.658, train_loss_epoch=0.658]\n",
"Epoch 39: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.658, train_loss_epoch=0.658]\n",
"Epoch 40: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.633, train_loss_epoch=0.633]\n",
"Epoch 40: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.633, train_loss_epoch=0.633]\n",
"Epoch 41: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.653, train_loss_epoch=0.653]\n",
"Epoch 41: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.653, train_loss_epoch=0.653]\n",
"Epoch 42: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.623, train_loss_epoch=0.623]\n",
"Epoch 42: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.623, train_loss_epoch=0.623]\n",
"Epoch 43: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.659, train_loss_epoch=0.659]\n",
"Epoch 43: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.659, train_loss_epoch=0.659]\n",
"Epoch 44: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.632, train_loss_epoch=0.632]\n",
"Epoch 44: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.632, train_loss_epoch=0.632]\n",
"Epoch 45: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.649, train_loss_epoch=0.649]\n",
"Epoch 45: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.649, train_loss_epoch=0.649]\n",
"Epoch 46: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.641, train_loss_epoch=0.641]\n",
"Epoch 46: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.641, train_loss_epoch=0.641]\n",
"Epoch 47: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.656, train_loss_epoch=0.656]\n",
"Epoch 47: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.656, train_loss_epoch=0.656]\n",
"Epoch 48: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.649, train_loss_epoch=0.649]\n",
"Epoch 48: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.649, train_loss_epoch=0.649]\n",
"Epoch 49: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.659, train_loss_epoch=0.659]\n",
"Epoch 49: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.659, train_loss_epoch=0.659]\n",
"Epoch 50: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.647, train_loss_epoch=0.647]\n",
"Epoch 50: 100%|██████████| 1/1 [00:00<00:00, 1.62it/s, v_num=0, train_loss_step=0.647, train_loss_epoch=0.647]\n",
"Epoch 51: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.652, train_loss_epoch=0.652]\n",
"Epoch 51: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.652, train_loss_epoch=0.652]\n",
"Epoch 52: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.643, train_loss_epoch=0.643]\n",
"Epoch 52: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.643, train_loss_epoch=0.643]\n",
"Epoch 53: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.650, train_loss_epoch=0.650]\n",
"Epoch 53: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.650, train_loss_epoch=0.650]\n",
"Epoch 54: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.641, train_loss_epoch=0.641]\n",
"Epoch 54: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.641, train_loss_epoch=0.641]\n",
"Epoch 55: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.637, train_loss_epoch=0.637]\n",
"Epoch 55: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.637, train_loss_epoch=0.637]\n",
"Epoch 56: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.625, train_loss_epoch=0.625]\n",
"Epoch 56: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.625, train_loss_epoch=0.625]\n",
"Epoch 57: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.651, train_loss_epoch=0.651]\n",
"Epoch 57: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.651, train_loss_epoch=0.651]\n",
"Epoch 57: 100%|██████████| 1/1 [00:00<00:00, 1.18it/s, v_num=0, train_loss_step=0.632, train_loss_epoch=0.651]\n",
"Epoch 58: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.632, train_loss_epoch=0.632]\n",
"Epoch 58: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.632, train_loss_epoch=0.632]\n",
"Epoch 59: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.636, train_loss_epoch=0.636]\n",
"Epoch 59: 100%|██████████| 1/1 [00:00<00:00, 1.76it/s, v_num=0, train_loss_step=0.636, train_loss_epoch=0.636]\n",
"Epoch 60: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.636, train_loss_epoch=0.636]\n",
"Epoch 60: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.636, train_loss_epoch=0.636]\n",
"Epoch 61: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.651, train_loss_epoch=0.651]\n",
"Epoch 61: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.651, train_loss_epoch=0.651]\n",
"Epoch 62: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.634, train_loss_epoch=0.634]\n",
"Epoch 62: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.634, train_loss_epoch=0.634]\n",
"Epoch 63: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.619, train_loss_epoch=0.619]\n",
"Epoch 63: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.619, train_loss_epoch=0.619]\n",
"Epoch 64: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.627, train_loss_epoch=0.627]\n",
"Epoch 64: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.627, train_loss_epoch=0.627]\n",
"Epoch 65: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.655, train_loss_epoch=0.655]\n",
"Epoch 65: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.655, train_loss_epoch=0.655]\n",
"Epoch 66: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.647, train_loss_epoch=0.647]\n",
"Epoch 66: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.647, train_loss_epoch=0.647]\n",
"Epoch 67: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.636, train_loss_epoch=0.636]\n",
"Epoch 67: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.636, train_loss_epoch=0.636]\n",
"Epoch 68: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.648, train_loss_epoch=0.648]\n",
"Epoch 68: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.648, train_loss_epoch=0.648]\n",
"Epoch 69: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.647, train_loss_epoch=0.647]\n",
"Epoch 69: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.647, train_loss_epoch=0.647]\n",
"Epoch 70: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.624, train_loss_epoch=0.624]\n",
"Epoch 70: 100%|██████████| 1/1 [00:00<00:00, 1.76it/s, v_num=0, train_loss_step=0.624, train_loss_epoch=0.624]\n",
"Epoch 71: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.633, train_loss_epoch=0.633]\n",
"Epoch 71: 100%|██████████| 1/1 [00:00<00:00, 1.76it/s, v_num=0, train_loss_step=0.633, train_loss_epoch=0.633]\n",
"Epoch 72: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.639, train_loss_epoch=0.639]\n",
"Epoch 72: 100%|██████████| 1/1 [00:00<00:00, 1.76it/s, v_num=0, train_loss_step=0.639, train_loss_epoch=0.639]\n",
"Epoch 73: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.635, train_loss_epoch=0.635]\n",
"Epoch 73: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.635, train_loss_epoch=0.635]\n",
"Epoch 74: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.640, train_loss_epoch=0.640]\n",
"Epoch 74: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.640, train_loss_epoch=0.640]\n",
"Epoch 75: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.622, train_loss_epoch=0.622]\n",
"Epoch 75: 100%|██████████| 1/1 [00:00<00:00, 1.76it/s, v_num=0, train_loss_step=0.622, train_loss_epoch=0.622]\n",
"Epoch 76: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.655, train_loss_epoch=0.655]\n",
"Epoch 76: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.655, train_loss_epoch=0.655]\n",
"Epoch 77: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.628, train_loss_epoch=0.628]\n",
"Epoch 77: 100%|██████████| 1/1 [00:00<00:00, 1.76it/s, v_num=0, train_loss_step=0.628, train_loss_epoch=0.628]\n",
"Epoch 78: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.645, train_loss_epoch=0.645]\n",
"Epoch 78: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.645, train_loss_epoch=0.645]\n",
"Epoch 79: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.631, train_loss_epoch=0.631]\n",
"Epoch 79: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.631, train_loss_epoch=0.631]\n",
"Epoch 80: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.625, train_loss_epoch=0.625]\n",
"Epoch 80: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.625, train_loss_epoch=0.625]\n",
"Epoch 80: 100%|██████████| 1/1 [00:00<00:00, 1.17it/s, v_num=0, train_loss_step=0.657, train_loss_epoch=0.625]\n",
"Epoch 81: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.657, train_loss_epoch=0.657]\n",
"Epoch 81: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.657, train_loss_epoch=0.657]\n",
"Epoch 82: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.638, train_loss_epoch=0.638]\n",
"Epoch 82: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.638, train_loss_epoch=0.638]\n",
"Epoch 82: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.630, train_loss_epoch=0.630] \n",
"Epoch 83: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.630, train_loss_epoch=0.630]\n",
"Epoch 83: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.630, train_loss_epoch=0.630]\n",
"Epoch 84: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.645, train_loss_epoch=0.645]\n",
"Epoch 84: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.645, train_loss_epoch=0.645]\n",
"Epoch 85: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.646, train_loss_epoch=0.646]\n",
"Epoch 85: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.646, train_loss_epoch=0.646]\n",
"Epoch 86: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.612, train_loss_epoch=0.612]\n",
"Epoch 86: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.612, train_loss_epoch=0.612]\n",
"Epoch 87: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592]\n",
"Epoch 87: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592]\n",
"Epoch 88: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.647, train_loss_epoch=0.647]\n",
"Epoch 88: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.647, train_loss_epoch=0.647]\n",
"Epoch 89: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.612, train_loss_epoch=0.612]\n",
"Epoch 89: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.612, train_loss_epoch=0.612]\n",
"Epoch 90: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.633, train_loss_epoch=0.633]\n",
"Epoch 90: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.633, train_loss_epoch=0.633]\n",
"Epoch 91: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.621, train_loss_epoch=0.621]\n",
"Epoch 91: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.621, train_loss_epoch=0.621]\n",
"Epoch 92: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.614, train_loss_epoch=0.614]\n",
"Epoch 92: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.614, train_loss_epoch=0.614]\n",
"Epoch 93: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.646, train_loss_epoch=0.646]\n",
"Epoch 93: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.646, train_loss_epoch=0.646]\n",
"Epoch 94: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.641, train_loss_epoch=0.641]\n",
"Epoch 94: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.641, train_loss_epoch=0.641]\n",
"Epoch 95: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.634, train_loss_epoch=0.634]\n",
"Epoch 95: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.634, train_loss_epoch=0.634]\n",
"Epoch 96: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.632, train_loss_epoch=0.632]\n",
"Epoch 96: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.632, train_loss_epoch=0.632]\n",
"Epoch 97: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.627, train_loss_epoch=0.627]\n",
"Epoch 97: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.627, train_loss_epoch=0.627]\n",
"Epoch 98: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.639, train_loss_epoch=0.639]\n",
"Epoch 98: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.639, train_loss_epoch=0.639]\n",
"Epoch 99: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.627, train_loss_epoch=0.627]\n",
"Epoch 99: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.627, train_loss_epoch=0.627]\n",
"Epoch 99: 100%|██████████| 1/1 [00:00<00:00, 1.15it/s, v_num=0, train_loss_step=0.644, train_loss_epoch=0.627]\n",
"Validation: | | 0/? [00:00, ?it/s]\u001b[A\n",
"Validation: 0%| | 0/1 [00:00, ?it/s]\u001b[A\n",
"Validation DataLoader 0: 0%| | 0/1 [00:00, ?it/s]\u001b[A\n",
"Validation DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 26.27it/s]\u001b[A\n",
"Epoch 100: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.644, train_loss_epoch=0.644, valid_loss=4.580]\n",
"Epoch 100: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.644, train_loss_epoch=0.644, valid_loss=4.580]\n",
"Epoch 100: 100%|██████████| 1/1 [00:00<00:00, 1.18it/s, v_num=0, train_loss_step=0.626, train_loss_epoch=0.644, valid_loss=4.580]\n",
"Epoch 100: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.626, train_loss_epoch=0.626, valid_loss=4.580] \n",
"Epoch 101: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.626, train_loss_epoch=0.626, valid_loss=4.580]\n",
"Epoch 101: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.626, train_loss_epoch=0.626, valid_loss=4.580]\n",
"Epoch 102: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.627, train_loss_epoch=0.627, valid_loss=4.580]\n",
"Epoch 102: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.627, train_loss_epoch=0.627, valid_loss=4.580]\n",
"Epoch 103: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.643, train_loss_epoch=0.643, valid_loss=4.580]\n",
"Epoch 103: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.643, train_loss_epoch=0.643, valid_loss=4.580]\n",
"Epoch 104: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.629, train_loss_epoch=0.629, valid_loss=4.580]\n",
"Epoch 104: 100%|██████████| 1/1 [00:00<00:00, 1.76it/s, v_num=0, train_loss_step=0.629, train_loss_epoch=0.629, valid_loss=4.580]\n",
"Epoch 105: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.627, train_loss_epoch=0.627, valid_loss=4.580]\n",
"Epoch 105: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.627, train_loss_epoch=0.627, valid_loss=4.580]\n",
"Epoch 106: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.639, train_loss_epoch=0.639, valid_loss=4.580]\n",
"Epoch 106: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.639, train_loss_epoch=0.639, valid_loss=4.580]\n",
"Epoch 107: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.642, train_loss_epoch=0.642, valid_loss=4.580]\n",
"Epoch 107: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.642, train_loss_epoch=0.642, valid_loss=4.580]\n",
"Epoch 108: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.642, train_loss_epoch=0.642, valid_loss=4.580]\n",
"Epoch 108: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.642, train_loss_epoch=0.642, valid_loss=4.580]\n",
"Epoch 109: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.628, train_loss_epoch=0.628, valid_loss=4.580]\n",
"Epoch 109: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.628, train_loss_epoch=0.628, valid_loss=4.580]\n",
"Epoch 110: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.641, train_loss_epoch=0.641, valid_loss=4.580]\n",
"Epoch 110: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.641, train_loss_epoch=0.641, valid_loss=4.580]\n",
"Epoch 111: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.619, train_loss_epoch=0.619, valid_loss=4.580]\n",
"Epoch 111: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.619, train_loss_epoch=0.619, valid_loss=4.580]\n",
"Epoch 112: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.629, train_loss_epoch=0.629, valid_loss=4.580]\n",
"Epoch 112: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.629, train_loss_epoch=0.629, valid_loss=4.580]\n",
"Epoch 113: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.640, train_loss_epoch=0.640, valid_loss=4.580]\n",
"Epoch 113: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.640, train_loss_epoch=0.640, valid_loss=4.580]\n",
"Epoch 114: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.638, train_loss_epoch=0.638, valid_loss=4.580]\n",
"Epoch 114: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.638, train_loss_epoch=0.638, valid_loss=4.580]\n",
"Epoch 115: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.623, train_loss_epoch=0.623, valid_loss=4.580]\n",
"Epoch 115: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.623, train_loss_epoch=0.623, valid_loss=4.580]\n",
"Epoch 116: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.646, train_loss_epoch=0.646, valid_loss=4.580]\n",
"Epoch 116: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.646, train_loss_epoch=0.646, valid_loss=4.580]\n",
"Epoch 117: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.631, train_loss_epoch=0.631, valid_loss=4.580]\n",
"Epoch 117: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.631, train_loss_epoch=0.631, valid_loss=4.580]\n",
"Epoch 118: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.623, train_loss_epoch=0.623, valid_loss=4.580]\n",
"Epoch 118: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.623, train_loss_epoch=0.623, valid_loss=4.580]\n",
"Epoch 119: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.649, train_loss_epoch=0.649, valid_loss=4.580]\n",
"Epoch 119: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.649, train_loss_epoch=0.649, valid_loss=4.580]\n",
"Epoch 120: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.629, train_loss_epoch=0.629, valid_loss=4.580]\n",
"Epoch 120: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.629, train_loss_epoch=0.629, valid_loss=4.580]\n",
"Epoch 121: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.651, train_loss_epoch=0.651, valid_loss=4.580]\n",
"Epoch 121: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.651, train_loss_epoch=0.651, valid_loss=4.580]\n",
"Epoch 122: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.661, train_loss_epoch=0.661, valid_loss=4.580]\n",
"Epoch 122: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.661, train_loss_epoch=0.661, valid_loss=4.580]\n",
"Epoch 123: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.637, train_loss_epoch=0.637, valid_loss=4.580]\n",
"Epoch 123: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.637, train_loss_epoch=0.637, valid_loss=4.580]\n",
"Epoch 124: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.626, train_loss_epoch=0.626, valid_loss=4.580]\n",
"Epoch 124: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.626, train_loss_epoch=0.626, valid_loss=4.580]\n",
"Epoch 125: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.636, train_loss_epoch=0.636, valid_loss=4.580]\n",
"Epoch 125: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.636, train_loss_epoch=0.636, valid_loss=4.580]\n",
"Epoch 126: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.622, train_loss_epoch=0.622, valid_loss=4.580]\n",
"Epoch 126: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.622, train_loss_epoch=0.622, valid_loss=4.580]\n",
"Epoch 127: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.629, train_loss_epoch=0.629, valid_loss=4.580]\n",
"Epoch 127: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.629, train_loss_epoch=0.629, valid_loss=4.580]\n",
"Epoch 128: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.618, train_loss_epoch=0.618, valid_loss=4.580]\n",
"Epoch 128: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.618, train_loss_epoch=0.618, valid_loss=4.580]\n",
"Epoch 129: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.658, train_loss_epoch=0.658, valid_loss=4.580]\n",
"Epoch 129: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.658, train_loss_epoch=0.658, valid_loss=4.580]\n",
"Epoch 130: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.659, train_loss_epoch=0.659, valid_loss=4.580]\n",
"Epoch 130: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.659, train_loss_epoch=0.659, valid_loss=4.580]\n",
"Epoch 131: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.644, train_loss_epoch=0.644, valid_loss=4.580]\n",
"Epoch 131: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.644, train_loss_epoch=0.644, valid_loss=4.580]\n",
"Epoch 132: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.639, train_loss_epoch=0.639, valid_loss=4.580]\n",
"Epoch 132: 100%|██████████| 1/1 [00:00<00:00, 1.68it/s, v_num=0, train_loss_step=0.639, train_loss_epoch=0.639, valid_loss=4.580]\n",
"Epoch 133: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.633, train_loss_epoch=0.633, valid_loss=4.580]\n",
"Epoch 133: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.633, train_loss_epoch=0.633, valid_loss=4.580]\n",
"Epoch 133: 100%|██████████| 1/1 [00:00<00:00, 1.15it/s, v_num=0, train_loss_step=0.626, train_loss_epoch=0.626, valid_loss=4.580]\n",
"Epoch 134: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.626, train_loss_epoch=0.626, valid_loss=4.580]\n",
"Epoch 134: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.626, train_loss_epoch=0.626, valid_loss=4.580]\n",
"Epoch 135: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.642, train_loss_epoch=0.642, valid_loss=4.580]\n",
"Epoch 135: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.642, train_loss_epoch=0.642, valid_loss=4.580]\n",
"Epoch 136: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.634, train_loss_epoch=0.634, valid_loss=4.580]\n",
"Epoch 136: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.634, train_loss_epoch=0.634, valid_loss=4.580]\n",
"Epoch 137: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.650, train_loss_epoch=0.650, valid_loss=4.580]\n",
"Epoch 137: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.650, train_loss_epoch=0.650, valid_loss=4.580]\n",
"Epoch 138: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.624, train_loss_epoch=0.624, valid_loss=4.580]\n",
"Epoch 138: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.624, train_loss_epoch=0.624, valid_loss=4.580]\n",
"Epoch 139: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.642, train_loss_epoch=0.642, valid_loss=4.580]\n",
"Epoch 139: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.642, train_loss_epoch=0.642, valid_loss=4.580]\n",
"Epoch 140: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.623, train_loss_epoch=0.623, valid_loss=4.580]\n",
"Epoch 140: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.623, train_loss_epoch=0.623, valid_loss=4.580]\n",
"Epoch 141: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.630, train_loss_epoch=0.630, valid_loss=4.580]\n",
"Epoch 141: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.630, train_loss_epoch=0.630, valid_loss=4.580]\n",
"Epoch 142: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.651, train_loss_epoch=0.651, valid_loss=4.580]\n",
"Epoch 142: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.651, train_loss_epoch=0.651, valid_loss=4.580]\n",
"Epoch 143: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.620, train_loss_epoch=0.620, valid_loss=4.580]\n",
"Epoch 143: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.620, train_loss_epoch=0.620, valid_loss=4.580]\n",
"Epoch 144: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.641, train_loss_epoch=0.641, valid_loss=4.580]\n",
"Epoch 144: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.641, train_loss_epoch=0.641, valid_loss=4.580]\n",
"Epoch 145: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.656, train_loss_epoch=0.656, valid_loss=4.580]\n",
"Epoch 145: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.656, train_loss_epoch=0.656, valid_loss=4.580]\n",
"Epoch 146: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.636, train_loss_epoch=0.636, valid_loss=4.580]\n",
"Epoch 146: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.636, train_loss_epoch=0.636, valid_loss=4.580]\n",
"Epoch 147: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.634, train_loss_epoch=0.634, valid_loss=4.580]\n",
"Epoch 147: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.634, train_loss_epoch=0.634, valid_loss=4.580]\n",
"Epoch 148: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.647, train_loss_epoch=0.647, valid_loss=4.580]\n",
"Epoch 148: 100%|██████████| 1/1 [00:00<00:00, 1.68it/s, v_num=0, train_loss_step=0.647, train_loss_epoch=0.647, valid_loss=4.580]\n",
"Epoch 149: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.622, train_loss_epoch=0.622, valid_loss=4.580]\n",
"Epoch 149: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.622, train_loss_epoch=0.622, valid_loss=4.580]\n",
"Epoch 150: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.634, train_loss_epoch=0.634, valid_loss=4.580]\n",
"Epoch 150: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.634, train_loss_epoch=0.634, valid_loss=4.580]\n",
"Epoch 151: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.650, train_loss_epoch=0.650, valid_loss=4.580]\n",
"Epoch 151: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.650, train_loss_epoch=0.650, valid_loss=4.580]\n",
"Epoch 152: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.641, train_loss_epoch=0.641, valid_loss=4.580]\n",
"Epoch 152: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.641, train_loss_epoch=0.641, valid_loss=4.580]\n",
"Epoch 153: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.665, train_loss_epoch=0.665, valid_loss=4.580]\n",
"Epoch 153: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.665, train_loss_epoch=0.665, valid_loss=4.580]\n",
"Epoch 154: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.621, train_loss_epoch=0.621, valid_loss=4.580]\n",
"Epoch 154: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.621, train_loss_epoch=0.621, valid_loss=4.580]\n",
"Epoch 155: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.627, train_loss_epoch=0.627, valid_loss=4.580]\n",
"Epoch 155: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.627, train_loss_epoch=0.627, valid_loss=4.580]\n",
"Epoch 156: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.662, train_loss_epoch=0.662, valid_loss=4.580]\n",
"Epoch 156: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.662, train_loss_epoch=0.662, valid_loss=4.580]\n",
"Epoch 157: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.620, train_loss_epoch=0.620, valid_loss=4.580]\n",
"Epoch 157: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.620, train_loss_epoch=0.620, valid_loss=4.580]\n",
"Epoch 158: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.642, train_loss_epoch=0.642, valid_loss=4.580]\n",
"Epoch 158: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.642, train_loss_epoch=0.642, valid_loss=4.580]\n",
"Epoch 159: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.628, train_loss_epoch=0.628, valid_loss=4.580]\n",
"Epoch 159: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.628, train_loss_epoch=0.628, valid_loss=4.580]\n",
"Epoch 160: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.611, train_loss_epoch=0.611, valid_loss=4.580]\n",
"Epoch 160: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.611, train_loss_epoch=0.611, valid_loss=4.580]\n",
"Epoch 161: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.610, train_loss_epoch=0.610, valid_loss=4.580]\n",
"Epoch 161: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.610, train_loss_epoch=0.610, valid_loss=4.580]\n",
"Epoch 162: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.624, train_loss_epoch=0.624, valid_loss=4.580]\n",
"Epoch 162: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.624, train_loss_epoch=0.624, valid_loss=4.580]\n",
"Epoch 163: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.613, train_loss_epoch=0.613, valid_loss=4.580]\n",
"Epoch 163: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.613, train_loss_epoch=0.613, valid_loss=4.580]\n",
"Epoch 164: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.624, train_loss_epoch=0.624, valid_loss=4.580]\n",
"Epoch 164: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.624, train_loss_epoch=0.624, valid_loss=4.580]\n",
"Epoch 165: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.640, train_loss_epoch=0.640, valid_loss=4.580]\n",
"Epoch 165: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.640, train_loss_epoch=0.640, valid_loss=4.580]\n",
"Epoch 166: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.622, train_loss_epoch=0.622, valid_loss=4.580]\n",
"Epoch 166: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.622, train_loss_epoch=0.622, valid_loss=4.580]\n",
"Epoch 167: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.647, train_loss_epoch=0.647, valid_loss=4.580]\n",
"Epoch 167: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.647, train_loss_epoch=0.647, valid_loss=4.580]\n",
"Epoch 168: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.621, train_loss_epoch=0.621, valid_loss=4.580]\n",
"Epoch 168: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.621, train_loss_epoch=0.621, valid_loss=4.580]\n",
"Epoch 169: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.611, train_loss_epoch=0.611, valid_loss=4.580]\n",
"Epoch 169: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.611, train_loss_epoch=0.611, valid_loss=4.580]\n",
"Epoch 170: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.628, train_loss_epoch=0.628, valid_loss=4.580]\n",
"Epoch 170: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.628, train_loss_epoch=0.628, valid_loss=4.580]\n",
"Epoch 171: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.625, train_loss_epoch=0.625, valid_loss=4.580]\n",
"Epoch 171: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.625, train_loss_epoch=0.625, valid_loss=4.580]\n",
"Epoch 172: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.618, train_loss_epoch=0.618, valid_loss=4.580]\n",
"Epoch 172: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.618, train_loss_epoch=0.618, valid_loss=4.580]\n",
"Epoch 173: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.646, train_loss_epoch=0.646, valid_loss=4.580]\n",
"Epoch 173: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.646, train_loss_epoch=0.646, valid_loss=4.580]\n",
"Epoch 174: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.646, train_loss_epoch=0.646, valid_loss=4.580]\n",
"Epoch 174: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.646, train_loss_epoch=0.646, valid_loss=4.580]\n",
"Epoch 175: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.654, train_loss_epoch=0.654, valid_loss=4.580]\n",
"Epoch 175: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.654, train_loss_epoch=0.654, valid_loss=4.580]\n",
"Epoch 176: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.637, train_loss_epoch=0.637, valid_loss=4.580]\n",
"Epoch 176: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.637, train_loss_epoch=0.637, valid_loss=4.580]\n",
"Epoch 177: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.620, train_loss_epoch=0.620, valid_loss=4.580]\n",
"Epoch 177: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.620, train_loss_epoch=0.620, valid_loss=4.580]\n",
"Epoch 178: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.648, train_loss_epoch=0.648, valid_loss=4.580]\n",
"Epoch 178: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.648, train_loss_epoch=0.648, valid_loss=4.580]\n",
"Epoch 179: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.632, train_loss_epoch=0.632, valid_loss=4.580]\n",
"Epoch 179: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.632, train_loss_epoch=0.632, valid_loss=4.580]\n",
"Epoch 180: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.617, train_loss_epoch=0.617, valid_loss=4.580]\n",
"Epoch 180: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.617, train_loss_epoch=0.617, valid_loss=4.580]\n",
"Epoch 181: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.620, train_loss_epoch=0.620, valid_loss=4.580]\n",
"Epoch 181: 100%|██████████| 1/1 [00:00<00:00, 1.67it/s, v_num=0, train_loss_step=0.620, train_loss_epoch=0.620, valid_loss=4.580]\n",
"Epoch 182: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.621, train_loss_epoch=0.621, valid_loss=4.580]\n",
"Epoch 182: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.621, train_loss_epoch=0.621, valid_loss=4.580]\n",
"Epoch 183: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.609, train_loss_epoch=0.609, valid_loss=4.580]\n",
"Epoch 183: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.609, train_loss_epoch=0.609, valid_loss=4.580]\n",
"Epoch 184: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.638, train_loss_epoch=0.638, valid_loss=4.580]\n",
"Epoch 184: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.638, train_loss_epoch=0.638, valid_loss=4.580]\n",
"Epoch 185: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.639, train_loss_epoch=0.639, valid_loss=4.580]\n",
"Epoch 185: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.639, train_loss_epoch=0.639, valid_loss=4.580]\n",
"Epoch 186: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.626, train_loss_epoch=0.626, valid_loss=4.580]\n",
"Epoch 186: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.626, train_loss_epoch=0.626, valid_loss=4.580]\n",
"Epoch 187: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.637, train_loss_epoch=0.637, valid_loss=4.580]\n",
"Epoch 187: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.637, train_loss_epoch=0.637, valid_loss=4.580]\n",
"Epoch 188: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.626, train_loss_epoch=0.626, valid_loss=4.580]\n",
"Epoch 188: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.626, train_loss_epoch=0.626, valid_loss=4.580]\n",
"Epoch 189: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.608, train_loss_epoch=0.608, valid_loss=4.580]\n",
"Epoch 189: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.608, train_loss_epoch=0.608, valid_loss=4.580]\n",
"Epoch 190: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.623, train_loss_epoch=0.623, valid_loss=4.580]\n",
"Epoch 190: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.623, train_loss_epoch=0.623, valid_loss=4.580]\n",
"Epoch 190: 100%|██████████| 1/1 [00:00<00:00, 1.18it/s, v_num=0, train_loss_step=0.621, train_loss_epoch=0.621, valid_loss=4.580]\n",
"Epoch 191: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.621, train_loss_epoch=0.621, valid_loss=4.580]\n",
"Epoch 191: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.621, train_loss_epoch=0.621, valid_loss=4.580]\n",
"Epoch 192: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.594, train_loss_epoch=0.594, valid_loss=4.580]\n",
"Epoch 192: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.594, train_loss_epoch=0.594, valid_loss=4.580]\n",
"Epoch 193: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.623, train_loss_epoch=0.623, valid_loss=4.580]\n",
"Epoch 193: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.623, train_loss_epoch=0.623, valid_loss=4.580]\n",
"Epoch 193: 100%|██████████| 1/1 [00:00<00:00, 1.18it/s, v_num=0, train_loss_step=0.624, train_loss_epoch=0.623, valid_loss=4.580]\n",
"Epoch 194: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.624, train_loss_epoch=0.624, valid_loss=4.580]\n",
"Epoch 194: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.624, train_loss_epoch=0.624, valid_loss=4.580]\n",
"Epoch 195: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.622, train_loss_epoch=0.622, valid_loss=4.580]\n",
"Epoch 195: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.622, train_loss_epoch=0.622, valid_loss=4.580]\n",
"Epoch 196: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.611, train_loss_epoch=0.611, valid_loss=4.580]\n",
"Epoch 196: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.611, train_loss_epoch=0.611, valid_loss=4.580]\n",
"Epoch 197: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.625, train_loss_epoch=0.625, valid_loss=4.580]\n",
"Epoch 197: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.625, train_loss_epoch=0.625, valid_loss=4.580]\n",
"Epoch 198: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.627, train_loss_epoch=0.627, valid_loss=4.580]\n",
"Epoch 198: 100%|██████████| 1/1 [00:00<00:00, 1.68it/s, v_num=0, train_loss_step=0.627, train_loss_epoch=0.627, valid_loss=4.580]\n",
"Epoch 199: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.610, train_loss_epoch=0.610, valid_loss=4.580]\n",
"Epoch 199: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.610, train_loss_epoch=0.610, valid_loss=4.580]\n",
"Epoch 199: 100%|██████████| 1/1 [00:00<00:00, 1.17it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.610, valid_loss=4.580]\n",
"Validation: | | 0/? [00:00, ?it/s]\u001b[A\n",
"Validation: 0%| | 0/1 [00:00, ?it/s]\u001b[A\n",
"Validation DataLoader 0: 0%| | 0/1 [00:00, ?it/s]\u001b[A\n",
"\u001b[36m(_train_tune pid=23277)\u001b[0m \n",
"Validation DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 24.62it/s]\u001b[A\n",
"Epoch 200: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.110]\n",
"Epoch 200: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.110]\n",
"Epoch 201: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.627, train_loss_epoch=0.627, valid_loss=4.110]\n",
"Epoch 201: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.627, train_loss_epoch=0.627, valid_loss=4.110]\n",
"Epoch 202: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.622, train_loss_epoch=0.622, valid_loss=4.110]\n",
"Epoch 202: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.622, train_loss_epoch=0.622, valid_loss=4.110]\n",
"Epoch 203: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.617, train_loss_epoch=0.617, valid_loss=4.110]\n",
"Epoch 203: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.617, train_loss_epoch=0.617, valid_loss=4.110]\n",
"Epoch 204: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.596, train_loss_epoch=0.596, valid_loss=4.110]\n",
"Epoch 204: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.596, train_loss_epoch=0.596, valid_loss=4.110]\n",
"Epoch 205: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.615, train_loss_epoch=0.615, valid_loss=4.110]\n",
"Epoch 205: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.615, train_loss_epoch=0.615, valid_loss=4.110]\n",
"Epoch 206: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.624, train_loss_epoch=0.624, valid_loss=4.110]\n",
"Epoch 206: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.624, train_loss_epoch=0.624, valid_loss=4.110]\n",
"Epoch 207: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.617, train_loss_epoch=0.617, valid_loss=4.110]\n",
"Epoch 207: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.617, train_loss_epoch=0.617, valid_loss=4.110]\n",
"Epoch 208: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.641, train_loss_epoch=0.641, valid_loss=4.110]\n",
"Epoch 208: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.641, train_loss_epoch=0.641, valid_loss=4.110]\n",
"Epoch 209: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.619, train_loss_epoch=0.619, valid_loss=4.110]\n",
"Epoch 209: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.619, train_loss_epoch=0.619, valid_loss=4.110]\n",
"Epoch 210: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.615, train_loss_epoch=0.615, valid_loss=4.110]\n",
"Epoch 210: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.615, train_loss_epoch=0.615, valid_loss=4.110]\n",
"Epoch 211: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.607, train_loss_epoch=0.607, valid_loss=4.110]\n",
"Epoch 211: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.607, train_loss_epoch=0.607, valid_loss=4.110]\n",
"Epoch 212: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.613, train_loss_epoch=0.613, valid_loss=4.110]\n",
"Epoch 212: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.613, train_loss_epoch=0.613, valid_loss=4.110]\n",
"Epoch 213: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.630, train_loss_epoch=0.630, valid_loss=4.110]\n",
"Epoch 213: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.630, train_loss_epoch=0.630, valid_loss=4.110]\n",
"Epoch 214: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.110]\n",
"Epoch 214: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.110]\n",
"Epoch 215: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.597, train_loss_epoch=0.597, valid_loss=4.110]\n",
"Epoch 215: 100%|██████████| 1/1 [00:00<00:00, 1.67it/s, v_num=0, train_loss_step=0.597, train_loss_epoch=0.597, valid_loss=4.110]\n",
"Epoch 216: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.605, train_loss_epoch=0.605, valid_loss=4.110]\n",
"Epoch 216: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.605, train_loss_epoch=0.605, valid_loss=4.110]\n",
"Epoch 217: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.609, train_loss_epoch=0.609, valid_loss=4.110]\n",
"Epoch 217: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.609, train_loss_epoch=0.609, valid_loss=4.110]\n",
"Epoch 218: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.620, train_loss_epoch=0.620, valid_loss=4.110]\n",
"Epoch 218: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.620, train_loss_epoch=0.620, valid_loss=4.110]\n",
"Epoch 219: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.609, train_loss_epoch=0.609, valid_loss=4.110]\n",
"Epoch 219: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.609, train_loss_epoch=0.609, valid_loss=4.110]\n",
"Epoch 220: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.615, train_loss_epoch=0.615, valid_loss=4.110]\n",
"Epoch 220: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.615, train_loss_epoch=0.615, valid_loss=4.110]\n",
"Epoch 221: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.613, train_loss_epoch=0.613, valid_loss=4.110]\n",
"Epoch 221: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.613, train_loss_epoch=0.613, valid_loss=4.110]\n",
"Epoch 222: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.603, train_loss_epoch=0.603, valid_loss=4.110]\n",
"Epoch 222: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.603, train_loss_epoch=0.603, valid_loss=4.110]\n",
"Epoch 223: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.633, train_loss_epoch=0.633, valid_loss=4.110]\n",
"Epoch 223: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.633, train_loss_epoch=0.633, valid_loss=4.110]\n",
"Epoch 224: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.609, train_loss_epoch=0.609, valid_loss=4.110]\n",
"Epoch 224: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.609, train_loss_epoch=0.609, valid_loss=4.110]\n",
"Epoch 225: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.620, train_loss_epoch=0.620, valid_loss=4.110]\n",
"Epoch 225: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.620, train_loss_epoch=0.620, valid_loss=4.110]\n",
"Epoch 226: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.613, train_loss_epoch=0.613, valid_loss=4.110]\n",
"Epoch 226: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.613, train_loss_epoch=0.613, valid_loss=4.110]\n",
"Epoch 227: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.626, train_loss_epoch=0.626, valid_loss=4.110]\n",
"Epoch 227: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.626, train_loss_epoch=0.626, valid_loss=4.110]\n",
"Epoch 228: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.586, train_loss_epoch=0.586, valid_loss=4.110]\n",
"Epoch 228: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.586, train_loss_epoch=0.586, valid_loss=4.110]\n",
"Epoch 229: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.600, train_loss_epoch=0.600, valid_loss=4.110]\n",
"Epoch 229: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.600, train_loss_epoch=0.600, valid_loss=4.110]\n",
"Epoch 230: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.613, train_loss_epoch=0.613, valid_loss=4.110]\n",
"Epoch 230: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.613, train_loss_epoch=0.613, valid_loss=4.110]\n",
"Epoch 231: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.597, train_loss_epoch=0.597, valid_loss=4.110]\n",
"Epoch 231: 100%|██████████| 1/1 [00:00<00:00, 1.68it/s, v_num=0, train_loss_step=0.597, train_loss_epoch=0.597, valid_loss=4.110]\n",
"Epoch 232: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.631, train_loss_epoch=0.631, valid_loss=4.110]\n",
"Epoch 232: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.631, train_loss_epoch=0.631, valid_loss=4.110]\n",
"Epoch 233: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.616, train_loss_epoch=0.616, valid_loss=4.110]\n",
"Epoch 233: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.616, train_loss_epoch=0.616, valid_loss=4.110]\n",
"Epoch 234: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.604, train_loss_epoch=0.604, valid_loss=4.110]\n",
"Epoch 234: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.604, train_loss_epoch=0.604, valid_loss=4.110]\n",
"Epoch 235: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.603, train_loss_epoch=0.603, valid_loss=4.110]\n",
"Epoch 235: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.603, train_loss_epoch=0.603, valid_loss=4.110]\n",
"Epoch 236: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.625, train_loss_epoch=0.625, valid_loss=4.110]\n",
"Epoch 236: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.625, train_loss_epoch=0.625, valid_loss=4.110]\n",
"Epoch 237: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.612, train_loss_epoch=0.612, valid_loss=4.110]\n",
"Epoch 237: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.612, train_loss_epoch=0.612, valid_loss=4.110]\n",
"Epoch 238: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.607, train_loss_epoch=0.607, valid_loss=4.110]\n",
"Epoch 238: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.607, train_loss_epoch=0.607, valid_loss=4.110]\n",
"Epoch 239: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.110]\n",
"Epoch 239: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.110]\n",
"Epoch 240: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.612, train_loss_epoch=0.612, valid_loss=4.110]\n",
"Epoch 240: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.612, train_loss_epoch=0.612, valid_loss=4.110]\n",
"Epoch 241: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.589, train_loss_epoch=0.589, valid_loss=4.110]\n",
"Epoch 241: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.589, train_loss_epoch=0.589, valid_loss=4.110]\n",
"Epoch 242: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.623, train_loss_epoch=0.623, valid_loss=4.110]\n",
"Epoch 242: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.623, train_loss_epoch=0.623, valid_loss=4.110]\n",
"Epoch 243: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.619, train_loss_epoch=0.619, valid_loss=4.110]\n",
"Epoch 243: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.619, train_loss_epoch=0.619, valid_loss=4.110]\n",
"Epoch 244: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.614, train_loss_epoch=0.614, valid_loss=4.110]\n",
"Epoch 244: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.614, train_loss_epoch=0.614, valid_loss=4.110]\n",
"Epoch 245: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.620, train_loss_epoch=0.620, valid_loss=4.110]\n",
"Epoch 245: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.620, train_loss_epoch=0.620, valid_loss=4.110]\n",
"Epoch 246: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.622, train_loss_epoch=0.622, valid_loss=4.110]\n",
"Epoch 246: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.622, train_loss_epoch=0.622, valid_loss=4.110]\n",
"Epoch 247: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.621, train_loss_epoch=0.621, valid_loss=4.110]\n",
"Epoch 247: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.621, train_loss_epoch=0.621, valid_loss=4.110]\n",
"Epoch 248: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.611, train_loss_epoch=0.611, valid_loss=4.110]\n",
"Epoch 248: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.611, train_loss_epoch=0.611, valid_loss=4.110]\n",
"Epoch 249: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.615, train_loss_epoch=0.615, valid_loss=4.110]\n",
"Epoch 249: 100%|██████████| 1/1 [00:00<00:00, 1.64it/s, v_num=0, train_loss_step=0.615, train_loss_epoch=0.615, valid_loss=4.110]\n",
"Epoch 250: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.609, train_loss_epoch=0.609, valid_loss=4.110]\n",
"Epoch 250: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.609, train_loss_epoch=0.609, valid_loss=4.110]\n",
"Epoch 251: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.628, train_loss_epoch=0.628, valid_loss=4.110]\n",
"Epoch 251: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.628, train_loss_epoch=0.628, valid_loss=4.110]\n",
"Epoch 252: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.611, train_loss_epoch=0.611, valid_loss=4.110]\n",
"Epoch 252: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.611, train_loss_epoch=0.611, valid_loss=4.110]\n",
"Epoch 253: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.621, train_loss_epoch=0.621, valid_loss=4.110]\n",
"Epoch 253: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.621, train_loss_epoch=0.621, valid_loss=4.110]\n",
"Epoch 254: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.601, train_loss_epoch=0.601, valid_loss=4.110]\n",
"Epoch 254: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.601, train_loss_epoch=0.601, valid_loss=4.110]\n",
"Epoch 255: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.601, train_loss_epoch=0.601, valid_loss=4.110]\n",
"Epoch 255: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.601, train_loss_epoch=0.601, valid_loss=4.110]\n",
"Epoch 256: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.609, train_loss_epoch=0.609, valid_loss=4.110]\n",
"Epoch 256: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.609, train_loss_epoch=0.609, valid_loss=4.110]\n",
"Epoch 257: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.601, train_loss_epoch=0.601, valid_loss=4.110]\n",
"Epoch 257: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.601, train_loss_epoch=0.601, valid_loss=4.110]\n",
"Epoch 258: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.608, train_loss_epoch=0.608, valid_loss=4.110]\n",
"Epoch 258: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.608, train_loss_epoch=0.608, valid_loss=4.110]\n",
"Epoch 259: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.601, train_loss_epoch=0.601, valid_loss=4.110]\n",
"Epoch 259: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.601, train_loss_epoch=0.601, valid_loss=4.110]\n",
"Epoch 260: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.110]\n",
"Epoch 260: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.110]\n",
"Epoch 261: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.610, train_loss_epoch=0.610, valid_loss=4.110]\n",
"Epoch 261: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.610, train_loss_epoch=0.610, valid_loss=4.110]\n",
"Epoch 262: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.610, train_loss_epoch=0.610, valid_loss=4.110]\n",
"Epoch 262: 100%|██████████| 1/1 [00:00<00:00, 1.68it/s, v_num=0, train_loss_step=0.610, train_loss_epoch=0.610, valid_loss=4.110]\n",
"Epoch 263: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.617, train_loss_epoch=0.617, valid_loss=4.110]\n",
"Epoch 263: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.617, train_loss_epoch=0.617, valid_loss=4.110]\n",
"Epoch 264: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.600, train_loss_epoch=0.600, valid_loss=4.110]\n",
"Epoch 264: 100%|██████████| 1/1 [00:00<00:00, 1.68it/s, v_num=0, train_loss_step=0.600, train_loss_epoch=0.600, valid_loss=4.110]\n",
"Epoch 265: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.612, train_loss_epoch=0.612, valid_loss=4.110]\n",
"Epoch 265: 100%|██████████| 1/1 [00:00<00:00, 1.65it/s, v_num=0, train_loss_step=0.612, train_loss_epoch=0.612, valid_loss=4.110]\n",
"Epoch 266: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.606, train_loss_epoch=0.606, valid_loss=4.110]\n",
"Epoch 266: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.606, train_loss_epoch=0.606, valid_loss=4.110]\n",
"Epoch 267: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.606, train_loss_epoch=0.606, valid_loss=4.110]\n",
"Epoch 267: 100%|██████████| 1/1 [00:00<00:00, 1.67it/s, v_num=0, train_loss_step=0.606, train_loss_epoch=0.606, valid_loss=4.110]\n",
"Epoch 268: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.604, train_loss_epoch=0.604, valid_loss=4.110]\n",
"Epoch 268: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.604, train_loss_epoch=0.604, valid_loss=4.110]\n",
"Epoch 269: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.616, train_loss_epoch=0.616, valid_loss=4.110]\n",
"Epoch 269: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.616, train_loss_epoch=0.616, valid_loss=4.110]\n",
"Epoch 270: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.611, train_loss_epoch=0.611, valid_loss=4.110]\n",
"Epoch 270: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.611, train_loss_epoch=0.611, valid_loss=4.110]\n",
"Epoch 271: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.591, train_loss_epoch=0.591, valid_loss=4.110]\n",
"Epoch 271: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.591, train_loss_epoch=0.591, valid_loss=4.110]\n",
"Epoch 272: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.624, train_loss_epoch=0.624, valid_loss=4.110]\n",
"Epoch 272: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.624, train_loss_epoch=0.624, valid_loss=4.110]\n",
"Epoch 273: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.597, train_loss_epoch=0.597, valid_loss=4.110]\n",
"Epoch 273: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.597, train_loss_epoch=0.597, valid_loss=4.110]\n",
"Epoch 274: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.110]\n",
"Epoch 274: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.110]\n",
"Epoch 275: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.614, train_loss_epoch=0.614, valid_loss=4.110]\n",
"Epoch 275: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.614, train_loss_epoch=0.614, valid_loss=4.110]\n",
"Epoch 276: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.600, train_loss_epoch=0.600, valid_loss=4.110]\n",
"Epoch 276: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.600, train_loss_epoch=0.600, valid_loss=4.110]\n",
"Epoch 277: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.609, train_loss_epoch=0.609, valid_loss=4.110]\n",
"Epoch 277: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.609, train_loss_epoch=0.609, valid_loss=4.110]\n",
"Epoch 278: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.618, train_loss_epoch=0.618, valid_loss=4.110]\n",
"Epoch 278: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.618, train_loss_epoch=0.618, valid_loss=4.110]\n",
"Epoch 279: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.617, train_loss_epoch=0.617, valid_loss=4.110]\n",
"Epoch 279: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.617, train_loss_epoch=0.617, valid_loss=4.110]\n",
"Epoch 280: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.575, valid_loss=4.110]\n",
"Epoch 280: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.575, valid_loss=4.110]\n",
"Epoch 281: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.614, train_loss_epoch=0.614, valid_loss=4.110]\n",
"Epoch 281: 100%|██████████| 1/1 [00:00<00:00, 1.68it/s, v_num=0, train_loss_step=0.614, train_loss_epoch=0.614, valid_loss=4.110]\n",
"Epoch 282: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.609, train_loss_epoch=0.609, valid_loss=4.110]\n",
"Epoch 282: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.609, train_loss_epoch=0.609, valid_loss=4.110]\n",
"Epoch 283: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.618, train_loss_epoch=0.618, valid_loss=4.110]\n",
"Epoch 283: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.618, train_loss_epoch=0.618, valid_loss=4.110]\n",
"Epoch 284: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.612, train_loss_epoch=0.612, valid_loss=4.110]\n",
"Epoch 284: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.612, train_loss_epoch=0.612, valid_loss=4.110]\n",
"Epoch 285: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.603, train_loss_epoch=0.603, valid_loss=4.110]\n",
"Epoch 285: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.603, train_loss_epoch=0.603, valid_loss=4.110]\n",
"Epoch 286: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.639, train_loss_epoch=0.639, valid_loss=4.110]\n",
"Epoch 286: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.639, train_loss_epoch=0.639, valid_loss=4.110]\n",
"Epoch 287: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.595, train_loss_epoch=0.595, valid_loss=4.110]\n",
"Epoch 287: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.595, train_loss_epoch=0.595, valid_loss=4.110]\n",
"Epoch 288: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.600, train_loss_epoch=0.600, valid_loss=4.110]\n",
"Epoch 288: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.600, train_loss_epoch=0.600, valid_loss=4.110]\n",
"Epoch 289: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.603, train_loss_epoch=0.603, valid_loss=4.110]\n",
"Epoch 289: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.603, train_loss_epoch=0.603, valid_loss=4.110]\n",
"Epoch 290: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.623, train_loss_epoch=0.623, valid_loss=4.110]\n",
"Epoch 290: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.623, train_loss_epoch=0.623, valid_loss=4.110]\n",
"Epoch 291: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.624, train_loss_epoch=0.624, valid_loss=4.110]\n",
"Epoch 291: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.624, train_loss_epoch=0.624, valid_loss=4.110]\n",
"Epoch 292: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.619, train_loss_epoch=0.619, valid_loss=4.110]\n",
"Epoch 292: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.619, train_loss_epoch=0.619, valid_loss=4.110]\n",
"Epoch 293: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.600, train_loss_epoch=0.600, valid_loss=4.110]\n",
"Epoch 293: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.600, train_loss_epoch=0.600, valid_loss=4.110]\n",
"Epoch 294: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.594, train_loss_epoch=0.594, valid_loss=4.110]\n",
"Epoch 294: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.594, train_loss_epoch=0.594, valid_loss=4.110]\n",
"Epoch 295: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.587, valid_loss=4.110]\n",
"Epoch 295: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.587, valid_loss=4.110]\n",
"Epoch 296: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.606, train_loss_epoch=0.606, valid_loss=4.110]\n",
"Epoch 296: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.606, train_loss_epoch=0.606, valid_loss=4.110]\n",
"Epoch 297: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.595, train_loss_epoch=0.595, valid_loss=4.110]\n",
"Epoch 297: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.595, train_loss_epoch=0.595, valid_loss=4.110]\n",
"Epoch 298: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.589, train_loss_epoch=0.589, valid_loss=4.110]\n",
"Epoch 298: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.589, train_loss_epoch=0.589, valid_loss=4.110]\n",
"Epoch 299: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.611, train_loss_epoch=0.611, valid_loss=4.110]\n",
"Epoch 299: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.611, train_loss_epoch=0.611, valid_loss=4.110]\n",
"Epoch 299: 100%|██████████| 1/1 [00:00<00:00, 1.16it/s, v_num=0, train_loss_step=0.606, train_loss_epoch=0.611, valid_loss=4.110]\n",
"Validation: | | 0/? [00:00, ?it/s]\u001b[A\n",
"Validation: 0%| | 0/1 [00:00, ?it/s]\u001b[A\n",
"Validation DataLoader 0: 0%| | 0/1 [00:00, ?it/s]\u001b[A\n",
"\u001b[36m(_train_tune pid=23277)\u001b[0m \n",
"Validation DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 28.82it/s]\u001b[A\n",
"\u001b[36m(_train_tune pid=23277)\u001b[0m \n",
"Epoch 300: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.606, train_loss_epoch=0.606, valid_loss=4.350]\n",
"Epoch 300: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.606, train_loss_epoch=0.606, valid_loss=4.350]\n",
"Epoch 301: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.615, train_loss_epoch=0.615, valid_loss=4.350]\n",
"Epoch 301: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.615, train_loss_epoch=0.615, valid_loss=4.350]\n",
"Epoch 302: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.601, train_loss_epoch=0.601, valid_loss=4.350]\n",
"Epoch 302: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.601, train_loss_epoch=0.601, valid_loss=4.350]\n",
"Epoch 303: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.599, train_loss_epoch=0.599, valid_loss=4.350]\n",
"Epoch 303: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.599, train_loss_epoch=0.599, valid_loss=4.350]\n",
"Epoch 304: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.597, train_loss_epoch=0.597, valid_loss=4.350]\n",
"Epoch 304: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.597, train_loss_epoch=0.597, valid_loss=4.350]\n",
"Epoch 305: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.608, train_loss_epoch=0.608, valid_loss=4.350]\n",
"Epoch 305: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.608, train_loss_epoch=0.608, valid_loss=4.350]\n",
"Epoch 306: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.574, train_loss_epoch=0.574, valid_loss=4.350]\n",
"Epoch 306: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.574, train_loss_epoch=0.574, valid_loss=4.350]\n",
"Epoch 307: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.609, train_loss_epoch=0.609, valid_loss=4.350]\n",
"Epoch 307: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.609, train_loss_epoch=0.609, valid_loss=4.350]\n",
"Epoch 308: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.601, train_loss_epoch=0.601, valid_loss=4.350]\n",
"Epoch 308: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.601, train_loss_epoch=0.601, valid_loss=4.350]\n",
"Epoch 309: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.577, train_loss_epoch=0.577, valid_loss=4.350]\n",
"Epoch 309: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.577, train_loss_epoch=0.577, valid_loss=4.350]\n",
"Epoch 310: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.611, train_loss_epoch=0.611, valid_loss=4.350]\n",
"Epoch 310: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.611, train_loss_epoch=0.611, valid_loss=4.350]\n",
"Epoch 311: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.620, train_loss_epoch=0.620, valid_loss=4.350]\n",
"Epoch 311: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.620, train_loss_epoch=0.620, valid_loss=4.350]\n",
"Epoch 312: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.603, train_loss_epoch=0.603, valid_loss=4.350]\n",
"Epoch 312: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.603, train_loss_epoch=0.603, valid_loss=4.350]\n",
"Epoch 313: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.619, train_loss_epoch=0.619, valid_loss=4.350]\n",
"Epoch 313: 100%|██████████| 1/1 [00:00<00:00, 1.68it/s, v_num=0, train_loss_step=0.619, train_loss_epoch=0.619, valid_loss=4.350]\n",
"Epoch 314: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.615, train_loss_epoch=0.615, valid_loss=4.350]\n",
"Epoch 314: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.615, train_loss_epoch=0.615, valid_loss=4.350]\n",
"Epoch 315: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.601, train_loss_epoch=0.601, valid_loss=4.350]\n",
"Epoch 315: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.601, train_loss_epoch=0.601, valid_loss=4.350]\n",
"Epoch 316: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.598, train_loss_epoch=0.598, valid_loss=4.350]\n",
"Epoch 316: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.598, train_loss_epoch=0.598, valid_loss=4.350]\n",
"Epoch 316: 100%|██████████| 1/1 [00:00<00:00, 1.17it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.598, valid_loss=4.350]\n",
"Epoch 317: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.350]\n",
"Epoch 317: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.350]\n",
"Epoch 318: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.350]\n",
"Epoch 318: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.350]\n",
"Epoch 319: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.598, train_loss_epoch=0.598, valid_loss=4.350]\n",
"Epoch 319: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.598, train_loss_epoch=0.598, valid_loss=4.350]\n",
"Epoch 320: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.605, train_loss_epoch=0.605, valid_loss=4.350]\n",
"Epoch 320: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.605, train_loss_epoch=0.605, valid_loss=4.350]\n",
"Epoch 321: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.596, train_loss_epoch=0.596, valid_loss=4.350]\n",
"Epoch 321: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.596, train_loss_epoch=0.596, valid_loss=4.350]\n",
"Epoch 322: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.607, train_loss_epoch=0.607, valid_loss=4.350]\n",
"Epoch 322: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.607, train_loss_epoch=0.607, valid_loss=4.350]\n",
"Epoch 323: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.596, train_loss_epoch=0.596, valid_loss=4.350]\n",
"Epoch 323: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.596, train_loss_epoch=0.596, valid_loss=4.350]\n",
"Epoch 324: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.597, train_loss_epoch=0.597, valid_loss=4.350]\n",
"Epoch 324: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.597, train_loss_epoch=0.597, valid_loss=4.350]\n",
"Epoch 325: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.582, train_loss_epoch=0.582, valid_loss=4.350]\n",
"Epoch 325: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.582, train_loss_epoch=0.582, valid_loss=4.350]\n",
"Epoch 326: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.596, train_loss_epoch=0.596, valid_loss=4.350]\n",
"Epoch 326: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.596, train_loss_epoch=0.596, valid_loss=4.350]\n",
"Epoch 327: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.627, train_loss_epoch=0.627, valid_loss=4.350]\n",
"Epoch 327: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.627, train_loss_epoch=0.627, valid_loss=4.350]\n",
"Epoch 328: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.604, train_loss_epoch=0.604, valid_loss=4.350]\n",
"Epoch 328: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.604, train_loss_epoch=0.604, valid_loss=4.350]\n",
"Epoch 329: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.589, train_loss_epoch=0.589, valid_loss=4.350]\n",
"Epoch 329: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.589, train_loss_epoch=0.589, valid_loss=4.350]\n",
"Epoch 330: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.595, train_loss_epoch=0.595, valid_loss=4.350]\n",
"Epoch 330: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.595, train_loss_epoch=0.595, valid_loss=4.350]\n",
"Epoch 331: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.350]\n",
"Epoch 331: 100%|██████████| 1/1 [00:00<00:00, 1.67it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.350]\n",
"Epoch 332: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.601, train_loss_epoch=0.601, valid_loss=4.350]\n",
"Epoch 332: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.601, train_loss_epoch=0.601, valid_loss=4.350]\n",
"Epoch 333: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.605, train_loss_epoch=0.605, valid_loss=4.350]\n",
"Epoch 333: 100%|██████████| 1/1 [00:00<00:00, 1.67it/s, v_num=0, train_loss_step=0.605, train_loss_epoch=0.605, valid_loss=4.350]\n",
"Epoch 334: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.600, train_loss_epoch=0.600, valid_loss=4.350]\n",
"Epoch 334: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.600, train_loss_epoch=0.600, valid_loss=4.350]\n",
"Epoch 335: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.611, train_loss_epoch=0.611, valid_loss=4.350]\n",
"Epoch 335: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.611, train_loss_epoch=0.611, valid_loss=4.350]\n",
"Epoch 336: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.618, train_loss_epoch=0.618, valid_loss=4.350]\n",
"Epoch 336: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.618, train_loss_epoch=0.618, valid_loss=4.350]\n",
"Epoch 337: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.588, train_loss_epoch=0.588, valid_loss=4.350]\n",
"Epoch 337: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.588, train_loss_epoch=0.588, valid_loss=4.350]\n",
"Epoch 338: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.587, valid_loss=4.350]\n",
"Epoch 338: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.587, valid_loss=4.350]\n",
"Epoch 339: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.603, train_loss_epoch=0.603, valid_loss=4.350]\n",
"Epoch 339: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.603, train_loss_epoch=0.603, valid_loss=4.350]\n",
"Epoch 340: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.587, valid_loss=4.350]\n",
"Epoch 340: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.587, valid_loss=4.350]\n",
"Epoch 341: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.611, train_loss_epoch=0.611, valid_loss=4.350]\n",
"Epoch 341: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.611, train_loss_epoch=0.611, valid_loss=4.350]\n",
"Epoch 342: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.584, train_loss_epoch=0.584, valid_loss=4.350]\n",
"Epoch 342: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.584, train_loss_epoch=0.584, valid_loss=4.350]\n",
"Epoch 343: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.601, train_loss_epoch=0.601, valid_loss=4.350]\n",
"Epoch 343: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.601, train_loss_epoch=0.601, valid_loss=4.350]\n",
"Epoch 344: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.350]\n",
"Epoch 344: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.350]\n",
"Epoch 345: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.600, train_loss_epoch=0.600, valid_loss=4.350]\n",
"Epoch 345: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.600, train_loss_epoch=0.600, valid_loss=4.350]\n",
"Epoch 346: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.591, train_loss_epoch=0.591, valid_loss=4.350]\n",
"Epoch 346: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.591, train_loss_epoch=0.591, valid_loss=4.350]\n",
"Epoch 347: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.604, train_loss_epoch=0.604, valid_loss=4.350]\n",
"Epoch 347: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.604, train_loss_epoch=0.604, valid_loss=4.350]\n",
"Epoch 348: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.573, train_loss_epoch=0.573, valid_loss=4.350]\n",
"Epoch 348: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.573, train_loss_epoch=0.573, valid_loss=4.350]\n",
"Epoch 349: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.601, train_loss_epoch=0.601, valid_loss=4.350]\n",
"Epoch 349: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.601, train_loss_epoch=0.601, valid_loss=4.350]\n",
"Epoch 350: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.571, valid_loss=4.350]\n",
"Epoch 350: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.571, valid_loss=4.350]\n",
"Epoch 351: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.350]\n",
"Epoch 351: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.350]\n",
"Epoch 352: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.589, train_loss_epoch=0.589, valid_loss=4.350]\n",
"Epoch 352: 100%|██████████| 1/1 [00:00<00:00, 1.76it/s, v_num=0, train_loss_step=0.589, train_loss_epoch=0.589, valid_loss=4.350]\n",
"Epoch 353: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.350]\n",
"Epoch 353: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.350]\n",
"Epoch 354: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.618, train_loss_epoch=0.618, valid_loss=4.350]\n",
"Epoch 354: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.618, train_loss_epoch=0.618, valid_loss=4.350]\n",
"Epoch 355: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.350]\n",
"Epoch 355: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.350]\n",
"Epoch 356: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.603, train_loss_epoch=0.603, valid_loss=4.350]\n",
"Epoch 356: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.603, train_loss_epoch=0.603, valid_loss=4.350]\n",
"Epoch 357: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.350]\n",
"Epoch 357: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.350]\n",
"Epoch 358: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.586, train_loss_epoch=0.586, valid_loss=4.350]\n",
"Epoch 358: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.586, train_loss_epoch=0.586, valid_loss=4.350]\n",
"Epoch 359: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.584, train_loss_epoch=0.584, valid_loss=4.350]\n",
"Epoch 359: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.584, train_loss_epoch=0.584, valid_loss=4.350]\n",
"Epoch 359: 100%|██████████| 1/1 [00:00<00:00, 1.18it/s, v_num=0, train_loss_step=0.597, train_loss_epoch=0.584, valid_loss=4.350]\n",
"Epoch 359: 100%|██████████| 1/1 [00:00<00:00, 1.17it/s, v_num=0, train_loss_step=0.597, train_loss_epoch=0.597, valid_loss=4.350]\n",
"Epoch 360: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.597, train_loss_epoch=0.597, valid_loss=4.350]\n",
"Epoch 360: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.597, train_loss_epoch=0.597, valid_loss=4.350]\n",
"Epoch 361: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.586, train_loss_epoch=0.586, valid_loss=4.350]\n",
"Epoch 361: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.586, train_loss_epoch=0.586, valid_loss=4.350]\n",
"Epoch 362: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.606, train_loss_epoch=0.606, valid_loss=4.350]\n",
"Epoch 362: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.606, train_loss_epoch=0.606, valid_loss=4.350]\n",
"Epoch 363: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.586, train_loss_epoch=0.586, valid_loss=4.350]\n",
"Epoch 363: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.586, train_loss_epoch=0.586, valid_loss=4.350]\n",
"Epoch 364: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.350]\n",
"Epoch 364: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.350]\n",
"Epoch 365: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.610, train_loss_epoch=0.610, valid_loss=4.350]\n",
"Epoch 365: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.610, train_loss_epoch=0.610, valid_loss=4.350]\n",
"Epoch 366: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.588, train_loss_epoch=0.588, valid_loss=4.350]\n",
"Epoch 366: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.588, train_loss_epoch=0.588, valid_loss=4.350]\n",
"Epoch 367: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.612, train_loss_epoch=0.612, valid_loss=4.350]\n",
"Epoch 367: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.612, train_loss_epoch=0.612, valid_loss=4.350]\n",
"Epoch 368: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.350]\n",
"Epoch 368: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.350]\n",
"Epoch 369: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.604, train_loss_epoch=0.604, valid_loss=4.350]\n",
"Epoch 369: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.604, train_loss_epoch=0.604, valid_loss=4.350]\n",
"Epoch 370: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.580, train_loss_epoch=0.580, valid_loss=4.350]\n",
"Epoch 370: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.580, train_loss_epoch=0.580, valid_loss=4.350]\n",
"Epoch 371: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.580, train_loss_epoch=0.580, valid_loss=4.350]\n",
"Epoch 371: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.580, train_loss_epoch=0.580, valid_loss=4.350]\n",
"Epoch 372: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.350]\n",
"Epoch 372: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.350]\n",
"Epoch 373: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.350]\n",
"Epoch 373: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.350]\n",
"Epoch 374: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.604, train_loss_epoch=0.604, valid_loss=4.350]\n",
"Epoch 374: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.604, train_loss_epoch=0.604, valid_loss=4.350]\n",
"Epoch 375: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.565, train_loss_epoch=0.565, valid_loss=4.350]\n",
"Epoch 375: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.565, train_loss_epoch=0.565, valid_loss=4.350]\n",
"Epoch 376: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.619, train_loss_epoch=0.619, valid_loss=4.350]\n",
"Epoch 376: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.619, train_loss_epoch=0.619, valid_loss=4.350]\n",
"Epoch 377: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.350]\n",
"Epoch 377: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.350]\n",
"Epoch 378: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.350]\n",
"Epoch 378: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.350]\n",
"Epoch 379: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.597, train_loss_epoch=0.597, valid_loss=4.350]\n",
"Epoch 379: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.597, train_loss_epoch=0.597, valid_loss=4.350]\n",
"Epoch 380: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.600, train_loss_epoch=0.600, valid_loss=4.350]\n",
"Epoch 380: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.600, train_loss_epoch=0.600, valid_loss=4.350]\n",
"Epoch 381: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.596, train_loss_epoch=0.596, valid_loss=4.350]\n",
"Epoch 381: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.596, train_loss_epoch=0.596, valid_loss=4.350]\n",
"Epoch 382: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.611, train_loss_epoch=0.611, valid_loss=4.350]\n",
"Epoch 382: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.611, train_loss_epoch=0.611, valid_loss=4.350]\n",
"Epoch 383: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.560, train_loss_epoch=0.560, valid_loss=4.350]\n",
"Epoch 383: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.560, train_loss_epoch=0.560, valid_loss=4.350]\n",
"Epoch 384: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.587, valid_loss=4.350]\n",
"Epoch 384: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.587, valid_loss=4.350]\n",
"Epoch 385: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.605, train_loss_epoch=0.605, valid_loss=4.350]\n",
"Epoch 385: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.605, train_loss_epoch=0.605, valid_loss=4.350]\n",
"Epoch 386: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.350]\n",
"Epoch 386: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.350]\n",
"Epoch 387: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.560, train_loss_epoch=0.560, valid_loss=4.350]\n",
"Epoch 387: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.560, train_loss_epoch=0.560, valid_loss=4.350]\n",
"Epoch 388: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.597, train_loss_epoch=0.597, valid_loss=4.350]\n",
"Epoch 388: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.597, train_loss_epoch=0.597, valid_loss=4.350]\n",
"Epoch 389: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.586, train_loss_epoch=0.586, valid_loss=4.350]\n",
"Epoch 389: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.586, train_loss_epoch=0.586, valid_loss=4.350]\n",
"Epoch 390: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.350]\n",
"Epoch 390: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.350]\n",
"Epoch 391: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.350]\n",
"Epoch 391: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.350]\n",
"Epoch 392: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.595, train_loss_epoch=0.595, valid_loss=4.350]\n",
"Epoch 392: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.595, train_loss_epoch=0.595, valid_loss=4.350]\n",
"Epoch 393: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.599, train_loss_epoch=0.599, valid_loss=4.350]\n",
"Epoch 393: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.599, train_loss_epoch=0.599, valid_loss=4.350]\n",
"Epoch 394: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.586, train_loss_epoch=0.586, valid_loss=4.350]\n",
"Epoch 394: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.586, train_loss_epoch=0.586, valid_loss=4.350]\n",
"Epoch 395: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.350]\n",
"Epoch 395: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.350]\n",
"Epoch 396: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.577, train_loss_epoch=0.577, valid_loss=4.350]\n",
"Epoch 396: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.577, train_loss_epoch=0.577, valid_loss=4.350]\n",
"Epoch 397: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.350]\n",
"Epoch 397: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.350]\n",
"Epoch 398: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.586, train_loss_epoch=0.586, valid_loss=4.350]\n",
"Epoch 398: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.586, train_loss_epoch=0.586, valid_loss=4.350]\n",
"Epoch 399: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.350]\n",
"Epoch 399: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.350]\n",
"Epoch 399: 100%|██████████| 1/1 [00:00<00:00, 1.16it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.350]\n",
"Validation: | | 0/? [00:00, ?it/s]\u001b[A\n",
"Validation: 0%| | 0/1 [00:00, ?it/s]\u001b[A\n",
"Validation DataLoader 0: 0%| | 0/1 [00:00, ?it/s]\u001b[A\n",
"Validation DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 27.49it/s]\u001b[A\n",
"\u001b[36m(_train_tune pid=23277)\u001b[0m \n",
"Epoch 400: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.550]\n",
"Epoch 400: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.550]\n",
"Epoch 401: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.600, train_loss_epoch=0.600, valid_loss=4.550]\n",
"Epoch 401: 100%|██████████| 1/1 [00:00<00:00, 1.67it/s, v_num=0, train_loss_step=0.600, train_loss_epoch=0.600, valid_loss=4.550]\n",
"Epoch 402: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.600, train_loss_epoch=0.600, valid_loss=4.550]\n",
"Epoch 402: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.600, train_loss_epoch=0.600, valid_loss=4.550]\n",
"Epoch 403: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.577, train_loss_epoch=0.577, valid_loss=4.550]\n",
"Epoch 403: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.577, train_loss_epoch=0.577, valid_loss=4.550]\n",
"Epoch 404: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.574, train_loss_epoch=0.574, valid_loss=4.550]\n",
"Epoch 404: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.574, train_loss_epoch=0.574, valid_loss=4.550]\n",
"Epoch 405: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.579, train_loss_epoch=0.579, valid_loss=4.550]\n",
"Epoch 405: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.579, train_loss_epoch=0.579, valid_loss=4.550]\n",
"Epoch 406: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.577, train_loss_epoch=0.577, valid_loss=4.550]\n",
"Epoch 406: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.577, train_loss_epoch=0.577, valid_loss=4.550]\n",
"Epoch 407: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.588, train_loss_epoch=0.588, valid_loss=4.550]\n",
"Epoch 407: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.588, train_loss_epoch=0.588, valid_loss=4.550]\n",
"Epoch 408: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.621, train_loss_epoch=0.621, valid_loss=4.550]\n",
"Epoch 408: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.621, train_loss_epoch=0.621, valid_loss=4.550]\n",
"Epoch 409: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.571, valid_loss=4.550]\n",
"Epoch 409: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.571, valid_loss=4.550]\n",
"Epoch 410: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.602, train_loss_epoch=0.602, valid_loss=4.550]\n",
"Epoch 410: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.602, train_loss_epoch=0.602, valid_loss=4.550]\n",
"Epoch 411: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.610, train_loss_epoch=0.610, valid_loss=4.550]\n",
"Epoch 411: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.610, train_loss_epoch=0.610, valid_loss=4.550]\n",
"Epoch 412: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.550]\n",
"Epoch 412: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.550]\n",
"Epoch 413: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.550]\n",
"Epoch 413: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.550]\n",
"Epoch 414: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.591, train_loss_epoch=0.591, valid_loss=4.550]\n",
"Epoch 414: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.591, train_loss_epoch=0.591, valid_loss=4.550]\n",
"Epoch 415: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.597, train_loss_epoch=0.597, valid_loss=4.550]\n",
"Epoch 415: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.597, train_loss_epoch=0.597, valid_loss=4.550]\n",
"Epoch 416: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.577, train_loss_epoch=0.577, valid_loss=4.550]\n",
"Epoch 416: 100%|██████████| 1/1 [00:00<00:00, 1.68it/s, v_num=0, train_loss_step=0.577, train_loss_epoch=0.577, valid_loss=4.550]\n",
"Epoch 417: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.577, train_loss_epoch=0.577, valid_loss=4.550]\n",
"Epoch 417: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.577, train_loss_epoch=0.577, valid_loss=4.550]\n",
"Epoch 418: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.607, train_loss_epoch=0.607, valid_loss=4.550]\n",
"Epoch 418: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.607, train_loss_epoch=0.607, valid_loss=4.550]\n",
"Epoch 419: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.550]\n",
"Epoch 419: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.550]\n",
"Epoch 420: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.575, valid_loss=4.550]\n",
"Epoch 420: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.575, valid_loss=4.550]\n",
"Epoch 421: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.574, train_loss_epoch=0.574, valid_loss=4.550]\n",
"Epoch 421: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.574, train_loss_epoch=0.574, valid_loss=4.550]\n",
"Epoch 422: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.594, train_loss_epoch=0.594, valid_loss=4.550]\n",
"Epoch 422: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.594, train_loss_epoch=0.594, valid_loss=4.550]\n",
"Epoch 423: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.594, train_loss_epoch=0.594, valid_loss=4.550]\n",
"Epoch 423: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.594, train_loss_epoch=0.594, valid_loss=4.550]\n",
"Epoch 424: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.597, train_loss_epoch=0.597, valid_loss=4.550]\n",
"Epoch 424: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.597, train_loss_epoch=0.597, valid_loss=4.550]\n",
"Epoch 425: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.591, train_loss_epoch=0.591, valid_loss=4.550]\n",
"Epoch 425: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.591, train_loss_epoch=0.591, valid_loss=4.550]\n",
"Epoch 426: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.550]\n",
"Epoch 426: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.550]\n",
"Epoch 427: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.550]\n",
"Epoch 427: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.550]\n",
"Epoch 428: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.594, train_loss_epoch=0.594, valid_loss=4.550]\n",
"Epoch 428: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.594, train_loss_epoch=0.594, valid_loss=4.550]\n",
"Epoch 429: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.572, train_loss_epoch=0.572, valid_loss=4.550]\n",
"Epoch 429: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.572, train_loss_epoch=0.572, valid_loss=4.550]\n",
"Epoch 430: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.550]\n",
"Epoch 430: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.550]\n",
"Epoch 430: 100%|██████████| 1/1 [00:00<00:00, 1.17it/s, v_num=0, train_loss_step=0.568, train_loss_epoch=0.568, valid_loss=4.550]\n",
"Epoch 431: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.568, train_loss_epoch=0.568, valid_loss=4.550]\n",
"Epoch 431: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.568, train_loss_epoch=0.568, valid_loss=4.550]\n",
"Epoch 432: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.610, train_loss_epoch=0.610, valid_loss=4.550]\n",
"Epoch 432: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.610, train_loss_epoch=0.610, valid_loss=4.550]\n",
"Epoch 433: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.594, train_loss_epoch=0.594, valid_loss=4.550]\n",
"Epoch 433: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.594, train_loss_epoch=0.594, valid_loss=4.550]\n",
"Epoch 434: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.550]\n",
"Epoch 434: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.550]\n",
"Epoch 435: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.555, train_loss_epoch=0.555, valid_loss=4.550]\n",
"Epoch 435: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.555, train_loss_epoch=0.555, valid_loss=4.550]\n",
"Epoch 436: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.578, train_loss_epoch=0.578, valid_loss=4.550]\n",
"Epoch 436: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.578, train_loss_epoch=0.578, valid_loss=4.550]\n",
"Epoch 437: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.576, train_loss_epoch=0.576, valid_loss=4.550]\n",
"Epoch 437: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.576, train_loss_epoch=0.576, valid_loss=4.550]\n",
"Epoch 438: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.582, train_loss_epoch=0.582, valid_loss=4.550]\n",
"Epoch 438: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.582, train_loss_epoch=0.582, valid_loss=4.550]\n",
"Epoch 439: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.588, train_loss_epoch=0.588, valid_loss=4.550]\n",
"Epoch 439: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.588, train_loss_epoch=0.588, valid_loss=4.550]\n",
"Epoch 440: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.569, train_loss_epoch=0.569, valid_loss=4.550]\n",
"Epoch 440: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.569, train_loss_epoch=0.569, valid_loss=4.550]\n",
"Epoch 440: 100%|██████████| 1/1 [00:00<00:00, 1.18it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.550]\n",
"Epoch 441: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.550]\n",
"Epoch 441: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.550]\n",
"Epoch 442: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.585, train_loss_epoch=0.585, valid_loss=4.550]\n",
"Epoch 442: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.585, train_loss_epoch=0.585, valid_loss=4.550]\n",
"Epoch 443: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.573, train_loss_epoch=0.573, valid_loss=4.550]\n",
"Epoch 443: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.573, train_loss_epoch=0.573, valid_loss=4.550]\n",
"Epoch 444: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.579, train_loss_epoch=0.579, valid_loss=4.550]\n",
"Epoch 444: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.579, train_loss_epoch=0.579, valid_loss=4.550]\n",
"Epoch 445: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.567, train_loss_epoch=0.567, valid_loss=4.550]\n",
"Epoch 445: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.567, train_loss_epoch=0.567, valid_loss=4.550]\n",
"Epoch 446: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.602, train_loss_epoch=0.602, valid_loss=4.550]\n",
"Epoch 446: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.602, train_loss_epoch=0.602, valid_loss=4.550]\n",
"Epoch 447: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.605, train_loss_epoch=0.605, valid_loss=4.550]\n",
"Epoch 447: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.605, train_loss_epoch=0.605, valid_loss=4.550]\n",
"Epoch 448: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.588, train_loss_epoch=0.588, valid_loss=4.550]\n",
"Epoch 448: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.588, train_loss_epoch=0.588, valid_loss=4.550]\n",
"Epoch 449: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.598, train_loss_epoch=0.598, valid_loss=4.550]\n",
"Epoch 449: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.598, train_loss_epoch=0.598, valid_loss=4.550]\n",
"Epoch 450: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.574, train_loss_epoch=0.574, valid_loss=4.550]\n",
"Epoch 450: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.574, train_loss_epoch=0.574, valid_loss=4.550]\n",
"Epoch 451: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.575, valid_loss=4.550]\n",
"Epoch 451: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.575, valid_loss=4.550]\n",
"Epoch 452: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.607, train_loss_epoch=0.607, valid_loss=4.550]\n",
"Epoch 452: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.607, train_loss_epoch=0.607, valid_loss=4.550]\n",
"Epoch 453: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.570, train_loss_epoch=0.570, valid_loss=4.550]\n",
"Epoch 453: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.570, train_loss_epoch=0.570, valid_loss=4.550]\n",
"Epoch 454: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.550]\n",
"Epoch 454: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.550]\n",
"Epoch 455: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.578, train_loss_epoch=0.578, valid_loss=4.550]\n",
"Epoch 455: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.578, train_loss_epoch=0.578, valid_loss=4.550]\n",
"Epoch 456: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.586, train_loss_epoch=0.586, valid_loss=4.550]\n",
"Epoch 456: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.586, train_loss_epoch=0.586, valid_loss=4.550]\n",
"Epoch 457: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.598, train_loss_epoch=0.598, valid_loss=4.550]\n",
"Epoch 457: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.598, train_loss_epoch=0.598, valid_loss=4.550]\n",
"Epoch 458: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.585, train_loss_epoch=0.585, valid_loss=4.550]\n",
"Epoch 458: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.585, train_loss_epoch=0.585, valid_loss=4.550]\n",
"Epoch 459: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.591, train_loss_epoch=0.591, valid_loss=4.550]\n",
"Epoch 459: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.591, train_loss_epoch=0.591, valid_loss=4.550]\n",
"Epoch 460: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.596, train_loss_epoch=0.596, valid_loss=4.550]\n",
"Epoch 460: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.596, train_loss_epoch=0.596, valid_loss=4.550]\n",
"Epoch 461: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.578, train_loss_epoch=0.578, valid_loss=4.550]\n",
"Epoch 461: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.578, train_loss_epoch=0.578, valid_loss=4.550]\n",
"Epoch 462: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.588, train_loss_epoch=0.588, valid_loss=4.550]\n",
"Epoch 462: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.588, train_loss_epoch=0.588, valid_loss=4.550]\n",
"Epoch 463: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.584, train_loss_epoch=0.584, valid_loss=4.550]\n",
"Epoch 463: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.584, train_loss_epoch=0.584, valid_loss=4.550]\n",
"Epoch 464: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.585, train_loss_epoch=0.585, valid_loss=4.550]\n",
"Epoch 464: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.585, train_loss_epoch=0.585, valid_loss=4.550]\n",
"Epoch 465: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.594, train_loss_epoch=0.594, valid_loss=4.550]\n",
"Epoch 465: 100%|██████████| 1/1 [00:00<00:00, 1.68it/s, v_num=0, train_loss_step=0.594, train_loss_epoch=0.594, valid_loss=4.550]\n",
"Epoch 466: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.591, train_loss_epoch=0.591, valid_loss=4.550]\n",
"Epoch 466: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.591, train_loss_epoch=0.591, valid_loss=4.550]\n",
"Epoch 467: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.569, train_loss_epoch=0.569, valid_loss=4.550]\n",
"Epoch 467: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.569, train_loss_epoch=0.569, valid_loss=4.550]\n",
"Epoch 468: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.621, train_loss_epoch=0.621, valid_loss=4.550]\n",
"Epoch 468: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.621, train_loss_epoch=0.621, valid_loss=4.550]\n",
"Epoch 469: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.550]\n",
"Epoch 469: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.550]\n",
"Epoch 470: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.582, train_loss_epoch=0.582, valid_loss=4.550]\n",
"Epoch 470: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.582, train_loss_epoch=0.582, valid_loss=4.550]\n",
"Epoch 471: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.579, train_loss_epoch=0.579, valid_loss=4.550]\n",
"Epoch 471: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.579, train_loss_epoch=0.579, valid_loss=4.550]\n",
"Epoch 472: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.598, train_loss_epoch=0.598, valid_loss=4.550]\n",
"Epoch 472: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.598, train_loss_epoch=0.598, valid_loss=4.550]\n",
"Epoch 473: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.550]\n",
"Epoch 473: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.550]\n",
"Epoch 474: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.589, train_loss_epoch=0.589, valid_loss=4.550]\n",
"Epoch 474: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.589, train_loss_epoch=0.589, valid_loss=4.550]\n",
"Epoch 475: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.550]\n",
"Epoch 475: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.550]\n",
"Epoch 476: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.550]\n",
"Epoch 476: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.550]\n",
"Epoch 477: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.550]\n",
"Epoch 477: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.550]\n",
"Epoch 478: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.580, train_loss_epoch=0.580, valid_loss=4.550]\n",
"Epoch 478: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.580, train_loss_epoch=0.580, valid_loss=4.550]\n",
"Epoch 479: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.602, train_loss_epoch=0.602, valid_loss=4.550]\n",
"Epoch 479: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.602, train_loss_epoch=0.602, valid_loss=4.550]\n",
"Epoch 480: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.575, valid_loss=4.550]\n",
"Epoch 480: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.575, valid_loss=4.550]\n",
"Epoch 481: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.584, train_loss_epoch=0.584, valid_loss=4.550]\n",
"Epoch 481: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.584, train_loss_epoch=0.584, valid_loss=4.550]\n",
"Epoch 482: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.609, train_loss_epoch=0.609, valid_loss=4.550]\n",
"Epoch 482: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.609, train_loss_epoch=0.609, valid_loss=4.550]\n",
"Epoch 483: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.562, train_loss_epoch=0.562, valid_loss=4.550]\n",
"Epoch 483: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.562, train_loss_epoch=0.562, valid_loss=4.550]\n",
"Epoch 484: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.565, train_loss_epoch=0.565, valid_loss=4.550]\n",
"Epoch 484: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.565, train_loss_epoch=0.565, valid_loss=4.550]\n",
"Epoch 485: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.582, train_loss_epoch=0.582, valid_loss=4.550]\n",
"Epoch 485: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.582, train_loss_epoch=0.582, valid_loss=4.550]\n",
"Epoch 486: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.586, train_loss_epoch=0.586, valid_loss=4.550]\n",
"Epoch 486: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.586, train_loss_epoch=0.586, valid_loss=4.550]\n",
"Epoch 487: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.586, train_loss_epoch=0.586, valid_loss=4.550]\n",
"Epoch 487: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.586, train_loss_epoch=0.586, valid_loss=4.550]\n",
"Epoch 488: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.561, train_loss_epoch=0.561, valid_loss=4.550]\n",
"Epoch 488: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.561, train_loss_epoch=0.561, valid_loss=4.550]\n",
"Epoch 489: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.561, train_loss_epoch=0.561, valid_loss=4.550]\n",
"Epoch 489: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.561, train_loss_epoch=0.561, valid_loss=4.550]\n",
"Epoch 490: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.571, valid_loss=4.550]\n",
"Epoch 490: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.571, valid_loss=4.550]\n",
"Epoch 491: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.550]\n",
"Epoch 491: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.550]\n",
"Epoch 492: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.550]\n",
"Epoch 492: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.550]\n",
"Epoch 493: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.573, train_loss_epoch=0.573, valid_loss=4.550]\n",
"Epoch 493: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.573, train_loss_epoch=0.573, valid_loss=4.550]\n",
"Epoch 494: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.594, train_loss_epoch=0.594, valid_loss=4.550]\n",
"Epoch 494: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.594, train_loss_epoch=0.594, valid_loss=4.550]\n",
"Epoch 495: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.550]\n",
"Epoch 495: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.550]\n",
"Epoch 496: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.594, train_loss_epoch=0.594, valid_loss=4.550]\n",
"Epoch 496: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.594, train_loss_epoch=0.594, valid_loss=4.550]\n",
"Epoch 497: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.589, train_loss_epoch=0.589, valid_loss=4.550]\n",
"Epoch 497: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.589, train_loss_epoch=0.589, valid_loss=4.550]\n",
"Epoch 498: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.577, train_loss_epoch=0.577, valid_loss=4.550]\n",
"Epoch 498: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.577, train_loss_epoch=0.577, valid_loss=4.550]\n",
"Epoch 499: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.550]\n",
"Epoch 499: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.550]\n",
"Epoch 499: 100%|██████████| 1/1 [00:00<00:00, 1.16it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.592, valid_loss=4.550]\n",
"Validation: | | 0/? [00:00, ?it/s]\u001b[A\n",
"Validation: 0%| | 0/1 [00:00, ?it/s]\u001b[A\n",
"Validation DataLoader 0: 0%| | 0/1 [00:00, ?it/s]\u001b[A\n",
"Validation DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 26.22it/s]\u001b[A\n",
"Epoch 500: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.587, valid_loss=4.230]\n",
"Epoch 500: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.587, valid_loss=4.230]\n",
"Epoch 501: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.574, train_loss_epoch=0.574, valid_loss=4.230]\n",
"Epoch 501: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.574, train_loss_epoch=0.574, valid_loss=4.230]\n",
"Epoch 502: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.578, train_loss_epoch=0.578, valid_loss=4.230]\n",
"Epoch 502: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.578, train_loss_epoch=0.578, valid_loss=4.230]\n",
"Epoch 503: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.563, train_loss_epoch=0.563, valid_loss=4.230]\n",
"Epoch 503: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.563, train_loss_epoch=0.563, valid_loss=4.230]\n",
"Epoch 504: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.574, train_loss_epoch=0.574, valid_loss=4.230]\n",
"Epoch 504: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.574, train_loss_epoch=0.574, valid_loss=4.230]\n",
"Epoch 505: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.564, train_loss_epoch=0.564, valid_loss=4.230]\n",
"Epoch 505: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.564, train_loss_epoch=0.564, valid_loss=4.230]\n",
"Epoch 506: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.571, valid_loss=4.230]\n",
"Epoch 506: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.571, valid_loss=4.230]\n",
"Epoch 507: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.576, train_loss_epoch=0.576, valid_loss=4.230]\n",
"Epoch 507: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.576, train_loss_epoch=0.576, valid_loss=4.230]\n",
"Epoch 508: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.230]\n",
"Epoch 508: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.230]\n",
"Epoch 509: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.585, train_loss_epoch=0.585, valid_loss=4.230]\n",
"Epoch 509: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.585, train_loss_epoch=0.585, valid_loss=4.230]\n",
"Epoch 510: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.579, train_loss_epoch=0.579, valid_loss=4.230]\n",
"Epoch 510: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.579, train_loss_epoch=0.579, valid_loss=4.230]\n",
"Epoch 511: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.579, train_loss_epoch=0.579, valid_loss=4.230]\n",
"Epoch 511: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.579, train_loss_epoch=0.579, valid_loss=4.230]\n",
"Epoch 512: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.568, train_loss_epoch=0.568, valid_loss=4.230]\n",
"Epoch 512: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.568, train_loss_epoch=0.568, valid_loss=4.230]\n",
"Epoch 513: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.601, train_loss_epoch=0.601, valid_loss=4.230]\n",
"Epoch 513: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.601, train_loss_epoch=0.601, valid_loss=4.230]\n",
"Epoch 514: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.576, train_loss_epoch=0.576, valid_loss=4.230]\n",
"Epoch 514: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.576, train_loss_epoch=0.576, valid_loss=4.230]\n",
"Epoch 515: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.575, valid_loss=4.230]\n",
"Epoch 515: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.575, valid_loss=4.230]\n",
"Epoch 516: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.563, train_loss_epoch=0.563, valid_loss=4.230]\n",
"Epoch 516: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.563, train_loss_epoch=0.563, valid_loss=4.230]\n",
"Epoch 517: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.576, train_loss_epoch=0.576, valid_loss=4.230]\n",
"Epoch 517: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.576, train_loss_epoch=0.576, valid_loss=4.230]\n",
"Epoch 518: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.576, train_loss_epoch=0.576, valid_loss=4.230]\n",
"Epoch 518: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.576, train_loss_epoch=0.576, valid_loss=4.230]\n",
"Epoch 519: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.571, valid_loss=4.230]\n",
"Epoch 519: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.571, valid_loss=4.230]\n",
"Epoch 520: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.568, train_loss_epoch=0.568, valid_loss=4.230]\n",
"Epoch 520: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.568, train_loss_epoch=0.568, valid_loss=4.230]\n",
"Epoch 521: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.572, train_loss_epoch=0.572, valid_loss=4.230]\n",
"Epoch 521: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.572, train_loss_epoch=0.572, valid_loss=4.230]\n",
"Epoch 522: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.230]\n",
"Epoch 522: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.230]\n",
"Epoch 523: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.578, train_loss_epoch=0.578, valid_loss=4.230]\n",
"Epoch 523: 100%|██████████| 1/1 [00:00<00:00, 1.76it/s, v_num=0, train_loss_step=0.578, train_loss_epoch=0.578, valid_loss=4.230]\n",
"Epoch 524: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.565, train_loss_epoch=0.565, valid_loss=4.230]\n",
"Epoch 524: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.565, train_loss_epoch=0.565, valid_loss=4.230]\n",
"Epoch 525: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.576, train_loss_epoch=0.576, valid_loss=4.230]\n",
"Epoch 525: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.576, train_loss_epoch=0.576, valid_loss=4.230]\n",
"Epoch 526: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.567, train_loss_epoch=0.567, valid_loss=4.230]\n",
"Epoch 526: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.567, train_loss_epoch=0.567, valid_loss=4.230]\n",
"Epoch 527: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.599, train_loss_epoch=0.599, valid_loss=4.230]\n",
"Epoch 527: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.599, train_loss_epoch=0.599, valid_loss=4.230]\n",
"Epoch 528: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.230]\n",
"Epoch 528: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.230]\n",
"Epoch 529: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.588, train_loss_epoch=0.588, valid_loss=4.230]\n",
"Epoch 529: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.588, train_loss_epoch=0.588, valid_loss=4.230]\n",
"Epoch 530: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.587, valid_loss=4.230]\n",
"Epoch 530: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.587, valid_loss=4.230]\n",
"Epoch 531: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.230]\n",
"Epoch 531: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.230]\n",
"Epoch 532: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.571, valid_loss=4.230]\n",
"Epoch 532: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.571, valid_loss=4.230]\n",
"Epoch 533: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.578, train_loss_epoch=0.578, valid_loss=4.230]\n",
"Epoch 533: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.578, train_loss_epoch=0.578, valid_loss=4.230]\n",
"Epoch 534: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.585, train_loss_epoch=0.585, valid_loss=4.230]\n",
"Epoch 534: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.585, train_loss_epoch=0.585, valid_loss=4.230]\n",
"Epoch 535: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.588, train_loss_epoch=0.588, valid_loss=4.230]\n",
"Epoch 535: 100%|██████████| 1/1 [00:00<00:00, 1.68it/s, v_num=0, train_loss_step=0.588, train_loss_epoch=0.588, valid_loss=4.230]\n",
"Epoch 536: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.573, train_loss_epoch=0.573, valid_loss=4.230]\n",
"Epoch 536: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.573, train_loss_epoch=0.573, valid_loss=4.230]\n",
"Epoch 537: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.578, train_loss_epoch=0.578, valid_loss=4.230]\n",
"Epoch 537: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.578, train_loss_epoch=0.578, valid_loss=4.230]\n",
"Epoch 538: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.570, train_loss_epoch=0.570, valid_loss=4.230]\n",
"Epoch 538: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.570, train_loss_epoch=0.570, valid_loss=4.230]\n",
"Epoch 539: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.545, train_loss_epoch=0.545, valid_loss=4.230]\n",
"Epoch 539: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.545, train_loss_epoch=0.545, valid_loss=4.230]\n",
"Epoch 540: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.568, train_loss_epoch=0.568, valid_loss=4.230]\n",
"Epoch 540: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.568, train_loss_epoch=0.568, valid_loss=4.230]\n",
"Epoch 541: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.230]\n",
"Epoch 541: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.230]\n",
"Epoch 542: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.544, train_loss_epoch=0.544, valid_loss=4.230]\n",
"Epoch 542: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.544, train_loss_epoch=0.544, valid_loss=4.230]\n",
"Epoch 543: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.555, train_loss_epoch=0.555, valid_loss=4.230]\n",
"Epoch 543: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.555, train_loss_epoch=0.555, valid_loss=4.230]\n",
"Epoch 544: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.552, train_loss_epoch=0.552, valid_loss=4.230]\n",
"Epoch 544: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.552, train_loss_epoch=0.552, valid_loss=4.230]\n",
"Epoch 545: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.576, train_loss_epoch=0.576, valid_loss=4.230]\n",
"Epoch 545: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.576, train_loss_epoch=0.576, valid_loss=4.230]\n",
"Epoch 546: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.557, train_loss_epoch=0.557, valid_loss=4.230]\n",
"Epoch 546: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.557, train_loss_epoch=0.557, valid_loss=4.230]\n",
"Epoch 546: 100%|██████████| 1/1 [00:00<00:00, 1.18it/s, v_num=0, train_loss_step=0.557, train_loss_epoch=0.557, valid_loss=4.230]\n",
"Epoch 547: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.557, train_loss_epoch=0.557, valid_loss=4.230]\n",
"Epoch 547: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.557, train_loss_epoch=0.557, valid_loss=4.230]\n",
"Epoch 548: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.580, train_loss_epoch=0.580, valid_loss=4.230]\n",
"Epoch 548: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.580, train_loss_epoch=0.580, valid_loss=4.230]\n",
"Epoch 549: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.548, train_loss_epoch=0.548, valid_loss=4.230]\n",
"Epoch 549: 100%|██████████| 1/1 [00:00<00:00, 1.68it/s, v_num=0, train_loss_step=0.548, train_loss_epoch=0.548, valid_loss=4.230]\n",
"Epoch 550: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.556, train_loss_epoch=0.556, valid_loss=4.230]\n",
"Epoch 550: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.556, train_loss_epoch=0.556, valid_loss=4.230]\n",
"Epoch 551: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.572, train_loss_epoch=0.572, valid_loss=4.230]\n",
"Epoch 551: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.572, train_loss_epoch=0.572, valid_loss=4.230]\n",
"Epoch 552: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.554, train_loss_epoch=0.554, valid_loss=4.230]\n",
"Epoch 552: 100%|██████████| 1/1 [00:00<00:00, 1.66it/s, v_num=0, train_loss_step=0.554, train_loss_epoch=0.554, valid_loss=4.230]\n",
"Epoch 553: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.549, train_loss_epoch=0.549, valid_loss=4.230]\n",
"Epoch 553: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.549, train_loss_epoch=0.549, valid_loss=4.230]\n",
"Epoch 554: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.560, train_loss_epoch=0.560, valid_loss=4.230]\n",
"Epoch 554: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.560, train_loss_epoch=0.560, valid_loss=4.230]\n",
"Epoch 555: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.568, train_loss_epoch=0.568, valid_loss=4.230]\n",
"Epoch 555: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.568, train_loss_epoch=0.568, valid_loss=4.230]\n",
"Epoch 556: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.659, train_loss_epoch=0.659, valid_loss=4.230]\n",
"Epoch 556: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.659, train_loss_epoch=0.659, valid_loss=4.230]\n",
"Epoch 557: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.596, train_loss_epoch=0.596, valid_loss=4.230]\n",
"Epoch 557: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.596, train_loss_epoch=0.596, valid_loss=4.230]\n",
"Epoch 558: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.608, train_loss_epoch=0.608, valid_loss=4.230]\n",
"Epoch 558: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.608, train_loss_epoch=0.608, valid_loss=4.230]\n",
"Epoch 559: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.584, train_loss_epoch=0.584, valid_loss=4.230]\n",
"Epoch 559: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.584, train_loss_epoch=0.584, valid_loss=4.230]\n",
"Epoch 560: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.617, train_loss_epoch=0.617, valid_loss=4.230]\n",
"Epoch 560: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.617, train_loss_epoch=0.617, valid_loss=4.230]\n",
"Epoch 561: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.636, train_loss_epoch=0.636, valid_loss=4.230]\n",
"Epoch 561: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.636, train_loss_epoch=0.636, valid_loss=4.230]\n",
"Epoch 562: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.625, train_loss_epoch=0.625, valid_loss=4.230]\n",
"Epoch 562: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.625, train_loss_epoch=0.625, valid_loss=4.230]\n",
"Epoch 562: 100%|██████████| 1/1 [00:00<00:00, 1.17it/s, v_num=0, train_loss_step=0.614, train_loss_epoch=0.614, valid_loss=4.230]\n",
"Epoch 563: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.614, train_loss_epoch=0.614, valid_loss=4.230]\n",
"Epoch 563: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.614, train_loss_epoch=0.614, valid_loss=4.230]\n",
"Epoch 564: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.596, train_loss_epoch=0.596, valid_loss=4.230]\n",
"Epoch 564: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.596, train_loss_epoch=0.596, valid_loss=4.230]\n",
"Epoch 565: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.613, train_loss_epoch=0.613, valid_loss=4.230]\n",
"Epoch 565: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.613, train_loss_epoch=0.613, valid_loss=4.230]\n",
"Epoch 566: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.230]\n",
"Epoch 566: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.230]\n",
"Epoch 567: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.601, train_loss_epoch=0.601, valid_loss=4.230]\n",
"Epoch 567: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.601, train_loss_epoch=0.601, valid_loss=4.230]\n",
"Epoch 568: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.230]\n",
"Epoch 568: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.230]\n",
"Epoch 569: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.230]\n",
"Epoch 569: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.230]\n",
"Epoch 570: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.598, train_loss_epoch=0.598, valid_loss=4.230]\n",
"Epoch 570: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.598, train_loss_epoch=0.598, valid_loss=4.230]\n",
"Epoch 571: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.603, train_loss_epoch=0.603, valid_loss=4.230]\n",
"Epoch 571: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.603, train_loss_epoch=0.603, valid_loss=4.230]\n",
"Epoch 572: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.620, train_loss_epoch=0.620, valid_loss=4.230]\n",
"Epoch 572: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.620, train_loss_epoch=0.620, valid_loss=4.230]\n",
"Epoch 573: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.596, train_loss_epoch=0.596, valid_loss=4.230]\n",
"Epoch 573: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.596, train_loss_epoch=0.596, valid_loss=4.230]\n",
"Epoch 574: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.599, train_loss_epoch=0.599, valid_loss=4.230]\n",
"Epoch 574: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.599, train_loss_epoch=0.599, valid_loss=4.230]\n",
"Epoch 575: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.610, train_loss_epoch=0.610, valid_loss=4.230]\n",
"Epoch 575: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.610, train_loss_epoch=0.610, valid_loss=4.230]\n",
"Epoch 576: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.606, train_loss_epoch=0.606, valid_loss=4.230]\n",
"Epoch 576: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.606, train_loss_epoch=0.606, valid_loss=4.230]\n",
"Epoch 577: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.584, train_loss_epoch=0.584, valid_loss=4.230]\n",
"Epoch 577: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.584, train_loss_epoch=0.584, valid_loss=4.230]\n",
"Epoch 578: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.611, train_loss_epoch=0.611, valid_loss=4.230]\n",
"Epoch 578: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.611, train_loss_epoch=0.611, valid_loss=4.230]\n",
"Epoch 579: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.230]\n",
"Epoch 579: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.230]\n",
"Epoch 580: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.603, train_loss_epoch=0.603, valid_loss=4.230]\n",
"Epoch 580: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.603, train_loss_epoch=0.603, valid_loss=4.230]\n",
"Epoch 581: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.230]\n",
"Epoch 581: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.230]\n",
"Epoch 582: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.601, train_loss_epoch=0.601, valid_loss=4.230]\n",
"Epoch 582: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.601, train_loss_epoch=0.601, valid_loss=4.230]\n",
"Epoch 583: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.613, train_loss_epoch=0.613, valid_loss=4.230]\n",
"Epoch 583: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.613, train_loss_epoch=0.613, valid_loss=4.230]\n",
"Epoch 584: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.567, train_loss_epoch=0.567, valid_loss=4.230]\n",
"Epoch 584: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.567, train_loss_epoch=0.567, valid_loss=4.230]\n",
"Epoch 585: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.577, train_loss_epoch=0.577, valid_loss=4.230]\n",
"Epoch 585: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.577, train_loss_epoch=0.577, valid_loss=4.230]\n",
"Epoch 586: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.564, train_loss_epoch=0.564, valid_loss=4.230]\n",
"Epoch 586: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.564, train_loss_epoch=0.564, valid_loss=4.230]\n",
"Epoch 587: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.572, train_loss_epoch=0.572, valid_loss=4.230]\n",
"Epoch 587: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.572, train_loss_epoch=0.572, valid_loss=4.230]\n",
"Epoch 588: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.577, train_loss_epoch=0.577, valid_loss=4.230]\n",
"Epoch 588: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.577, train_loss_epoch=0.577, valid_loss=4.230]\n",
"Epoch 589: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.604, train_loss_epoch=0.604, valid_loss=4.230]\n",
"Epoch 589: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.604, train_loss_epoch=0.604, valid_loss=4.230]\n",
"Epoch 590: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.230]\n",
"Epoch 590: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.230]\n",
"Epoch 591: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.587, valid_loss=4.230]\n",
"Epoch 591: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.587, valid_loss=4.230]\n",
"Epoch 592: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.589, train_loss_epoch=0.589, valid_loss=4.230]\n",
"Epoch 592: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.589, train_loss_epoch=0.589, valid_loss=4.230]\n",
"Epoch 593: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.588, train_loss_epoch=0.588, valid_loss=4.230]\n",
"Epoch 593: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.588, train_loss_epoch=0.588, valid_loss=4.230]\n",
"Epoch 594: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.591, train_loss_epoch=0.591, valid_loss=4.230]\n",
"Epoch 594: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.591, train_loss_epoch=0.591, valid_loss=4.230]\n",
"Epoch 595: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.602, train_loss_epoch=0.602, valid_loss=4.230]\n",
"Epoch 595: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.602, train_loss_epoch=0.602, valid_loss=4.230]\n",
"Epoch 596: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.602, train_loss_epoch=0.602, valid_loss=4.230]\n",
"Epoch 596: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.602, train_loss_epoch=0.602, valid_loss=4.230]\n",
"Epoch 597: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.587, valid_loss=4.230]\n",
"Epoch 597: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.587, valid_loss=4.230]\n",
"Epoch 598: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.594, train_loss_epoch=0.594, valid_loss=4.230]\n",
"Epoch 598: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.594, train_loss_epoch=0.594, valid_loss=4.230]\n",
"Epoch 599: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.578, train_loss_epoch=0.578, valid_loss=4.230]\n",
"Epoch 599: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.578, train_loss_epoch=0.578, valid_loss=4.230]\n",
"Epoch 599: 100%|██████████| 1/1 [00:00<00:00, 1.18it/s, v_num=0, train_loss_step=0.591, train_loss_epoch=0.578, valid_loss=4.230]\n",
"Validation: | | 0/? [00:00, ?it/s]\u001b[A\n",
"Validation: 0%| | 0/1 [00:00, ?it/s]\u001b[A\n",
"Validation DataLoader 0: 0%| | 0/1 [00:00, ?it/s]\u001b[A\n",
"Validation DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 31.24it/s]\u001b[A\n",
"Epoch 600: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.591, train_loss_epoch=0.591, valid_loss=4.470]\n",
"Epoch 600: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.591, train_loss_epoch=0.591, valid_loss=4.470]\n",
"Epoch 601: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.602, train_loss_epoch=0.602, valid_loss=4.470]\n",
"Epoch 601: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.602, train_loss_epoch=0.602, valid_loss=4.470]\n",
"Epoch 602: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.599, train_loss_epoch=0.599, valid_loss=4.470]\n",
"Epoch 602: 100%|██████████| 1/1 [00:00<00:00, 1.67it/s, v_num=0, train_loss_step=0.599, train_loss_epoch=0.599, valid_loss=4.470]\n",
"Epoch 603: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.470]\n",
"Epoch 603: 100%|██████████| 1/1 [00:00<00:00, 1.68it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.470]\n",
"Epoch 603: 100%|██████████| 1/1 [00:00<00:00, 1.15it/s, v_num=0, train_loss_step=0.573, train_loss_epoch=0.581, valid_loss=4.470]\n",
"Epoch 604: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.573, train_loss_epoch=0.573, valid_loss=4.470]\n",
"Epoch 604: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.573, train_loss_epoch=0.573, valid_loss=4.470]\n",
"Epoch 605: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.573, train_loss_epoch=0.573, valid_loss=4.470]\n",
"Epoch 605: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.573, train_loss_epoch=0.573, valid_loss=4.470]\n",
"Epoch 606: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.591, train_loss_epoch=0.591, valid_loss=4.470]\n",
"Epoch 606: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.591, train_loss_epoch=0.591, valid_loss=4.470]\n",
"Epoch 607: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.470]\n",
"Epoch 607: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.470]\n",
"Epoch 608: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.587, valid_loss=4.470]\n",
"Epoch 608: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.587, valid_loss=4.470]\n",
"Epoch 609: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.584, train_loss_epoch=0.584, valid_loss=4.470]\n",
"Epoch 609: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.584, train_loss_epoch=0.584, valid_loss=4.470]\n",
"Epoch 610: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.587, valid_loss=4.470]\n",
"Epoch 610: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.587, valid_loss=4.470]\n",
"Epoch 611: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.470]\n",
"Epoch 611: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.470]\n",
"Epoch 612: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.591, train_loss_epoch=0.591, valid_loss=4.470]\n",
"Epoch 612: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.591, train_loss_epoch=0.591, valid_loss=4.470]\n",
"Epoch 613: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.470]\n",
"Epoch 613: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.470]\n",
"Epoch 614: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.470]\n",
"Epoch 614: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.470]\n",
"Epoch 615: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.598, train_loss_epoch=0.598, valid_loss=4.470]\n",
"Epoch 615: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.598, train_loss_epoch=0.598, valid_loss=4.470]\n",
"Epoch 616: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.591, train_loss_epoch=0.591, valid_loss=4.470]\n",
"Epoch 616: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.591, train_loss_epoch=0.591, valid_loss=4.470]\n",
"Epoch 617: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.576, train_loss_epoch=0.576, valid_loss=4.470]\n",
"Epoch 617: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.576, train_loss_epoch=0.576, valid_loss=4.470]\n",
"Epoch 618: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.584, train_loss_epoch=0.584, valid_loss=4.470]\n",
"Epoch 618: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.584, train_loss_epoch=0.584, valid_loss=4.470]\n",
"Epoch 619: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.589, train_loss_epoch=0.589, valid_loss=4.470]\n",
"Epoch 619: 100%|██████████| 1/1 [00:00<00:00, 1.64it/s, v_num=0, train_loss_step=0.589, train_loss_epoch=0.589, valid_loss=4.470]\n",
"Epoch 620: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.601, train_loss_epoch=0.601, valid_loss=4.470]\n",
"Epoch 620: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.601, train_loss_epoch=0.601, valid_loss=4.470]\n",
"Epoch 621: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.577, train_loss_epoch=0.577, valid_loss=4.470]\n",
"Epoch 621: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.577, train_loss_epoch=0.577, valid_loss=4.470]\n",
"Epoch 622: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.580, train_loss_epoch=0.580, valid_loss=4.470]\n",
"Epoch 622: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.580, train_loss_epoch=0.580, valid_loss=4.470]\n",
"Epoch 623: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.584, train_loss_epoch=0.584, valid_loss=4.470]\n",
"Epoch 623: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.584, train_loss_epoch=0.584, valid_loss=4.470]\n",
"Epoch 624: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.607, train_loss_epoch=0.607, valid_loss=4.470]\n",
"Epoch 624: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.607, train_loss_epoch=0.607, valid_loss=4.470]\n",
"Epoch 625: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.575, valid_loss=4.470]\n",
"Epoch 625: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.575, valid_loss=4.470]\n",
"Epoch 626: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.585, train_loss_epoch=0.585, valid_loss=4.470]\n",
"Epoch 626: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.585, train_loss_epoch=0.585, valid_loss=4.470]\n",
"Epoch 627: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.601, train_loss_epoch=0.601, valid_loss=4.470]\n",
"Epoch 627: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.601, train_loss_epoch=0.601, valid_loss=4.470]\n",
"Epoch 628: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.599, train_loss_epoch=0.599, valid_loss=4.470]\n",
"Epoch 628: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.599, train_loss_epoch=0.599, valid_loss=4.470]\n",
"Epoch 629: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.606, train_loss_epoch=0.606, valid_loss=4.470]\n",
"Epoch 629: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.606, train_loss_epoch=0.606, valid_loss=4.470]\n",
"Epoch 630: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.575, valid_loss=4.470]\n",
"Epoch 630: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.575, valid_loss=4.470]\n",
"Epoch 631: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.470]\n",
"Epoch 631: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.470]\n",
"Epoch 632: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.588, train_loss_epoch=0.588, valid_loss=4.470]\n",
"Epoch 632: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.588, train_loss_epoch=0.588, valid_loss=4.470]\n",
"Epoch 633: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.600, train_loss_epoch=0.600, valid_loss=4.470]\n",
"Epoch 633: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.600, train_loss_epoch=0.600, valid_loss=4.470]\n",
"Epoch 634: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.470]\n",
"Epoch 634: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.470]\n",
"Epoch 635: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.470]\n",
"Epoch 635: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.470]\n",
"Epoch 636: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.470]\n",
"Epoch 636: 100%|██████████| 1/1 [00:00<00:00, 1.65it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.470]\n",
"Epoch 637: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.470]\n",
"Epoch 637: 100%|██████████| 1/1 [00:00<00:00, 1.65it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.470]\n",
"Epoch 638: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.591, train_loss_epoch=0.591, valid_loss=4.470]\n",
"Epoch 638: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.591, train_loss_epoch=0.591, valid_loss=4.470]\n",
"Epoch 639: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.595, train_loss_epoch=0.595, valid_loss=4.470]\n",
"Epoch 639: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.595, train_loss_epoch=0.595, valid_loss=4.470]\n",
"Epoch 640: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.568, train_loss_epoch=0.568, valid_loss=4.470]\n",
"Epoch 640: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.568, train_loss_epoch=0.568, valid_loss=4.470]\n",
"Epoch 641: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.564, train_loss_epoch=0.564, valid_loss=4.470]\n",
"Epoch 641: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.564, train_loss_epoch=0.564, valid_loss=4.470]\n",
"Epoch 642: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.575, valid_loss=4.470]\n",
"Epoch 642: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.575, valid_loss=4.470]\n",
"Epoch 643: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.585, train_loss_epoch=0.585, valid_loss=4.470]\n",
"Epoch 643: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.585, train_loss_epoch=0.585, valid_loss=4.470]\n",
"Epoch 644: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.574, train_loss_epoch=0.574, valid_loss=4.470]\n",
"Epoch 644: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.574, train_loss_epoch=0.574, valid_loss=4.470]\n",
"Epoch 645: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.580, train_loss_epoch=0.580, valid_loss=4.470]\n",
"Epoch 645: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.580, train_loss_epoch=0.580, valid_loss=4.470]\n",
"Epoch 646: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.576, train_loss_epoch=0.576, valid_loss=4.470]\n",
"Epoch 646: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.576, train_loss_epoch=0.576, valid_loss=4.470]\n",
"Epoch 647: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.605, train_loss_epoch=0.605, valid_loss=4.470]\n",
"Epoch 647: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.605, train_loss_epoch=0.605, valid_loss=4.470]\n",
"Epoch 648: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.571, valid_loss=4.470]\n",
"Epoch 648: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.571, valid_loss=4.470]\n",
"Epoch 649: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.470]\n",
"Epoch 649: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.470]\n",
"Epoch 650: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.577, train_loss_epoch=0.577, valid_loss=4.470]\n",
"Epoch 650: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.577, train_loss_epoch=0.577, valid_loss=4.470]\n",
"Epoch 651: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.589, train_loss_epoch=0.589, valid_loss=4.470]\n",
"Epoch 651: 100%|██████████| 1/1 [00:00<00:00, 1.68it/s, v_num=0, train_loss_step=0.589, train_loss_epoch=0.589, valid_loss=4.470]\n",
"Epoch 652: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.594, train_loss_epoch=0.594, valid_loss=4.470]\n",
"Epoch 652: 100%|██████████| 1/1 [00:00<00:00, 1.67it/s, v_num=0, train_loss_step=0.594, train_loss_epoch=0.594, valid_loss=4.470]\n",
"Epoch 653: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.577, train_loss_epoch=0.577, valid_loss=4.470]\n",
"Epoch 653: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.577, train_loss_epoch=0.577, valid_loss=4.470]\n",
"Epoch 654: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.606, train_loss_epoch=0.606, valid_loss=4.470]\n",
"Epoch 654: 100%|██████████| 1/1 [00:00<00:00, 1.67it/s, v_num=0, train_loss_step=0.606, train_loss_epoch=0.606, valid_loss=4.470]\n",
"Epoch 655: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.575, valid_loss=4.470]\n",
"Epoch 655: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.575, valid_loss=4.470]\n",
"Epoch 656: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.577, train_loss_epoch=0.577, valid_loss=4.470]\n",
"Epoch 656: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.577, train_loss_epoch=0.577, valid_loss=4.470]\n",
"Epoch 657: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.470]\n",
"Epoch 657: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.470]\n",
"Epoch 658: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.591, train_loss_epoch=0.591, valid_loss=4.470]\n",
"Epoch 658: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.591, train_loss_epoch=0.591, valid_loss=4.470]\n",
"Epoch 659: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.603, train_loss_epoch=0.603, valid_loss=4.470]\n",
"Epoch 659: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.603, train_loss_epoch=0.603, valid_loss=4.470]\n",
"Epoch 660: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.562, train_loss_epoch=0.562, valid_loss=4.470]\n",
"Epoch 660: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.562, train_loss_epoch=0.562, valid_loss=4.470]\n",
"Epoch 661: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.601, train_loss_epoch=0.601, valid_loss=4.470]\n",
"Epoch 661: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.601, train_loss_epoch=0.601, valid_loss=4.470]\n",
"Epoch 662: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.574, train_loss_epoch=0.574, valid_loss=4.470]\n",
"Epoch 662: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.574, train_loss_epoch=0.574, valid_loss=4.470]\n",
"Epoch 663: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.570, train_loss_epoch=0.570, valid_loss=4.470]\n",
"Epoch 663: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.570, train_loss_epoch=0.570, valid_loss=4.470]\n",
"Epoch 664: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.470]\n",
"Epoch 664: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.470]\n",
"Epoch 665: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.595, train_loss_epoch=0.595, valid_loss=4.470]\n",
"Epoch 665: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.595, train_loss_epoch=0.595, valid_loss=4.470]\n",
"Epoch 665: 100%|██████████| 1/1 [00:00<00:00, 1.18it/s, v_num=0, train_loss_step=0.554, train_loss_epoch=0.595, valid_loss=4.470]\n",
"Epoch 665: 100%|██████████| 1/1 [00:00<00:00, 1.17it/s, v_num=0, train_loss_step=0.554, train_loss_epoch=0.554, valid_loss=4.470]\n",
"Epoch 666: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.554, train_loss_epoch=0.554, valid_loss=4.470]\n",
"Epoch 666: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.554, train_loss_epoch=0.554, valid_loss=4.470]\n",
"Epoch 667: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.584, train_loss_epoch=0.584, valid_loss=4.470]\n",
"Epoch 667: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.584, train_loss_epoch=0.584, valid_loss=4.470]\n",
"Epoch 668: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.594, train_loss_epoch=0.594, valid_loss=4.470]\n",
"Epoch 668: 100%|██████████| 1/1 [00:00<00:00, 1.68it/s, v_num=0, train_loss_step=0.594, train_loss_epoch=0.594, valid_loss=4.470]\n",
"Epoch 669: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.568, train_loss_epoch=0.568, valid_loss=4.470]\n",
"Epoch 669: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.568, train_loss_epoch=0.568, valid_loss=4.470]\n",
"Epoch 670: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.603, train_loss_epoch=0.603, valid_loss=4.470]\n",
"Epoch 670: 100%|██████████| 1/1 [00:00<00:00, 1.64it/s, v_num=0, train_loss_step=0.603, train_loss_epoch=0.603, valid_loss=4.470]\n",
"Epoch 671: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.588, train_loss_epoch=0.588, valid_loss=4.470]\n",
"Epoch 671: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.588, train_loss_epoch=0.588, valid_loss=4.470]\n",
"Epoch 672: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.571, valid_loss=4.470]\n",
"Epoch 672: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.571, valid_loss=4.470]\n",
"Epoch 673: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.600, train_loss_epoch=0.600, valid_loss=4.470]\n",
"Epoch 673: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.600, train_loss_epoch=0.600, valid_loss=4.470]\n",
"Epoch 674: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.580, train_loss_epoch=0.580, valid_loss=4.470]\n",
"Epoch 674: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.580, train_loss_epoch=0.580, valid_loss=4.470]\n",
"Epoch 675: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.591, train_loss_epoch=0.591, valid_loss=4.470]\n",
"Epoch 675: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.591, train_loss_epoch=0.591, valid_loss=4.470]\n",
"Epoch 676: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.470]\n",
"Epoch 676: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.470]\n",
"Epoch 677: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.587, valid_loss=4.470]\n",
"Epoch 677: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.587, valid_loss=4.470]\n",
"Epoch 678: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.470]\n",
"Epoch 678: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.470]\n",
"Epoch 679: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.578, train_loss_epoch=0.578, valid_loss=4.470]\n",
"Epoch 679: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.578, train_loss_epoch=0.578, valid_loss=4.470]\n",
"Epoch 680: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.556, train_loss_epoch=0.556, valid_loss=4.470]\n",
"Epoch 680: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.556, train_loss_epoch=0.556, valid_loss=4.470]\n",
"Epoch 681: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.470]\n",
"Epoch 681: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.470]\n",
"Epoch 682: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.587, valid_loss=4.470]\n",
"Epoch 682: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.587, valid_loss=4.470]\n",
"Epoch 683: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.601, train_loss_epoch=0.601, valid_loss=4.470]\n",
"Epoch 683: 100%|██████████| 1/1 [00:00<00:00, 1.68it/s, v_num=0, train_loss_step=0.601, train_loss_epoch=0.601, valid_loss=4.470]\n",
"Epoch 684: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.568, train_loss_epoch=0.568, valid_loss=4.470]\n",
"Epoch 684: 100%|██████████| 1/1 [00:00<00:00, 1.66it/s, v_num=0, train_loss_step=0.568, train_loss_epoch=0.568, valid_loss=4.470]\n",
"Epoch 685: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.586, train_loss_epoch=0.586, valid_loss=4.470]\n",
"Epoch 685: 100%|██████████| 1/1 [00:00<00:00, 1.68it/s, v_num=0, train_loss_step=0.586, train_loss_epoch=0.586, valid_loss=4.470]\n",
"Epoch 686: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.470]\n",
"Epoch 686: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.470]\n",
"Epoch 687: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.567, train_loss_epoch=0.567, valid_loss=4.470]\n",
"Epoch 687: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.567, train_loss_epoch=0.567, valid_loss=4.470]\n",
"Epoch 688: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.578, train_loss_epoch=0.578, valid_loss=4.470]\n",
"Epoch 688: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.578, train_loss_epoch=0.578, valid_loss=4.470]\n",
"Epoch 689: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.470]\n",
"Epoch 689: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.470]\n",
"Epoch 690: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.470]\n",
"Epoch 690: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.470]\n",
"Epoch 691: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.621, train_loss_epoch=0.621, valid_loss=4.470]\n",
"Epoch 691: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.621, train_loss_epoch=0.621, valid_loss=4.470]\n",
"Epoch 692: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.608, train_loss_epoch=0.608, valid_loss=4.470]\n",
"Epoch 692: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.608, train_loss_epoch=0.608, valid_loss=4.470]\n",
"Epoch 693: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.588, train_loss_epoch=0.588, valid_loss=4.470]\n",
"Epoch 693: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.588, train_loss_epoch=0.588, valid_loss=4.470]\n",
"Epoch 694: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.568, train_loss_epoch=0.568, valid_loss=4.470]\n",
"Epoch 694: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.568, train_loss_epoch=0.568, valid_loss=4.470]\n",
"Epoch 695: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.582, train_loss_epoch=0.582, valid_loss=4.470]\n",
"Epoch 695: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.582, train_loss_epoch=0.582, valid_loss=4.470]\n",
"Epoch 696: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.470]\n",
"Epoch 696: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.470]\n",
"Epoch 696: 100%|██████████| 1/1 [00:00<00:00, 1.18it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.470]\n",
"Epoch 697: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.470]\n",
"Epoch 697: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.470]\n",
"Epoch 698: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.470]\n",
"Epoch 698: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.470]\n",
"Epoch 699: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.571, valid_loss=4.470]\n",
"Epoch 699: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.571, valid_loss=4.470]\n",
"Epoch 699: 100%|██████████| 1/1 [00:00<00:00, 1.17it/s, v_num=0, train_loss_step=0.598, train_loss_epoch=0.571, valid_loss=4.470]\n",
"Validation: | | 0/? [00:00, ?it/s]\u001b[A\n",
"Validation: 0%| | 0/1 [00:00, ?it/s]\u001b[A\n",
"Validation DataLoader 0: 0%| | 0/1 [00:00, ?it/s]\u001b[A\n",
"Validation DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 23.76it/s]\u001b[A\n",
"Epoch 700: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.598, train_loss_epoch=0.598, valid_loss=4.530]\n",
"Epoch 700: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.598, train_loss_epoch=0.598, valid_loss=4.530]\n",
"Epoch 701: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.578, train_loss_epoch=0.578, valid_loss=4.530]\n",
"Epoch 701: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.578, train_loss_epoch=0.578, valid_loss=4.530]\n",
"Epoch 702: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.568, train_loss_epoch=0.568, valid_loss=4.530]\n",
"Epoch 702: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.568, train_loss_epoch=0.568, valid_loss=4.530]\n",
"Epoch 703: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.530]\n",
"Epoch 703: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.530]\n",
"Epoch 704: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.530]\n",
"Epoch 704: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.530]\n",
"Epoch 705: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.530]\n",
"Epoch 705: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.530]\n",
"Epoch 706: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.574, train_loss_epoch=0.574, valid_loss=4.530]\n",
"Epoch 706: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.574, train_loss_epoch=0.574, valid_loss=4.530]\n",
"Epoch 707: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.572, train_loss_epoch=0.572, valid_loss=4.530]\n",
"Epoch 707: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.572, train_loss_epoch=0.572, valid_loss=4.530]\n",
"Epoch 708: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.595, train_loss_epoch=0.595, valid_loss=4.530]\n",
"Epoch 708: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.595, train_loss_epoch=0.595, valid_loss=4.530]\n",
"Epoch 709: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.595, train_loss_epoch=0.595, valid_loss=4.530]\n",
"Epoch 709: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.595, train_loss_epoch=0.595, valid_loss=4.530]\n",
"Epoch 710: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.567, train_loss_epoch=0.567, valid_loss=4.530]\n",
"Epoch 710: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.567, train_loss_epoch=0.567, valid_loss=4.530]\n",
"Epoch 711: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.569, train_loss_epoch=0.569, valid_loss=4.530]\n",
"Epoch 711: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.569, train_loss_epoch=0.569, valid_loss=4.530]\n",
"Epoch 712: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.561, train_loss_epoch=0.561, valid_loss=4.530]\n",
"Epoch 712: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.561, train_loss_epoch=0.561, valid_loss=4.530]\n",
"Epoch 713: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.585, train_loss_epoch=0.585, valid_loss=4.530]\n",
"Epoch 713: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.585, train_loss_epoch=0.585, valid_loss=4.530]\n",
"Epoch 714: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.530]\n",
"Epoch 714: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.530]\n",
"Epoch 715: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.580, train_loss_epoch=0.580, valid_loss=4.530]\n",
"Epoch 715: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.580, train_loss_epoch=0.580, valid_loss=4.530]\n",
"Epoch 716: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.566, train_loss_epoch=0.566, valid_loss=4.530]\n",
"Epoch 716: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.566, train_loss_epoch=0.566, valid_loss=4.530]\n",
"Epoch 717: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.579, train_loss_epoch=0.579, valid_loss=4.530]\n",
"Epoch 717: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.579, train_loss_epoch=0.579, valid_loss=4.530]\n",
"Epoch 718: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.586, train_loss_epoch=0.586, valid_loss=4.530]\n",
"Epoch 718: 100%|██████████| 1/1 [00:00<00:00, 1.66it/s, v_num=0, train_loss_step=0.586, train_loss_epoch=0.586, valid_loss=4.530]\n",
"Epoch 719: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.576, train_loss_epoch=0.576, valid_loss=4.530]\n",
"Epoch 719: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.576, train_loss_epoch=0.576, valid_loss=4.530]\n",
"Epoch 719: 100%|██████████| 1/1 [00:00<00:00, 1.17it/s, v_num=0, train_loss_step=0.577, train_loss_epoch=0.577, valid_loss=4.530]\n",
"Epoch 720: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.577, train_loss_epoch=0.577, valid_loss=4.530]\n",
"Epoch 720: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.577, train_loss_epoch=0.577, valid_loss=4.530]\n",
"Epoch 721: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.577, train_loss_epoch=0.577, valid_loss=4.530]\n",
"Epoch 721: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.577, train_loss_epoch=0.577, valid_loss=4.530]\n",
"Epoch 722: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.530]\n",
"Epoch 722: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.530]\n",
"Epoch 723: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.567, train_loss_epoch=0.567, valid_loss=4.530]\n",
"Epoch 723: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.567, train_loss_epoch=0.567, valid_loss=4.530]\n",
"Epoch 724: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.570, train_loss_epoch=0.570, valid_loss=4.530]\n",
"Epoch 724: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.570, train_loss_epoch=0.570, valid_loss=4.530]\n",
"Epoch 725: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.530]\n",
"Epoch 725: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.530]\n",
"Epoch 726: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.574, train_loss_epoch=0.574, valid_loss=4.530]\n",
"Epoch 726: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.574, train_loss_epoch=0.574, valid_loss=4.530]\n",
"Epoch 727: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.574, train_loss_epoch=0.574, valid_loss=4.530]\n",
"Epoch 727: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.574, train_loss_epoch=0.574, valid_loss=4.530]\n",
"Epoch 728: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.576, train_loss_epoch=0.576, valid_loss=4.530]\n",
"Epoch 728: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.576, train_loss_epoch=0.576, valid_loss=4.530]\n",
"Epoch 729: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.576, train_loss_epoch=0.576, valid_loss=4.530]\n",
"Epoch 729: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.576, train_loss_epoch=0.576, valid_loss=4.530]\n",
"Epoch 730: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.598, train_loss_epoch=0.598, valid_loss=4.530]\n",
"Epoch 730: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.598, train_loss_epoch=0.598, valid_loss=4.530]\n",
"Epoch 731: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.588, train_loss_epoch=0.588, valid_loss=4.530]\n",
"Epoch 731: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.588, train_loss_epoch=0.588, valid_loss=4.530]\n",
"Epoch 732: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.586, train_loss_epoch=0.586, valid_loss=4.530]\n",
"Epoch 732: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.586, train_loss_epoch=0.586, valid_loss=4.530]\n",
"Epoch 733: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.561, train_loss_epoch=0.561, valid_loss=4.530]\n",
"Epoch 733: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.561, train_loss_epoch=0.561, valid_loss=4.530]\n",
"Epoch 734: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.530]\n",
"Epoch 734: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.530]\n",
"Epoch 735: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.569, train_loss_epoch=0.569, valid_loss=4.530]\n",
"Epoch 735: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.569, train_loss_epoch=0.569, valid_loss=4.530]\n",
"Epoch 736: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.530]\n",
"Epoch 736: 100%|██████████| 1/1 [00:00<00:00, 1.64it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.530]\n",
"Epoch 737: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.569, train_loss_epoch=0.569, valid_loss=4.530]\n",
"Epoch 737: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.569, train_loss_epoch=0.569, valid_loss=4.530]\n",
"Epoch 738: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.585, train_loss_epoch=0.585, valid_loss=4.530]\n",
"Epoch 738: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.585, train_loss_epoch=0.585, valid_loss=4.530]\n",
"Epoch 739: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.586, train_loss_epoch=0.586, valid_loss=4.530]\n",
"Epoch 739: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.586, train_loss_epoch=0.586, valid_loss=4.530]\n",
"Epoch 740: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.589, train_loss_epoch=0.589, valid_loss=4.530]\n",
"Epoch 740: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.589, train_loss_epoch=0.589, valid_loss=4.530]\n",
"Epoch 741: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.578, train_loss_epoch=0.578, valid_loss=4.530]\n",
"Epoch 741: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.578, train_loss_epoch=0.578, valid_loss=4.530]\n",
"Epoch 742: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.557, train_loss_epoch=0.557, valid_loss=4.530]\n",
"Epoch 742: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.557, train_loss_epoch=0.557, valid_loss=4.530]\n",
"Epoch 743: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.568, train_loss_epoch=0.568, valid_loss=4.530]\n",
"Epoch 743: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.568, train_loss_epoch=0.568, valid_loss=4.530]\n",
"Epoch 744: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.582, train_loss_epoch=0.582, valid_loss=4.530]\n",
"Epoch 744: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.582, train_loss_epoch=0.582, valid_loss=4.530]\n",
"Epoch 745: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.571, valid_loss=4.530]\n",
"Epoch 745: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.571, valid_loss=4.530]\n",
"Epoch 746: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.530]\n",
"Epoch 746: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.530]\n",
"Epoch 747: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.530]\n",
"Epoch 747: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.530]\n",
"Epoch 748: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.586, train_loss_epoch=0.586, valid_loss=4.530]\n",
"Epoch 748: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.586, train_loss_epoch=0.586, valid_loss=4.530]\n",
"Epoch 749: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.563, train_loss_epoch=0.563, valid_loss=4.530]\n",
"Epoch 749: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.563, train_loss_epoch=0.563, valid_loss=4.530]\n",
"Epoch 750: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.575, valid_loss=4.530]\n",
"Epoch 750: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.575, valid_loss=4.530]\n",
"Epoch 751: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.559, train_loss_epoch=0.559, valid_loss=4.530]\n",
"Epoch 751: 100%|██████████| 1/1 [00:00<00:00, 1.68it/s, v_num=0, train_loss_step=0.559, train_loss_epoch=0.559, valid_loss=4.530]\n",
"Epoch 752: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.573, train_loss_epoch=0.573, valid_loss=4.530]\n",
"Epoch 752: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.573, train_loss_epoch=0.573, valid_loss=4.530]\n",
"Epoch 753: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.530]\n",
"Epoch 753: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.592, train_loss_epoch=0.592, valid_loss=4.530]\n",
"Epoch 754: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.530]\n",
"Epoch 754: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.530]\n",
"Epoch 755: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.563, train_loss_epoch=0.563, valid_loss=4.530]\n",
"Epoch 755: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.563, train_loss_epoch=0.563, valid_loss=4.530]\n",
"Epoch 756: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.579, train_loss_epoch=0.579, valid_loss=4.530]\n",
"Epoch 756: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.579, train_loss_epoch=0.579, valid_loss=4.530]\n",
"Epoch 757: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.580, train_loss_epoch=0.580, valid_loss=4.530]\n",
"Epoch 757: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.580, train_loss_epoch=0.580, valid_loss=4.530]\n",
"Epoch 758: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.569, train_loss_epoch=0.569, valid_loss=4.530]\n",
"Epoch 758: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.569, train_loss_epoch=0.569, valid_loss=4.530]\n",
"Epoch 759: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.580, train_loss_epoch=0.580, valid_loss=4.530]\n",
"Epoch 759: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.580, train_loss_epoch=0.580, valid_loss=4.530]\n",
"Epoch 760: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.584, train_loss_epoch=0.584, valid_loss=4.530]\n",
"Epoch 760: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.584, train_loss_epoch=0.584, valid_loss=4.530]\n",
"Epoch 761: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.564, train_loss_epoch=0.564, valid_loss=4.530]\n",
"Epoch 761: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.564, train_loss_epoch=0.564, valid_loss=4.530]\n",
"Epoch 762: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.579, train_loss_epoch=0.579, valid_loss=4.530]\n",
"Epoch 762: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.579, train_loss_epoch=0.579, valid_loss=4.530]\n",
"Epoch 763: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.564, train_loss_epoch=0.564, valid_loss=4.530]\n",
"Epoch 763: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.564, train_loss_epoch=0.564, valid_loss=4.530]\n",
"Epoch 764: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.560, train_loss_epoch=0.560, valid_loss=4.530]\n",
"Epoch 764: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.560, train_loss_epoch=0.560, valid_loss=4.530]\n",
"Epoch 765: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.580, train_loss_epoch=0.580, valid_loss=4.530]\n",
"Epoch 765: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.580, train_loss_epoch=0.580, valid_loss=4.530]\n",
"Epoch 766: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.578, train_loss_epoch=0.578, valid_loss=4.530]\n",
"Epoch 766: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.578, train_loss_epoch=0.578, valid_loss=4.530]\n",
"Epoch 767: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.567, train_loss_epoch=0.567, valid_loss=4.530]\n",
"Epoch 767: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.567, train_loss_epoch=0.567, valid_loss=4.530]\n",
"Epoch 768: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.575, valid_loss=4.530]\n",
"Epoch 768: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.575, valid_loss=4.530]\n",
"Epoch 769: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.559, train_loss_epoch=0.559, valid_loss=4.530]\n",
"Epoch 769: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.559, train_loss_epoch=0.559, valid_loss=4.530]\n",
"Epoch 770: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.530]\n",
"Epoch 770: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.530]\n",
"Epoch 771: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.574, train_loss_epoch=0.574, valid_loss=4.530]\n",
"Epoch 771: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.574, train_loss_epoch=0.574, valid_loss=4.530]\n",
"Epoch 772: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.561, train_loss_epoch=0.561, valid_loss=4.530]\n",
"Epoch 772: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.561, train_loss_epoch=0.561, valid_loss=4.530]\n",
"Epoch 773: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.589, train_loss_epoch=0.589, valid_loss=4.530]\n",
"Epoch 773: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.589, train_loss_epoch=0.589, valid_loss=4.530]\n",
"Epoch 774: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.597, train_loss_epoch=0.597, valid_loss=4.530]\n",
"Epoch 774: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.597, train_loss_epoch=0.597, valid_loss=4.530]\n",
"Epoch 775: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.573, train_loss_epoch=0.573, valid_loss=4.530]\n",
"Epoch 775: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.573, train_loss_epoch=0.573, valid_loss=4.530]\n",
"Epoch 776: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.579, train_loss_epoch=0.579, valid_loss=4.530]\n",
"Epoch 776: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.579, train_loss_epoch=0.579, valid_loss=4.530]\n",
"Epoch 777: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.577, train_loss_epoch=0.577, valid_loss=4.530]\n",
"Epoch 777: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.577, train_loss_epoch=0.577, valid_loss=4.530]\n",
"Epoch 778: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.589, train_loss_epoch=0.589, valid_loss=4.530]\n",
"Epoch 778: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.589, train_loss_epoch=0.589, valid_loss=4.530]\n",
"Epoch 779: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.567, train_loss_epoch=0.567, valid_loss=4.530]\n",
"Epoch 779: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.567, train_loss_epoch=0.567, valid_loss=4.530]\n",
"Epoch 779: 100%|██████████| 1/1 [00:00<00:00, 1.18it/s, v_num=0, train_loss_step=0.596, train_loss_epoch=0.596, valid_loss=4.530]\n",
"Epoch 780: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.596, train_loss_epoch=0.596, valid_loss=4.530]\n",
"Epoch 780: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.596, train_loss_epoch=0.596, valid_loss=4.530]\n",
"Epoch 781: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.594, train_loss_epoch=0.594, valid_loss=4.530]\n",
"Epoch 781: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.594, train_loss_epoch=0.594, valid_loss=4.530]\n",
"Epoch 782: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.578, train_loss_epoch=0.578, valid_loss=4.530]\n",
"Epoch 782: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.578, train_loss_epoch=0.578, valid_loss=4.530]\n",
"Epoch 783: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.572, train_loss_epoch=0.572, valid_loss=4.530]\n",
"Epoch 783: 100%|██████████| 1/1 [00:00<00:00, 1.67it/s, v_num=0, train_loss_step=0.572, train_loss_epoch=0.572, valid_loss=4.530]\n",
"Epoch 784: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.594, train_loss_epoch=0.594, valid_loss=4.530]\n",
"Epoch 784: 100%|██████████| 1/1 [00:00<00:00, 1.68it/s, v_num=0, train_loss_step=0.594, train_loss_epoch=0.594, valid_loss=4.530]\n",
"Epoch 784: 100%|██████████| 1/1 [00:00<00:00, 1.15it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.594, valid_loss=4.530]\n",
"Epoch 785: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.587, valid_loss=4.530]\n",
"Epoch 785: 100%|██████████| 1/1 [00:00<00:00, 1.67it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.587, valid_loss=4.530]\n",
"Epoch 786: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.530]\n",
"Epoch 786: 100%|██████████| 1/1 [00:00<00:00, 1.68it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.530]\n",
"Epoch 787: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.571, valid_loss=4.530]\n",
"Epoch 787: 100%|██████████| 1/1 [00:00<00:00, 1.67it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.571, valid_loss=4.530]\n",
"Epoch 788: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.572, train_loss_epoch=0.572, valid_loss=4.530]\n",
"Epoch 788: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.572, train_loss_epoch=0.572, valid_loss=4.530]\n",
"Epoch 789: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.569, train_loss_epoch=0.569, valid_loss=4.530]\n",
"Epoch 789: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.569, train_loss_epoch=0.569, valid_loss=4.530]\n",
"Epoch 790: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.566, train_loss_epoch=0.566, valid_loss=4.530]\n",
"Epoch 790: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.566, train_loss_epoch=0.566, valid_loss=4.530]\n",
"Epoch 791: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.595, train_loss_epoch=0.595, valid_loss=4.530]\n",
"Epoch 791: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.595, train_loss_epoch=0.595, valid_loss=4.530]\n",
"Epoch 792: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.560, train_loss_epoch=0.560, valid_loss=4.530]\n",
"Epoch 792: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.560, train_loss_epoch=0.560, valid_loss=4.530]\n",
"Epoch 793: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.594, train_loss_epoch=0.594, valid_loss=4.530]\n",
"Epoch 793: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.594, train_loss_epoch=0.594, valid_loss=4.530]\n",
"Epoch 794: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.571, valid_loss=4.530]\n",
"Epoch 794: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.571, valid_loss=4.530]\n",
"Epoch 795: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.597, train_loss_epoch=0.597, valid_loss=4.530]\n",
"Epoch 795: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.597, train_loss_epoch=0.597, valid_loss=4.530]\n",
"Epoch 796: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.588, train_loss_epoch=0.588, valid_loss=4.530]\n",
"Epoch 796: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.588, train_loss_epoch=0.588, valid_loss=4.530]\n",
"Epoch 797: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.587, valid_loss=4.530]\n",
"Epoch 797: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.587, valid_loss=4.530]\n",
"Epoch 798: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.596, train_loss_epoch=0.596, valid_loss=4.530]\n",
"Epoch 798: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.596, train_loss_epoch=0.596, valid_loss=4.530]\n",
"Epoch 799: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.575, valid_loss=4.530]\n",
"Epoch 799: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.575, valid_loss=4.530]\n",
"Epoch 799: 100%|██████████| 1/1 [00:00<00:00, 1.18it/s, v_num=0, train_loss_step=0.576, train_loss_epoch=0.575, valid_loss=4.530]\n",
"Validation: | | 0/? [00:00, ?it/s]\u001b[A\n",
"Validation: 0%| | 0/1 [00:00, ?it/s]\u001b[A\n",
"Validation DataLoader 0: 0%| | 0/1 [00:00, ?it/s]\u001b[A\n",
"Validation DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 25.63it/s]\u001b[A\n",
"\u001b[36m(_train_tune pid=23277)\u001b[0m \n",
"Epoch 800: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.576, train_loss_epoch=0.576, valid_loss=4.160]\n",
"Epoch 800: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.576, train_loss_epoch=0.576, valid_loss=4.160]\n",
"Epoch 801: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.578, train_loss_epoch=0.578, valid_loss=4.160]\n",
"Epoch 801: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.578, train_loss_epoch=0.578, valid_loss=4.160]\n",
"Epoch 802: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.589, train_loss_epoch=0.589, valid_loss=4.160]\n",
"Epoch 802: 100%|██████████| 1/1 [00:00<00:00, 1.66it/s, v_num=0, train_loss_step=0.589, train_loss_epoch=0.589, valid_loss=4.160]\n",
"Epoch 803: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.604, train_loss_epoch=0.604, valid_loss=4.160]\n",
"Epoch 803: 100%|██████████| 1/1 [00:00<00:00, 1.68it/s, v_num=0, train_loss_step=0.604, train_loss_epoch=0.604, valid_loss=4.160]\n",
"Epoch 804: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.586, train_loss_epoch=0.586, valid_loss=4.160]\n",
"Epoch 804: 100%|██████████| 1/1 [00:00<00:00, 1.67it/s, v_num=0, train_loss_step=0.586, train_loss_epoch=0.586, valid_loss=4.160]\n",
"Epoch 805: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.595, train_loss_epoch=0.595, valid_loss=4.160]\n",
"Epoch 805: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.595, train_loss_epoch=0.595, valid_loss=4.160]\n",
"Epoch 806: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.576, train_loss_epoch=0.576, valid_loss=4.160]\n",
"Epoch 806: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.576, train_loss_epoch=0.576, valid_loss=4.160]\n",
"Epoch 807: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.608, train_loss_epoch=0.608, valid_loss=4.160]\n",
"Epoch 807: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.608, train_loss_epoch=0.608, valid_loss=4.160]\n",
"Epoch 808: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.575, valid_loss=4.160]\n",
"Epoch 808: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.575, valid_loss=4.160]\n",
"Epoch 809: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.608, train_loss_epoch=0.608, valid_loss=4.160]\n",
"Epoch 809: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.608, train_loss_epoch=0.608, valid_loss=4.160]\n",
"Epoch 810: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.576, train_loss_epoch=0.576, valid_loss=4.160]\n",
"Epoch 810: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.576, train_loss_epoch=0.576, valid_loss=4.160]\n",
"Epoch 811: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.579, train_loss_epoch=0.579, valid_loss=4.160]\n",
"Epoch 811: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.579, train_loss_epoch=0.579, valid_loss=4.160]\n",
"Epoch 812: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.571, valid_loss=4.160]\n",
"Epoch 812: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.571, valid_loss=4.160]\n",
"Epoch 813: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.160]\n",
"Epoch 813: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.160]\n",
"Epoch 814: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.576, train_loss_epoch=0.576, valid_loss=4.160]\n",
"Epoch 814: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.576, train_loss_epoch=0.576, valid_loss=4.160]\n",
"Epoch 815: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.160]\n",
"Epoch 815: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.160]\n",
"Epoch 816: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.580, train_loss_epoch=0.580, valid_loss=4.160]\n",
"Epoch 816: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.580, train_loss_epoch=0.580, valid_loss=4.160]\n",
"Epoch 817: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.579, train_loss_epoch=0.579, valid_loss=4.160]\n",
"Epoch 817: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.579, train_loss_epoch=0.579, valid_loss=4.160]\n",
"Epoch 818: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.632, train_loss_epoch=0.632, valid_loss=4.160]\n",
"Epoch 818: 100%|██████████| 1/1 [00:00<00:00, 1.67it/s, v_num=0, train_loss_step=0.632, train_loss_epoch=0.632, valid_loss=4.160]\n",
"Epoch 819: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.587, valid_loss=4.160]\n",
"Epoch 819: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.587, valid_loss=4.160]\n",
"Epoch 820: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.585, train_loss_epoch=0.585, valid_loss=4.160]\n",
"Epoch 820: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.585, train_loss_epoch=0.585, valid_loss=4.160]\n",
"Epoch 821: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.584, train_loss_epoch=0.584, valid_loss=4.160]\n",
"Epoch 821: 100%|██████████| 1/1 [00:00<00:00, 1.66it/s, v_num=0, train_loss_step=0.584, train_loss_epoch=0.584, valid_loss=4.160]\n",
"Epoch 822: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.160]\n",
"Epoch 822: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.160]\n",
"Epoch 823: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.160]\n",
"Epoch 823: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.160]\n",
"Epoch 823: 100%|██████████| 1/1 [00:00<00:00, 1.18it/s, v_num=0, train_loss_step=0.576, train_loss_epoch=0.576, valid_loss=4.160]\n",
"Epoch 824: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.576, train_loss_epoch=0.576, valid_loss=4.160]\n",
"Epoch 824: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.576, train_loss_epoch=0.576, valid_loss=4.160]\n",
"Epoch 825: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.563, train_loss_epoch=0.563, valid_loss=4.160]\n",
"Epoch 825: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.563, train_loss_epoch=0.563, valid_loss=4.160]\n",
"Epoch 826: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.561, train_loss_epoch=0.561, valid_loss=4.160]\n",
"Epoch 826: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.561, train_loss_epoch=0.561, valid_loss=4.160]\n",
"Epoch 826: 100%|██████████| 1/1 [00:00<00:00, 1.17it/s, v_num=0, train_loss_step=0.588, train_loss_epoch=0.588, valid_loss=4.160]\n",
"Epoch 827: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.588, train_loss_epoch=0.588, valid_loss=4.160]\n",
"Epoch 827: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.588, train_loss_epoch=0.588, valid_loss=4.160]\n",
"Epoch 828: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.579, train_loss_epoch=0.579, valid_loss=4.160]\n",
"Epoch 828: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.579, train_loss_epoch=0.579, valid_loss=4.160]\n",
"Epoch 829: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.160]\n",
"Epoch 829: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.160]\n",
"Epoch 830: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.591, train_loss_epoch=0.591, valid_loss=4.160]\n",
"Epoch 830: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.591, train_loss_epoch=0.591, valid_loss=4.160]\n",
"Epoch 831: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.579, train_loss_epoch=0.579, valid_loss=4.160]\n",
"Epoch 831: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.579, train_loss_epoch=0.579, valid_loss=4.160]\n",
"Epoch 832: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.577, train_loss_epoch=0.577, valid_loss=4.160]\n",
"Epoch 832: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.577, train_loss_epoch=0.577, valid_loss=4.160]\n",
"Epoch 833: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.561, train_loss_epoch=0.561, valid_loss=4.160]\n",
"Epoch 833: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.561, train_loss_epoch=0.561, valid_loss=4.160]\n",
"Epoch 834: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.579, train_loss_epoch=0.579, valid_loss=4.160]\n",
"Epoch 834: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.579, train_loss_epoch=0.579, valid_loss=4.160]\n",
"Epoch 835: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.600, train_loss_epoch=0.600, valid_loss=4.160]\n",
"Epoch 835: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.600, train_loss_epoch=0.600, valid_loss=4.160]\n",
"Epoch 836: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.595, train_loss_epoch=0.595, valid_loss=4.160]\n",
"Epoch 836: 100%|██████████| 1/1 [00:00<00:00, 1.68it/s, v_num=0, train_loss_step=0.595, train_loss_epoch=0.595, valid_loss=4.160]\n",
"Epoch 837: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.580, train_loss_epoch=0.580, valid_loss=4.160]\n",
"Epoch 837: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.580, train_loss_epoch=0.580, valid_loss=4.160]\n",
"Epoch 838: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.589, train_loss_epoch=0.589, valid_loss=4.160]\n",
"Epoch 838: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.589, train_loss_epoch=0.589, valid_loss=4.160]\n",
"Epoch 839: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.160]\n",
"Epoch 839: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.160]\n",
"Epoch 840: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.617, train_loss_epoch=0.617, valid_loss=4.160]\n",
"Epoch 840: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.617, train_loss_epoch=0.617, valid_loss=4.160]\n",
"Epoch 841: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.599, train_loss_epoch=0.599, valid_loss=4.160]\n",
"Epoch 841: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.599, train_loss_epoch=0.599, valid_loss=4.160]\n",
"Epoch 842: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.609, train_loss_epoch=0.609, valid_loss=4.160]\n",
"Epoch 842: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.609, train_loss_epoch=0.609, valid_loss=4.160]\n",
"Epoch 843: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.601, train_loss_epoch=0.601, valid_loss=4.160]\n",
"Epoch 843: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.601, train_loss_epoch=0.601, valid_loss=4.160]\n",
"Epoch 844: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.594, train_loss_epoch=0.594, valid_loss=4.160]\n",
"Epoch 844: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.594, train_loss_epoch=0.594, valid_loss=4.160]\n",
"Epoch 845: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.606, train_loss_epoch=0.606, valid_loss=4.160]\n",
"Epoch 845: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.606, train_loss_epoch=0.606, valid_loss=4.160]\n",
"Epoch 846: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.584, train_loss_epoch=0.584, valid_loss=4.160]\n",
"Epoch 846: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.584, train_loss_epoch=0.584, valid_loss=4.160]\n",
"Epoch 847: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.603, train_loss_epoch=0.603, valid_loss=4.160]\n",
"Epoch 847: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.603, train_loss_epoch=0.603, valid_loss=4.160]\n",
"Epoch 848: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.602, train_loss_epoch=0.602, valid_loss=4.160]\n",
"Epoch 848: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.602, train_loss_epoch=0.602, valid_loss=4.160]\n",
"Epoch 849: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.580, train_loss_epoch=0.580, valid_loss=4.160]\n",
"Epoch 849: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.580, train_loss_epoch=0.580, valid_loss=4.160]\n",
"Epoch 850: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.587, valid_loss=4.160]\n",
"Epoch 850: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.587, valid_loss=4.160]\n",
"Epoch 851: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.597, train_loss_epoch=0.597, valid_loss=4.160]\n",
"Epoch 851: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.597, train_loss_epoch=0.597, valid_loss=4.160]\n",
"Epoch 852: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.589, train_loss_epoch=0.589, valid_loss=4.160]\n",
"Epoch 852: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.589, train_loss_epoch=0.589, valid_loss=4.160]\n",
"Epoch 853: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.160]\n",
"Epoch 853: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.160]\n",
"Epoch 854: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.576, train_loss_epoch=0.576, valid_loss=4.160]\n",
"Epoch 854: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.576, train_loss_epoch=0.576, valid_loss=4.160]\n",
"Epoch 855: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.585, train_loss_epoch=0.585, valid_loss=4.160]\n",
"Epoch 855: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.585, train_loss_epoch=0.585, valid_loss=4.160]\n",
"Epoch 856: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.582, train_loss_epoch=0.582, valid_loss=4.160]\n",
"Epoch 856: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.582, train_loss_epoch=0.582, valid_loss=4.160]\n",
"Epoch 857: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.597, train_loss_epoch=0.597, valid_loss=4.160]\n",
"Epoch 857: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.597, train_loss_epoch=0.597, valid_loss=4.160]\n",
"Epoch 858: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.579, train_loss_epoch=0.579, valid_loss=4.160]\n",
"Epoch 858: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.579, train_loss_epoch=0.579, valid_loss=4.160]\n",
"Epoch 859: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.587, valid_loss=4.160]\n",
"Epoch 859: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.587, valid_loss=4.160]\n",
"Epoch 860: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.160]\n",
"Epoch 860: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.160]\n",
"Epoch 861: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.571, valid_loss=4.160]\n",
"Epoch 861: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.571, valid_loss=4.160]\n",
"Epoch 862: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.575, valid_loss=4.160]\n",
"Epoch 862: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.575, valid_loss=4.160]\n",
"Epoch 863: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.587, valid_loss=4.160]\n",
"Epoch 863: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.587, valid_loss=4.160]\n",
"Epoch 864: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.582, train_loss_epoch=0.582, valid_loss=4.160]\n",
"Epoch 864: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.582, train_loss_epoch=0.582, valid_loss=4.160]\n",
"Epoch 865: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.585, train_loss_epoch=0.585, valid_loss=4.160]\n",
"Epoch 865: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.585, train_loss_epoch=0.585, valid_loss=4.160]\n",
"Epoch 866: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.574, train_loss_epoch=0.574, valid_loss=4.160]\n",
"Epoch 866: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.574, train_loss_epoch=0.574, valid_loss=4.160]\n",
"Epoch 867: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.598, train_loss_epoch=0.598, valid_loss=4.160]\n",
"Epoch 867: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.598, train_loss_epoch=0.598, valid_loss=4.160]\n",
"Epoch 868: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.586, train_loss_epoch=0.586, valid_loss=4.160]\n",
"Epoch 868: 100%|██████████| 1/1 [00:00<00:00, 1.67it/s, v_num=0, train_loss_step=0.586, train_loss_epoch=0.586, valid_loss=4.160]\n",
"Epoch 869: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.571, valid_loss=4.160]\n",
"Epoch 869: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.571, valid_loss=4.160]\n",
"Epoch 870: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.556, train_loss_epoch=0.556, valid_loss=4.160]\n",
"Epoch 870: 100%|██████████| 1/1 [00:00<00:00, 1.68it/s, v_num=0, train_loss_step=0.556, train_loss_epoch=0.556, valid_loss=4.160]\n",
"Epoch 871: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.572, train_loss_epoch=0.572, valid_loss=4.160]\n",
"Epoch 871: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.572, train_loss_epoch=0.572, valid_loss=4.160]\n",
"Epoch 872: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.573, train_loss_epoch=0.573, valid_loss=4.160]\n",
"Epoch 872: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.573, train_loss_epoch=0.573, valid_loss=4.160]\n",
"Epoch 873: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.557, train_loss_epoch=0.557, valid_loss=4.160]\n",
"Epoch 873: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.557, train_loss_epoch=0.557, valid_loss=4.160]\n",
"Epoch 874: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.573, train_loss_epoch=0.573, valid_loss=4.160]\n",
"Epoch 874: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.573, train_loss_epoch=0.573, valid_loss=4.160]\n",
"Epoch 875: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.567, train_loss_epoch=0.567, valid_loss=4.160]\n",
"Epoch 875: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.567, train_loss_epoch=0.567, valid_loss=4.160]\n",
"Epoch 876: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.568, train_loss_epoch=0.568, valid_loss=4.160]\n",
"Epoch 876: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.568, train_loss_epoch=0.568, valid_loss=4.160]\n",
"Epoch 877: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.573, train_loss_epoch=0.573, valid_loss=4.160]\n",
"Epoch 877: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.573, train_loss_epoch=0.573, valid_loss=4.160]\n",
"Epoch 878: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.600, train_loss_epoch=0.600, valid_loss=4.160]\n",
"Epoch 878: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.600, train_loss_epoch=0.600, valid_loss=4.160]\n",
"Epoch 879: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.587, valid_loss=4.160]\n",
"Epoch 879: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.587, valid_loss=4.160]\n",
"Epoch 880: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.580, train_loss_epoch=0.580, valid_loss=4.160]\n",
"Epoch 880: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.580, train_loss_epoch=0.580, valid_loss=4.160]\n",
"Epoch 881: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.160]\n",
"Epoch 881: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.160]\n",
"Epoch 882: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.591, train_loss_epoch=0.591, valid_loss=4.160]\n",
"Epoch 882: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.591, train_loss_epoch=0.591, valid_loss=4.160]\n",
"Epoch 883: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.554, train_loss_epoch=0.554, valid_loss=4.160]\n",
"Epoch 883: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.554, train_loss_epoch=0.554, valid_loss=4.160]\n",
"Epoch 884: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.578, train_loss_epoch=0.578, valid_loss=4.160]\n",
"Epoch 884: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.578, train_loss_epoch=0.578, valid_loss=4.160]\n",
"Epoch 885: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.560, train_loss_epoch=0.560, valid_loss=4.160]\n",
"Epoch 885: 100%|██████████| 1/1 [00:00<00:00, 1.68it/s, v_num=0, train_loss_step=0.560, train_loss_epoch=0.560, valid_loss=4.160]\n",
"Epoch 886: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.575, valid_loss=4.160]\n",
"Epoch 886: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.575, valid_loss=4.160]\n",
"Epoch 887: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.585, train_loss_epoch=0.585, valid_loss=4.160]\n",
"Epoch 887: 100%|██████████| 1/1 [00:00<00:00, 1.62it/s, v_num=0, train_loss_step=0.585, train_loss_epoch=0.585, valid_loss=4.160]\n",
"Epoch 888: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.575, valid_loss=4.160]\n",
"Epoch 888: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.575, valid_loss=4.160]\n",
"Epoch 889: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.579, train_loss_epoch=0.579, valid_loss=4.160]\n",
"Epoch 889: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.579, train_loss_epoch=0.579, valid_loss=4.160]\n",
"Epoch 890: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.572, train_loss_epoch=0.572, valid_loss=4.160]\n",
"Epoch 890: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.572, train_loss_epoch=0.572, valid_loss=4.160]\n",
"Epoch 891: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.565, train_loss_epoch=0.565, valid_loss=4.160]\n",
"Epoch 891: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.565, train_loss_epoch=0.565, valid_loss=4.160]\n",
"Epoch 892: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.568, train_loss_epoch=0.568, valid_loss=4.160]\n",
"Epoch 892: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.568, train_loss_epoch=0.568, valid_loss=4.160]\n",
"Epoch 893: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.598, train_loss_epoch=0.598, valid_loss=4.160]\n",
"Epoch 893: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.598, train_loss_epoch=0.598, valid_loss=4.160]\n",
"Epoch 894: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.585, train_loss_epoch=0.585, valid_loss=4.160]\n",
"Epoch 894: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.585, train_loss_epoch=0.585, valid_loss=4.160]\n",
"Epoch 895: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.160]\n",
"Epoch 895: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.160]\n",
"Epoch 896: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.160]\n",
"Epoch 896: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.160]\n",
"Epoch 897: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.576, train_loss_epoch=0.576, valid_loss=4.160]\n",
"Epoch 897: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.576, train_loss_epoch=0.576, valid_loss=4.160]\n",
"Epoch 898: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.582, train_loss_epoch=0.582, valid_loss=4.160]\n",
"Epoch 898: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.582, train_loss_epoch=0.582, valid_loss=4.160]\n",
"Epoch 899: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.563, train_loss_epoch=0.563, valid_loss=4.160]\n",
"Epoch 899: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.563, train_loss_epoch=0.563, valid_loss=4.160]\n",
"Epoch 899: 100%|██████████| 1/1 [00:00<00:00, 1.17it/s, v_num=0, train_loss_step=0.547, train_loss_epoch=0.563, valid_loss=4.160]\n",
"Validation: | | 0/? [00:00, ?it/s]\u001b[A\n",
"Validation: 0%| | 0/1 [00:00, ?it/s]\u001b[A\n",
"Validation DataLoader 0: 0%| | 0/1 [00:00, ?it/s]\u001b[A\n",
"Validation DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 30.94it/s]\u001b[A\n",
"Epoch 900: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.547, train_loss_epoch=0.547, valid_loss=4.580]\n",
"Epoch 900: 100%|██████████| 1/1 [00:00<00:00, 1.76it/s, v_num=0, train_loss_step=0.547, train_loss_epoch=0.547, valid_loss=4.580]\n",
"Epoch 901: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.573, train_loss_epoch=0.573, valid_loss=4.580]\n",
"Epoch 901: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.573, train_loss_epoch=0.573, valid_loss=4.580]\n",
"Epoch 901: 100%|██████████| 1/1 [00:00<00:00, 1.18it/s, v_num=0, train_loss_step=0.573, train_loss_epoch=0.573, valid_loss=4.580]\n",
"Epoch 902: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.573, train_loss_epoch=0.573, valid_loss=4.580]\n",
"Epoch 902: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.573, train_loss_epoch=0.573, valid_loss=4.580]\n",
"Epoch 903: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.580]\n",
"Epoch 903: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.580]\n",
"Epoch 903: 100%|██████████| 1/1 [00:00<00:00, 1.17it/s, v_num=0, train_loss_step=0.580, train_loss_epoch=0.581, valid_loss=4.580]\n",
"Epoch 904: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.580, train_loss_epoch=0.580, valid_loss=4.580]\n",
"Epoch 904: 100%|██████████| 1/1 [00:00<00:00, 1.66it/s, v_num=0, train_loss_step=0.580, train_loss_epoch=0.580, valid_loss=4.580]\n",
"Epoch 905: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.587, valid_loss=4.580]\n",
"Epoch 905: 100%|██████████| 1/1 [00:00<00:00, 1.68it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.587, valid_loss=4.580]\n",
"Epoch 906: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.600, train_loss_epoch=0.600, valid_loss=4.580]\n",
"Epoch 906: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.600, train_loss_epoch=0.600, valid_loss=4.580]\n",
"Epoch 907: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.580]\n",
"Epoch 907: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.580]\n",
"Epoch 908: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.580]\n",
"Epoch 908: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.580]\n",
"Epoch 909: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.586, train_loss_epoch=0.586, valid_loss=4.580]\n",
"Epoch 909: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.586, train_loss_epoch=0.586, valid_loss=4.580]\n",
"Epoch 910: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.569, train_loss_epoch=0.569, valid_loss=4.580]\n",
"Epoch 910: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.569, train_loss_epoch=0.569, valid_loss=4.580]\n",
"Epoch 911: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.575, valid_loss=4.580]\n",
"Epoch 911: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.575, valid_loss=4.580]\n",
"Epoch 912: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.567, train_loss_epoch=0.567, valid_loss=4.580]\n",
"Epoch 912: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.567, train_loss_epoch=0.567, valid_loss=4.580]\n",
"Epoch 913: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.564, train_loss_epoch=0.564, valid_loss=4.580]\n",
"Epoch 913: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.564, train_loss_epoch=0.564, valid_loss=4.580]\n",
"Epoch 914: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.564, train_loss_epoch=0.564, valid_loss=4.580]\n",
"Epoch 914: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.564, train_loss_epoch=0.564, valid_loss=4.580]\n",
"Epoch 915: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.586, train_loss_epoch=0.586, valid_loss=4.580]\n",
"Epoch 915: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.586, train_loss_epoch=0.586, valid_loss=4.580]\n",
"Epoch 916: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.580]\n",
"Epoch 916: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.580]\n",
"Epoch 917: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.596, train_loss_epoch=0.596, valid_loss=4.580]\n",
"Epoch 917: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.596, train_loss_epoch=0.596, valid_loss=4.580]\n",
"Epoch 918: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.574, train_loss_epoch=0.574, valid_loss=4.580]\n",
"Epoch 918: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.574, train_loss_epoch=0.574, valid_loss=4.580]\n",
"Epoch 919: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.562, train_loss_epoch=0.562, valid_loss=4.580]\n",
"Epoch 919: 100%|██████████| 1/1 [00:00<00:00, 1.68it/s, v_num=0, train_loss_step=0.562, train_loss_epoch=0.562, valid_loss=4.580]\n",
"Epoch 920: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.557, train_loss_epoch=0.557, valid_loss=4.580]\n",
"Epoch 920: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.557, train_loss_epoch=0.557, valid_loss=4.580]\n",
"Epoch 921: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.589, train_loss_epoch=0.589, valid_loss=4.580]\n",
"Epoch 921: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.589, train_loss_epoch=0.589, valid_loss=4.580]\n",
"Epoch 922: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.571, valid_loss=4.580]\n",
"Epoch 922: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.571, valid_loss=4.580]\n",
"Epoch 923: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.571, valid_loss=4.580]\n",
"Epoch 923: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.571, valid_loss=4.580]\n",
"Epoch 924: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.578, train_loss_epoch=0.578, valid_loss=4.580]\n",
"Epoch 924: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.578, train_loss_epoch=0.578, valid_loss=4.580]\n",
"Epoch 925: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.585, train_loss_epoch=0.585, valid_loss=4.580]\n",
"Epoch 925: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.585, train_loss_epoch=0.585, valid_loss=4.580]\n",
"Epoch 926: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.594, train_loss_epoch=0.594, valid_loss=4.580]\n",
"Epoch 926: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.594, train_loss_epoch=0.594, valid_loss=4.580]\n",
"Epoch 927: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.585, train_loss_epoch=0.585, valid_loss=4.580]\n",
"Epoch 927: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.585, train_loss_epoch=0.585, valid_loss=4.580]\n",
"Epoch 928: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.575, valid_loss=4.580]\n",
"Epoch 928: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.575, valid_loss=4.580]\n",
"Epoch 929: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.580, train_loss_epoch=0.580, valid_loss=4.580]\n",
"Epoch 929: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.580, train_loss_epoch=0.580, valid_loss=4.580]\n",
"Epoch 930: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.580]\n",
"Epoch 930: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.580]\n",
"Epoch 931: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.571, valid_loss=4.580]\n",
"Epoch 931: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.571, valid_loss=4.580]\n",
"Epoch 932: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.579, train_loss_epoch=0.579, valid_loss=4.580]\n",
"Epoch 932: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.579, train_loss_epoch=0.579, valid_loss=4.580]\n",
"Epoch 933: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.569, train_loss_epoch=0.569, valid_loss=4.580]\n",
"Epoch 933: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.569, train_loss_epoch=0.569, valid_loss=4.580]\n",
"Epoch 934: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.572, train_loss_epoch=0.572, valid_loss=4.580]\n",
"Epoch 934: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.572, train_loss_epoch=0.572, valid_loss=4.580]\n",
"Epoch 935: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.561, train_loss_epoch=0.561, valid_loss=4.580]\n",
"Epoch 935: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.561, train_loss_epoch=0.561, valid_loss=4.580]\n",
"Epoch 936: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.547, train_loss_epoch=0.547, valid_loss=4.580]\n",
"Epoch 936: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.547, train_loss_epoch=0.547, valid_loss=4.580]\n",
"Epoch 937: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.566, train_loss_epoch=0.566, valid_loss=4.580]\n",
"Epoch 937: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.566, train_loss_epoch=0.566, valid_loss=4.580]\n",
"Epoch 938: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.559, train_loss_epoch=0.559, valid_loss=4.580]\n",
"Epoch 938: 100%|██████████| 1/1 [00:00<00:00, 1.67it/s, v_num=0, train_loss_step=0.559, train_loss_epoch=0.559, valid_loss=4.580]\n",
"Epoch 939: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.570, train_loss_epoch=0.570, valid_loss=4.580]\n",
"Epoch 939: 100%|██████████| 1/1 [00:00<00:00, 1.65it/s, v_num=0, train_loss_step=0.570, train_loss_epoch=0.570, valid_loss=4.580]\n",
"Epoch 940: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.575, valid_loss=4.580]\n",
"Epoch 940: 100%|██████████| 1/1 [00:00<00:00, 1.67it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.575, valid_loss=4.580]\n",
"Epoch 941: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.571, valid_loss=4.580]\n",
"Epoch 941: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.571, valid_loss=4.580]\n",
"Epoch 942: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.580]\n",
"Epoch 942: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.580]\n",
"Epoch 943: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.553, train_loss_epoch=0.553, valid_loss=4.580]\n",
"Epoch 943: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.553, train_loss_epoch=0.553, valid_loss=4.580]\n",
"Epoch 944: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.563, train_loss_epoch=0.563, valid_loss=4.580]\n",
"Epoch 944: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.563, train_loss_epoch=0.563, valid_loss=4.580]\n",
"Epoch 945: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.575, valid_loss=4.580]\n",
"Epoch 945: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.575, valid_loss=4.580]\n",
"Epoch 946: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.572, train_loss_epoch=0.572, valid_loss=4.580]\n",
"Epoch 946: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.572, train_loss_epoch=0.572, valid_loss=4.580]\n",
"Epoch 947: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.588, train_loss_epoch=0.588, valid_loss=4.580]\n",
"Epoch 947: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.588, train_loss_epoch=0.588, valid_loss=4.580]\n",
"Epoch 948: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.563, train_loss_epoch=0.563, valid_loss=4.580]\n",
"Epoch 948: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.563, train_loss_epoch=0.563, valid_loss=4.580]\n",
"Epoch 949: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.591, train_loss_epoch=0.591, valid_loss=4.580]\n",
"Epoch 949: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.591, train_loss_epoch=0.591, valid_loss=4.580]\n",
"Epoch 950: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.572, train_loss_epoch=0.572, valid_loss=4.580]\n",
"Epoch 950: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.572, train_loss_epoch=0.572, valid_loss=4.580]\n",
"Epoch 951: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.580]\n",
"Epoch 951: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.580]\n",
"Epoch 952: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.598, train_loss_epoch=0.598, valid_loss=4.580]\n",
"Epoch 952: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.598, train_loss_epoch=0.598, valid_loss=4.580]\n",
"Epoch 953: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.584, train_loss_epoch=0.584, valid_loss=4.580]\n",
"Epoch 953: 100%|██████████| 1/1 [00:00<00:00, 1.68it/s, v_num=0, train_loss_step=0.584, train_loss_epoch=0.584, valid_loss=4.580]\n",
"Epoch 954: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.604, train_loss_epoch=0.604, valid_loss=4.580]\n",
"Epoch 954: 100%|██████████| 1/1 [00:00<00:00, 1.64it/s, v_num=0, train_loss_step=0.604, train_loss_epoch=0.604, valid_loss=4.580]\n",
"Epoch 955: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.591, train_loss_epoch=0.591, valid_loss=4.580]\n",
"Epoch 955: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.591, train_loss_epoch=0.591, valid_loss=4.580]\n",
"Epoch 956: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.595, train_loss_epoch=0.595, valid_loss=4.580]\n",
"Epoch 956: 100%|██████████| 1/1 [00:00<00:00, 1.63it/s, v_num=0, train_loss_step=0.595, train_loss_epoch=0.595, valid_loss=4.580]\n",
"Epoch 956: 100%|██████████| 1/1 [00:00<00:00, 1.13it/s, v_num=0, train_loss_step=0.597, train_loss_epoch=0.597, valid_loss=4.580]\n",
"Epoch 957: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.597, train_loss_epoch=0.597, valid_loss=4.580]\n",
"Epoch 957: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.597, train_loss_epoch=0.597, valid_loss=4.580]\n",
"Epoch 958: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.564, train_loss_epoch=0.564, valid_loss=4.580]\n",
"Epoch 958: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.564, train_loss_epoch=0.564, valid_loss=4.580]\n",
"Epoch 959: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.566, train_loss_epoch=0.566, valid_loss=4.580]\n",
"Epoch 959: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.566, train_loss_epoch=0.566, valid_loss=4.580]\n",
"Epoch 959: 100%|██████████| 1/1 [00:00<00:00, 1.18it/s, v_num=0, train_loss_step=0.567, train_loss_epoch=0.566, valid_loss=4.580]\n",
"Epoch 960: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.567, train_loss_epoch=0.567, valid_loss=4.580]\n",
"Epoch 960: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.567, train_loss_epoch=0.567, valid_loss=4.580]\n",
"Epoch 961: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.571, valid_loss=4.580]\n",
"Epoch 961: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.571, valid_loss=4.580]\n",
"Epoch 962: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.559, train_loss_epoch=0.559, valid_loss=4.580]\n",
"Epoch 962: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.559, train_loss_epoch=0.559, valid_loss=4.580]\n",
"Epoch 963: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.582, train_loss_epoch=0.582, valid_loss=4.580]\n",
"Epoch 963: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.582, train_loss_epoch=0.582, valid_loss=4.580]\n",
"Epoch 964: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.584, train_loss_epoch=0.584, valid_loss=4.580]\n",
"Epoch 964: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.584, train_loss_epoch=0.584, valid_loss=4.580]\n",
"Epoch 965: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.549, train_loss_epoch=0.549, valid_loss=4.580]\n",
"Epoch 965: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.549, train_loss_epoch=0.549, valid_loss=4.580]\n",
"Epoch 966: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.591, train_loss_epoch=0.591, valid_loss=4.580]\n",
"Epoch 966: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.591, train_loss_epoch=0.591, valid_loss=4.580]\n",
"Epoch 967: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.596, train_loss_epoch=0.596, valid_loss=4.580]\n",
"Epoch 967: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.596, train_loss_epoch=0.596, valid_loss=4.580]\n",
"Epoch 968: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.606, train_loss_epoch=0.606, valid_loss=4.580]\n",
"Epoch 968: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.606, train_loss_epoch=0.606, valid_loss=4.580]\n",
"Epoch 969: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.598, train_loss_epoch=0.598, valid_loss=4.580]\n",
"Epoch 969: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.598, train_loss_epoch=0.598, valid_loss=4.580]\n",
"Epoch 970: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.580]\n",
"Epoch 970: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.580]\n",
"Epoch 971: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.580]\n",
"Epoch 971: 100%|██████████| 1/1 [00:00<00:00, 1.65it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.580]\n",
"Epoch 972: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.565, train_loss_epoch=0.565, valid_loss=4.580]\n",
"Epoch 972: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.565, train_loss_epoch=0.565, valid_loss=4.580]\n",
"Epoch 973: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.580]\n",
"Epoch 973: 100%|██████████| 1/1 [00:00<00:00, 1.64it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.580]\n",
"Epoch 974: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.561, train_loss_epoch=0.561, valid_loss=4.580]\n",
"Epoch 974: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.561, train_loss_epoch=0.561, valid_loss=4.580]\n",
"Epoch 975: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.572, train_loss_epoch=0.572, valid_loss=4.580]\n",
"Epoch 975: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.572, train_loss_epoch=0.572, valid_loss=4.580]\n",
"Epoch 976: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.599, train_loss_epoch=0.599, valid_loss=4.580]\n",
"Epoch 976: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.599, train_loss_epoch=0.599, valid_loss=4.580]\n",
"Epoch 977: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.584, train_loss_epoch=0.584, valid_loss=4.580]\n",
"Epoch 977: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.584, train_loss_epoch=0.584, valid_loss=4.580]\n",
"Epoch 978: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.566, train_loss_epoch=0.566, valid_loss=4.580]\n",
"Epoch 978: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.566, train_loss_epoch=0.566, valid_loss=4.580]\n",
"Epoch 979: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.580]\n",
"Epoch 979: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.580]\n",
"Epoch 980: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.585, train_loss_epoch=0.585, valid_loss=4.580]\n",
"Epoch 980: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.585, train_loss_epoch=0.585, valid_loss=4.580]\n",
"Epoch 980: 100%|██████████| 1/1 [00:00<00:00, 1.17it/s, v_num=0, train_loss_step=0.597, train_loss_epoch=0.597, valid_loss=4.580]\n",
"Epoch 981: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.597, train_loss_epoch=0.597, valid_loss=4.580]\n",
"Epoch 981: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.597, train_loss_epoch=0.597, valid_loss=4.580]\n",
"Epoch 982: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.564, train_loss_epoch=0.564, valid_loss=4.580]\n",
"Epoch 982: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.564, train_loss_epoch=0.564, valid_loss=4.580]\n",
"Epoch 983: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.580]\n",
"Epoch 983: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.580]\n",
"Epoch 984: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.604, train_loss_epoch=0.604, valid_loss=4.580]\n",
"Epoch 984: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.604, train_loss_epoch=0.604, valid_loss=4.580]\n",
"Epoch 985: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.585, train_loss_epoch=0.585, valid_loss=4.580]\n",
"Epoch 985: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.585, train_loss_epoch=0.585, valid_loss=4.580]\n",
"Epoch 986: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.579, train_loss_epoch=0.579, valid_loss=4.580]\n",
"Epoch 986: 100%|██████████| 1/1 [00:00<00:00, 1.73it/s, v_num=0, train_loss_step=0.579, train_loss_epoch=0.579, valid_loss=4.580]\n",
"Epoch 987: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.597, train_loss_epoch=0.597, valid_loss=4.580]\n",
"Epoch 987: 100%|██████████| 1/1 [00:00<00:00, 1.71it/s, v_num=0, train_loss_step=0.597, train_loss_epoch=0.597, valid_loss=4.580]\n",
"Epoch 988: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.580]\n",
"Epoch 988: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.581, valid_loss=4.580]\n",
"Epoch 989: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.580]\n",
"Epoch 989: 100%|██████████| 1/1 [00:00<00:00, 1.67it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.580]\n",
"Epoch 990: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.613, train_loss_epoch=0.613, valid_loss=4.580]\n",
"Epoch 990: 100%|██████████| 1/1 [00:00<00:00, 1.70it/s, v_num=0, train_loss_step=0.613, train_loss_epoch=0.613, valid_loss=4.580]\n",
"Epoch 991: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.580]\n",
"Epoch 991: 100%|██████████| 1/1 [00:00<00:00, 1.72it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.583, valid_loss=4.580]\n",
"Epoch 992: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.582, train_loss_epoch=0.582, valid_loss=4.580]\n",
"Epoch 992: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.582, train_loss_epoch=0.582, valid_loss=4.580]\n",
"Epoch 993: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.558, train_loss_epoch=0.558, valid_loss=4.580]\n",
"Epoch 993: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.558, train_loss_epoch=0.558, valid_loss=4.580]\n",
"Epoch 994: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.574, train_loss_epoch=0.574, valid_loss=4.580]\n",
"Epoch 994: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.574, train_loss_epoch=0.574, valid_loss=4.580]\n",
"Epoch 995: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.577, train_loss_epoch=0.577, valid_loss=4.580]\n",
"Epoch 995: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.577, train_loss_epoch=0.577, valid_loss=4.580]\n",
"Epoch 996: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.580]\n",
"Epoch 996: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.593, train_loss_epoch=0.593, valid_loss=4.580]\n",
"Epoch 997: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.580]\n",
"Epoch 997: 100%|██████████| 1/1 [00:00<00:00, 1.75it/s, v_num=0, train_loss_step=0.590, train_loss_epoch=0.590, valid_loss=4.580]\n",
"Epoch 998: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.568, train_loss_epoch=0.568, valid_loss=4.580]\n",
"Epoch 998: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.568, train_loss_epoch=0.568, valid_loss=4.580]\n",
"Epoch 999: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=0.566, train_loss_epoch=0.566, valid_loss=4.580]\n",
"Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 1.74it/s, v_num=0, train_loss_step=0.566, train_loss_epoch=0.566, valid_loss=4.580]\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\u001b[36m(_train_tune pid=23277)\u001b[0m `Trainer.fit` stopped: `max_steps=1000` reached.\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[36m(_train_tune pid=23277)\u001b[0m \rEpoch 999: 100%|██████████| 1/1 [00:00<00:00, 1.18it/s, v_num=0, train_loss_step=0.580, train_loss_epoch=0.566, valid_loss=4.580]\n",
"\u001b[36m(_train_tune pid=23277)\u001b[0m \rValidation: | | 0/? [00:00, ?it/s]\u001b[A\n",
"\u001b[36m(_train_tune pid=23277)\u001b[0m \rValidation: 0%| | 0/1 [00:00, ?it/s]\u001b[A\n",
"\u001b[36m(_train_tune pid=23277)\u001b[0m \rValidation DataLoader 0: 0%| | 0/1 [00:00, ?it/s]\u001b[A\n",
"\u001b[36m(_train_tune pid=23277)\u001b[0m \rValidation DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 41.82it/s]\u001b[A\n",
"\u001b[36m(_train_tune pid=23277)\u001b[0m \r \u001b[A\rEpoch 999: 100%|██████████| 1/1 [00:00<00:00, 1.13it/s, v_num=0, train_loss_step=0.580, train_loss_epoch=0.566, valid_loss=4.300]\rEpoch 999: 100%|██████████| 1/1 [00:00<00:00, 1.13it/s, v_num=0, train_loss_step=0.580, train_loss_epoch=0.580, valid_loss=4.300]\rEpoch 999: 100%|██████████| 1/1 [00:00<00:00, 1.13it/s, v_num=0, train_loss_step=0.580, train_loss_epoch=0.580, valid_loss=4.300]\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\u001b[36m(pid=26992)\u001b[0m /usr/local/lib/python3.10/dist-packages/dask/dataframe/__init__.py:42: FutureWarning: \n",
"\u001b[36m(pid=26992)\u001b[0m Dask dataframe query planning is disabled because dask-expr is not installed.\n",
"\u001b[36m(pid=26992)\u001b[0m \n",
"\u001b[36m(pid=26992)\u001b[0m You can install it with `pip install dask[dataframe]` or `conda install dask`.\n",
"\u001b[36m(pid=26992)\u001b[0m This will raise in a future version.\n",
"\u001b[36m(pid=26992)\u001b[0m \n",
"\u001b[36m(pid=26992)\u001b[0m warnings.warn(msg, FutureWarning)\n",
"\u001b[36m(_train_tune pid=26992)\u001b[0m /usr/local/lib/python3.10/dist-packages/ray/tune/integration/pytorch_lightning.py:198: `ray.tune.integration.pytorch_lightning.TuneReportCallback` is deprecated. Use `ray.tune.integration.pytorch_lightning.TuneReportCheckpointCallback` instead.\n",
"\u001b[36m(_train_tune pid=26992)\u001b[0m Seed set to 8\n",
"\u001b[36m(_train_tune pid=26992)\u001b[0m GPU available: True (cuda), used: True\n",
"\u001b[36m(_train_tune pid=26992)\u001b[0m TPU available: False, using: 0 TPU cores\n",
"\u001b[36m(_train_tune pid=26992)\u001b[0m HPU available: False, using: 0 HPUs\n",
"\u001b[36m(_train_tune pid=26992)\u001b[0m 2025-01-07 14:37:02.254333: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"\u001b[36m(_train_tune pid=26992)\u001b[0m 2025-01-07 14:37:02.280866: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"\u001b[36m(_train_tune pid=26992)\u001b[0m 2025-01-07 14:37:02.288400: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
"\u001b[36m(_train_tune pid=26992)\u001b[0m 2025-01-07 14:37:03.447186: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
"\u001b[36m(_train_tune pid=26992)\u001b[0m LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
"\u001b[36m(_train_tune pid=26992)\u001b[0m \n",
"\u001b[36m(_train_tune pid=26992)\u001b[0m | Name | Type | Params | Mode \n",
"\u001b[36m(_train_tune pid=26992)\u001b[0m --------------------------------------------------------\n",
"\u001b[36m(_train_tune pid=26992)\u001b[0m 0 | loss | MQLoss | 5 | train\n",
"\u001b[36m(_train_tune pid=26992)\u001b[0m 1 | padder_train | ConstantPad1d | 0 | train\n",
"\u001b[36m(_train_tune pid=26992)\u001b[0m 2 | scaler | TemporalNorm | 0 | train\n",
"\u001b[36m(_train_tune pid=26992)\u001b[0m 3 | decomp | SeriesDecomp | 0 | train\n",
"\u001b[36m(_train_tune pid=26992)\u001b[0m 4 | enc_embedding | DataEmbedding | 384 | train\n",
"\u001b[36m(_train_tune pid=26992)\u001b[0m 5 | dec_embedding | DataEmbedding | 384 | train\n",
"\u001b[36m(_train_tune pid=26992)\u001b[0m 6 | encoder | Encoder | 297 K | train\n",
"\u001b[36m(_train_tune pid=26992)\u001b[0m 7 | decoder | Decoder | 143 K | train\n",
"\u001b[36m(_train_tune pid=26992)\u001b[0m --------------------------------------------------------\n",
"\u001b[36m(_train_tune pid=26992)\u001b[0m 441 K Trainable params\n",
"\u001b[36m(_train_tune pid=26992)\u001b[0m 5 Non-trainable params\n",
"\u001b[36m(_train_tune pid=26992)\u001b[0m 441 K Total params\n",
"\u001b[36m(_train_tune pid=26992)\u001b[0m 1.764 Total estimated model params size (MB)\n",
"\u001b[36m(_train_tune pid=26992)\u001b[0m 119 Modules in train mode\n",
"\u001b[36m(_train_tune pid=26992)\u001b[0m 0 Modules in eval mode\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[36m(_train_tune pid=26992)\u001b[0m \rSanity Checking: | | 0/? [00:00, ?it/s]\n",
"Sanity Checking DataLoader 0: 0%| | 0/2 [00:00, ?it/s]\n",
"Epoch 0: 0%| | 0/3 [00:00, ?it/s] \n",
"Epoch 0: 100%|██████████| 3/3 [00:02<00:00, 1.19it/s]\n",
"Epoch 1: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.520, train_loss_epoch=1.480]\n",
"Epoch 1: 100%|██████████| 3/3 [00:02<00:00, 1.31it/s, v_num=0, train_loss_step=1.520, train_loss_epoch=1.480]\n",
"Epoch 2: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.490, train_loss_epoch=1.420]\n",
"Epoch 2: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=1.490, train_loss_epoch=1.420]\n",
"Epoch 3: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.400, train_loss_epoch=1.440]\n",
"Epoch 3: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=1.400, train_loss_epoch=1.440]\n",
"Epoch 4: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.330, train_loss_epoch=1.380]\n",
"Epoch 4: 100%|██████████| 3/3 [00:02<00:00, 1.31it/s, v_num=0, train_loss_step=1.330, train_loss_epoch=1.380]\n",
"Epoch 5: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.350, train_loss_epoch=1.320]\n",
"Epoch 5: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=1.350, train_loss_epoch=1.320]\n",
"Epoch 5: 100%|██████████| 3/3 [00:02<00:00, 1.16it/s, v_num=0, train_loss_step=1.310, train_loss_epoch=1.320]\n",
"Epoch 6: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.310, train_loss_epoch=1.280]\n",
"Epoch 6: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=1.310, train_loss_epoch=1.280]\n",
"Epoch 7: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.170, train_loss_epoch=1.220]\n",
"Epoch 7: 100%|██████████| 3/3 [00:02<00:00, 1.28it/s, v_num=0, train_loss_step=1.170, train_loss_epoch=1.220]\n",
"Epoch 8: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.140, train_loss_epoch=1.140]\n",
"Epoch 8: 100%|██████████| 3/3 [00:02<00:00, 1.27it/s, v_num=0, train_loss_step=1.140, train_loss_epoch=1.140]\n",
"Epoch 9: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.995, train_loss_epoch=1.010]\n",
"Epoch 9: 100%|██████████| 3/3 [00:02<00:00, 1.28it/s, v_num=0, train_loss_step=0.995, train_loss_epoch=1.010]\n",
"Epoch 10: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.879, train_loss_epoch=0.884]\n",
"Epoch 10: 100%|██████████| 3/3 [00:02<00:00, 1.28it/s, v_num=0, train_loss_step=0.879, train_loss_epoch=0.884]\n",
"Epoch 10: 100%|██████████| 3/3 [00:02<00:00, 1.14it/s, v_num=0, train_loss_step=0.777, train_loss_epoch=0.749]\n",
"Epoch 11: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.777, train_loss_epoch=0.749]\n",
"Epoch 11: 100%|██████████| 3/3 [00:02<00:00, 1.28it/s, v_num=0, train_loss_step=0.777, train_loss_epoch=0.749]\n",
"Epoch 11: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.722, train_loss_epoch=0.703] \n",
"Epoch 12: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.722, train_loss_epoch=0.703]\n",
"Epoch 12: 100%|██████████| 3/3 [00:02<00:00, 1.28it/s, v_num=0, train_loss_step=0.722, train_loss_epoch=0.703]\n",
"Epoch 13: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.699, train_loss_epoch=0.693]\n",
"Epoch 13: 100%|██████████| 3/3 [00:02<00:00, 1.26it/s, v_num=0, train_loss_step=0.699, train_loss_epoch=0.693]\n",
"Epoch 14: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.726, train_loss_epoch=0.681]\n",
"Epoch 14: 100%|██████████| 3/3 [00:02<00:00, 1.28it/s, v_num=0, train_loss_step=0.726, train_loss_epoch=0.681]\n",
"Epoch 15: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.703, train_loss_epoch=0.674]\n",
"Epoch 15: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.703, train_loss_epoch=0.674]\n",
"Epoch 16: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.669, train_loss_epoch=0.685]\n",
"Epoch 16: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.669, train_loss_epoch=0.685]\n",
"Epoch 17: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.653, train_loss_epoch=0.683]\n",
"Epoch 17: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.653, train_loss_epoch=0.683]\n",
"Epoch 18: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.641, train_loss_epoch=0.679]\n",
"Epoch 18: 100%|██████████| 3/3 [00:02<00:00, 1.28it/s, v_num=0, train_loss_step=0.641, train_loss_epoch=0.679]\n",
"Epoch 19: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.663, train_loss_epoch=0.668]\n",
"Epoch 19: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.663, train_loss_epoch=0.668]\n",
"Epoch 20: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.624, train_loss_epoch=0.661]\n",
"Epoch 20: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.624, train_loss_epoch=0.661]\n",
"Epoch 21: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.723, train_loss_epoch=0.669]\n",
"Epoch 21: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.723, train_loss_epoch=0.669]\n",
"Epoch 22: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.660, train_loss_epoch=0.656]\n",
"Epoch 22: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.660, train_loss_epoch=0.656]\n",
"Epoch 23: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.662, train_loss_epoch=0.667]\n",
"Epoch 23: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.662, train_loss_epoch=0.667]\n",
"Epoch 24: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.702, train_loss_epoch=0.658]\n",
"Epoch 24: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.702, train_loss_epoch=0.658]\n",
"Epoch 25: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.693, train_loss_epoch=0.650]\n",
"Epoch 25: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.693, train_loss_epoch=0.650]\n",
"Epoch 26: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.639, train_loss_epoch=0.650]\n",
"Epoch 26: 100%|██████████| 3/3 [00:02<00:00, 1.31it/s, v_num=0, train_loss_step=0.639, train_loss_epoch=0.650]\n",
"Epoch 27: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.696, train_loss_epoch=0.649]\n",
"Epoch 27: 100%|██████████| 3/3 [00:02<00:00, 1.31it/s, v_num=0, train_loss_step=0.696, train_loss_epoch=0.649]\n",
"Epoch 28: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.691, train_loss_epoch=0.674]\n",
"Epoch 28: 100%|██████████| 3/3 [00:02<00:00, 1.31it/s, v_num=0, train_loss_step=0.691, train_loss_epoch=0.674]\n",
"Epoch 29: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.605, train_loss_epoch=0.657]\n",
"Epoch 29: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.605, train_loss_epoch=0.657]\n",
"Epoch 30: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.644, train_loss_epoch=0.651]\n",
"Epoch 30: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.644, train_loss_epoch=0.651]\n",
"Epoch 31: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.649, train_loss_epoch=0.655]\n",
"Epoch 31: 100%|██████████| 3/3 [00:02<00:00, 1.31it/s, v_num=0, train_loss_step=0.649, train_loss_epoch=0.655]\n",
"Epoch 32: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.618, train_loss_epoch=0.658]\n",
"Epoch 32: 100%|██████████| 3/3 [00:02<00:00, 1.31it/s, v_num=0, train_loss_step=0.618, train_loss_epoch=0.658]\n",
"Epoch 33: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.699, train_loss_epoch=0.651]\n",
"\u001b[36m(_train_tune pid=26992)\u001b[0m \n",
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"Validation DataLoader 0: 100%|██████████| 3/3 [00:00<00:00, 41.56it/s]\u001b[A\n",
"Epoch 33: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.697, train_loss_epoch=0.651, valid_loss=5.110]\n",
"Epoch 33: 100%|██████████| 3/3 [00:02<00:00, 1.27it/s, v_num=0, train_loss_step=0.697, train_loss_epoch=0.651, valid_loss=5.110]\n",
"Epoch 34: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.599, train_loss_epoch=0.644, valid_loss=5.110]\n",
"Epoch 34: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.599, train_loss_epoch=0.644, valid_loss=5.110]\n",
"Epoch 35: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.602, train_loss_epoch=0.648, valid_loss=5.110]\n",
"Epoch 35: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.602, train_loss_epoch=0.648, valid_loss=5.110]\n",
"Epoch 36: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.639, train_loss_epoch=0.640, valid_loss=5.110]\n",
"Epoch 36: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.639, train_loss_epoch=0.640, valid_loss=5.110]\n",
"Epoch 37: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.660, train_loss_epoch=0.657, valid_loss=5.110]\n",
"Epoch 37: 100%|██████████| 3/3 [00:02<00:00, 1.31it/s, v_num=0, train_loss_step=0.660, train_loss_epoch=0.657, valid_loss=5.110]\n",
"Epoch 38: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.603, train_loss_epoch=0.655, valid_loss=5.110]\n",
"Epoch 38: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.603, train_loss_epoch=0.655, valid_loss=5.110]\n",
"Epoch 39: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.681, train_loss_epoch=0.634, valid_loss=5.110]\n",
"Epoch 39: 100%|██████████| 3/3 [00:02<00:00, 1.31it/s, v_num=0, train_loss_step=0.681, train_loss_epoch=0.634, valid_loss=5.110]\n",
"Epoch 40: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.638, train_loss_epoch=0.639, valid_loss=5.110]\n",
"Epoch 40: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.638, train_loss_epoch=0.639, valid_loss=5.110]\n",
"Epoch 41: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.585, train_loss_epoch=0.628, valid_loss=5.110]\n",
"Epoch 41: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.585, train_loss_epoch=0.628, valid_loss=5.110]\n",
"Epoch 42: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.647, train_loss_epoch=0.646, valid_loss=5.110]\n",
"Epoch 42: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.647, train_loss_epoch=0.646, valid_loss=5.110]\n",
"Epoch 43: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.687, train_loss_epoch=0.635, valid_loss=5.110]\n",
"Epoch 43: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.687, train_loss_epoch=0.635, valid_loss=5.110]\n",
"Epoch 43: 100%|██████████| 3/3 [00:02<00:00, 1.15it/s, v_num=0, train_loss_step=0.584, train_loss_epoch=0.635, valid_loss=5.110]\n",
"Epoch 44: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.584, train_loss_epoch=0.629, valid_loss=5.110]\n",
"Epoch 44: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.584, train_loss_epoch=0.629, valid_loss=5.110]\n",
"Epoch 45: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.676, train_loss_epoch=0.626, valid_loss=5.110]\n",
"Epoch 45: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.676, train_loss_epoch=0.626, valid_loss=5.110]\n",
"Epoch 46: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.657, train_loss_epoch=0.620, valid_loss=5.110]\n",
"Epoch 46: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.657, train_loss_epoch=0.620, valid_loss=5.110]\n",
"Epoch 47: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.680, train_loss_epoch=0.632, valid_loss=5.110]\n",
"Epoch 47: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.680, train_loss_epoch=0.632, valid_loss=5.110]\n",
"Epoch 48: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.663, train_loss_epoch=0.617, valid_loss=5.110]\n",
"Epoch 48: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.663, train_loss_epoch=0.617, valid_loss=5.110]\n",
"Epoch 49: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.582, train_loss_epoch=0.627, valid_loss=5.110]\n",
"Epoch 49: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.582, train_loss_epoch=0.627, valid_loss=5.110]\n",
"Epoch 50: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.621, valid_loss=5.110]\n",
"Epoch 50: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.583, train_loss_epoch=0.621, valid_loss=5.110]\n",
"Epoch 51: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.626, train_loss_epoch=0.624, valid_loss=5.110]\n",
"Epoch 51: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.626, train_loss_epoch=0.624, valid_loss=5.110]\n",
"Epoch 52: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.654, train_loss_epoch=0.611, valid_loss=5.110]\n",
"Epoch 52: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.654, train_loss_epoch=0.611, valid_loss=5.110]\n",
"Epoch 53: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.631, train_loss_epoch=0.621, valid_loss=5.110]\n",
"Epoch 53: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.631, train_loss_epoch=0.621, valid_loss=5.110]\n",
"Epoch 54: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.694, train_loss_epoch=0.634, valid_loss=5.110]\n",
"Epoch 54: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.694, train_loss_epoch=0.634, valid_loss=5.110]\n",
"Epoch 55: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.596, train_loss_epoch=0.652, valid_loss=5.110]\n",
"Epoch 55: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.596, train_loss_epoch=0.652, valid_loss=5.110]\n",
"Epoch 56: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.625, train_loss_epoch=0.630, valid_loss=5.110]\n",
"Epoch 56: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.625, train_loss_epoch=0.630, valid_loss=5.110]\n",
"Epoch 57: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.692, train_loss_epoch=0.636, valid_loss=5.110]\n",
"Epoch 57: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.692, train_loss_epoch=0.636, valid_loss=5.110]\n",
"Epoch 58: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.613, train_loss_epoch=0.644, valid_loss=5.110]\n",
"Epoch 58: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.613, train_loss_epoch=0.644, valid_loss=5.110]\n",
"Epoch 59: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.665, train_loss_epoch=0.628, valid_loss=5.110]\n",
"Epoch 59: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.665, train_loss_epoch=0.628, valid_loss=5.110]\n",
"Epoch 60: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.695, train_loss_epoch=0.645, valid_loss=5.110]\n",
"Epoch 60: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.695, train_loss_epoch=0.645, valid_loss=5.110]\n",
"Epoch 61: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.609, train_loss_epoch=0.631, valid_loss=5.110]\n",
"Epoch 61: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.609, train_loss_epoch=0.631, valid_loss=5.110]\n",
"Epoch 62: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.673, train_loss_epoch=0.640, valid_loss=5.110]\n",
"Epoch 62: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.673, train_loss_epoch=0.640, valid_loss=5.110]\n",
"Epoch 63: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.666, train_loss_epoch=0.635, valid_loss=5.110]\n",
"Epoch 63: 100%|██████████| 3/3 [00:02<00:00, 1.28it/s, v_num=0, train_loss_step=0.666, train_loss_epoch=0.635, valid_loss=5.110]\n",
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"Epoch 64: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.613, train_loss_epoch=0.618, valid_loss=5.110]\n",
"Epoch 65: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.713, train_loss_epoch=0.636, valid_loss=5.110]\n",
"Epoch 65: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.713, train_loss_epoch=0.636, valid_loss=5.110]\n",
"Epoch 66: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.655, train_loss_epoch=0.638, valid_loss=5.110]\n",
"\u001b[36m(_train_tune pid=26992)\u001b[0m \n",
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"\u001b[36m(_train_tune pid=26992)\u001b[0m \n",
"Validation DataLoader 0: 100%|██████████| 3/3 [00:00<00:00, 40.78it/s]\u001b[A\n",
"Epoch 66: 0%| | 0/3 [00:01, ?it/s, v_num=0, train_loss_step=0.607, train_loss_epoch=0.638, valid_loss=5.100]\n",
"Epoch 66: 100%|██████████| 3/3 [00:02<00:00, 1.26it/s, v_num=0, train_loss_step=0.607, train_loss_epoch=0.638, valid_loss=5.100]\n",
"Epoch 67: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.627, train_loss_epoch=0.638, valid_loss=5.100]\n",
"Epoch 67: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.627, train_loss_epoch=0.638, valid_loss=5.100]\n",
"Epoch 68: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.641, train_loss_epoch=0.631, valid_loss=5.100]\n",
"Epoch 68: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.641, train_loss_epoch=0.631, valid_loss=5.100]\n",
"Epoch 69: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.642, train_loss_epoch=0.628, valid_loss=5.100]\n",
"Epoch 69: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.642, train_loss_epoch=0.628, valid_loss=5.100]\n",
"Epoch 70: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.677, train_loss_epoch=0.631, valid_loss=5.100]\n",
"Epoch 70: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.677, train_loss_epoch=0.631, valid_loss=5.100]\n",
"Epoch 71: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.625, valid_loss=5.100]\n",
"Epoch 71: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.625, valid_loss=5.100]\n",
"Epoch 72: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.611, valid_loss=5.100]\n",
"Epoch 72: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.575, train_loss_epoch=0.611, valid_loss=5.100]\n",
"Epoch 73: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.619, valid_loss=5.100]\n",
"Epoch 73: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.571, train_loss_epoch=0.619, valid_loss=5.100]\n",
"Epoch 73: 100%|██████████| 3/3 [00:02<00:00, 1.15it/s, v_num=0, train_loss_step=0.577, train_loss_epoch=0.615, valid_loss=5.100]\n",
"Epoch 74: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.577, train_loss_epoch=0.615, valid_loss=5.100]\n",
"Epoch 74: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.577, train_loss_epoch=0.615, valid_loss=5.100]\n",
"Epoch 75: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.603, train_loss_epoch=0.608, valid_loss=5.100]\n",
"Epoch 75: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.603, train_loss_epoch=0.608, valid_loss=5.100]\n",
"Epoch 76: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.651, train_loss_epoch=0.614, valid_loss=5.100]\n",
"Epoch 76: 100%|██████████| 3/3 [00:02<00:00, 1.31it/s, v_num=0, train_loss_step=0.651, train_loss_epoch=0.614, valid_loss=5.100]\n",
"Epoch 77: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.664, train_loss_epoch=0.620, valid_loss=5.100]\n",
"Epoch 77: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.664, train_loss_epoch=0.620, valid_loss=5.100]\n",
"Epoch 78: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.565, train_loss_epoch=0.596, valid_loss=5.100]\n",
"Epoch 78: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.565, train_loss_epoch=0.596, valid_loss=5.100]\n",
"Epoch 79: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.659, train_loss_epoch=0.611, valid_loss=5.100]\n",
"Epoch 79: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.659, train_loss_epoch=0.611, valid_loss=5.100]\n",
"Epoch 80: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.616, train_loss_epoch=0.612, valid_loss=5.100]\n",
"Epoch 80: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.616, train_loss_epoch=0.612, valid_loss=5.100]\n",
"Epoch 81: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.660, train_loss_epoch=0.618, valid_loss=5.100]\n",
"Epoch 81: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.660, train_loss_epoch=0.618, valid_loss=5.100]\n",
"Epoch 82: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.562, train_loss_epoch=0.607, valid_loss=5.100]\n",
"Epoch 82: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.562, train_loss_epoch=0.607, valid_loss=5.100]\n",
"Epoch 83: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.624, train_loss_epoch=0.623, valid_loss=5.100]\n",
"Epoch 83: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.624, train_loss_epoch=0.623, valid_loss=5.100]\n",
"Epoch 84: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.564, train_loss_epoch=0.607, valid_loss=5.100]\n",
"Epoch 84: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.564, train_loss_epoch=0.607, valid_loss=5.100]\n",
"Epoch 85: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.644, train_loss_epoch=0.607, valid_loss=5.100]\n",
"Epoch 85: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.644, train_loss_epoch=0.607, valid_loss=5.100]\n",
"Epoch 86: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.634, train_loss_epoch=0.604, valid_loss=5.100]\n",
"Epoch 86: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.634, train_loss_epoch=0.604, valid_loss=5.100]\n",
"Epoch 87: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.613, train_loss_epoch=0.603, valid_loss=5.100]\n",
"Epoch 87: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.613, train_loss_epoch=0.603, valid_loss=5.100]\n",
"Epoch 88: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.621, train_loss_epoch=0.600, valid_loss=5.100]\n",
"Epoch 88: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.621, train_loss_epoch=0.600, valid_loss=5.100]\n",
"Epoch 89: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.585, train_loss_epoch=0.592, valid_loss=5.100]\n",
"Epoch 89: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.585, train_loss_epoch=0.592, valid_loss=5.100]\n",
"Epoch 90: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.586, train_loss_epoch=0.591, valid_loss=5.100]\n",
"Epoch 90: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.586, train_loss_epoch=0.591, valid_loss=5.100]\n",
"Epoch 91: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.631, train_loss_epoch=0.597, valid_loss=5.100]\n",
"Epoch 91: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.631, train_loss_epoch=0.597, valid_loss=5.100]\n",
"Epoch 92: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.643, train_loss_epoch=0.598, valid_loss=5.100]\n",
"Epoch 92: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.643, train_loss_epoch=0.598, valid_loss=5.100]\n",
"Epoch 93: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.605, train_loss_epoch=0.586, valid_loss=5.100]\n",
"Epoch 93: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.605, train_loss_epoch=0.586, valid_loss=5.100]\n",
"Epoch 94: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.537, train_loss_epoch=0.584, valid_loss=5.100]\n",
"Epoch 94: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.537, train_loss_epoch=0.584, valid_loss=5.100]\n",
"Epoch 94: 100%|██████████| 3/3 [00:02<00:00, 1.16it/s, v_num=0, train_loss_step=0.605, train_loss_epoch=0.593, valid_loss=5.100]\n",
"Epoch 95: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.605, train_loss_epoch=0.593, valid_loss=5.100]\n",
"Epoch 95: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.605, train_loss_epoch=0.593, valid_loss=5.100]\n",
"Epoch 96: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.558, train_loss_epoch=0.593, valid_loss=5.100]\n",
"Epoch 96: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.558, train_loss_epoch=0.593, valid_loss=5.100]\n",
"Epoch 97: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.543, train_loss_epoch=0.589, valid_loss=5.100]\n",
"Epoch 97: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.543, train_loss_epoch=0.589, valid_loss=5.100]\n",
"Epoch 98: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.598, train_loss_epoch=0.598, valid_loss=5.100]\n",
"Epoch 98: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.598, train_loss_epoch=0.598, valid_loss=5.100]\n",
"Epoch 99: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.537, train_loss_epoch=0.592, valid_loss=5.100]\n",
"Epoch 99: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.537, train_loss_epoch=0.592, valid_loss=5.100]\n",
"Epoch 99: 100%|██████████| 3/3 [00:02<00:00, 1.16it/s, v_num=0, train_loss_step=0.603, train_loss_epoch=0.592, valid_loss=5.100]\n",
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"Validation: 0%| | 0/3 [00:00, ?it/s]\u001b[A\n",
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"Validation DataLoader 0: 100%|██████████| 3/3 [00:00<00:00, 41.81it/s]\u001b[A\n",
"Epoch 100: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.603, train_loss_epoch=0.592, valid_loss=5.230]\n",
"Epoch 100: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.603, train_loss_epoch=0.592, valid_loss=5.230]\n",
"Epoch 101: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.559, train_loss_epoch=0.595, valid_loss=5.230]\n",
"Epoch 101: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.559, train_loss_epoch=0.595, valid_loss=5.230]\n",
"Epoch 102: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.578, valid_loss=5.230]\n",
"Epoch 102: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.587, train_loss_epoch=0.578, valid_loss=5.230]\n",
"Epoch 103: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.555, train_loss_epoch=0.590, valid_loss=5.230]\n",
"Epoch 103: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.555, train_loss_epoch=0.590, valid_loss=5.230]\n",
"Epoch 104: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.563, train_loss_epoch=0.594, valid_loss=5.230]\n",
"Epoch 104: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.563, train_loss_epoch=0.594, valid_loss=5.230]\n",
"Epoch 105: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.594, train_loss_epoch=0.591, valid_loss=5.230]\n",
"Epoch 105: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.594, train_loss_epoch=0.591, valid_loss=5.230]\n",
"Epoch 106: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.557, train_loss_epoch=0.591, valid_loss=5.230]\n",
"Epoch 106: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.557, train_loss_epoch=0.591, valid_loss=5.230]\n",
"Epoch 107: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.582, train_loss_epoch=0.576, valid_loss=5.230]\n",
"Epoch 107: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.582, train_loss_epoch=0.576, valid_loss=5.230]\n",
"Epoch 108: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.544, train_loss_epoch=0.586, valid_loss=5.230]\n",
"Epoch 108: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.544, train_loss_epoch=0.586, valid_loss=5.230]\n",
"Epoch 109: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.531, train_loss_epoch=0.578, valid_loss=5.230]\n",
"Epoch 109: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.531, train_loss_epoch=0.578, valid_loss=5.230]\n",
"Epoch 110: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.529, train_loss_epoch=0.576, valid_loss=5.230]\n",
"Epoch 110: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.529, train_loss_epoch=0.576, valid_loss=5.230]\n",
"Epoch 111: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.603, train_loss_epoch=0.574, valid_loss=5.230]\n",
"Epoch 111: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.603, train_loss_epoch=0.574, valid_loss=5.230]\n",
"Epoch 112: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.600, train_loss_epoch=0.572, valid_loss=5.230]\n",
"Epoch 112: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.600, train_loss_epoch=0.572, valid_loss=5.230]\n",
"Epoch 113: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.531, train_loss_epoch=0.566, valid_loss=5.230]\n",
"Epoch 113: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.531, train_loss_epoch=0.566, valid_loss=5.230]\n",
"Epoch 114: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.515, train_loss_epoch=0.567, valid_loss=5.230]\n",
"Epoch 114: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.515, train_loss_epoch=0.567, valid_loss=5.230]\n",
"Epoch 115: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.601, train_loss_epoch=0.566, valid_loss=5.230]\n",
"Epoch 115: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.601, train_loss_epoch=0.566, valid_loss=5.230]\n",
"Epoch 116: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.523, train_loss_epoch=0.564, valid_loss=5.230]\n",
"Epoch 116: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.523, train_loss_epoch=0.564, valid_loss=5.230]\n",
"Epoch 117: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.520, train_loss_epoch=0.568, valid_loss=5.230]\n",
"Epoch 117: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.520, train_loss_epoch=0.568, valid_loss=5.230]\n",
"Epoch 118: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.524, train_loss_epoch=0.567, valid_loss=5.230]\n",
"Epoch 118: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.524, train_loss_epoch=0.567, valid_loss=5.230]\n",
"Epoch 119: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.512, train_loss_epoch=0.567, valid_loss=5.230]\n",
"Epoch 119: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.512, train_loss_epoch=0.567, valid_loss=5.230]\n",
"Epoch 120: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.523, train_loss_epoch=0.573, valid_loss=5.230]\n",
"Epoch 120: 100%|██████████| 3/3 [00:02<00:00, 1.31it/s, v_num=0, train_loss_step=0.523, train_loss_epoch=0.573, valid_loss=5.230]\n",
"Epoch 121: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.623, train_loss_epoch=0.577, valid_loss=5.230]\n",
"Epoch 121: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.623, train_loss_epoch=0.577, valid_loss=5.230]\n",
"Epoch 122: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.519, train_loss_epoch=0.575, valid_loss=5.230]\n",
"Epoch 122: 100%|██████████| 3/3 [00:02<00:00, 1.31it/s, v_num=0, train_loss_step=0.519, train_loss_epoch=0.575, valid_loss=5.230]\n",
"Epoch 123: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.521, train_loss_epoch=0.567, valid_loss=5.230]\n",
"Epoch 123: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.521, train_loss_epoch=0.567, valid_loss=5.230]\n",
"Epoch 124: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.572, train_loss_epoch=0.558, valid_loss=5.230]\n",
"Epoch 124: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.572, train_loss_epoch=0.558, valid_loss=5.230]\n",
"Epoch 125: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.563, train_loss_epoch=0.561, valid_loss=5.230]\n",
"Epoch 125: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.563, train_loss_epoch=0.561, valid_loss=5.230]\n",
"Epoch 126: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.603, train_loss_epoch=0.556, valid_loss=5.230]\n",
"Epoch 126: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.603, train_loss_epoch=0.556, valid_loss=5.230]\n",
"Epoch 127: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.512, train_loss_epoch=0.555, valid_loss=5.230]\n",
"Epoch 127: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.512, train_loss_epoch=0.555, valid_loss=5.230]\n",
"Epoch 128: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.618, train_loss_epoch=0.564, valid_loss=5.230]\n",
"Epoch 128: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.618, train_loss_epoch=0.564, valid_loss=5.230]\n",
"Epoch 129: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.572, train_loss_epoch=0.556, valid_loss=5.230]\n",
"Epoch 129: 100%|██████████| 3/3 [00:02<00:00, 1.28it/s, v_num=0, train_loss_step=0.572, train_loss_epoch=0.556, valid_loss=5.230]\n",
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"Epoch 130: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.580, train_loss_epoch=0.561, valid_loss=5.230]\n",
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"Epoch 131: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.505, train_loss_epoch=0.555, valid_loss=5.230]\n",
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"Epoch 132: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.596, train_loss_epoch=0.560, valid_loss=5.230]\n",
"Epoch 132: 100%|██████████| 3/3 [00:02<00:00, 1.16it/s, v_num=0, train_loss_step=0.551, train_loss_epoch=0.552, valid_loss=5.230]\n",
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"Epoch 133: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.551, train_loss_epoch=0.552, valid_loss=5.230]\n",
"\u001b[36m(_train_tune pid=26992)\u001b[0m \n",
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"Validation DataLoader 0: 100%|██████████| 3/3 [00:00<00:00, 41.50it/s]\u001b[A\n",
"Epoch 133: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.548, train_loss_epoch=0.552, valid_loss=7.940]\n",
"Epoch 133: 100%|██████████| 3/3 [00:02<00:00, 1.26it/s, v_num=0, train_loss_step=0.548, train_loss_epoch=0.552, valid_loss=7.940]\n",
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"Epoch 134: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.581, train_loss_epoch=0.538, valid_loss=7.940]\n",
"Epoch 135: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.570, train_loss_epoch=0.538, valid_loss=7.940]\n",
"Epoch 135: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.570, train_loss_epoch=0.538, valid_loss=7.940]\n",
"Epoch 136: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.540, train_loss_epoch=0.537, valid_loss=7.940]\n",
"Epoch 136: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.540, train_loss_epoch=0.537, valid_loss=7.940]\n",
"Epoch 137: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.483, train_loss_epoch=0.538, valid_loss=7.940]\n",
"Epoch 137: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.483, train_loss_epoch=0.538, valid_loss=7.940]\n",
"Epoch 138: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.557, train_loss_epoch=0.535, valid_loss=7.940]\n",
"Epoch 138: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.557, train_loss_epoch=0.535, valid_loss=7.940]\n",
"Epoch 139: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.543, train_loss_epoch=0.539, valid_loss=7.940]\n",
"Epoch 139: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.543, train_loss_epoch=0.539, valid_loss=7.940]\n",
"Epoch 140: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.493, train_loss_epoch=0.543, valid_loss=7.940]\n",
"Epoch 140: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.493, train_loss_epoch=0.543, valid_loss=7.940]\n",
"Epoch 141: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.489, train_loss_epoch=0.535, valid_loss=7.940]\n",
"Epoch 141: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.489, train_loss_epoch=0.535, valid_loss=7.940]\n",
"Epoch 142: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.570, train_loss_epoch=0.529, valid_loss=7.940]\n",
"Epoch 142: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.570, train_loss_epoch=0.529, valid_loss=7.940]\n",
"Epoch 143: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.519, train_loss_epoch=0.520, valid_loss=7.940]\n",
"Epoch 143: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.519, train_loss_epoch=0.520, valid_loss=7.940]\n",
"Epoch 144: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.469, train_loss_epoch=0.514, valid_loss=7.940]\n",
"Epoch 144: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.469, train_loss_epoch=0.514, valid_loss=7.940]\n",
"Epoch 145: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.562, train_loss_epoch=0.515, valid_loss=7.940]\n",
"Epoch 145: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.562, train_loss_epoch=0.515, valid_loss=7.940]\n",
"Epoch 146: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.480, train_loss_epoch=0.519, valid_loss=7.940]\n",
"Epoch 146: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.480, train_loss_epoch=0.519, valid_loss=7.940]\n",
"Epoch 147: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.492, train_loss_epoch=0.521, valid_loss=7.940]\n",
"Epoch 147: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.492, train_loss_epoch=0.521, valid_loss=7.940]\n",
"Epoch 148: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.477, train_loss_epoch=0.517, valid_loss=7.940]\n",
"Epoch 148: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.477, train_loss_epoch=0.517, valid_loss=7.940]\n",
"Epoch 149: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.516, train_loss_epoch=0.533, valid_loss=7.940]\n",
"Epoch 149: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.516, train_loss_epoch=0.533, valid_loss=7.940]\n",
"Epoch 150: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.480, train_loss_epoch=0.516, valid_loss=7.940]\n",
"Epoch 150: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.480, train_loss_epoch=0.516, valid_loss=7.940]\n",
"Epoch 151: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.564, train_loss_epoch=0.525, valid_loss=7.940]\n",
"Epoch 151: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.564, train_loss_epoch=0.525, valid_loss=7.940]\n",
"Epoch 152: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.540, train_loss_epoch=0.514, valid_loss=7.940]\n",
"Epoch 152: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.540, train_loss_epoch=0.514, valid_loss=7.940]\n",
"Epoch 153: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.535, train_loss_epoch=0.521, valid_loss=7.940]\n",
"Epoch 153: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.535, train_loss_epoch=0.521, valid_loss=7.940]\n",
"Epoch 154: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.484, train_loss_epoch=0.522, valid_loss=7.940]\n",
"Epoch 154: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.484, train_loss_epoch=0.522, valid_loss=7.940]\n",
"Epoch 155: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.470, train_loss_epoch=0.502, valid_loss=7.940]\n",
"Epoch 155: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.470, train_loss_epoch=0.502, valid_loss=7.940]\n",
"Epoch 156: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.509, train_loss_epoch=0.509, valid_loss=7.940]\n",
"Epoch 156: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.509, train_loss_epoch=0.509, valid_loss=7.940]\n",
"Epoch 157: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.551, train_loss_epoch=0.506, valid_loss=7.940]\n",
"Epoch 157: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.551, train_loss_epoch=0.506, valid_loss=7.940]\n",
"Epoch 158: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.516, train_loss_epoch=0.495, valid_loss=7.940]\n",
"Epoch 158: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.516, train_loss_epoch=0.495, valid_loss=7.940]\n",
"Epoch 159: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.473, train_loss_epoch=0.496, valid_loss=7.940]\n",
"Epoch 159: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.473, train_loss_epoch=0.496, valid_loss=7.940]\n",
"Epoch 160: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.502, train_loss_epoch=0.508, valid_loss=7.940]\n",
"Epoch 160: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.502, train_loss_epoch=0.508, valid_loss=7.940]\n",
"Epoch 161: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.495, train_loss_epoch=0.493, valid_loss=7.940]\n",
"Epoch 161: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.495, train_loss_epoch=0.493, valid_loss=7.940]\n",
"Epoch 162: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.512, train_loss_epoch=0.498, valid_loss=7.940]\n",
"Epoch 162: 100%|██████████| 3/3 [00:02<00:00, 1.28it/s, v_num=0, train_loss_step=0.512, train_loss_epoch=0.498, valid_loss=7.940]\n",
"Epoch 163: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.525, train_loss_epoch=0.520, valid_loss=7.940]\n",
"Epoch 163: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.525, train_loss_epoch=0.520, valid_loss=7.940]\n",
"Epoch 164: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.529, train_loss_epoch=0.530, valid_loss=7.940]\n",
"Epoch 164: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.529, train_loss_epoch=0.530, valid_loss=7.940]\n",
"Epoch 165: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.552, train_loss_epoch=0.526, valid_loss=7.940]\n",
"Epoch 165: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.552, train_loss_epoch=0.526, valid_loss=7.940]\n",
"Epoch 166: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.511, train_loss_epoch=0.504, valid_loss=7.940]\n",
"\u001b[36m(_train_tune pid=26992)\u001b[0m \n",
"Validation: | | 0/? [00:00, ?it/s]\u001b[A\n",
"Validation: 0%| | 0/3 [00:00, ?it/s]\u001b[A\n",
"Validation DataLoader 0: 0%| | 0/3 [00:00, ?it/s]\u001b[A\n",
"\u001b[36m(_train_tune pid=26992)\u001b[0m \n",
"Validation DataLoader 0: 100%|██████████| 3/3 [00:00<00:00, 41.68it/s]\u001b[A\n",
"Epoch 166: 0%| | 0/3 [00:01, ?it/s, v_num=0, train_loss_step=0.519, train_loss_epoch=0.504, valid_loss=6.200]\n",
"Epoch 166: 100%|██████████| 3/3 [00:02<00:00, 1.26it/s, v_num=0, train_loss_step=0.519, train_loss_epoch=0.504, valid_loss=6.200]\n",
"Epoch 167: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.475, train_loss_epoch=0.511, valid_loss=6.200]\n",
"Epoch 167: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.475, train_loss_epoch=0.511, valid_loss=6.200]\n",
"Epoch 168: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.517, train_loss_epoch=0.504, valid_loss=6.200]\n",
"Epoch 168: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.517, train_loss_epoch=0.504, valid_loss=6.200]\n",
"Epoch 169: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.492, train_loss_epoch=0.493, valid_loss=6.200]\n",
"Epoch 169: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.492, train_loss_epoch=0.493, valid_loss=6.200]\n",
"Epoch 170: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.498, train_loss_epoch=0.487, valid_loss=6.200]\n",
"Epoch 170: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.498, train_loss_epoch=0.487, valid_loss=6.200]\n",
"Epoch 171: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.539, train_loss_epoch=0.497, valid_loss=6.200]\n",
"Epoch 171: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.539, train_loss_epoch=0.497, valid_loss=6.200]\n",
"Epoch 172: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.490, train_loss_epoch=0.480, valid_loss=6.200]\n",
"Epoch 172: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.490, train_loss_epoch=0.480, valid_loss=6.200]\n",
"Epoch 173: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.499, train_loss_epoch=0.484, valid_loss=6.200]\n",
"Epoch 173: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.499, train_loss_epoch=0.484, valid_loss=6.200]\n",
"Epoch 174: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.559, train_loss_epoch=0.508, valid_loss=6.200]\n",
"Epoch 174: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.559, train_loss_epoch=0.508, valid_loss=6.200]\n",
"Epoch 175: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.517, train_loss_epoch=0.506, valid_loss=6.200]\n",
"Epoch 175: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.517, train_loss_epoch=0.506, valid_loss=6.200]\n",
"Epoch 176: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.523, train_loss_epoch=0.516, valid_loss=6.200]\n",
"Epoch 176: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.523, train_loss_epoch=0.516, valid_loss=6.200]\n",
"Epoch 177: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.477, train_loss_epoch=0.500, valid_loss=6.200]\n",
"Epoch 177: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.477, train_loss_epoch=0.500, valid_loss=6.200]\n",
"Epoch 178: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.578, train_loss_epoch=0.534, valid_loss=6.200]\n",
"Epoch 178: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.578, train_loss_epoch=0.534, valid_loss=6.200]\n",
"Epoch 179: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.498, train_loss_epoch=0.492, valid_loss=6.200]\n",
"Epoch 179: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.498, train_loss_epoch=0.492, valid_loss=6.200]\n",
"Epoch 180: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.539, train_loss_epoch=0.497, valid_loss=6.200]\n",
"Epoch 180: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.539, train_loss_epoch=0.497, valid_loss=6.200]\n",
"Epoch 181: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.525, train_loss_epoch=0.527, valid_loss=6.200]\n",
"Epoch 181: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.525, train_loss_epoch=0.527, valid_loss=6.200]\n",
"Epoch 182: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.520, train_loss_epoch=0.512, valid_loss=6.200]\n",
"Epoch 182: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.520, train_loss_epoch=0.512, valid_loss=6.200]\n",
"Epoch 183: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.457, train_loss_epoch=0.495, valid_loss=6.200]\n",
"Epoch 183: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.457, train_loss_epoch=0.495, valid_loss=6.200]\n",
"Epoch 184: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.503, train_loss_epoch=0.500, valid_loss=6.200]\n",
"Epoch 184: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.503, train_loss_epoch=0.500, valid_loss=6.200]\n",
"Epoch 185: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.452, train_loss_epoch=0.499, valid_loss=6.200]\n",
"Epoch 185: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.452, train_loss_epoch=0.499, valid_loss=6.200]\n",
"Epoch 186: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.514, train_loss_epoch=0.495, valid_loss=6.200]\n",
"Epoch 186: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.514, train_loss_epoch=0.495, valid_loss=6.200]\n",
"Epoch 187: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.512, train_loss_epoch=0.490, valid_loss=6.200]\n",
"Epoch 187: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.512, train_loss_epoch=0.490, valid_loss=6.200]\n",
"Epoch 188: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.488, train_loss_epoch=0.479, valid_loss=6.200]\n",
"Epoch 188: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.488, train_loss_epoch=0.479, valid_loss=6.200]\n",
"Epoch 189: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.445, train_loss_epoch=0.481, valid_loss=6.200]\n",
"Epoch 189: 100%|██████████| 3/3 [00:02<00:00, 1.28it/s, v_num=0, train_loss_step=0.445, train_loss_epoch=0.481, valid_loss=6.200]\n",
"Epoch 190: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.482, train_loss_epoch=0.476, valid_loss=6.200]\n",
"Epoch 190: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.482, train_loss_epoch=0.476, valid_loss=6.200]\n",
"Epoch 191: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.482, train_loss_epoch=0.472, valid_loss=6.200]\n",
"Epoch 191: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.482, train_loss_epoch=0.472, valid_loss=6.200]\n",
"Epoch 192: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.449, train_loss_epoch=0.475, valid_loss=6.200]\n",
"Epoch 192: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.449, train_loss_epoch=0.475, valid_loss=6.200]\n",
"Epoch 193: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.483, train_loss_epoch=0.470, valid_loss=6.200]\n",
"Epoch 193: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.483, train_loss_epoch=0.470, valid_loss=6.200]\n",
"Epoch 194: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.460, train_loss_epoch=0.480, valid_loss=6.200]\n",
"Epoch 194: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.460, train_loss_epoch=0.480, valid_loss=6.200]\n",
"Epoch 195: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.502, train_loss_epoch=0.472, valid_loss=6.200]\n",
"Epoch 195: 100%|██████████| 3/3 [00:02<00:00, 1.28it/s, v_num=0, train_loss_step=0.502, train_loss_epoch=0.472, valid_loss=6.200]\n",
"Epoch 196: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.494, train_loss_epoch=0.471, valid_loss=6.200]\n",
"Epoch 196: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.494, train_loss_epoch=0.471, valid_loss=6.200]\n",
"Epoch 197: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.485, train_loss_epoch=0.479, valid_loss=6.200]\n",
"Epoch 197: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.485, train_loss_epoch=0.479, valid_loss=6.200]\n",
"Epoch 198: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.490, train_loss_epoch=0.484, valid_loss=6.200]\n",
"Epoch 198: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.490, train_loss_epoch=0.484, valid_loss=6.200]\n",
"Epoch 199: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.490, train_loss_epoch=0.476, valid_loss=6.200]\n",
"Epoch 199: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.490, train_loss_epoch=0.476, valid_loss=6.200]\n",
"Epoch 199: 100%|██████████| 3/3 [00:02<00:00, 1.15it/s, v_num=0, train_loss_step=0.438, train_loss_epoch=0.476, valid_loss=6.200]\n",
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"\u001b[36m(_train_tune pid=26992)\u001b[0m \n",
"Validation DataLoader 0: 100%|██████████| 3/3 [00:00<00:00, 29.59it/s]\u001b[A\n",
"Epoch 200: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.438, train_loss_epoch=0.474, valid_loss=9.050]\n",
"Epoch 200: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.438, train_loss_epoch=0.474, valid_loss=9.050]\n",
"Epoch 201: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.496, train_loss_epoch=0.491, valid_loss=9.050]\n",
"Epoch 201: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.496, train_loss_epoch=0.491, valid_loss=9.050]\n",
"Epoch 202: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.496, train_loss_epoch=0.500, valid_loss=9.050]\n",
"Epoch 202: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.496, train_loss_epoch=0.500, valid_loss=9.050]\n",
"Epoch 203: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.483, train_loss_epoch=0.482, valid_loss=9.050]\n",
"Epoch 203: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.483, train_loss_epoch=0.482, valid_loss=9.050]\n",
"Epoch 204: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.448, train_loss_epoch=0.485, valid_loss=9.050]\n",
"Epoch 204: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.448, train_loss_epoch=0.485, valid_loss=9.050]\n",
"Epoch 204: 100%|██████████| 3/3 [00:02<00:00, 1.15it/s, v_num=0, train_loss_step=0.459, train_loss_epoch=0.488, valid_loss=9.050]\n",
"Epoch 205: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.459, train_loss_epoch=0.488, valid_loss=9.050]\n",
"Epoch 205: 100%|██████████| 3/3 [00:02<00:00, 1.26it/s, v_num=0, train_loss_step=0.459, train_loss_epoch=0.488, valid_loss=9.050]\n",
"Epoch 206: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.502, train_loss_epoch=0.487, valid_loss=9.050]\n",
"Epoch 206: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.502, train_loss_epoch=0.487, valid_loss=9.050]\n",
"Epoch 207: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.509, train_loss_epoch=0.488, valid_loss=9.050]\n",
"Epoch 207: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.509, train_loss_epoch=0.488, valid_loss=9.050]\n",
"Epoch 208: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.518, train_loss_epoch=0.534, valid_loss=9.050]\n",
"Epoch 208: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.518, train_loss_epoch=0.534, valid_loss=9.050]\n",
"Epoch 209: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.462, train_loss_epoch=0.540, valid_loss=9.050]\n",
"Epoch 209: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.462, train_loss_epoch=0.540, valid_loss=9.050]\n",
"Epoch 210: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.451, train_loss_epoch=0.509, valid_loss=9.050]\n",
"Epoch 210: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.451, train_loss_epoch=0.509, valid_loss=9.050]\n",
"Epoch 211: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.446, train_loss_epoch=0.487, valid_loss=9.050]\n",
"Epoch 211: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.446, train_loss_epoch=0.487, valid_loss=9.050]\n",
"Epoch 212: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.500, train_loss_epoch=0.485, valid_loss=9.050]\n",
"Epoch 212: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.500, train_loss_epoch=0.485, valid_loss=9.050]\n",
"Epoch 213: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.453, train_loss_epoch=0.486, valid_loss=9.050]\n",
"Epoch 213: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.453, train_loss_epoch=0.486, valid_loss=9.050]\n",
"Epoch 214: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.452, train_loss_epoch=0.480, valid_loss=9.050]\n",
"Epoch 214: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.452, train_loss_epoch=0.480, valid_loss=9.050]\n",
"Epoch 215: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.449, train_loss_epoch=0.487, valid_loss=9.050]\n",
"Epoch 215: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.449, train_loss_epoch=0.487, valid_loss=9.050]\n",
"Epoch 215: 100%|██████████| 3/3 [00:02<00:00, 1.15it/s, v_num=0, train_loss_step=0.490, train_loss_epoch=0.487, valid_loss=9.050]\n",
"Epoch 216: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.490, train_loss_epoch=0.472, valid_loss=9.050]\n",
"Epoch 216: 100%|██████████| 3/3 [00:02<00:00, 1.28it/s, v_num=0, train_loss_step=0.490, train_loss_epoch=0.472, valid_loss=9.050]\n",
"Epoch 217: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.473, train_loss_epoch=0.469, valid_loss=9.050]\n",
"Epoch 217: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.473, train_loss_epoch=0.469, valid_loss=9.050]\n",
"Epoch 218: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.436, train_loss_epoch=0.468, valid_loss=9.050]\n",
"Epoch 218: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.436, train_loss_epoch=0.468, valid_loss=9.050]\n",
"Epoch 219: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.482, train_loss_epoch=0.467, valid_loss=9.050]\n",
"Epoch 219: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.482, train_loss_epoch=0.467, valid_loss=9.050]\n",
"Epoch 220: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.426, train_loss_epoch=0.460, valid_loss=9.050]\n",
"Epoch 220: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.426, train_loss_epoch=0.460, valid_loss=9.050]\n",
"Epoch 221: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.471, train_loss_epoch=0.461, valid_loss=9.050]\n",
"Epoch 221: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.471, train_loss_epoch=0.461, valid_loss=9.050]\n",
"Epoch 222: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.465, train_loss_epoch=0.453, valid_loss=9.050]\n",
"Epoch 222: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.465, train_loss_epoch=0.453, valid_loss=9.050]\n",
"Epoch 223: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.446, train_loss_epoch=0.463, valid_loss=9.050]\n",
"Epoch 223: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.446, train_loss_epoch=0.463, valid_loss=9.050]\n",
"Epoch 224: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.472, train_loss_epoch=0.466, valid_loss=9.050]\n",
"Epoch 224: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.472, train_loss_epoch=0.466, valid_loss=9.050]\n",
"Epoch 225: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.420, train_loss_epoch=0.449, valid_loss=9.050]\n",
"Epoch 225: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.420, train_loss_epoch=0.449, valid_loss=9.050]\n",
"Epoch 226: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.423, train_loss_epoch=0.447, valid_loss=9.050]\n",
"Epoch 226: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.423, train_loss_epoch=0.447, valid_loss=9.050]\n",
"Epoch 227: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.453, train_loss_epoch=0.443, valid_loss=9.050]\n",
"Epoch 227: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.453, train_loss_epoch=0.443, valid_loss=9.050]\n",
"Epoch 228: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.420, train_loss_epoch=0.441, valid_loss=9.050]\n",
"Epoch 228: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.420, train_loss_epoch=0.441, valid_loss=9.050]\n",
"Epoch 229: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.449, train_loss_epoch=0.442, valid_loss=9.050]\n",
"Epoch 229: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.449, train_loss_epoch=0.442, valid_loss=9.050]\n",
"Epoch 229: 100%|██████████| 3/3 [00:02<00:00, 1.16it/s, v_num=0, train_loss_step=0.479, train_loss_epoch=0.454, valid_loss=9.050]\n",
"Epoch 230: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.479, train_loss_epoch=0.454, valid_loss=9.050]\n",
"Epoch 230: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.479, train_loss_epoch=0.454, valid_loss=9.050]\n",
"Epoch 231: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.443, train_loss_epoch=0.443, valid_loss=9.050]\n",
"Epoch 231: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.443, train_loss_epoch=0.443, valid_loss=9.050]\n",
"Epoch 232: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.433, train_loss_epoch=0.448, valid_loss=9.050]\n",
"Epoch 232: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.433, train_loss_epoch=0.448, valid_loss=9.050]\n",
"Epoch 233: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.451, train_loss_epoch=0.447, valid_loss=9.050]\n",
"\u001b[36m(_train_tune pid=26992)\u001b[0m \n",
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"\u001b[36m(_train_tune pid=26992)\u001b[0m \n",
"Validation DataLoader 0: 100%|██████████| 3/3 [00:00<00:00, 26.98it/s]\u001b[A\n",
"\u001b[36m(_train_tune pid=26992)\u001b[0m \n",
"Epoch 233: 0%| | 0/3 [00:01, ?it/s, v_num=0, train_loss_step=0.446, train_loss_epoch=0.447, valid_loss=12.10]\n",
"Epoch 233: 100%|██████████| 3/3 [00:02<00:00, 1.22it/s, v_num=0, train_loss_step=0.446, train_loss_epoch=0.447, valid_loss=12.10]\n",
"Epoch 234: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.460, train_loss_epoch=0.440, valid_loss=12.10]\n",
"Epoch 234: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.460, train_loss_epoch=0.440, valid_loss=12.10]\n",
"Epoch 235: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.458, train_loss_epoch=0.443, valid_loss=12.10]\n",
"Epoch 235: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.458, train_loss_epoch=0.443, valid_loss=12.10]\n",
"Epoch 236: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.517, train_loss_epoch=0.465, valid_loss=12.10]\n",
"Epoch 236: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.517, train_loss_epoch=0.465, valid_loss=12.10]\n",
"Epoch 237: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.497, train_loss_epoch=0.483, valid_loss=12.10]\n",
"Epoch 237: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.497, train_loss_epoch=0.483, valid_loss=12.10]\n",
"Epoch 238: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.463, train_loss_epoch=0.456, valid_loss=12.10]\n",
"Epoch 238: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.463, train_loss_epoch=0.456, valid_loss=12.10]\n",
"Epoch 239: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.447, train_loss_epoch=0.472, valid_loss=12.10]\n",
"Epoch 239: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.447, train_loss_epoch=0.472, valid_loss=12.10]\n",
"Epoch 240: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.436, train_loss_epoch=0.454, valid_loss=12.10]\n",
"Epoch 240: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.436, train_loss_epoch=0.454, valid_loss=12.10]\n",
"Epoch 241: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.454, train_loss_epoch=0.447, valid_loss=12.10]\n",
"Epoch 241: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.454, train_loss_epoch=0.447, valid_loss=12.10]\n",
"Epoch 242: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.416, train_loss_epoch=0.442, valid_loss=12.10]\n",
"Epoch 242: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.416, train_loss_epoch=0.442, valid_loss=12.10]\n",
"Epoch 243: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.428, train_loss_epoch=0.447, valid_loss=12.10]\n",
"Epoch 243: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.428, train_loss_epoch=0.447, valid_loss=12.10]\n",
"Epoch 244: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.416, train_loss_epoch=0.430, valid_loss=12.10]\n",
"Epoch 244: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.416, train_loss_epoch=0.430, valid_loss=12.10]\n",
"Epoch 245: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.440, train_loss_epoch=0.435, valid_loss=12.10]\n",
"Epoch 245: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.440, train_loss_epoch=0.435, valid_loss=12.10]\n",
"Epoch 246: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.449, train_loss_epoch=0.443, valid_loss=12.10]\n",
"Epoch 246: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.449, train_loss_epoch=0.443, valid_loss=12.10]\n",
"Epoch 247: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.445, train_loss_epoch=0.432, valid_loss=12.10]\n",
"Epoch 247: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.445, train_loss_epoch=0.432, valid_loss=12.10]\n",
"Epoch 248: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.432, train_loss_epoch=0.422, valid_loss=12.10]\n",
"Epoch 248: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.432, train_loss_epoch=0.422, valid_loss=12.10]\n",
"Epoch 249: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.409, train_loss_epoch=0.428, valid_loss=12.10]\n",
"Epoch 249: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.409, train_loss_epoch=0.428, valid_loss=12.10]\n",
"Epoch 250: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.429, train_loss_epoch=0.424, valid_loss=12.10]\n",
"Epoch 250: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.429, train_loss_epoch=0.424, valid_loss=12.10]\n",
"Epoch 251: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.467, train_loss_epoch=0.444, valid_loss=12.10]\n",
"Epoch 251: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.467, train_loss_epoch=0.444, valid_loss=12.10]\n",
"Epoch 252: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.412, train_loss_epoch=0.433, valid_loss=12.10]\n",
"Epoch 252: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.412, train_loss_epoch=0.433, valid_loss=12.10]\n",
"Epoch 253: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.442, train_loss_epoch=0.427, valid_loss=12.10]\n",
"Epoch 253: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.442, train_loss_epoch=0.427, valid_loss=12.10]\n",
"Epoch 254: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.433, train_loss_epoch=0.426, valid_loss=12.10]\n",
"Epoch 254: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.433, train_loss_epoch=0.426, valid_loss=12.10]\n",
"Epoch 255: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.433, train_loss_epoch=0.434, valid_loss=12.10]\n",
"Epoch 255: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.433, train_loss_epoch=0.434, valid_loss=12.10]\n",
"Epoch 256: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.422, train_loss_epoch=0.436, valid_loss=12.10]\n",
"Epoch 256: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.422, train_loss_epoch=0.436, valid_loss=12.10]\n",
"Epoch 257: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.399, train_loss_epoch=0.422, valid_loss=12.10]\n",
"Epoch 257: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.399, train_loss_epoch=0.422, valid_loss=12.10]\n",
"Epoch 258: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.415, train_loss_epoch=0.426, valid_loss=12.10]\n",
"Epoch 258: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.415, train_loss_epoch=0.426, valid_loss=12.10]\n",
"Epoch 258: 100%|██████████| 3/3 [00:02<00:00, 1.16it/s, v_num=0, train_loss_step=0.415, train_loss_epoch=0.426, valid_loss=12.10]\n",
"Epoch 259: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.415, train_loss_epoch=0.430, valid_loss=12.10]\n",
"Epoch 259: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.415, train_loss_epoch=0.430, valid_loss=12.10]\n",
"Epoch 260: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.426, train_loss_epoch=0.427, valid_loss=12.10]\n",
"Epoch 260: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.426, train_loss_epoch=0.427, valid_loss=12.10]\n",
"Epoch 261: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.407, train_loss_epoch=0.421, valid_loss=12.10]\n",
"Epoch 261: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.407, train_loss_epoch=0.421, valid_loss=12.10]\n",
"Epoch 262: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.418, train_loss_epoch=0.422, valid_loss=12.10]\n",
"Epoch 262: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.418, train_loss_epoch=0.422, valid_loss=12.10]\n",
"Epoch 263: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.401, train_loss_epoch=0.415, valid_loss=12.10]\n",
"Epoch 263: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.401, train_loss_epoch=0.415, valid_loss=12.10]\n",
"Epoch 264: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.400, train_loss_epoch=0.410, valid_loss=12.10]\n",
"Epoch 264: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.400, train_loss_epoch=0.410, valid_loss=12.10]\n",
"Epoch 265: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.436, train_loss_epoch=0.422, valid_loss=12.10]\n",
"Epoch 265: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.436, train_loss_epoch=0.422, valid_loss=12.10]\n",
"Epoch 266: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.412, train_loss_epoch=0.414, valid_loss=12.10]\n",
"\u001b[36m(_train_tune pid=26992)\u001b[0m \n",
"Validation: | | 0/? [00:00, ?it/s]\u001b[A\n",
"Validation: 0%| | 0/3 [00:00, ?it/s]\u001b[A\n",
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"\u001b[36m(_train_tune pid=26992)\u001b[0m \n",
"Validation DataLoader 0: 100%|██████████| 3/3 [00:00<00:00, 41.86it/s]\u001b[A\n",
"Epoch 266: 0%| | 0/3 [00:01, ?it/s, v_num=0, train_loss_step=0.419, train_loss_epoch=0.414, valid_loss=10.80]\n",
"Epoch 266: 100%|██████████| 3/3 [00:02<00:00, 1.25it/s, v_num=0, train_loss_step=0.419, train_loss_epoch=0.414, valid_loss=10.80]\n",
"Epoch 267: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.401, train_loss_epoch=0.415, valid_loss=10.80]\n",
"Epoch 267: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.401, train_loss_epoch=0.415, valid_loss=10.80]\n",
"Epoch 268: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.404, train_loss_epoch=0.418, valid_loss=10.80]\n",
"Epoch 268: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.404, train_loss_epoch=0.418, valid_loss=10.80]\n",
"Epoch 269: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.437, train_loss_epoch=0.426, valid_loss=10.80]\n",
"Epoch 269: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.437, train_loss_epoch=0.426, valid_loss=10.80]\n",
"Epoch 270: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.418, train_loss_epoch=0.421, valid_loss=10.80]\n",
"Epoch 270: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.418, train_loss_epoch=0.421, valid_loss=10.80]\n",
"Epoch 271: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.435, train_loss_epoch=0.415, valid_loss=10.80]\n",
"Epoch 271: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.435, train_loss_epoch=0.415, valid_loss=10.80]\n",
"Epoch 272: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.419, train_loss_epoch=0.423, valid_loss=10.80]\n",
"Epoch 272: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.419, train_loss_epoch=0.423, valid_loss=10.80]\n",
"Epoch 273: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.424, train_loss_epoch=0.420, valid_loss=10.80]\n",
"Epoch 273: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.424, train_loss_epoch=0.420, valid_loss=10.80]\n",
"Epoch 274: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.432, train_loss_epoch=0.424, valid_loss=10.80]\n",
"Epoch 274: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.432, train_loss_epoch=0.424, valid_loss=10.80]\n",
"Epoch 275: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.430, train_loss_epoch=0.428, valid_loss=10.80]\n",
"Epoch 275: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.430, train_loss_epoch=0.428, valid_loss=10.80]\n",
"Epoch 275: 100%|██████████| 3/3 [00:02<00:00, 1.15it/s, v_num=0, train_loss_step=0.422, train_loss_epoch=0.416, valid_loss=10.80]\n",
"Epoch 276: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.422, train_loss_epoch=0.416, valid_loss=10.80]\n",
"Epoch 276: 100%|██████████| 3/3 [00:02<00:00, 1.28it/s, v_num=0, train_loss_step=0.422, train_loss_epoch=0.416, valid_loss=10.80]\n",
"Epoch 276: 100%|██████████| 3/3 [00:02<00:00, 1.14it/s, v_num=0, train_loss_step=0.425, train_loss_epoch=0.416, valid_loss=10.80]\n",
"Epoch 277: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.425, train_loss_epoch=0.412, valid_loss=10.80]\n",
"Epoch 277: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.425, train_loss_epoch=0.412, valid_loss=10.80]\n",
"Epoch 278: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.419, train_loss_epoch=0.419, valid_loss=10.80]\n",
"Epoch 278: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.419, train_loss_epoch=0.419, valid_loss=10.80]\n",
"Epoch 279: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.414, train_loss_epoch=0.421, valid_loss=10.80]\n",
"Epoch 279: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.414, train_loss_epoch=0.421, valid_loss=10.80]\n",
"Epoch 280: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.445, train_loss_epoch=0.422, valid_loss=10.80]\n",
"Epoch 280: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.445, train_loss_epoch=0.422, valid_loss=10.80]\n",
"Epoch 281: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.425, train_loss_epoch=0.433, valid_loss=10.80]\n",
"Epoch 281: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.425, train_loss_epoch=0.433, valid_loss=10.80]\n",
"Epoch 282: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.429, train_loss_epoch=0.440, valid_loss=10.80]\n",
"Epoch 282: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.429, train_loss_epoch=0.440, valid_loss=10.80]\n",
"Epoch 283: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.432, train_loss_epoch=0.431, valid_loss=10.80]\n",
"Epoch 283: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.432, train_loss_epoch=0.431, valid_loss=10.80]\n",
"Epoch 284: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.429, train_loss_epoch=0.448, valid_loss=10.80]\n",
"Epoch 284: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.429, train_loss_epoch=0.448, valid_loss=10.80]\n",
"Epoch 285: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.406, train_loss_epoch=0.416, valid_loss=10.80]\n",
"Epoch 285: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.406, train_loss_epoch=0.416, valid_loss=10.80]\n",
"Epoch 286: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.440, train_loss_epoch=0.431, valid_loss=10.80]\n",
"Epoch 286: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.440, train_loss_epoch=0.431, valid_loss=10.80]\n",
"Epoch 287: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.451, train_loss_epoch=0.433, valid_loss=10.80]\n",
"Epoch 287: 100%|██████████| 3/3 [00:02<00:00, 1.28it/s, v_num=0, train_loss_step=0.451, train_loss_epoch=0.433, valid_loss=10.80]\n",
"Epoch 288: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.435, train_loss_epoch=0.418, valid_loss=10.80]\n",
"Epoch 288: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.435, train_loss_epoch=0.418, valid_loss=10.80]\n",
"Epoch 289: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.423, train_loss_epoch=0.416, valid_loss=10.80]\n",
"Epoch 289: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.423, train_loss_epoch=0.416, valid_loss=10.80]\n",
"Epoch 290: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.415, train_loss_epoch=0.407, valid_loss=10.80]\n",
"Epoch 290: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.415, train_loss_epoch=0.407, valid_loss=10.80]\n",
"Epoch 291: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.413, train_loss_epoch=0.415, valid_loss=10.80]\n",
"Epoch 291: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.413, train_loss_epoch=0.415, valid_loss=10.80]\n",
"Epoch 292: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.399, train_loss_epoch=0.411, valid_loss=10.80]\n",
"Epoch 292: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.399, train_loss_epoch=0.411, valid_loss=10.80]\n",
"Epoch 293: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.431, train_loss_epoch=0.417, valid_loss=10.80]\n",
"Epoch 293: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.431, train_loss_epoch=0.417, valid_loss=10.80]\n",
"Epoch 294: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.410, train_loss_epoch=0.417, valid_loss=10.80]\n",
"Epoch 294: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.410, train_loss_epoch=0.417, valid_loss=10.80]\n",
"Epoch 295: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.422, train_loss_epoch=0.407, valid_loss=10.80]\n",
"Epoch 295: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.422, train_loss_epoch=0.407, valid_loss=10.80]\n",
"Epoch 296: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.392, train_loss_epoch=0.407, valid_loss=10.80]\n",
"Epoch 296: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.392, train_loss_epoch=0.407, valid_loss=10.80]\n",
"Epoch 297: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.418, train_loss_epoch=0.417, valid_loss=10.80]\n",
"Epoch 297: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.418, train_loss_epoch=0.417, valid_loss=10.80]\n",
"Epoch 298: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.430, train_loss_epoch=0.411, valid_loss=10.80]\n",
"Epoch 298: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.430, train_loss_epoch=0.411, valid_loss=10.80]\n",
"Epoch 299: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.401, train_loss_epoch=0.411, valid_loss=10.80]\n",
"Epoch 299: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.401, train_loss_epoch=0.411, valid_loss=10.80]\n",
"Epoch 299: 100%|██████████| 3/3 [00:02<00:00, 1.15it/s, v_num=0, train_loss_step=0.414, train_loss_epoch=0.411, valid_loss=10.80]\n",
"\u001b[36m(_train_tune pid=26992)\u001b[0m \n",
"Validation: | | 0/? [00:00, ?it/s]\u001b[A\n",
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"\u001b[36m(_train_tune pid=26992)\u001b[0m \n",
"Validation DataLoader 0: 100%|██████████| 3/3 [00:00<00:00, 40.00it/s]\u001b[A\n",
"Epoch 300: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.414, train_loss_epoch=0.411, valid_loss=9.750]\n",
"Epoch 300: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.414, train_loss_epoch=0.411, valid_loss=9.750]\n",
"Epoch 301: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.403, train_loss_epoch=0.407, valid_loss=9.750]\n",
"Epoch 301: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.403, train_loss_epoch=0.407, valid_loss=9.750]\n",
"Epoch 302: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.396, train_loss_epoch=0.397, valid_loss=9.750]\n",
"Epoch 302: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.396, train_loss_epoch=0.397, valid_loss=9.750]\n",
"Epoch 303: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.397, train_loss_epoch=0.396, valid_loss=9.750]\n",
"Epoch 303: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.397, train_loss_epoch=0.396, valid_loss=9.750]\n",
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"Epoch 304: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.403, train_loss_epoch=0.404, valid_loss=9.750]\n",
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"Epoch 306: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.399, train_loss_epoch=0.395, valid_loss=9.750]\n",
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"Epoch 307: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.390, train_loss_epoch=0.399, valid_loss=9.750]\n",
"Epoch 308: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.391, train_loss_epoch=0.392, valid_loss=9.750]\n",
"Epoch 308: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.391, train_loss_epoch=0.392, valid_loss=9.750]\n",
"Epoch 309: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.388, train_loss_epoch=0.390, valid_loss=9.750]\n",
"Epoch 309: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.388, train_loss_epoch=0.390, valid_loss=9.750]\n",
"Epoch 310: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.400, train_loss_epoch=0.397, valid_loss=9.750]\n",
"Epoch 310: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.400, train_loss_epoch=0.397, valid_loss=9.750]\n",
"Epoch 311: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.395, train_loss_epoch=0.395, valid_loss=9.750]\n",
"Epoch 311: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.395, train_loss_epoch=0.395, valid_loss=9.750]\n",
"Epoch 312: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.393, train_loss_epoch=0.391, valid_loss=9.750]\n",
"Epoch 312: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.393, train_loss_epoch=0.391, valid_loss=9.750]\n",
"Epoch 313: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.428, train_loss_epoch=0.406, valid_loss=9.750]\n",
"Epoch 313: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.428, train_loss_epoch=0.406, valid_loss=9.750]\n",
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"Epoch 314: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.404, train_loss_epoch=0.401, valid_loss=9.750]\n",
"Epoch 315: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.398, train_loss_epoch=0.399, valid_loss=9.750]\n",
"Epoch 315: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.398, train_loss_epoch=0.399, valid_loss=9.750]\n",
"Epoch 316: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.412, train_loss_epoch=0.401, valid_loss=9.750]\n",
"Epoch 316: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.412, train_loss_epoch=0.401, valid_loss=9.750]\n",
"Epoch 317: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.417, train_loss_epoch=0.418, valid_loss=9.750]\n",
"Epoch 317: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.417, train_loss_epoch=0.418, valid_loss=9.750]\n",
"Epoch 317: 100%|██████████| 3/3 [00:02<00:00, 1.16it/s, v_num=0, train_loss_step=0.421, train_loss_epoch=0.419, valid_loss=9.750]\n",
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"Epoch 318: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.421, train_loss_epoch=0.419, valid_loss=9.750]\n",
"Epoch 319: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.432, train_loss_epoch=0.423, valid_loss=9.750]\n",
"Epoch 319: 100%|██████████| 3/3 [00:02<00:00, 1.28it/s, v_num=0, train_loss_step=0.432, train_loss_epoch=0.423, valid_loss=9.750]\n",
"Epoch 320: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.439, train_loss_epoch=0.414, valid_loss=9.750]\n",
"Epoch 320: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.439, train_loss_epoch=0.414, valid_loss=9.750]\n",
"Epoch 321: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.447, train_loss_epoch=0.447, valid_loss=9.750]\n",
"Epoch 321: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.447, train_loss_epoch=0.447, valid_loss=9.750]\n",
"Epoch 322: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.421, train_loss_epoch=0.427, valid_loss=9.750]\n",
"Epoch 322: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.421, train_loss_epoch=0.427, valid_loss=9.750]\n",
"Epoch 323: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.430, train_loss_epoch=0.427, valid_loss=9.750]\n",
"Epoch 323: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.430, train_loss_epoch=0.427, valid_loss=9.750]\n",
"Epoch 324: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.428, train_loss_epoch=0.413, valid_loss=9.750]\n",
"Epoch 324: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.428, train_loss_epoch=0.413, valid_loss=9.750]\n",
"Epoch 325: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.385, train_loss_epoch=0.415, valid_loss=9.750]\n",
"Epoch 325: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.385, train_loss_epoch=0.415, valid_loss=9.750]\n",
"Epoch 326: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.387, train_loss_epoch=0.398, valid_loss=9.750]\n",
"Epoch 326: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.387, train_loss_epoch=0.398, valid_loss=9.750]\n",
"Epoch 327: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.389, train_loss_epoch=0.403, valid_loss=9.750]\n",
"Epoch 327: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.389, train_loss_epoch=0.403, valid_loss=9.750]\n",
"Epoch 328: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.379, train_loss_epoch=0.394, valid_loss=9.750]\n",
"Epoch 328: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.379, train_loss_epoch=0.394, valid_loss=9.750]\n",
"Epoch 329: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.391, train_loss_epoch=0.388, valid_loss=9.750]\n",
"Epoch 329: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.391, train_loss_epoch=0.388, valid_loss=9.750]\n",
"Epoch 330: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.376, train_loss_epoch=0.391, valid_loss=9.750]\n",
"Epoch 330: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.376, train_loss_epoch=0.391, valid_loss=9.750]\n",
"Epoch 331: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.405, train_loss_epoch=0.394, valid_loss=9.750]\n",
"Epoch 331: 100%|██████████| 3/3 [00:02<00:00, 1.29it/s, v_num=0, train_loss_step=0.405, train_loss_epoch=0.394, valid_loss=9.750]\n",
"Epoch 331: 100%|██████████| 3/3 [00:02<00:00, 1.15it/s, v_num=0, train_loss_step=0.412, train_loss_epoch=0.408, valid_loss=9.750]\n",
"Epoch 332: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.412, train_loss_epoch=0.408, valid_loss=9.750]\n",
"Epoch 332: 100%|██████████| 3/3 [00:02<00:00, 1.30it/s, v_num=0, train_loss_step=0.412, train_loss_epoch=0.408, valid_loss=9.750]\n",
"Epoch 333: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.374, train_loss_epoch=0.385, valid_loss=9.750]\n"
]
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"\u001b[36m(_train_tune pid=26992)\u001b[0m `Trainer.fit` stopped: `max_steps=1000` reached.\n"
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"\u001b[36m(_train_tune pid=26992)\u001b[0m \n",
"\u001b[36m(_train_tune pid=26992)\u001b[0m \rValidation: | | 0/? [00:00, ?it/s]\u001b[A\n",
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"\u001b[36m(_train_tune pid=26992)\u001b[0m \rValidation DataLoader 0: 100%|██████████| 3/3 [00:00<00:00, 43.62it/s]\u001b[A\n",
"\u001b[36m(_train_tune pid=26992)\u001b[0m \r \u001b[A\rEpoch 333: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.401, train_loss_epoch=0.385, valid_loss=9.030]\rEpoch 333: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.401, train_loss_epoch=0.401, valid_loss=9.030]\rEpoch 333: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.401, train_loss_epoch=0.401, valid_loss=9.030]\n"
]
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"2025-01-07 14:51:35,357\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/root/ray_results/_train_tune_2025-01-07_14-22-15' in 0.0072s.\n",
"INFO:lightning_fabric.utilities.seed:Seed set to 12\n",
"INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
"INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
"INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
"INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
"INFO:pytorch_lightning.callbacks.model_summary:\n",
" | Name | Type | Params | Mode \n",
"--------------------------------------------------------\n",
"0 | loss | MQLoss | 5 | eval \n",
"1 | padder_train | ConstantPad1d | 0 | train\n",
"2 | scaler | TemporalNorm | 0 | train\n",
"3 | decomp | SeriesDecomp | 0 | train\n",
"4 | enc_embedding | DataEmbedding | 384 | train\n",
"5 | dec_embedding | DataEmbedding | 384 | train\n",
"6 | encoder | Encoder | 297 K | train\n",
"7 | decoder | Decoder | 143 K | train\n",
"--------------------------------------------------------\n",
"441 K Trainable params\n",
"5 Non-trainable params\n",
"441 K Total params\n",
"1.764 Total estimated model params size (MB)\n",
"118 Modules in train mode\n",
"1 Modules in eval mode\n"
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"INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_steps=1000` reached.\n"
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"text": [
"+--------------------------------------------------------------------+\n",
"| Configuration for experiment _train_tune_2025-01-07_15-06-01 |\n",
"+--------------------------------------------------------------------+\n",
"| Search algorithm BasicVariantGenerator |\n",
"| Scheduler FIFOScheduler |\n",
"| Number of trials 5 |\n",
"+--------------------------------------------------------------------+\n",
"\n",
"View detailed results here: /root/ray_results/_train_tune_2025-01-07_15-06-01\n",
"To visualize your results with TensorBoard, run: `tensorboard --logdir /tmp/ray/session_2025-01-07_14-22-15_419131_253/artifacts/2025-01-07_15-06-01/_train_tune_2025-01-07_15-06-01/driver_artifacts`\n"
]
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"text": [
"\u001b[36m(pid=34407)\u001b[0m /usr/local/lib/python3.10/dist-packages/dask/dataframe/__init__.py:42: FutureWarning: \n",
"\u001b[36m(pid=34407)\u001b[0m Dask dataframe query planning is disabled because dask-expr is not installed.\n",
"\u001b[36m(pid=34407)\u001b[0m \n",
"\u001b[36m(pid=34407)\u001b[0m You can install it with `pip install dask[dataframe]` or `conda install dask`.\n",
"\u001b[36m(pid=34407)\u001b[0m This will raise in a future version.\n",
"\u001b[36m(pid=34407)\u001b[0m \n",
"\u001b[36m(pid=34407)\u001b[0m warnings.warn(msg, FutureWarning)\n",
"\u001b[36m(_train_tune pid=34407)\u001b[0m /usr/local/lib/python3.10/dist-packages/ray/tune/integration/pytorch_lightning.py:198: `ray.tune.integration.pytorch_lightning.TuneReportCallback` is deprecated. Use `ray.tune.integration.pytorch_lightning.TuneReportCheckpointCallback` instead.\n",
"\u001b[36m(_train_tune pid=34407)\u001b[0m Seed set to 15\n",
"\u001b[36m(_train_tune pid=34407)\u001b[0m GPU available: True (cuda), used: True\n",
"\u001b[36m(_train_tune pid=34407)\u001b[0m TPU available: False, using: 0 TPU cores\n",
"\u001b[36m(_train_tune pid=34407)\u001b[0m HPU available: False, using: 0 HPUs\n",
"\u001b[36m(_train_tune pid=34407)\u001b[0m 2025-01-07 15:06:10.106542: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"\u001b[36m(_train_tune pid=34407)\u001b[0m 2025-01-07 15:06:10.150141: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"\u001b[36m(_train_tune pid=34407)\u001b[0m 2025-01-07 15:06:10.162500: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
"\u001b[36m(_train_tune pid=34407)\u001b[0m 2025-01-07 15:06:11.370991: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
"\u001b[36m(_train_tune pid=34407)\u001b[0m LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
"\u001b[36m(_train_tune pid=34407)\u001b[0m \n",
"\u001b[36m(_train_tune pid=34407)\u001b[0m | Name | Type | Params | Mode \n",
"\u001b[36m(_train_tune pid=34407)\u001b[0m -------------------------------------------------------\n",
"\u001b[36m(_train_tune pid=34407)\u001b[0m 0 | loss | MQLoss | 5 | train\n",
"\u001b[36m(_train_tune pid=34407)\u001b[0m 1 | padder_train | ConstantPad1d | 0 | train\n",
"\u001b[36m(_train_tune pid=34407)\u001b[0m 2 | scaler | TemporalNorm | 0 | train\n",
"\u001b[36m(_train_tune pid=34407)\u001b[0m 3 | blocks | ModuleList | 151 K | train\n",
"\u001b[36m(_train_tune pid=34407)\u001b[0m -------------------------------------------------------\n",
"\u001b[36m(_train_tune pid=34407)\u001b[0m 151 K Trainable params\n",
"\u001b[36m(_train_tune pid=34407)\u001b[0m 5 Non-trainable params\n",
"\u001b[36m(_train_tune pid=34407)\u001b[0m 151 K Total params\n",
"\u001b[36m(_train_tune pid=34407)\u001b[0m 0.605 Total estimated model params size (MB)\n",
"\u001b[36m(_train_tune pid=34407)\u001b[0m 40 Modules in train mode\n",
"\u001b[36m(_train_tune pid=34407)\u001b[0m 0 Modules in eval mode\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Sanity Checking DataLoader 0: 0%| | 0/2 [00:00, ?it/s]\n",
"Epoch 0: 0%| | 0/3 [00:00, ?it/s] \n",
"Epoch 3: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=2.420, train_loss_epoch=1.900]\n",
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"Epoch 9: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=2.170, train_loss_epoch=1.500]\n",
"Epoch 12: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.670, train_loss_epoch=1.280]\n",
"Epoch 15: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=2.060, train_loss_epoch=1.400]\n",
"Epoch 17: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.230, train_loss_epoch=1.380]\n",
"Epoch 20: 100%|██████████| 3/3 [00:00<00:00, 88.21it/s, v_num=0, train_loss_step=0.998, train_loss_epoch=1.360]\n",
"Epoch 21: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.998, train_loss_epoch=1.360]\n",
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"Epoch 30: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.938, train_loss_epoch=1.330]\n",
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"Validation DataLoader 0: 100%|██████████| 3/3 [00:00<00:00, 175.13it/s]\u001b[A\n",
"Epoch 35: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.956, train_loss_epoch=1.410, valid_loss=3.380]\n",
"Epoch 38: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.290, train_loss_epoch=1.290, valid_loss=3.380]\n",
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"Epoch 43: 100%|██████████| 3/3 [00:00<00:00, 84.61it/s, v_num=0, train_loss_step=1.690, train_loss_epoch=1.380, valid_loss=3.380]\n",
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"Epoch 67: 100%|██████████| 3/3 [00:00<00:00, 86.47it/s, v_num=0, train_loss_step=1.150, train_loss_epoch=1.390, valid_loss=3.740]\n",
"Epoch 67: 100%|██████████| 3/3 [00:00<00:00, 81.77it/s, v_num=0, train_loss_step=1.150, train_loss_epoch=1.300, valid_loss=3.740]\n",
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"Epoch 98: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.840, train_loss_epoch=1.300, valid_loss=3.740]\n",
"Epoch 99: 100%|██████████| 3/3 [00:00<00:00, 98.46it/s, v_num=0, train_loss_step=1.190, train_loss_epoch=1.380, valid_loss=3.740]\n",
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"Epoch 118: 100%|██████████| 3/3 [00:00<00:00, 96.22it/s, v_num=0, train_loss_step=1.290, train_loss_epoch=1.310, valid_loss=3.830]\n",
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"Epoch 152: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.810, train_loss_epoch=1.330, valid_loss=3.340]\n",
"Epoch 154: 100%|██████████| 3/3 [00:00<00:00, 91.82it/s, v_num=0, train_loss_step=1.830, train_loss_epoch=1.340, valid_loss=3.340]\n",
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"Epoch 194: 100%|██████████| 3/3 [00:00<00:00, 52.81it/s, v_num=0, train_loss_step=1.950, train_loss_epoch=1.400, valid_loss=3.530]\n",
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"Epoch 199: 100%|██████████| 3/3 [00:00<00:00, 47.92it/s, v_num=0, train_loss_step=1.690, train_loss_epoch=1.370, valid_loss=3.530]\n",
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"Epoch 239: 100%|██████████| 3/3 [00:00<00:00, 67.40it/s, v_num=0, train_loss_step=1.800, train_loss_epoch=1.340, valid_loss=3.310]\n",
"Epoch 239: 100%|██████████| 3/3 [00:00<00:00, 66.29it/s, v_num=0, train_loss_step=1.800, train_loss_epoch=1.340, valid_loss=3.310]\n",
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"Epoch 299: 100%|██████████| 3/3 [00:00<00:00, 97.17it/s, v_num=0, train_loss_step=1.240, train_loss_epoch=1.310, valid_loss=3.800]\n",
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"Epoch 300: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.240, train_loss_epoch=1.420, valid_loss=3.790]\n",
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"Epoch 303: 100%|██████████| 3/3 [00:00<00:00, 84.49it/s, v_num=0, train_loss_step=1.200, train_loss_epoch=1.300, valid_loss=3.790]\n",
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"Epoch 318: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.898, train_loss_epoch=1.310, valid_loss=3.790]\n",
"Epoch 321: 100%|██████████| 3/3 [00:00<00:00, 86.91it/s, v_num=0, train_loss_step=1.920, train_loss_epoch=1.360, valid_loss=3.790]\n",
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"\u001b[36m(_train_tune pid=34407)\u001b[0m \n",
"Validation DataLoader 0: 100%|██████████| 3/3 [00:00<00:00, 195.65it/s]\u001b[A\n",
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]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\u001b[36m(_train_tune pid=34407)\u001b[0m `Trainer.fit` stopped: `max_steps=1000` reached.\n",
"\u001b[36m(pid=34562)\u001b[0m /usr/local/lib/python3.10/dist-packages/dask/dataframe/__init__.py:42: FutureWarning: \n",
"\u001b[36m(pid=34562)\u001b[0m Dask dataframe query planning is disabled because dask-expr is not installed.\n",
"\u001b[36m(pid=34562)\u001b[0m \n",
"\u001b[36m(pid=34562)\u001b[0m You can install it with `pip install dask[dataframe]` or `conda install dask`.\n",
"\u001b[36m(pid=34562)\u001b[0m This will raise in a future version.\n",
"\u001b[36m(pid=34562)\u001b[0m \n",
"\u001b[36m(pid=34562)\u001b[0m warnings.warn(msg, FutureWarning)\n",
"\u001b[36m(_train_tune pid=34562)\u001b[0m /usr/local/lib/python3.10/dist-packages/ray/tune/integration/pytorch_lightning.py:198: `ray.tune.integration.pytorch_lightning.TuneReportCallback` is deprecated. Use `ray.tune.integration.pytorch_lightning.TuneReportCheckpointCallback` instead.\n",
"\u001b[36m(_train_tune pid=34562)\u001b[0m Seed set to 19\n",
"\u001b[36m(_train_tune pid=34562)\u001b[0m GPU available: True (cuda), used: True\n",
"\u001b[36m(_train_tune pid=34562)\u001b[0m TPU available: False, using: 0 TPU cores\n",
"\u001b[36m(_train_tune pid=34562)\u001b[0m HPU available: False, using: 0 HPUs\n",
"\u001b[36m(_train_tune pid=34562)\u001b[0m 2025-01-07 15:06:33.802530: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"\u001b[36m(_train_tune pid=34562)\u001b[0m 2025-01-07 15:06:33.826529: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"\u001b[36m(_train_tune pid=34562)\u001b[0m 2025-01-07 15:06:33.834189: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
"\u001b[36m(_train_tune pid=34562)\u001b[0m 2025-01-07 15:06:35.073856: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
"\u001b[36m(_train_tune pid=34562)\u001b[0m LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
"\u001b[36m(_train_tune pid=34562)\u001b[0m \n",
"\u001b[36m(_train_tune pid=34562)\u001b[0m | Name | Type | Params | Mode \n",
"\u001b[36m(_train_tune pid=34562)\u001b[0m -------------------------------------------------------\n",
"\u001b[36m(_train_tune pid=34562)\u001b[0m 0 | loss | MQLoss | 5 | train\n",
"\u001b[36m(_train_tune pid=34562)\u001b[0m 1 | padder_train | ConstantPad1d | 0 | train\n",
"\u001b[36m(_train_tune pid=34562)\u001b[0m 2 | scaler | TemporalNorm | 0 | train\n",
"\u001b[36m(_train_tune pid=34562)\u001b[0m 3 | blocks | ModuleList | 87.9 K | train\n",
"\u001b[36m(_train_tune pid=34562)\u001b[0m -------------------------------------------------------\n",
"\u001b[36m(_train_tune pid=34562)\u001b[0m 87.9 K Trainable params\n",
"\u001b[36m(_train_tune pid=34562)\u001b[0m 5 Non-trainable params\n",
"\u001b[36m(_train_tune pid=34562)\u001b[0m 87.9 K Total params\n",
"\u001b[36m(_train_tune pid=34562)\u001b[0m 0.352 Total estimated model params size (MB)\n",
"\u001b[36m(_train_tune pid=34562)\u001b[0m 40 Modules in train mode\n",
"\u001b[36m(_train_tune pid=34562)\u001b[0m 0 Modules in eval mode\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Sanity Checking DataLoader 0: 0%| | 0/1 [00:00, ?it/s]\n",
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"Epoch 2: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=3.210, train_loss_epoch=3.210]\n",
"Epoch 7: 100%|██████████| 1/1 [00:00<00:00, 62.03it/s, v_num=0, train_loss_step=2.620, train_loss_epoch=2.620]\n",
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"Epoch 24: 100%|██████████| 1/1 [00:00<00:00, 67.12it/s, v_num=0, train_loss_step=2.280, train_loss_epoch=2.550]\n",
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"Epoch 99: 100%|██████████| 1/1 [00:00<00:00, 96.50it/s, v_num=0, train_loss_step=1.490, train_loss_epoch=1.580]\n",
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"Epoch 107: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.360, train_loss_epoch=1.360, valid_loss=3.400]\n",
"Epoch 115: 100%|██████████| 1/1 [00:00<00:00, 94.07it/s, v_num=0, train_loss_step=1.300, train_loss_epoch=1.250, valid_loss=3.400]\n",
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"Epoch 140: 100%|██████████| 1/1 [00:00<00:00, 78.75it/s, v_num=0, train_loss_step=1.400, train_loss_epoch=1.400, valid_loss=3.400]\n",
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"Epoch 149: 100%|██████████| 1/1 [00:00<00:00, 88.81it/s, v_num=0, train_loss_step=1.440, train_loss_epoch=1.360, valid_loss=3.400]\n",
"Epoch 149: 100%|██████████| 1/1 [00:00<00:00, 76.45it/s, v_num=0, train_loss_step=1.440, train_loss_epoch=1.440, valid_loss=3.400]\n",
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"Epoch 157: 100%|██████████| 1/1 [00:00<00:00, 58.48it/s, v_num=0, train_loss_step=1.360, train_loss_epoch=1.350, valid_loss=3.400]\n",
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"Epoch 199: 100%|██████████| 1/1 [00:00<00:00, 89.05it/s, v_num=0, train_loss_step=1.510, train_loss_epoch=1.150, valid_loss=3.400]\n",
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"Epoch 202: 100%|██████████| 1/1 [00:00<00:00, 77.04it/s, v_num=0, train_loss_step=1.210, train_loss_epoch=1.570, valid_loss=3.180]\n",
"Epoch 202: 100%|██████████| 1/1 [00:00<00:00, 65.52it/s, v_num=0, train_loss_step=1.210, train_loss_epoch=1.210, valid_loss=3.180]\n",
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"Epoch 265: 100%|██████████| 1/1 [00:00<00:00, 79.92it/s, v_num=0, train_loss_step=1.470, train_loss_epoch=1.470, valid_loss=3.180]\n",
"Epoch 273: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.410, train_loss_epoch=1.410, valid_loss=3.180]\n",
"Epoch 281: 100%|██████████| 1/1 [00:00<00:00, 69.74it/s, v_num=0, train_loss_step=1.250, train_loss_epoch=1.250, valid_loss=3.180]\n",
"Epoch 281: 100%|██████████| 1/1 [00:00<00:00, 57.51it/s, v_num=0, train_loss_step=1.460, train_loss_epoch=1.460, valid_loss=3.180]\n",
"Epoch 282: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.460, train_loss_epoch=1.460, valid_loss=3.180]\n",
"Epoch 290: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.490, train_loss_epoch=1.490, valid_loss=3.180]\n",
"Epoch 297: 100%|██████████| 1/1 [00:00<00:00, 56.14it/s, v_num=0, train_loss_step=1.320, train_loss_epoch=1.320, valid_loss=3.180]\n",
"Epoch 297: 100%|██████████| 1/1 [00:00<00:00, 55.02it/s, v_num=0, train_loss_step=1.490, train_loss_epoch=1.320, valid_loss=3.180]\n",
"Epoch 299: 100%|██████████| 1/1 [00:00<00:00, 93.78it/s, v_num=0, train_loss_step=1.370, train_loss_epoch=1.480, valid_loss=3.180]\n",
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"Epoch 304: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.320, train_loss_epoch=1.320, valid_loss=3.570]\n",
"Epoch 304: 100%|██████████| 1/1 [00:00<00:00, 83.83it/s, v_num=0, train_loss_step=1.300, train_loss_epoch=1.300, valid_loss=3.570]\n",
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"Epoch 312: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.380, train_loss_epoch=1.380, valid_loss=3.570]\n",
"Epoch 319: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.240, train_loss_epoch=1.240, valid_loss=3.570]\n",
"Epoch 327: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.260, train_loss_epoch=1.260, valid_loss=3.570]\n",
"Epoch 335: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.310, train_loss_epoch=1.310, valid_loss=3.570]\n",
"Epoch 344: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.450, train_loss_epoch=1.450, valid_loss=3.570]\n",
"Epoch 351: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.560, train_loss_epoch=1.560, valid_loss=3.570]\n",
"Epoch 359: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.320, train_loss_epoch=1.320, valid_loss=3.570]\n",
"Epoch 359: 100%|██████████| 1/1 [00:00<00:00, 60.97it/s, v_num=0, train_loss_step=1.570, train_loss_epoch=1.570, valid_loss=3.570]\n",
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"Epoch 368: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.520, train_loss_epoch=1.520, valid_loss=3.570]\n",
"Epoch 376: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.410, train_loss_epoch=1.410, valid_loss=3.570]\n",
"Epoch 384: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.420, train_loss_epoch=1.420, valid_loss=3.570]\n",
"Epoch 392: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.550, train_loss_epoch=1.550, valid_loss=3.570]\n",
"Epoch 399: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.280, train_loss_epoch=1.280, valid_loss=3.570]\n",
"Epoch 399: 100%|██████████| 1/1 [00:00<00:00, 78.95it/s, v_num=0, train_loss_step=1.480, train_loss_epoch=1.280, valid_loss=3.570]\n",
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"Epoch 406: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.300, train_loss_epoch=1.300, valid_loss=3.250]\n",
"Epoch 414: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.310, train_loss_epoch=1.310, valid_loss=3.250]\n",
"Epoch 422: 100%|██████████| 1/1 [00:00<00:00, 61.88it/s, v_num=0, train_loss_step=1.370, train_loss_epoch=1.370, valid_loss=3.250]\n",
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"Epoch 423: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.370, train_loss_epoch=1.370, valid_loss=3.250]\n",
"Epoch 431: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.450, train_loss_epoch=1.450, valid_loss=3.250]\n",
"Epoch 439: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.210, train_loss_epoch=1.210, valid_loss=3.250]\n",
"Epoch 447: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.500, train_loss_epoch=1.500, valid_loss=3.250]\n",
"Epoch 455: 100%|██████████| 1/1 [00:00<00:00, 90.63it/s, v_num=0, train_loss_step=1.450, train_loss_epoch=1.300, valid_loss=3.250]\n",
"Epoch 456: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.450, train_loss_epoch=1.450, valid_loss=3.250]\n",
"Epoch 463: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.530, train_loss_epoch=1.530, valid_loss=3.250]\n",
"Epoch 464: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.280, train_loss_epoch=1.280, valid_loss=3.250]\n",
"Epoch 471: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.470, train_loss_epoch=1.470, valid_loss=3.250]\n",
"Epoch 479: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.370, train_loss_epoch=1.370, valid_loss=3.250]\n",
"Epoch 487: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.590, train_loss_epoch=1.590, valid_loss=3.250]\n",
"Epoch 495: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.320, train_loss_epoch=1.320, valid_loss=3.250]\n",
"Epoch 495: 100%|██████████| 1/1 [00:00<00:00, 89.47it/s, v_num=0, train_loss_step=1.370, train_loss_epoch=1.320, valid_loss=3.250]\n",
"Epoch 495: 100%|██████████| 1/1 [00:00<00:00, 82.49it/s, v_num=0, train_loss_step=1.370, train_loss_epoch=1.370, valid_loss=3.250]\n",
"Epoch 499: 100%|██████████| 1/1 [00:00<00:00, 93.44it/s, v_num=0, train_loss_step=1.210, train_loss_epoch=1.270, valid_loss=3.250]\n",
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"Epoch 502: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.280, train_loss_epoch=1.280, valid_loss=3.410]\n",
"Epoch 510: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.380, train_loss_epoch=1.380, valid_loss=3.410]\n",
"Epoch 518: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.190, train_loss_epoch=1.190, valid_loss=3.410]\n",
"Epoch 526: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.430, train_loss_epoch=1.430, valid_loss=3.410]\n",
"Epoch 534: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.180, train_loss_epoch=1.180, valid_loss=3.410]\n",
"Epoch 542: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.450, train_loss_epoch=1.450, valid_loss=3.410]\n",
"Epoch 549: 100%|██████████| 1/1 [00:00<00:00, 52.02it/s, v_num=0, train_loss_step=1.510, train_loss_epoch=1.290, valid_loss=3.410]\n",
"Epoch 549: 100%|██████████| 1/1 [00:00<00:00, 47.09it/s, v_num=0, train_loss_step=1.510, train_loss_epoch=1.510, valid_loss=3.410]\n",
"Epoch 557: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.460, train_loss_epoch=1.460, valid_loss=3.410]\n",
"Epoch 565: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.470, train_loss_epoch=1.470, valid_loss=3.410]\n",
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"Epoch 574: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.170, train_loss_epoch=1.170, valid_loss=3.410]\n",
"Epoch 582: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.400, train_loss_epoch=1.400, valid_loss=3.410]\n",
"Epoch 590: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.430, train_loss_epoch=1.430, valid_loss=3.410]\n",
"Epoch 599: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.410, train_loss_epoch=1.410, valid_loss=3.410]\n",
"Epoch 599: 100%|██████████| 1/1 [00:00<00:00, 50.25it/s, v_num=0, train_loss_step=1.400, train_loss_epoch=1.410, valid_loss=3.410]\n",
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"Epoch 606: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.380, train_loss_epoch=1.380, valid_loss=3.550]\n",
"Epoch 613: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.140, train_loss_epoch=1.140, valid_loss=3.550] \n",
"Epoch 614: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.140, train_loss_epoch=1.140, valid_loss=3.550]\n",
"Epoch 622: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.360, train_loss_epoch=1.360, valid_loss=3.550]\n",
"Epoch 629: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.550, train_loss_epoch=1.550, valid_loss=3.550]\n",
"Epoch 629: 100%|██████████| 1/1 [00:00<00:00, 70.84it/s, v_num=0, train_loss_step=1.500, train_loss_epoch=1.500, valid_loss=3.550]\n",
"Epoch 637: 100%|██████████| 1/1 [00:00<00:00, 90.58it/s, v_num=0, train_loss_step=1.360, train_loss_epoch=1.360, valid_loss=3.550]\n",
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"Epoch 646: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.270, train_loss_epoch=1.270, valid_loss=3.550]\n",
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"Epoch 661: 100%|██████████| 1/1 [00:00<00:00, 72.26it/s, v_num=0, train_loss_step=1.230, train_loss_epoch=1.230, valid_loss=3.550]\n",
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"Epoch 670: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.120, train_loss_epoch=1.120, valid_loss=3.550]\n",
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"Epoch 699: 100%|██████████| 1/1 [00:00<00:00, 92.28it/s, v_num=0, train_loss_step=1.370, train_loss_epoch=1.340, valid_loss=3.550]\n",
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"Epoch 700: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.370, train_loss_epoch=1.370, valid_loss=3.300]\n",
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"Epoch 723: 100%|██████████| 1/1 [00:00<00:00, 89.64it/s, v_num=0, train_loss_step=1.360, train_loss_epoch=1.360, valid_loss=3.300]\n",
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"Epoch 732: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.270, train_loss_epoch=1.270, valid_loss=3.300]\n",
"Epoch 740: 100%|██████████| 1/1 [00:00<00:00, 72.28it/s, v_num=0, train_loss_step=1.170, train_loss_epoch=1.330, valid_loss=3.300]\n",
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"Epoch 757: 100%|██████████| 1/1 [00:00<00:00, 83.02it/s, v_num=0, train_loss_step=1.370, train_loss_epoch=1.370, valid_loss=3.300]\n",
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"Epoch 796: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.350, train_loss_epoch=1.350, valid_loss=3.300]\n",
"Epoch 799: 100%|██████████| 1/1 [00:00<00:00, 88.75it/s, v_num=0, train_loss_step=1.520, train_loss_epoch=1.290, valid_loss=3.300]\n",
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"Epoch 810: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.340, train_loss_epoch=1.340, valid_loss=3.400]\n",
"Epoch 810: 100%|██████████| 1/1 [00:00<00:00, 79.10it/s, v_num=0, train_loss_step=1.340, train_loss_epoch=1.340, valid_loss=3.400]\n",
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"Epoch 818: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.220, train_loss_epoch=1.220, valid_loss=3.400]\n",
"Epoch 824: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.350, train_loss_epoch=1.350, valid_loss=3.400]\n",
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"Epoch 834: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.490, train_loss_epoch=1.490, valid_loss=3.400]\n",
"Epoch 839: 100%|██████████| 1/1 [00:00<00:00, 64.15it/s, v_num=0, train_loss_step=1.330, train_loss_epoch=1.330, valid_loss=3.400]\n",
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"Epoch 850: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.460, train_loss_epoch=1.460, valid_loss=3.400]\n",
"Epoch 855: 100%|██████████| 1/1 [00:00<00:00, 74.16it/s, v_num=0, train_loss_step=1.300, train_loss_epoch=1.440, valid_loss=3.400]\n",
"Epoch 855: 100%|██████████| 1/1 [00:00<00:00, 65.27it/s, v_num=0, train_loss_step=1.300, train_loss_epoch=1.300, valid_loss=3.400]\n",
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"Epoch 868: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.210, train_loss_epoch=1.210, valid_loss=3.400]\n",
"Epoch 874: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.430, train_loss_epoch=1.430, valid_loss=3.400]\n",
"Epoch 880: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.280, train_loss_epoch=1.280, valid_loss=3.400]\n",
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"Epoch 899: 100%|██████████| 1/1 [00:00<00:00, 69.03it/s, v_num=0, train_loss_step=1.230, train_loss_epoch=1.320, valid_loss=3.400]\n",
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"Epoch 969: 100%|██████████| 1/1 [00:00<00:00, 53.30it/s, v_num=0, train_loss_step=1.260, train_loss_epoch=1.460, valid_loss=3.390]\n",
"Epoch 969: 100%|██████████| 1/1 [00:00<00:00, 43.03it/s, v_num=0, train_loss_step=1.260, train_loss_epoch=1.260, valid_loss=3.390]\n",
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]
},
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"name": "stderr",
"text": [
"\u001b[36m(_train_tune pid=34562)\u001b[0m `Trainer.fit` stopped: `max_steps=1000` reached.\n"
]
},
{
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"name": "stdout",
"text": [
"\u001b[36m(_train_tune pid=34562)\u001b[0m \rEpoch 997: 100%|██████████| 1/1 [00:00<00:00, 54.17it/s, v_num=0, train_loss_step=1.320, train_loss_epoch=1.320, valid_loss=3.390]\rEpoch 997: 100%|██████████| 1/1 [00:00<00:00, 53.08it/s, v_num=0, train_loss_step=1.340, train_loss_epoch=1.320, valid_loss=3.390]\rEpoch 997: 100%|██████████| 1/1 [00:00<00:00, 51.11it/s, v_num=0, train_loss_step=1.340, train_loss_epoch=1.340, valid_loss=3.390]\rEpoch 997: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.340, train_loss_epoch=1.340, valid_loss=3.390] \rEpoch 998: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.340, train_loss_epoch=1.340, valid_loss=3.390]\rEpoch 998: 100%|██████████| 1/1 [00:00<00:00, 69.52it/s, v_num=0, train_loss_step=1.340, train_loss_epoch=1.340, valid_loss=3.390]\rEpoch 998: 100%|██████████| 1/1 [00:00<00:00, 67.57it/s, v_num=0, train_loss_step=1.350, train_loss_epoch=1.340, valid_loss=3.390]\rEpoch 998: 100%|██████████| 1/1 [00:00<00:00, 64.67it/s, v_num=0, train_loss_step=1.350, train_loss_epoch=1.350, valid_loss=3.390]\rEpoch 998: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.350, train_loss_epoch=1.350, valid_loss=3.390] \rEpoch 999: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.350, train_loss_epoch=1.350, valid_loss=3.390]\rEpoch 999: 100%|██████████| 1/1 [00:00<00:00, 68.19it/s, v_num=0, train_loss_step=1.350, train_loss_epoch=1.350, valid_loss=3.390]\rEpoch 999: 100%|██████████| 1/1 [00:00<00:00, 66.54it/s, v_num=0, train_loss_step=1.330, train_loss_epoch=1.350, valid_loss=3.390]\n",
"\u001b[36m(_train_tune pid=34562)\u001b[0m \rValidation: | | 0/? [00:00, ?it/s]\u001b[A\n",
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"\u001b[36m(_train_tune pid=34562)\u001b[0m \rValidation DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 171.48it/s]\u001b[A\n",
"\u001b[36m(_train_tune pid=34562)\u001b[0m \r \u001b[A\rEpoch 999: 100%|██████████| 1/1 [00:00<00:00, 27.96it/s, v_num=0, train_loss_step=1.330, train_loss_epoch=1.350, valid_loss=3.440]\rEpoch 999: 100%|██████████| 1/1 [00:00<00:00, 27.11it/s, v_num=0, train_loss_step=1.330, train_loss_epoch=1.330, valid_loss=3.440]\rEpoch 999: 100%|██████████| 1/1 [00:00<00:00, 26.26it/s, v_num=0, train_loss_step=1.330, train_loss_epoch=1.330, valid_loss=3.440]\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\u001b[36m(pid=34719)\u001b[0m /usr/local/lib/python3.10/dist-packages/dask/dataframe/__init__.py:42: FutureWarning: \n",
"\u001b[36m(pid=34719)\u001b[0m Dask dataframe query planning is disabled because dask-expr is not installed.\n",
"\u001b[36m(pid=34719)\u001b[0m \n",
"\u001b[36m(pid=34719)\u001b[0m You can install it with `pip install dask[dataframe]` or `conda install dask`.\n",
"\u001b[36m(pid=34719)\u001b[0m This will raise in a future version.\n",
"\u001b[36m(pid=34719)\u001b[0m \n",
"\u001b[36m(pid=34719)\u001b[0m warnings.warn(msg, FutureWarning)\n",
"\u001b[36m(_train_tune pid=34719)\u001b[0m /usr/local/lib/python3.10/dist-packages/ray/tune/integration/pytorch_lightning.py:198: `ray.tune.integration.pytorch_lightning.TuneReportCallback` is deprecated. Use `ray.tune.integration.pytorch_lightning.TuneReportCheckpointCallback` instead.\n",
"\u001b[36m(_train_tune pid=34719)\u001b[0m Seed set to 5\n",
"\u001b[36m(_train_tune pid=34719)\u001b[0m GPU available: True (cuda), used: True\n",
"\u001b[36m(_train_tune pid=34719)\u001b[0m TPU available: False, using: 0 TPU cores\n",
"\u001b[36m(_train_tune pid=34719)\u001b[0m HPU available: False, using: 0 HPUs\n",
"\u001b[36m(_train_tune pid=34719)\u001b[0m 2025-01-07 15:07:01.420302: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"\u001b[36m(_train_tune pid=34719)\u001b[0m 2025-01-07 15:07:01.443842: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"\u001b[36m(_train_tune pid=34719)\u001b[0m 2025-01-07 15:07:01.451144: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
"\u001b[36m(_train_tune pid=34719)\u001b[0m 2025-01-07 15:07:02.631234: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
"\u001b[36m(_train_tune pid=34719)\u001b[0m LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
"\u001b[36m(_train_tune pid=34719)\u001b[0m \n",
"\u001b[36m(_train_tune pid=34719)\u001b[0m | Name | Type | Params | Mode \n",
"\u001b[36m(_train_tune pid=34719)\u001b[0m -------------------------------------------------------\n",
"\u001b[36m(_train_tune pid=34719)\u001b[0m 0 | loss | MQLoss | 5 | train\n",
"\u001b[36m(_train_tune pid=34719)\u001b[0m 1 | padder_train | ConstantPad1d | 0 | train\n",
"\u001b[36m(_train_tune pid=34719)\u001b[0m 2 | scaler | TemporalNorm | 0 | train\n",
"\u001b[36m(_train_tune pid=34719)\u001b[0m 3 | blocks | ModuleList | 121 K | train\n",
"\u001b[36m(_train_tune pid=34719)\u001b[0m -------------------------------------------------------\n",
"\u001b[36m(_train_tune pid=34719)\u001b[0m 121 K Trainable params\n",
"\u001b[36m(_train_tune pid=34719)\u001b[0m 5 Non-trainable params\n",
"\u001b[36m(_train_tune pid=34719)\u001b[0m 121 K Total params\n",
"\u001b[36m(_train_tune pid=34719)\u001b[0m 0.484 Total estimated model params size (MB)\n",
"\u001b[36m(_train_tune pid=34719)\u001b[0m 40 Modules in train mode\n",
"\u001b[36m(_train_tune pid=34719)\u001b[0m 0 Modules in eval mode\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[36m(_train_tune pid=34719)\u001b[0m \rSanity Checking: | | 0/? [00:00, ?it/s]\n",
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"Epoch 7: 100%|██████████| 1/1 [00:00<00:00, 56.32it/s, v_num=0, train_loss_step=2.770, train_loss_epoch=2.770]\n",
"Epoch 12: 100%|██████████| 1/1 [00:00<00:00, 49.46it/s, v_num=0, train_loss_step=2.800, train_loss_epoch=2.800]\n",
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"Epoch 55: 100%|██████████| 1/1 [00:00<00:00, 51.58it/s, v_num=0, train_loss_step=1.390, train_loss_epoch=1.430]\n",
"Epoch 55: 100%|██████████| 1/1 [00:00<00:00, 49.65it/s, v_num=0, train_loss_step=1.390, train_loss_epoch=1.390]\n",
"Epoch 60: 100%|██████████| 1/1 [00:00<00:00, 58.40it/s, v_num=0, train_loss_step=1.320, train_loss_epoch=1.320]\n",
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"Epoch 231: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.430, train_loss_epoch=1.430, valid_loss=3.330]\n",
"Epoch 238: 100%|██████████| 1/1 [00:00<00:00, 83.27it/s, v_num=0, train_loss_step=1.330, train_loss_epoch=1.390, valid_loss=3.330]\n",
"Epoch 238: 100%|██████████| 1/1 [00:00<00:00, 77.85it/s, v_num=0, train_loss_step=1.330, train_loss_epoch=1.330, valid_loss=3.330]\n",
"Epoch 239: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.330, train_loss_epoch=1.330, valid_loss=3.330]\n",
"Epoch 246: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.480, train_loss_epoch=1.480, valid_loss=3.330]\n",
"Epoch 254: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.330, train_loss_epoch=1.330, valid_loss=3.330]\n",
"Epoch 261: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.390, train_loss_epoch=1.390, valid_loss=3.330]\n",
"Epoch 268: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.370, train_loss_epoch=1.370, valid_loss=3.330]\n",
"Epoch 275: 100%|██████████| 1/1 [00:00<00:00, 69.14it/s, v_num=0, train_loss_step=1.410, train_loss_epoch=1.410, valid_loss=3.330]\n",
"Epoch 276: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.410, train_loss_epoch=1.410, valid_loss=3.330]\n",
"Epoch 283: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.560, train_loss_epoch=1.560, valid_loss=3.330]\n",
"Epoch 290: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.320, train_loss_epoch=1.320, valid_loss=3.330]\n",
"Epoch 290: 100%|██████████| 1/1 [00:00<00:00, 67.61it/s, v_num=0, train_loss_step=1.350, train_loss_epoch=1.320, valid_loss=3.330]\n",
"Epoch 290: 100%|██████████| 1/1 [00:00<00:00, 58.87it/s, v_num=0, train_loss_step=1.350, train_loss_epoch=1.350, valid_loss=3.330]\n",
"Epoch 291: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.350, train_loss_epoch=1.350, valid_loss=3.330]\n",
"Epoch 298: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.410, train_loss_epoch=1.410, valid_loss=3.330]\n",
"Epoch 299: 100%|██████████| 1/1 [00:00<00:00, 83.36it/s, v_num=0, train_loss_step=1.380, train_loss_epoch=1.420, valid_loss=3.330]\n",
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"Epoch 304: 100%|██████████| 1/1 [00:00<00:00, 61.13it/s, v_num=0, train_loss_step=1.380, train_loss_epoch=1.380, valid_loss=3.460]\n",
"Epoch 305: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.380, train_loss_epoch=1.380, valid_loss=3.460]\n",
"Epoch 312: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.480, train_loss_epoch=1.480, valid_loss=3.460]\n",
"Epoch 319: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.360, train_loss_epoch=1.360, valid_loss=3.460]\n",
"Epoch 319: 100%|██████████| 1/1 [00:00<00:00, 67.53it/s, v_num=0, train_loss_step=1.360, train_loss_epoch=1.360, valid_loss=3.460]\n",
"Epoch 319: 100%|██████████| 1/1 [00:00<00:00, 53.10it/s, v_num=0, train_loss_step=1.410, train_loss_epoch=1.410, valid_loss=3.460]\n",
"Epoch 320: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.410, train_loss_epoch=1.410, valid_loss=3.460]\n",
"Epoch 327: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.340, train_loss_epoch=1.340, valid_loss=3.460]\n",
"Epoch 333: 100%|██████████| 1/1 [00:00<00:00, 65.92it/s, v_num=0, train_loss_step=1.280, train_loss_epoch=1.360, valid_loss=3.460]\n",
"Epoch 333: 100%|██████████| 1/1 [00:00<00:00, 58.75it/s, v_num=0, train_loss_step=1.280, train_loss_epoch=1.280, valid_loss=3.460]\n",
"Epoch 341: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.350, train_loss_epoch=1.350, valid_loss=3.460]\n",
"Epoch 348: 100%|██████████| 1/1 [00:00<00:00, 67.46it/s, v_num=0, train_loss_step=1.370, train_loss_epoch=1.370, valid_loss=3.460]\n",
"Epoch 349: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.370, train_loss_epoch=1.370, valid_loss=3.460]\n",
"Epoch 356: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.380, train_loss_epoch=1.380, valid_loss=3.460]\n",
"Epoch 363: 100%|██████████| 1/1 [00:00<00:00, 68.04it/s, v_num=0, train_loss_step=1.320, train_loss_epoch=1.320, valid_loss=3.460]\n",
"Epoch 364: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.320, train_loss_epoch=1.320, valid_loss=3.460]\n",
"Epoch 371: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.330, train_loss_epoch=1.330, valid_loss=3.460]\n",
"Epoch 378: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.400, train_loss_epoch=1.400, valid_loss=3.460]\n",
"Epoch 379: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.400, train_loss_epoch=1.400, valid_loss=3.460]\n",
"Epoch 386: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.390, train_loss_epoch=1.390, valid_loss=3.460]\n",
"Epoch 394: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.340, train_loss_epoch=1.340, valid_loss=3.460]\n",
"Epoch 399: 100%|██████████| 1/1 [00:00<00:00, 74.08it/s, v_num=0, train_loss_step=1.290, train_loss_epoch=1.310, valid_loss=3.460]\n",
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"Validation DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 227.99it/s]\u001b[A\n",
"Epoch 400: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.290, train_loss_epoch=1.290, valid_loss=3.460]\n",
"Epoch 406: 100%|██████████| 1/1 [00:00<00:00, 83.33it/s, v_num=0, train_loss_step=1.360, train_loss_epoch=1.530, valid_loss=3.460]\n",
"Epoch 406: 100%|██████████| 1/1 [00:00<00:00, 69.67it/s, v_num=0, train_loss_step=1.360, train_loss_epoch=1.360, valid_loss=3.460]\n",
"Epoch 407: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.360, train_loss_epoch=1.360, valid_loss=3.460]\n",
"Epoch 414: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.330, train_loss_epoch=1.330, valid_loss=3.460]\n",
"Epoch 421: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.360, train_loss_epoch=1.360, valid_loss=3.460]\n",
"Epoch 429: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.440, train_loss_epoch=1.440, valid_loss=3.460]\n",
"Epoch 436: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.400, train_loss_epoch=1.400, valid_loss=3.460]\n",
"Epoch 436: 100%|██████████| 1/1 [00:00<00:00, 58.53it/s, v_num=0, train_loss_step=1.410, train_loss_epoch=1.410, valid_loss=3.460]\n",
"Epoch 437: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.410, train_loss_epoch=1.410, valid_loss=3.460]\n",
"Epoch 445: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.270, train_loss_epoch=1.270, valid_loss=3.460]\n",
"Epoch 452: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.350, train_loss_epoch=1.350, valid_loss=3.460]\n",
"Epoch 459: 100%|██████████| 1/1 [00:00<00:00, 84.18it/s, v_num=0, train_loss_step=1.310, train_loss_epoch=1.300, valid_loss=3.460]\n",
"Epoch 459: 100%|██████████| 1/1 [00:00<00:00, 79.56it/s, v_num=0, train_loss_step=1.310, train_loss_epoch=1.310, valid_loss=3.460]\n",
"Epoch 460: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.310, train_loss_epoch=1.310, valid_loss=3.460]\n",
"Epoch 467: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.340, train_loss_epoch=1.340, valid_loss=3.460]\n",
"Epoch 474: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.360, train_loss_epoch=1.360, valid_loss=3.460]\n",
"Epoch 481: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.320, train_loss_epoch=1.320, valid_loss=3.460]\n",
"Epoch 488: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.360, train_loss_epoch=1.360, valid_loss=3.460]\n",
"Epoch 488: 100%|██████████| 1/1 [00:00<00:00, 72.17it/s, v_num=0, train_loss_step=1.360, train_loss_epoch=1.360, valid_loss=3.460]\n",
"Epoch 488: 100%|██████████| 1/1 [00:00<00:00, 61.79it/s, v_num=0, train_loss_step=1.370, train_loss_epoch=1.370, valid_loss=3.460]\n",
"Epoch 496: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.400, train_loss_epoch=1.400, valid_loss=3.460]\n",
"Epoch 499: 100%|██████████| 1/1 [00:00<00:00, 83.99it/s, v_num=0, train_loss_step=1.280, train_loss_epoch=1.280, valid_loss=3.460]\n",
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"Validation DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 231.93it/s]\u001b[A\n",
"Epoch 502: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.440, train_loss_epoch=1.440, valid_loss=3.340]\n",
"Epoch 510: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.300, train_loss_epoch=1.300, valid_loss=3.340]\n",
"Epoch 517: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.310, train_loss_epoch=1.310, valid_loss=3.340]\n",
"Epoch 517: 100%|██████████| 1/1 [00:00<00:00, 64.43it/s, v_num=0, train_loss_step=1.340, train_loss_epoch=1.310, valid_loss=3.340]\n",
"Epoch 525: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.320, train_loss_epoch=1.320, valid_loss=3.340]\n",
"Epoch 533: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.390, train_loss_epoch=1.390, valid_loss=3.340]\n",
"Epoch 540: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.420, train_loss_epoch=1.420, valid_loss=3.340]\n",
"Epoch 547: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.260, train_loss_epoch=1.260, valid_loss=3.340]\n",
"Epoch 554: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.370, train_loss_epoch=1.370, valid_loss=3.340]\n",
"Epoch 561: 100%|██████████| 1/1 [00:00<00:00, 85.11it/s, v_num=0, train_loss_step=1.360, train_loss_epoch=1.360, valid_loss=3.340]\n",
"Epoch 561: 100%|██████████| 1/1 [00:00<00:00, 76.78it/s, v_num=0, train_loss_step=1.360, train_loss_epoch=1.360, valid_loss=3.340]\n",
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"Epoch 577: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.400, train_loss_epoch=1.400, valid_loss=3.340]\n",
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"Epoch 584: 100%|██████████| 1/1 [00:00<00:00, 64.69it/s, v_num=0, train_loss_step=1.460, train_loss_epoch=1.460, valid_loss=3.340]\n",
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"Epoch 599: 100%|██████████| 1/1 [00:00<00:00, 87.83it/s, v_num=0, train_loss_step=1.290, train_loss_epoch=1.320, valid_loss=3.340]\n",
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"\u001b[36m(_train_tune pid=34719)\u001b[0m \n",
"Epoch 606: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.290, train_loss_epoch=1.290, valid_loss=3.310]\n",
"Epoch 606: 100%|██████████| 1/1 [00:00<00:00, 65.30it/s, v_num=0, train_loss_step=1.440, train_loss_epoch=1.290, valid_loss=3.310]\n",
"Epoch 606: 100%|██████████| 1/1 [00:00<00:00, 57.51it/s, v_num=0, train_loss_step=1.440, train_loss_epoch=1.440, valid_loss=3.310]\n",
"Epoch 614: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.340, train_loss_epoch=1.340, valid_loss=3.310]\n",
"Epoch 621: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.430, train_loss_epoch=1.430, valid_loss=3.310]\n",
"Epoch 628: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.470, train_loss_epoch=1.470, valid_loss=3.310]\n",
"Epoch 628: 100%|██████████| 1/1 [00:00<00:00, 60.75it/s, v_num=0, train_loss_step=1.370, train_loss_epoch=1.370, valid_loss=3.310]\n",
"Epoch 629: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.370, train_loss_epoch=1.370, valid_loss=3.310]\n",
"Epoch 636: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.390, train_loss_epoch=1.390, valid_loss=3.310]\n",
"Epoch 643: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.400, train_loss_epoch=1.400, valid_loss=3.310]\n",
"Epoch 643: 100%|██████████| 1/1 [00:00<00:00, 51.02it/s, v_num=0, train_loss_step=1.400, train_loss_epoch=1.400, valid_loss=3.310]\n",
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"Epoch 644: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.350, train_loss_epoch=1.350, valid_loss=3.310]\n",
"Epoch 651: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.440, train_loss_epoch=1.440, valid_loss=3.310]\n",
"Epoch 658: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.430, train_loss_epoch=1.430, valid_loss=3.310]\n",
"Epoch 658: 100%|██████████| 1/1 [00:00<00:00, 58.47it/s, v_num=0, train_loss_step=1.400, train_loss_epoch=1.400, valid_loss=3.310]\n",
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"Epoch 666: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.470, train_loss_epoch=1.470, valid_loss=3.310]\n",
"Epoch 673: 100%|██████████| 1/1 [00:00<00:00, 69.83it/s, v_num=0, train_loss_step=1.420, train_loss_epoch=1.420, valid_loss=3.310]\n",
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"Epoch 695: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.410, train_loss_epoch=1.410, valid_loss=3.310]\n",
"Epoch 699: 100%|██████████| 1/1 [00:00<00:00, 85.29it/s, v_num=0, train_loss_step=1.360, train_loss_epoch=1.510, valid_loss=3.310]\n",
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"Validation DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 251.10it/s]\u001b[A\n",
"Epoch 700: 100%|██████████| 1/1 [00:00<00:00, 69.34it/s, v_num=0, train_loss_step=1.360, train_loss_epoch=1.360, valid_loss=3.410]\n",
"Epoch 701: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.360, train_loss_epoch=1.360, valid_loss=3.410]\n",
"Epoch 708: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.330, train_loss_epoch=1.330, valid_loss=3.410]\n",
"Epoch 715: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.380, train_loss_epoch=1.380, valid_loss=3.410]\n",
"Epoch 715: 100%|██████████| 1/1 [00:00<00:00, 58.01it/s, v_num=0, train_loss_step=1.400, train_loss_epoch=1.400, valid_loss=3.410]\n",
"Epoch 723: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.470, train_loss_epoch=1.470, valid_loss=3.410]\n",
"Epoch 730: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.420, train_loss_epoch=1.420, valid_loss=3.410]\n",
"Epoch 731: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.350, train_loss_epoch=1.350, valid_loss=3.410]\n",
"Epoch 738: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.360, train_loss_epoch=1.360, valid_loss=3.410]\n",
"Epoch 745: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.430, train_loss_epoch=1.430, valid_loss=3.410]\n",
"Epoch 752: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.390, train_loss_epoch=1.390, valid_loss=3.410]\n",
"Epoch 758: 100%|██████████| 1/1 [00:00<00:00, 53.75it/s, v_num=0, train_loss_step=1.370, train_loss_epoch=1.370, valid_loss=3.410]\n",
"Epoch 759: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.370, train_loss_epoch=1.370, valid_loss=3.410]\n",
"Epoch 766: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.410, train_loss_epoch=1.410, valid_loss=3.410]\n",
"Epoch 766: 100%|██████████| 1/1 [00:00<00:00, 85.19it/s, v_num=0, train_loss_step=1.410, train_loss_epoch=1.410, valid_loss=3.410]\n",
"Epoch 766: 100%|██████████| 1/1 [00:00<00:00, 82.53it/s, v_num=0, train_loss_step=1.410, train_loss_epoch=1.410, valid_loss=3.410]\n",
"Epoch 766: 100%|██████████| 1/1 [00:00<00:00, 71.69it/s, v_num=0, train_loss_step=1.410, train_loss_epoch=1.410, valid_loss=3.410]\n",
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"Epoch 781: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.390, train_loss_epoch=1.390, valid_loss=3.410]\n",
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"Epoch 799: 100%|██████████| 1/1 [00:00<00:00, 47.01it/s, v_num=0, train_loss_step=1.270, train_loss_epoch=1.350, valid_loss=3.410]\n",
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"Epoch 801: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.400, train_loss_epoch=1.400, valid_loss=3.310]\n",
"Epoch 806: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.330, train_loss_epoch=1.330, valid_loss=3.310]\n",
"Epoch 806: 100%|██████████| 1/1 [00:00<00:00, 70.83it/s, v_num=0, train_loss_step=1.330, train_loss_epoch=1.330, valid_loss=3.310]\n",
"Epoch 806: 100%|██████████| 1/1 [00:00<00:00, 60.09it/s, v_num=0, train_loss_step=1.310, train_loss_epoch=1.330, valid_loss=3.310]\n",
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"Epoch 811: 100%|██████████| 1/1 [00:00<00:00, 40.65it/s, v_num=0, train_loss_step=1.310, train_loss_epoch=1.310, valid_loss=3.310]\n",
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"Epoch 850: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.370, train_loss_epoch=1.370, valid_loss=3.310]\n",
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"Epoch 899: 100%|██████████| 1/1 [00:00<00:00, 44.36it/s, v_num=0, train_loss_step=1.390, train_loss_epoch=1.400, valid_loss=3.310]\n",
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"Epoch 904: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.440, train_loss_epoch=1.440, valid_loss=3.410]\n",
"Epoch 909: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.340, train_loss_epoch=1.340, valid_loss=3.410]\n",
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"Epoch 924: 100%|██████████| 1/1 [00:00<00:00, 63.97it/s, v_num=0, train_loss_step=1.410, train_loss_epoch=1.410, valid_loss=3.410]\n",
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"Epoch 984: 100%|██████████| 1/1 [00:00<00:00, 56.49it/s, v_num=0, train_loss_step=1.360, train_loss_epoch=1.360, valid_loss=3.410]\n",
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"Epoch 995: 100%|██████████| 1/1 [00:00<00:00, 58.04it/s, v_num=0, train_loss_step=1.350, train_loss_epoch=1.380, valid_loss=3.410]\n",
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]
},
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"output_type": "stream",
"name": "stderr",
"text": [
"\u001b[36m(_train_tune pid=34719)\u001b[0m `Trainer.fit` stopped: `max_steps=1000` reached.\n"
]
},
{
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"text": [
"\u001b[36m(_train_tune pid=34719)\u001b[0m \rEpoch 996: 100%|██████████| 1/1 [00:00<00:00, 57.54it/s, v_num=0, train_loss_step=1.350, train_loss_epoch=1.350, valid_loss=3.410]\rEpoch 996: 100%|██████████| 1/1 [00:00<00:00, 56.25it/s, v_num=0, train_loss_step=1.390, train_loss_epoch=1.350, valid_loss=3.410]\rEpoch 996: 100%|██████████| 1/1 [00:00<00:00, 54.00it/s, v_num=0, train_loss_step=1.390, train_loss_epoch=1.390, valid_loss=3.410]\rEpoch 996: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.390, train_loss_epoch=1.390, valid_loss=3.410] \rEpoch 997: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.390, train_loss_epoch=1.390, valid_loss=3.410]\rEpoch 997: 100%|██████████| 1/1 [00:00<00:00, 58.44it/s, v_num=0, train_loss_step=1.390, train_loss_epoch=1.390, valid_loss=3.410]\rEpoch 997: 100%|██████████| 1/1 [00:00<00:00, 57.12it/s, v_num=0, train_loss_step=1.340, train_loss_epoch=1.390, valid_loss=3.410]\rEpoch 997: 100%|██████████| 1/1 [00:00<00:00, 54.76it/s, v_num=0, train_loss_step=1.340, train_loss_epoch=1.340, valid_loss=3.410]\rEpoch 997: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.340, train_loss_epoch=1.340, valid_loss=3.410] \rEpoch 998: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.340, train_loss_epoch=1.340, valid_loss=3.410]\rEpoch 998: 100%|██████████| 1/1 [00:00<00:00, 58.48it/s, v_num=0, train_loss_step=1.340, train_loss_epoch=1.340, valid_loss=3.410]\rEpoch 998: 100%|██████████| 1/1 [00:00<00:00, 57.07it/s, v_num=0, train_loss_step=1.340, train_loss_epoch=1.340, valid_loss=3.410]\rEpoch 998: 100%|██████████| 1/1 [00:00<00:00, 54.92it/s, v_num=0, train_loss_step=1.340, train_loss_epoch=1.340, valid_loss=3.410]\rEpoch 998: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.340, train_loss_epoch=1.340, valid_loss=3.410] \rEpoch 999: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.340, train_loss_epoch=1.340, valid_loss=3.410]\rEpoch 999: 100%|██████████| 1/1 [00:00<00:00, 57.88it/s, v_num=0, train_loss_step=1.340, train_loss_epoch=1.340, valid_loss=3.410]\rEpoch 999: 100%|██████████| 1/1 [00:00<00:00, 56.59it/s, v_num=0, train_loss_step=1.430, train_loss_epoch=1.340, valid_loss=3.410]\n",
"\u001b[36m(_train_tune pid=34719)\u001b[0m \rValidation: | | 0/? [00:00, ?it/s]\u001b[A\n",
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"\u001b[36m(_train_tune pid=34719)\u001b[0m \rValidation DataLoader 0: 0%| | 0/1 [00:00, ?it/s]\u001b[A\n",
"\u001b[36m(_train_tune pid=34719)\u001b[0m \rValidation DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 150.64it/s]\u001b[A\n",
"\u001b[36m(_train_tune pid=34719)\u001b[0m \r \u001b[A\rEpoch 999: 100%|██████████| 1/1 [00:00<00:00, 24.33it/s, v_num=0, train_loss_step=1.430, train_loss_epoch=1.340, valid_loss=3.360]\rEpoch 999: 100%|██████████| 1/1 [00:00<00:00, 23.44it/s, v_num=0, train_loss_step=1.430, train_loss_epoch=1.430, valid_loss=3.360]\rEpoch 999: 100%|██████████| 1/1 [00:00<00:00, 22.13it/s, v_num=0, train_loss_step=1.430, train_loss_epoch=1.430, valid_loss=3.360]\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\u001b[36m(pid=34886)\u001b[0m /usr/local/lib/python3.10/dist-packages/dask/dataframe/__init__.py:42: FutureWarning: \n",
"\u001b[36m(pid=34886)\u001b[0m Dask dataframe query planning is disabled because dask-expr is not installed.\n",
"\u001b[36m(pid=34886)\u001b[0m \n",
"\u001b[36m(pid=34886)\u001b[0m You can install it with `pip install dask[dataframe]` or `conda install dask`.\n",
"\u001b[36m(pid=34886)\u001b[0m This will raise in a future version.\n",
"\u001b[36m(pid=34886)\u001b[0m \n",
"\u001b[36m(pid=34886)\u001b[0m warnings.warn(msg, FutureWarning)\n",
"\u001b[36m(_train_tune pid=34886)\u001b[0m /usr/local/lib/python3.10/dist-packages/ray/tune/integration/pytorch_lightning.py:198: `ray.tune.integration.pytorch_lightning.TuneReportCallback` is deprecated. Use `ray.tune.integration.pytorch_lightning.TuneReportCheckpointCallback` instead.\n",
"\u001b[36m(_train_tune pid=34886)\u001b[0m Seed set to 13\n",
"\u001b[36m(_train_tune pid=34886)\u001b[0m GPU available: True (cuda), used: True\n",
"\u001b[36m(_train_tune pid=34886)\u001b[0m TPU available: False, using: 0 TPU cores\n",
"\u001b[36m(_train_tune pid=34886)\u001b[0m HPU available: False, using: 0 HPUs\n",
"\u001b[36m(_train_tune pid=34886)\u001b[0m 2025-01-07 15:07:29.622233: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"\u001b[36m(_train_tune pid=34886)\u001b[0m 2025-01-07 15:07:29.657615: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"\u001b[36m(_train_tune pid=34886)\u001b[0m 2025-01-07 15:07:29.664853: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
"\u001b[36m(_train_tune pid=34886)\u001b[0m 2025-01-07 15:07:30.802412: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
"\u001b[36m(_train_tune pid=34886)\u001b[0m LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
"\u001b[36m(_train_tune pid=34886)\u001b[0m \n",
"\u001b[36m(_train_tune pid=34886)\u001b[0m | Name | Type | Params | Mode \n",
"\u001b[36m(_train_tune pid=34886)\u001b[0m -------------------------------------------------------\n",
"\u001b[36m(_train_tune pid=34886)\u001b[0m 0 | loss | MQLoss | 5 | train\n",
"\u001b[36m(_train_tune pid=34886)\u001b[0m 1 | padder_train | ConstantPad1d | 0 | train\n",
"\u001b[36m(_train_tune pid=34886)\u001b[0m 2 | scaler | TemporalNorm | 0 | train\n",
"\u001b[36m(_train_tune pid=34886)\u001b[0m 3 | blocks | ModuleList | 137 K | train\n",
"\u001b[36m(_train_tune pid=34886)\u001b[0m -------------------------------------------------------\n",
"\u001b[36m(_train_tune pid=34886)\u001b[0m 137 K Trainable params\n",
"\u001b[36m(_train_tune pid=34886)\u001b[0m 5 Non-trainable params\n",
"\u001b[36m(_train_tune pid=34886)\u001b[0m 137 K Total params\n",
"\u001b[36m(_train_tune pid=34886)\u001b[0m 0.549 Total estimated model params size (MB)\n",
"\u001b[36m(_train_tune pid=34886)\u001b[0m 40 Modules in train mode\n",
"\u001b[36m(_train_tune pid=34886)\u001b[0m 0 Modules in eval mode\n"
]
},
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"Sanity Checking DataLoader 0: 0%| | 0/2 [00:00, ?it/s]\n",
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"Epoch 29: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.330, train_loss_epoch=1.430]\n",
"Epoch 31: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.050, train_loss_epoch=1.390]\n",
"Epoch 33: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.948, train_loss_epoch=1.330]\n",
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"\u001b[36m(_train_tune pid=34886)\u001b[0m \n",
"Validation DataLoader 0: 100%|██████████| 3/3 [00:00<00:00, 130.96it/s]\u001b[A\n",
"Epoch 34: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.280, train_loss_epoch=1.370, valid_loss=3.350]\n",
"Epoch 36: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.860, train_loss_epoch=1.370, valid_loss=3.350]\n",
"Epoch 38: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.260, train_loss_epoch=1.380, valid_loss=3.350]\n",
"Epoch 40: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.800, train_loss_epoch=1.350, valid_loss=3.350]\n",
"Epoch 42: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.941, train_loss_epoch=1.360, valid_loss=3.350]\n",
"Epoch 43: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.000, train_loss_epoch=1.370, valid_loss=3.350]\n",
"Epoch 45: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.860, train_loss_epoch=1.400, valid_loss=3.350]\n",
"Epoch 47: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.840, train_loss_epoch=1.350, valid_loss=3.350]\n",
"Epoch 49: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.957, train_loss_epoch=1.410, valid_loss=3.350]\n",
"Epoch 51: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.880, train_loss_epoch=1.410, valid_loss=3.350]\n",
"Epoch 53: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.250, train_loss_epoch=1.350, valid_loss=3.350]\n",
"Epoch 54: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.954, train_loss_epoch=1.340, valid_loss=3.350]\n",
"Epoch 57: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.971, train_loss_epoch=1.380, valid_loss=3.350]\n",
"Epoch 59: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.949, train_loss_epoch=1.370, valid_loss=3.350]\n",
"Epoch 62: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.350, train_loss_epoch=1.360, valid_loss=3.350]\n",
"Epoch 64: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.270, train_loss_epoch=1.350, valid_loss=3.350]\n",
"Epoch 66: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.840, train_loss_epoch=1.360, valid_loss=3.350]\n",
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"Validation DataLoader 0: 100%|██████████| 3/3 [00:00<00:00, 225.85it/s]\u001b[A\n",
"Epoch 66: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.840, train_loss_epoch=1.360, valid_loss=3.510]\n",
"Epoch 69: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.890, train_loss_epoch=1.370, valid_loss=3.510]\n",
"Epoch 72: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.930, train_loss_epoch=1.400, valid_loss=3.510]\n",
"Epoch 74: 100%|██████████| 3/3 [00:00<00:00, 79.22it/s, v_num=0, train_loss_step=1.350, train_loss_epoch=1.400, valid_loss=3.510]\n",
"Epoch 74: 100%|██████████| 3/3 [00:00<00:00, 76.57it/s, v_num=0, train_loss_step=1.860, train_loss_epoch=1.390, valid_loss=3.510]\n",
"Epoch 75: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.860, train_loss_epoch=1.390, valid_loss=3.510]\n",
"Epoch 77: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.940, train_loss_epoch=1.330, valid_loss=3.510]\n",
"Epoch 77: 100%|██████████| 3/3 [00:00<00:00, 80.73it/s, v_num=0, train_loss_step=1.880, train_loss_epoch=1.330, valid_loss=3.510]\n",
"Epoch 78: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.880, train_loss_epoch=1.380, valid_loss=3.510]\n",
"Epoch 80: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.300, train_loss_epoch=1.380, valid_loss=3.510]\n",
"Epoch 83: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.870, train_loss_epoch=1.360, valid_loss=3.510]\n",
"Epoch 86: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.969, train_loss_epoch=1.340, valid_loss=3.510]\n",
"Epoch 89: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.910, train_loss_epoch=1.380, valid_loss=3.510]\n",
"Epoch 91: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.860, train_loss_epoch=1.340, valid_loss=3.510]\n",
"Epoch 94: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.981, train_loss_epoch=1.420, valid_loss=3.510]\n",
"Epoch 96: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.230, train_loss_epoch=1.360, valid_loss=3.510]\n",
"Epoch 98: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.770, train_loss_epoch=1.320, valid_loss=3.510]\n",
"Epoch 99: 100%|██████████| 3/3 [00:00<00:00, 84.77it/s, v_num=0, train_loss_step=0.936, train_loss_epoch=1.340, valid_loss=3.510]\n",
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"Validation DataLoader 0: 100%|██████████| 3/3 [00:00<00:00, 231.46it/s]\u001b[A\n",
"Epoch 100: 100%|██████████| 3/3 [00:00<00:00, 81.05it/s, v_num=0, train_loss_step=0.936, train_loss_epoch=1.370, valid_loss=3.430]\n",
"Epoch 101: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.936, train_loss_epoch=1.370, valid_loss=3.430]\n",
"Epoch 103: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.800, train_loss_epoch=1.370, valid_loss=3.430]\n",
"Epoch 106: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.730, train_loss_epoch=1.320, valid_loss=3.430]\n",
"Epoch 109: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.945, train_loss_epoch=1.350, valid_loss=3.430]\n",
"Epoch 112: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.993, train_loss_epoch=1.400, valid_loss=3.430]\n",
"Epoch 114: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.963, train_loss_epoch=1.330, valid_loss=3.430]\n",
"Epoch 114: 100%|██████████| 3/3 [00:00<00:00, 72.07it/s, v_num=0, train_loss_step=0.963, train_loss_epoch=1.330, valid_loss=3.430]\n",
"Epoch 114: 100%|██████████| 3/3 [00:00<00:00, 69.87it/s, v_num=0, train_loss_step=0.943, train_loss_epoch=1.370, valid_loss=3.430]\n",
"Epoch 115: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.943, train_loss_epoch=1.370, valid_loss=3.430]\n",
"Epoch 117: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.390, train_loss_epoch=1.420, valid_loss=3.430]\n",
"Epoch 120: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.985, train_loss_epoch=1.390, valid_loss=3.430]\n",
"Epoch 122: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.936, train_loss_epoch=1.350, valid_loss=3.430]\n",
"Epoch 125: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.760, train_loss_epoch=1.310, valid_loss=3.430]\n",
"Epoch 128: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.950, train_loss_epoch=1.390, valid_loss=3.430]\n",
"Epoch 131: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.180, train_loss_epoch=1.340, valid_loss=3.430]\n",
"Epoch 133: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.790, train_loss_epoch=1.330, valid_loss=3.430]\n",
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"Validation DataLoader 0: 100%|██████████| 3/3 [00:00<00:00, 235.83it/s]\u001b[A\n",
"Epoch 133: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.270, train_loss_epoch=1.330, valid_loss=3.380]\n",
"Epoch 136: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.870, train_loss_epoch=1.370, valid_loss=3.380]\n",
"Epoch 139: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.971, train_loss_epoch=1.380, valid_loss=3.380]\n",
"Epoch 141: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.250, train_loss_epoch=1.350, valid_loss=3.380]\n",
"Epoch 144: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.780, train_loss_epoch=1.320, valid_loss=3.380]\n",
"Epoch 146: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.840, train_loss_epoch=1.340, valid_loss=3.380]\n",
"Epoch 146: 100%|██████████| 3/3 [00:00<00:00, 81.84it/s, v_num=0, train_loss_step=1.240, train_loss_epoch=1.330, valid_loss=3.380]\n",
"Epoch 147: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.240, train_loss_epoch=1.330, valid_loss=3.380]\n",
"Epoch 149: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.890, train_loss_epoch=1.370, valid_loss=3.380]\n",
"Epoch 152: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.979, train_loss_epoch=1.380, valid_loss=3.380]\n",
"Epoch 155: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.840, train_loss_epoch=1.340, valid_loss=3.380]\n",
"Epoch 158: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.964, train_loss_epoch=1.340, valid_loss=3.380]\n",
"Epoch 160: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.953, train_loss_epoch=1.400, valid_loss=3.380]\n",
"Epoch 161: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.000, train_loss_epoch=1.390, valid_loss=3.380]\n",
"Epoch 163: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.971, train_loss_epoch=1.400, valid_loss=3.380]\n",
"Epoch 166: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.690, train_loss_epoch=1.330, valid_loss=3.380]\n",
"\u001b[36m(_train_tune pid=34886)\u001b[0m \n",
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"Validation DataLoader 0: 100%|██████████| 3/3 [00:00<00:00, 233.34it/s]\u001b[A\n",
"Epoch 168: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.987, train_loss_epoch=1.370, valid_loss=3.390]\n",
"Epoch 171: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.210, train_loss_epoch=1.340, valid_loss=3.390]\n",
"Epoch 173: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.929, train_loss_epoch=1.310, valid_loss=3.390]\n",
"Epoch 176: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.910, train_loss_epoch=1.370, valid_loss=3.390]\n",
"Epoch 179: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.886, train_loss_epoch=1.320, valid_loss=3.390]\n",
"Epoch 182: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.030, train_loss_epoch=1.350, valid_loss=3.390]\n",
"Epoch 185: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.350, train_loss_epoch=1.360, valid_loss=3.390]\n",
"Epoch 187: 100%|██████████| 3/3 [00:00<00:00, 82.40it/s, v_num=0, train_loss_step=0.907, train_loss_epoch=1.310, valid_loss=3.390]\n",
"Epoch 187: 100%|██████████| 3/3 [00:00<00:00, 79.73it/s, v_num=0, train_loss_step=0.963, train_loss_epoch=1.350, valid_loss=3.390]\n",
"Epoch 188: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.963, train_loss_epoch=1.350, valid_loss=3.390]\n",
"Epoch 190: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.870, train_loss_epoch=1.370, valid_loss=3.390]\n",
"Epoch 193: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.940, train_loss_epoch=1.410, valid_loss=3.390]\n",
"Epoch 196: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.330, train_loss_epoch=1.380, valid_loss=3.390]\n",
"Epoch 199: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.840, train_loss_epoch=1.370, valid_loss=3.390]\n",
"Epoch 199: 100%|██████████| 3/3 [00:00<00:00, 79.48it/s, v_num=0, train_loss_step=0.918, train_loss_epoch=1.370, valid_loss=3.390]\n",
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"Validation DataLoader 0: 100%|██████████| 3/3 [00:00<00:00, 235.03it/s]\u001b[A\n",
"Epoch 201: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.870, train_loss_epoch=1.360, valid_loss=3.430]\n",
"Epoch 203: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.900, train_loss_epoch=1.390, valid_loss=3.430]\n",
"Epoch 206: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.770, train_loss_epoch=1.310, valid_loss=3.430]\n",
"Epoch 209: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.840, train_loss_epoch=1.360, valid_loss=3.430]\n",
"Epoch 212: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.210, train_loss_epoch=1.350, valid_loss=3.430]\n",
"Epoch 215: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.220, train_loss_epoch=1.280, valid_loss=3.430]\n",
"Epoch 218: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.210, train_loss_epoch=1.330, valid_loss=3.430]\n",
"Epoch 220: 100%|██████████| 3/3 [00:00<00:00, 85.44it/s, v_num=0, train_loss_step=1.300, train_loss_epoch=1.330, valid_loss=3.430]\n",
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"Epoch 221: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.230, train_loss_epoch=1.350, valid_loss=3.430]\n",
"Epoch 223: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.927, train_loss_epoch=1.360, valid_loss=3.430]\n",
"Epoch 226: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.910, train_loss_epoch=1.380, valid_loss=3.430]\n",
"Epoch 229: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.810, train_loss_epoch=1.340, valid_loss=3.430]\n",
"Epoch 231: 100%|██████████| 3/3 [00:00<00:00, 85.16it/s, v_num=0, train_loss_step=0.945, train_loss_epoch=1.380, valid_loss=3.430]\n",
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"Validation DataLoader 0: 100%|██████████| 3/3 [00:00<00:00, 245.63it/s]\u001b[A\n",
"Epoch 234: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.830, train_loss_epoch=1.330, valid_loss=3.420]\n",
"Epoch 237: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.992, train_loss_epoch=1.330, valid_loss=3.420]\n",
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"Epoch 251: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.730, train_loss_epoch=1.310, valid_loss=3.420]\n",
"Epoch 254: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.946, train_loss_epoch=1.310, valid_loss=3.420]\n",
"Epoch 256: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.840, train_loss_epoch=1.330, valid_loss=3.420]\n",
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"Epoch 262: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.990, train_loss_epoch=1.400, valid_loss=3.420]\n",
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"Epoch 275: 100%|██████████| 3/3 [00:00<00:00, 74.77it/s, v_num=0, train_loss_step=0.962, train_loss_epoch=1.350, valid_loss=3.470]\n",
"Epoch 275: 100%|██████████| 3/3 [00:00<00:00, 73.41it/s, v_num=0, train_loss_step=0.962, train_loss_epoch=1.340, valid_loss=3.470]\n",
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]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\u001b[36m(_train_tune pid=34886)\u001b[0m `Trainer.fit` stopped: `max_steps=1000` reached.\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[36m(_train_tune pid=34886)\u001b[0m \rEpoch 332: 100%|██████████| 3/3 [00:00<00:00, 62.38it/s, v_num=0, train_loss_step=0.979, train_loss_epoch=1.380, valid_loss=3.440]\rEpoch 332: 100%|██████████| 3/3 [00:00<00:00, 61.90it/s, v_num=0, train_loss_step=0.998, train_loss_epoch=1.380, valid_loss=3.440]\rEpoch 332: 100%|██████████| 3/3 [00:00<00:00, 61.09it/s, v_num=0, train_loss_step=0.998, train_loss_epoch=1.330, valid_loss=3.440]\rEpoch 332: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.998, train_loss_epoch=1.330, valid_loss=3.440] \rEpoch 333: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=0.998, train_loss_epoch=1.330, valid_loss=3.440]\n",
"\u001b[36m(_train_tune pid=34886)\u001b[0m \rValidation: | | 0/? [00:00, ?it/s]\u001b[A\n",
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"\u001b[36m(_train_tune pid=34886)\u001b[0m \rValidation DataLoader 0: 0%| | 0/3 [00:00, ?it/s]\u001b[A\n",
"\u001b[36m(_train_tune pid=34886)\u001b[0m \rValidation DataLoader 0: 100%|██████████| 3/3 [00:00<00:00, 194.79it/s]\u001b[A\n",
"\u001b[36m(_train_tune pid=34886)\u001b[0m \r \u001b[A\rEpoch 333: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.850, train_loss_epoch=1.330, valid_loss=3.440]\rEpoch 333: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.850, train_loss_epoch=1.850, valid_loss=3.440]\rEpoch 333: 0%| | 0/3 [00:00, ?it/s, v_num=0, train_loss_step=1.850, train_loss_epoch=1.850, valid_loss=3.440]\n"
]
},
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"name": "stderr",
"text": [
"\u001b[36m(pid=35046)\u001b[0m /usr/local/lib/python3.10/dist-packages/dask/dataframe/__init__.py:42: FutureWarning: \n",
"\u001b[36m(pid=35046)\u001b[0m Dask dataframe query planning is disabled because dask-expr is not installed.\n",
"\u001b[36m(pid=35046)\u001b[0m \n",
"\u001b[36m(pid=35046)\u001b[0m You can install it with `pip install dask[dataframe]` or `conda install dask`.\n",
"\u001b[36m(pid=35046)\u001b[0m This will raise in a future version.\n",
"\u001b[36m(pid=35046)\u001b[0m \n",
"\u001b[36m(pid=35046)\u001b[0m warnings.warn(msg, FutureWarning)\n",
"\u001b[36m(_train_tune pid=35046)\u001b[0m /usr/local/lib/python3.10/dist-packages/ray/tune/integration/pytorch_lightning.py:198: `ray.tune.integration.pytorch_lightning.TuneReportCallback` is deprecated. Use `ray.tune.integration.pytorch_lightning.TuneReportCheckpointCallback` instead.\n",
"\u001b[36m(_train_tune pid=35046)\u001b[0m Seed set to 6\n",
"\u001b[36m(_train_tune pid=35046)\u001b[0m GPU available: True (cuda), used: True\n",
"\u001b[36m(_train_tune pid=35046)\u001b[0m TPU available: False, using: 0 TPU cores\n",
"\u001b[36m(_train_tune pid=35046)\u001b[0m HPU available: False, using: 0 HPUs\n",
"\u001b[36m(_train_tune pid=35046)\u001b[0m 2025-01-07 15:07:56.865609: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"\u001b[36m(_train_tune pid=35046)\u001b[0m 2025-01-07 15:07:56.892328: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"\u001b[36m(_train_tune pid=35046)\u001b[0m 2025-01-07 15:07:56.901304: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
"\u001b[36m(_train_tune pid=35046)\u001b[0m 2025-01-07 15:07:58.109397: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
"\u001b[36m(_train_tune pid=35046)\u001b[0m LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
"\u001b[36m(_train_tune pid=35046)\u001b[0m \n",
"\u001b[36m(_train_tune pid=35046)\u001b[0m | Name | Type | Params | Mode \n",
"\u001b[36m(_train_tune pid=35046)\u001b[0m -------------------------------------------------------\n",
"\u001b[36m(_train_tune pid=35046)\u001b[0m 0 | loss | MQLoss | 5 | train\n",
"\u001b[36m(_train_tune pid=35046)\u001b[0m 1 | padder_train | ConstantPad1d | 0 | train\n",
"\u001b[36m(_train_tune pid=35046)\u001b[0m 2 | scaler | TemporalNorm | 0 | train\n",
"\u001b[36m(_train_tune pid=35046)\u001b[0m 3 | blocks | ModuleList | 87.9 K | train\n",
"\u001b[36m(_train_tune pid=35046)\u001b[0m -------------------------------------------------------\n",
"\u001b[36m(_train_tune pid=35046)\u001b[0m 87.9 K Trainable params\n",
"\u001b[36m(_train_tune pid=35046)\u001b[0m 5 Non-trainable params\n",
"\u001b[36m(_train_tune pid=35046)\u001b[0m 87.9 K Total params\n",
"\u001b[36m(_train_tune pid=35046)\u001b[0m 0.352 Total estimated model params size (MB)\n",
"\u001b[36m(_train_tune pid=35046)\u001b[0m 40 Modules in train mode\n",
"\u001b[36m(_train_tune pid=35046)\u001b[0m 0 Modules in eval mode\n"
]
},
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"Sanity Checking DataLoader 0: 0%| | 0/1 [00:00, ?it/s]\n",
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"Epoch 251: 100%|██████████| 1/1 [00:00<00:00, 56.92it/s, v_num=0, train_loss_step=1.190, train_loss_epoch=1.260, valid_loss=3.360]\n",
"Epoch 257: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.300, train_loss_epoch=1.300, valid_loss=3.360]\n",
"Epoch 263: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.660, train_loss_epoch=1.660, valid_loss=3.360]\n",
"Epoch 269: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.190, train_loss_epoch=1.190, valid_loss=3.360]\n",
"Epoch 274: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.270, train_loss_epoch=1.270, valid_loss=3.360]\n",
"Epoch 279: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.290, train_loss_epoch=1.290, valid_loss=3.360]\n",
"Epoch 285: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.480, train_loss_epoch=1.480, valid_loss=3.360]\n",
"Epoch 291: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.350, train_loss_epoch=1.350, valid_loss=3.360]\n",
"Epoch 296: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.430, train_loss_epoch=1.430, valid_loss=3.360]\n",
"Epoch 299: 100%|██████████| 1/1 [00:00<00:00, 68.83it/s, v_num=0, train_loss_step=1.170, train_loss_epoch=1.240, valid_loss=3.360]\n",
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"Epoch 300: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.170, train_loss_epoch=1.170, valid_loss=3.300]\n",
"Epoch 306: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.140, train_loss_epoch=1.140, valid_loss=3.300]\n",
"Epoch 311: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.550, train_loss_epoch=1.550, valid_loss=3.300]\n",
"Epoch 316: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.480, train_loss_epoch=1.480, valid_loss=3.300]\n",
"Epoch 321: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.590, train_loss_epoch=1.590, valid_loss=3.300]\n",
"Epoch 326: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.490, train_loss_epoch=1.490, valid_loss=3.300]\n",
"Epoch 332: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.400, train_loss_epoch=1.400, valid_loss=3.300]\n",
"Epoch 340: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.240, train_loss_epoch=1.240, valid_loss=3.300]\n",
"Epoch 349: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.400, train_loss_epoch=1.400, valid_loss=3.300]\n",
"Epoch 357: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.520, train_loss_epoch=1.520, valid_loss=3.300]\n",
"Epoch 365: 100%|██████████| 1/1 [00:00<00:00, 60.87it/s, v_num=0, train_loss_step=1.410, train_loss_epoch=1.410, valid_loss=3.300]\n",
"Epoch 374: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.330, train_loss_epoch=1.330, valid_loss=3.300]\n",
"Epoch 382: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.220, train_loss_epoch=1.220, valid_loss=3.300]\n",
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"Epoch 398: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.340, train_loss_epoch=1.340, valid_loss=3.300]\n",
"Epoch 399: 100%|██████████| 1/1 [00:00<00:00, 93.36it/s, v_num=0, train_loss_step=1.300, train_loss_epoch=1.450, valid_loss=3.300]\n",
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"Epoch 412: 100%|██████████| 1/1 [00:00<00:00, 62.74it/s, v_num=0, train_loss_step=1.320, train_loss_epoch=1.320, valid_loss=3.360]\n",
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"Epoch 421: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.510, train_loss_epoch=1.510, valid_loss=3.360]\n",
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"Epoch 436: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.160, train_loss_epoch=1.160, valid_loss=3.360]\n",
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"Epoch 453: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.430, train_loss_epoch=1.430, valid_loss=3.360]\n",
"Epoch 461: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.400, train_loss_epoch=1.400, valid_loss=3.360]\n",
"Epoch 470: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.410, train_loss_epoch=1.410, valid_loss=3.360]\n",
"Epoch 470: 100%|██████████| 1/1 [00:00<00:00, 66.65it/s, v_num=0, train_loss_step=1.410, train_loss_epoch=1.410, valid_loss=3.360]\n",
"Epoch 471: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.400, train_loss_epoch=1.400, valid_loss=3.360]\n",
"Epoch 478: 100%|██████████| 1/1 [00:00<00:00, 88.13it/s, v_num=0, train_loss_step=1.290, train_loss_epoch=1.290, valid_loss=3.360]\n",
"Epoch 486: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.510, train_loss_epoch=1.510, valid_loss=3.360]\n",
"Epoch 494: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.500, train_loss_epoch=1.500, valid_loss=3.360]\n",
"Epoch 499: 100%|██████████| 1/1 [00:00<00:00, 74.45it/s, v_num=0, train_loss_step=1.380, train_loss_epoch=1.240, valid_loss=3.360]\n",
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"Epoch 506: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.340, train_loss_epoch=1.340, valid_loss=3.420]\n",
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"Epoch 532: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.480, train_loss_epoch=1.480, valid_loss=3.420]\n",
"Epoch 540: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.390, train_loss_epoch=1.390, valid_loss=3.420]\n",
"Epoch 548: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.420, train_loss_epoch=1.420, valid_loss=3.420]\n",
"Epoch 556: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.260, train_loss_epoch=1.260, valid_loss=3.420]\n",
"Epoch 563: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.270, train_loss_epoch=1.270, valid_loss=3.420]\n",
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"Epoch 571: 100%|██████████| 1/1 [00:00<00:00, 61.63it/s, v_num=0, train_loss_step=1.310, train_loss_epoch=1.310, valid_loss=3.420]\n",
"Epoch 579: 100%|██████████| 1/1 [00:00<00:00, 88.43it/s, v_num=0, train_loss_step=1.270, train_loss_epoch=1.270, valid_loss=3.420]\n",
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"Epoch 587: 100%|██████████| 1/1 [00:00<00:00, 67.00it/s, v_num=0, train_loss_step=1.520, train_loss_epoch=1.520, valid_loss=3.420]\n",
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"Epoch 596: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.610, train_loss_epoch=1.610, valid_loss=3.420]\n",
"Epoch 599: 100%|██████████| 1/1 [00:00<00:00, 92.35it/s, v_num=0, train_loss_step=1.260, train_loss_epoch=1.400, valid_loss=3.420]\n",
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"Epoch 611: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.470, train_loss_epoch=1.470, valid_loss=3.270]\n",
"Epoch 619: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.230, train_loss_epoch=1.230, valid_loss=3.270]\n",
"Epoch 626: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.110, train_loss_epoch=1.110, valid_loss=3.270]\n",
"Epoch 634: 100%|██████████| 1/1 [00:00<00:00, 89.15it/s, v_num=0, train_loss_step=1.500, train_loss_epoch=1.500, valid_loss=3.270]\n",
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"Epoch 659: 100%|██████████| 1/1 [00:00<00:00, 68.91it/s, v_num=0, train_loss_step=1.290, train_loss_epoch=1.290, valid_loss=3.270]\n",
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"Epoch 699: 100%|██████████| 1/1 [00:00<00:00, 93.74it/s, v_num=0, train_loss_step=1.390, train_loss_epoch=1.380, valid_loss=3.270]\n",
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"Epoch 708: 100%|██████████| 1/1 [00:00<00:00, 70.06it/s, v_num=0, train_loss_step=1.200, train_loss_epoch=1.200, valid_loss=3.320]\n",
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"Epoch 757: 100%|██████████| 1/1 [00:00<00:00, 66.61it/s, v_num=0, train_loss_step=1.170, train_loss_epoch=1.170, valid_loss=3.320]\n",
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"Epoch 766: 100%|██████████| 1/1 [00:00<00:00, 92.32it/s, v_num=0, train_loss_step=1.340, train_loss_epoch=1.340, valid_loss=3.320]\n",
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"Epoch 799: 100%|██████████| 1/1 [00:00<00:00, 68.03it/s, v_num=0, train_loss_step=1.390, train_loss_epoch=1.620, valid_loss=3.320]\n",
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"Epoch 805: 100%|██████████| 1/1 [00:00<00:00, 66.98it/s, v_num=0, train_loss_step=1.460, train_loss_epoch=1.460, valid_loss=3.370]\n",
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"Epoch 821: 100%|██████████| 1/1 [00:00<00:00, 66.20it/s, v_num=0, train_loss_step=1.350, train_loss_epoch=1.350, valid_loss=3.370]\n",
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"Epoch 862: 100%|██████████| 1/1 [00:00<00:00, 80.64it/s, v_num=0, train_loss_step=1.330, train_loss_epoch=1.330, valid_loss=3.370]\n",
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"Epoch 899: 100%|██████████| 1/1 [00:00<00:00, 95.53it/s, v_num=0, train_loss_step=1.320, train_loss_epoch=1.300, valid_loss=3.370]\n",
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"Epoch 902: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.290, train_loss_epoch=1.290, valid_loss=3.350]\n",
"Epoch 910: 100%|██████████| 1/1 [00:00<00:00, 66.18it/s, v_num=0, train_loss_step=1.450, train_loss_epoch=1.450, valid_loss=3.350]\n",
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"Epoch 927: 100%|██████████| 1/1 [00:00<00:00, 59.65it/s, v_num=0, train_loss_step=1.460, train_loss_epoch=1.460, valid_loss=3.350]\n",
"Epoch 936: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.340, train_loss_epoch=1.340, valid_loss=3.350]\n",
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"Epoch 944: 100%|██████████| 1/1 [00:00<00:00, 59.68it/s, v_num=0, train_loss_step=1.290, train_loss_epoch=1.290, valid_loss=3.350]\n",
"Epoch 952: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.310, train_loss_epoch=1.310, valid_loss=3.350]\n",
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]
},
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"2025-01-07 15:08:14,947\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/root/ray_results/_train_tune_2025-01-07_15-06-01' in 0.0089s.\n",
"INFO:lightning_fabric.utilities.seed:Seed set to 6\n"
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"name": "stdout",
"text": [
"\u001b[36m(_train_tune pid=35046)\u001b[0m \rEpoch 984: 100%|██████████| 1/1 [00:00<00:00, 83.36it/s, v_num=0, train_loss_step=1.040, train_loss_epoch=1.040, valid_loss=3.350]\rEpoch 984: 100%|██████████| 1/1 [00:00<00:00, 81.32it/s, v_num=0, train_loss_step=1.330, train_loss_epoch=1.040, valid_loss=3.350]\rEpoch 984: 100%|██████████| 1/1 [00:00<00:00, 77.54it/s, v_num=0, train_loss_step=1.330, train_loss_epoch=1.330, valid_loss=3.350]\rEpoch 984: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.330, train_loss_epoch=1.330, valid_loss=3.350] \rEpoch 985: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.330, train_loss_epoch=1.330, valid_loss=3.350]\rEpoch 985: 100%|██████████| 1/1 [00:00<00:00, 82.10it/s, v_num=0, train_loss_step=1.330, train_loss_epoch=1.330, valid_loss=3.350]\rEpoch 985: 100%|██████████| 1/1 [00:00<00:00, 79.89it/s, v_num=0, train_loss_step=1.380, train_loss_epoch=1.330, valid_loss=3.350]\rEpoch 985: 100%|██████████| 1/1 [00:00<00:00, 76.17it/s, v_num=0, train_loss_step=1.380, train_loss_epoch=1.380, valid_loss=3.350]\rEpoch 985: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.380, train_loss_epoch=1.380, valid_loss=3.350] \rEpoch 986: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.380, train_loss_epoch=1.380, valid_loss=3.350]\rEpoch 986: 100%|██████████| 1/1 [00:00<00:00, 81.40it/s, v_num=0, train_loss_step=1.380, train_loss_epoch=1.380, valid_loss=3.350]\rEpoch 986: 100%|██████████| 1/1 [00:00<00:00, 79.72it/s, v_num=0, train_loss_step=1.330, train_loss_epoch=1.380, valid_loss=3.350]\rEpoch 986: 100%|██████████| 1/1 [00:00<00:00, 76.67it/s, v_num=0, train_loss_step=1.330, train_loss_epoch=1.330, valid_loss=3.350]\rEpoch 986: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.330, train_loss_epoch=1.330, valid_loss=3.350] \rEpoch 987: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.330, train_loss_epoch=1.330, valid_loss=3.350]\rEpoch 987: 100%|██████████| 1/1 [00:00<00:00, 96.31it/s, v_num=0, train_loss_step=1.330, train_loss_epoch=1.330, valid_loss=3.350]\rEpoch 987: 100%|██████████| 1/1 [00:00<00:00, 94.00it/s, v_num=0, train_loss_step=1.270, train_loss_epoch=1.330, valid_loss=3.350]\rEpoch 987: 100%|██████████| 1/1 [00:00<00:00, 89.82it/s, v_num=0, train_loss_step=1.270, train_loss_epoch=1.270, valid_loss=3.350]\rEpoch 987: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.270, train_loss_epoch=1.270, valid_loss=3.350] \rEpoch 988: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.270, train_loss_epoch=1.270, valid_loss=3.350]\rEpoch 988: 100%|██████████| 1/1 [00:00<00:00, 90.33it/s, v_num=0, train_loss_step=1.270, train_loss_epoch=1.270, valid_loss=3.350]\rEpoch 988: 100%|██████████| 1/1 [00:00<00:00, 87.91it/s, v_num=0, train_loss_step=1.210, train_loss_epoch=1.270, valid_loss=3.350]\rEpoch 988: 100%|██████████| 1/1 [00:00<00:00, 83.86it/s, v_num=0, train_loss_step=1.210, train_loss_epoch=1.210, valid_loss=3.350]\rEpoch 988: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.210, train_loss_epoch=1.210, valid_loss=3.350] \rEpoch 989: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.210, train_loss_epoch=1.210, valid_loss=3.350]\rEpoch 989: 100%|██████████| 1/1 [00:00<00:00, 90.99it/s, v_num=0, train_loss_step=1.210, train_loss_epoch=1.210, valid_loss=3.350]\rEpoch 989: 100%|██████████| 1/1 [00:00<00:00, 88.60it/s, v_num=0, train_loss_step=1.240, train_loss_epoch=1.210, valid_loss=3.350]\rEpoch 989: 100%|██████████| 1/1 [00:00<00:00, 84.24it/s, v_num=0, train_loss_step=1.240, train_loss_epoch=1.240, valid_loss=3.350]\rEpoch 989: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.240, train_loss_epoch=1.240, valid_loss=3.350] \rEpoch 990: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.240, train_loss_epoch=1.240, valid_loss=3.350]\rEpoch 990: 100%|██████████| 1/1 [00:00<00:00, 96.36it/s, v_num=0, train_loss_step=1.240, train_loss_epoch=1.240, valid_loss=3.350]\rEpoch 990: 100%|██████████| 1/1 [00:00<00:00, 93.57it/s, v_num=0, train_loss_step=1.390, train_loss_epoch=1.240, valid_loss=3.350]\rEpoch 990: 100%|██████████| 1/1 [00:00<00:00, 89.63it/s, v_num=0, train_loss_step=1.390, train_loss_epoch=1.390, valid_loss=3.350]\rEpoch 990: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.390, train_loss_epoch=1.390, valid_loss=3.350] \rEpoch 991: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.390, train_loss_epoch=1.390, valid_loss=3.350]\rEpoch 991: 100%|██████████| 1/1 [00:00<00:00, 95.96it/s, v_num=0, train_loss_step=1.390, train_loss_epoch=1.390, valid_loss=3.350]\rEpoch 991: 100%|██████████| 1/1 [00:00<00:00, 92.30it/s, v_num=0, train_loss_step=1.480, train_loss_epoch=1.390, valid_loss=3.350]\rEpoch 991: 100%|██████████| 1/1 [00:00<00:00, 87.91it/s, v_num=0, train_loss_step=1.480, train_loss_epoch=1.480, valid_loss=3.350]\rEpoch 991: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.480, train_loss_epoch=1.480, valid_loss=3.350] \rEpoch 992: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.480, train_loss_epoch=1.480, valid_loss=3.350]\n",
"\u001b[36m(_train_tune pid=35046)\u001b[0m \rEpoch 992: 100%|██████████| 1/1 [00:00<00:00, 77.98it/s, v_num=0, train_loss_step=1.480, train_loss_epoch=1.480, valid_loss=3.350]\rEpoch 992: 100%|██████████| 1/1 [00:00<00:00, 75.10it/s, v_num=0, train_loss_step=1.370, train_loss_epoch=1.480, valid_loss=3.350]\rEpoch 992: 100%|██████████| 1/1 [00:00<00:00, 66.45it/s, v_num=0, train_loss_step=1.370, train_loss_epoch=1.370, valid_loss=3.350]\rEpoch 992: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.370, train_loss_epoch=1.370, valid_loss=3.350] \rEpoch 993: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.370, train_loss_epoch=1.370, valid_loss=3.350]\n",
"\u001b[36m(_train_tune pid=35046)\u001b[0m \rEpoch 993: 100%|██████████| 1/1 [00:00<00:00, 67.66it/s, v_num=0, train_loss_step=1.370, train_loss_epoch=1.370, valid_loss=3.350]\rEpoch 993: 100%|██████████| 1/1 [00:00<00:00, 66.18it/s, v_num=0, train_loss_step=1.380, train_loss_epoch=1.370, valid_loss=3.350]\rEpoch 993: 100%|██████████| 1/1 [00:00<00:00, 63.84it/s, v_num=0, train_loss_step=1.380, train_loss_epoch=1.380, valid_loss=3.350]\rEpoch 993: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.380, train_loss_epoch=1.380, valid_loss=3.350] \rEpoch 994: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.380, train_loss_epoch=1.380, valid_loss=3.350]\rEpoch 994: 100%|██████████| 1/1 [00:00<00:00, 89.84it/s, v_num=0, train_loss_step=1.380, train_loss_epoch=1.380, valid_loss=3.350]\rEpoch 994: 100%|██████████| 1/1 [00:00<00:00, 87.55it/s, v_num=0, train_loss_step=1.520, train_loss_epoch=1.380, valid_loss=3.350]\rEpoch 994: 100%|██████████| 1/1 [00:00<00:00, 83.00it/s, v_num=0, train_loss_step=1.520, train_loss_epoch=1.520, valid_loss=3.350]\rEpoch 994: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.520, train_loss_epoch=1.520, valid_loss=3.350] \rEpoch 995: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.520, train_loss_epoch=1.520, valid_loss=3.350]\rEpoch 995: 100%|██████████| 1/1 [00:00<00:00, 85.50it/s, v_num=0, train_loss_step=1.520, train_loss_epoch=1.520, valid_loss=3.350]\rEpoch 995: 100%|██████████| 1/1 [00:00<00:00, 83.00it/s, v_num=0, train_loss_step=1.490, train_loss_epoch=1.520, valid_loss=3.350]\rEpoch 995: 100%|██████████| 1/1 [00:00<00:00, 79.31it/s, v_num=0, train_loss_step=1.490, train_loss_epoch=1.490, valid_loss=3.350]\rEpoch 995: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.490, train_loss_epoch=1.490, valid_loss=3.350] \rEpoch 996: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.490, train_loss_epoch=1.490, valid_loss=3.350]\rEpoch 996: 100%|██████████| 1/1 [00:00<00:00, 91.11it/s, v_num=0, train_loss_step=1.490, train_loss_epoch=1.490, valid_loss=3.350]\rEpoch 996: 100%|██████████| 1/1 [00:00<00:00, 88.39it/s, v_num=0, train_loss_step=1.190, train_loss_epoch=1.490, valid_loss=3.350]\rEpoch 996: 100%|██████████| 1/1 [00:00<00:00, 84.69it/s, v_num=0, train_loss_step=1.190, train_loss_epoch=1.190, valid_loss=3.350]\rEpoch 996: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.190, train_loss_epoch=1.190, valid_loss=3.350] \rEpoch 997: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.190, train_loss_epoch=1.190, valid_loss=3.350]\rEpoch 997: 100%|██████████| 1/1 [00:00<00:00, 83.89it/s, v_num=0, train_loss_step=1.190, train_loss_epoch=1.190, valid_loss=3.350]\rEpoch 997: 100%|██████████| 1/1 [00:00<00:00, 81.95it/s, v_num=0, train_loss_step=1.600, train_loss_epoch=1.190, valid_loss=3.350]\rEpoch 997: 100%|██████████| 1/1 [00:00<00:00, 77.84it/s, v_num=0, train_loss_step=1.600, train_loss_epoch=1.600, valid_loss=3.350]\rEpoch 997: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.600, train_loss_epoch=1.600, valid_loss=3.350] \rEpoch 998: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.600, train_loss_epoch=1.600, valid_loss=3.350]\rEpoch 998: 100%|██████████| 1/1 [00:00<00:00, 77.21it/s, v_num=0, train_loss_step=1.600, train_loss_epoch=1.600, valid_loss=3.350]\rEpoch 998: 100%|██████████| 1/1 [00:00<00:00, 75.25it/s, v_num=0, train_loss_step=1.870, train_loss_epoch=1.600, valid_loss=3.350]\rEpoch 998: 100%|██████████| 1/1 [00:00<00:00, 71.87it/s, v_num=0, train_loss_step=1.870, train_loss_epoch=1.870, valid_loss=3.350]\rEpoch 998: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.870, train_loss_epoch=1.870, valid_loss=3.350] \rEpoch 999: 0%| | 0/1 [00:00, ?it/s, v_num=0, train_loss_step=1.870, train_loss_epoch=1.870, valid_loss=3.350]\rEpoch 999: 100%|██████████| 1/1 [00:00<00:00, 94.29it/s, v_num=0, train_loss_step=1.870, train_loss_epoch=1.870, valid_loss=3.350]\rEpoch 999: 100%|██████████| 1/1 [00:00<00:00, 92.02it/s, v_num=0, train_loss_step=1.620, train_loss_epoch=1.870, valid_loss=3.350]\n",
"\u001b[36m(_train_tune pid=35046)\u001b[0m \rValidation: | | 0/? [00:00, ?it/s]\u001b[A\n",
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"\u001b[36m(_train_tune pid=35046)\u001b[0m \rValidation DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 229.75it/s]\u001b[A\n",
"\n"
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"\u001b[36m(_train_tune pid=35046)\u001b[0m `Trainer.fit` stopped: `max_steps=1000` reached.\n",
"INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
"INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
"INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
"INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
"INFO:pytorch_lightning.callbacks.model_summary:\n",
" | Name | Type | Params | Mode \n",
"-------------------------------------------------------\n",
"0 | loss | MQLoss | 5 | eval \n",
"1 | padder_train | ConstantPad1d | 0 | train\n",
"2 | scaler | TemporalNorm | 0 | train\n",
"3 | blocks | ModuleList | 87.9 K | train\n",
"-------------------------------------------------------\n",
"87.9 K Trainable params\n",
"5 Non-trainable params\n",
"87.9 K Total params\n",
"0.352 Total estimated model params size (MB)\n",
"39 Modules in train mode\n",
"1 Modules in eval mode\n"
]
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"\u001b[36m(_train_tune pid=35046)\u001b[0m \n",
"\u001b[36m(_train_tune pid=35046)\u001b[0m \r \u001b[A\rEpoch 999: 100%|██████████| 1/1 [00:00<00:00, 35.79it/s, v_num=0, train_loss_step=1.620, train_loss_epoch=1.870, valid_loss=3.300]\rEpoch 999: 100%|██████████| 1/1 [00:00<00:00, 34.33it/s, v_num=0, train_loss_step=1.620, train_loss_epoch=1.620, valid_loss=3.300]\rEpoch 999: 100%|██████████| 1/1 [00:00<00:00, 33.19it/s, v_num=0, train_loss_step=1.620, train_loss_epoch=1.620, valid_loss=3.300]\n"
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"/usr/local/lib/python3.10/dist-packages/utilsforecast/processing.py:384: FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead.\n",
" freq = pd.tseries.frequencies.to_offset(freq)\n",
"/usr/local/lib/python3.10/dist-packages/utilsforecast/processing.py:440: FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead.\n",
" freq = pd.tseries.frequencies.to_offset(freq)\n",
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}
},
"metadata": {},
"execution_count": 52
}
]
},
{
"cell_type": "code",
"source": [
"from statsforecast import StatsForecast\n",
"StatsForecast.plot(hist, fcst_df, engine='matplotlib', max_insample_length=48 * 3, level=[80, 90])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 102
},
"id": "dgAjN8YByRNo",
"outputId": "3b5515de-f8ed-4343-8b48-6f6c4476c4d1"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
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""
],
"image/png": "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\n"
},
"metadata": {},
"execution_count": 53
}
]
},
{
"cell_type": "code",
"source": [
"StatsForecast.plot(hist, fcst_df, models=[\"AutoNHITS\"], engine='matplotlib', max_insample_length=48 * 3, level=[80, 90])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 102
},
"id": "LW0MNnBq4DYH",
"outputId": "95b9dedf-c121-4d6e-85ff-66a5b588065f"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {},
"execution_count": 54
}
]
},
{
"cell_type": "code",
"source": [
"StatsForecast.plot(hist, fcst_df, models=[\"AutoAutoformer\"], engine='matplotlib', max_insample_length=48 * 3, level=[80, 90])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 102
},
"id": "7Aiu3ojNJymG",
"outputId": "d6eced7c-8c66-4c72-bd20-0a8bd294ac2d"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {},
"execution_count": 55
}
]
},
{
"cell_type": "markdown",
"source": [
"# Zwischenergebnis\n",
"1. NHITS & Autoformer\n",
"2. AutoNHITS & AutoAutoformer\n",
"NEXT: FEDfirmer, PoatchTST, Informer && M6 Spearman comparison of prediction quality"
],
"metadata": {
"id": "i8IEjEcXNWyl"
}
},
{
"cell_type": "markdown",
"source": [
"# FEDformer (oho)"
],
"metadata": {
"id": "Czs3Q9MyXjYq"
}
},
{
"cell_type": "code",
"source": [
"# FEDformer Example\n",
"# This cell is analogous to the Autoformer snippet, but uses FEDformer instead.\n",
"\n",
"from neuralforecast.models import FEDformer\n",
"from neuralforecast import NeuralForecast\n",
"\n",
"# Define the forecast horizon (how many steps ahead we predict)\n",
"horizon = 12 # or whatever suits your data\n",
"\n",
"# Instantiate the FEDformer model with some basic hyperparameters\n",
"models = [\n",
" FEDformer(\n",
" h=horizon, # Forecast horizon\n",
" input_size=horizon, # Length of input sequence\n",
" max_steps=100, # Number of training steps\n",
" val_check_steps=10,# Check validation loss every X steps\n",
" early_stop_patience_steps=3\n",
" )\n",
"]\n",
"\n",
"# Create the NeuralForecast pipeline\n",
"# 'hist' should be your DataFrame with columns: [unique_id, ds, y, (exogenous...)]\n",
"nf = NeuralForecast(models=models, freq='M') # freq='M' for monthly data, adjust as needed\n",
"\n",
"# Train the FEDformer model\n",
"# 'val_size' is how many of the last data points to hold out as a validation set\n",
"nf.fit(df=hist, val_size=horizon)\n",
"\n",
"# Get in-sample predictions\n",
"Y_hat_fedformer = nf.predict_insample(step_size=horizon)\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 637,
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{
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"name": "stderr",
"text": [
"INFO:lightning_fabric.utilities.seed:Seed set to 1\n",
"INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
"INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
"INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
"INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
"INFO:pytorch_lightning.callbacks.model_summary:\n",
" | Name | Type | Params | Mode \n",
"--------------------------------------------------------\n",
"0 | loss | MAE | 0 | train\n",
"1 | padder_train | ConstantPad1d | 0 | train\n",
"2 | scaler | TemporalNorm | 0 | train\n",
"3 | decomp | SeriesDecomp | 0 | train\n",
"4 | enc_embedding | DataEmbedding | 384 | train\n",
"5 | dec_embedding | DataEmbedding | 384 | train\n",
"6 | encoder | Encoder | 161 K | train\n",
"7 | decoder | Decoder | 177 K | train\n",
"--------------------------------------------------------\n",
"339 K Trainable params\n",
"0 Non-trainable params\n",
"339 K Total params\n",
"1.359 Total estimated model params size (MB)\n",
"80 Modules in train mode\n",
"0 Modules in eval mode\n"
]
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"text": [
"/usr/local/lib/python3.10/dist-packages/utilsforecast/processing.py:384: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
" freq = pd.tseries.frequencies.to_offset(freq)\n",
"INFO:pytorch_lightning.utilities.rank_zero:Trainer already configured with model summary callbacks: []. Skipping setting a default `ModelSummary` callback.\n",
"INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
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},
{
"cell_type": "markdown",
"source": [
"### Visualization FEDformer"
],
"metadata": {
"id": "rPeD-ENxJ5F9"
}
},
{
"cell_type": "code",
"source": [
"#print(f\"FEFformer prediction score: {nf.predict_insample(step_size=horizon)} .\")\n",
"\n",
"# Plot the results\n",
"#import matplotlib.pyplot as plt\n",
"#\n",
"#plt.figure(figsize=(10, 5))\n",
"#for unique_id in Y_hat_insample.index.unique():\n",
"# stock_data = Y_hat_insample.loc[unique_id]\n",
"#\n",
"# # Plot true values and forecast\n",
"# plt.plot(stock_data['ds'], stock_data['y'], label=f'True ({unique_id})')\n",
"# plt.plot(stock_data['ds'], stock_data['FEDformer'],\n",
"# label=f'Forecast ({unique_id})', linestyle='--')\n",
"#\n",
"# # Mark the train-val split (assuming the last 'horizon' points are val)\n",
"# if len(stock_data) > horizon:\n",
"# plt.axvline(stock_data['ds'].iloc[-horizon], color='black',\n",
"# linestyle='dotted', alpha=0.7)\n",
"#\n",
"#plt.xlabel('Timestamp [t]')\n",
"#plt.ylabel('Stock value')\n",
"#plt.title('True vs. Forecast Values per Stock with FEDformer')\n",
"#plt.grid(alpha=0.4)\n",
"#\n",
"## Adjust legend to fit better\n",
"#plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
"#\n",
"#plt.tight_layout()\n",
"#plt.show()"
],
"metadata": {
"id": "3chdGjvSJ9RE"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# PatchTST (oho)"
],
"metadata": {
"id": "qNGg6rtrYk22"
}
},
{
"cell_type": "code",
"source": [
"# PatchTST Example\n",
"# Similar to the Autoformer or FEDformer snippet, but uses PatchTST instead.\n",
"\n",
"from neuralforecast.models import PatchTST\n",
"from neuralforecast import NeuralForecast\n",
"\n",
"# Define the forecast horizon (how many steps ahead we predict)\n",
"horizon = 12 # or whichever suits your data\n",
"\n",
"# Instantiate the PatchTST model with some basic hyperparameters\n",
"models = [\n",
" PatchTST(\n",
" h=horizon, # Forecast horizon\n",
" input_size=horizon, # Length of input sequence\n",
" max_steps=1000, # Number of training steps\n",
" val_check_steps=100,# Check validation loss every X steps\n",
" early_stop_patience_steps=3,\n",
" hidden_size=128 # e.g., dimension of patch embeddings (tune as needed)\n",
" )\n",
"]\n",
"\n",
"# Create the NeuralForecast pipeline\n",
"# 'hist' should be your DataFrame with columns: [unique_id, ds, y, ...]\n",
"nf = NeuralForecast(models=models, freq='M') # freq='M' for monthly data, adjust as needed\n",
"\n",
"# Train the PatchTST model\n",
"# 'val_size' is how many of the last data points to hold out as a validation set\n",
"nf.fit(df=hist, val_size=horizon)\n",
"\n",
"# Get in-sample predictions\n",
"Y_hat_insample = nf.predict_insample(step_size=horizon)\n",
"\n"
],
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{
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"name": "stderr",
"text": [
"INFO:lightning_fabric.utilities.seed:Seed set to 1\n",
"INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
"INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
"INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
"INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
"INFO:pytorch_lightning.callbacks.model_summary:\n",
" | Name | Type | Params | Mode \n",
"-----------------------------------------------------------\n",
"0 | loss | MAE | 0 | train\n",
"1 | padder_train | ConstantPad1d | 0 | train\n",
"2 | scaler | TemporalNorm | 0 | train\n",
"3 | model | PatchTST_backbone | 401 K | train\n",
"-----------------------------------------------------------\n",
"401 K Trainable params\n",
"3 Non-trainable params\n",
"401 K Total params\n",
"1.605 Total estimated model params size (MB)\n",
"90 Modules in train mode\n",
"0 Modules in eval mode\n"
]
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"INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_steps=1000` reached.\n",
"/usr/local/lib/python3.10/dist-packages/utilsforecast/processing.py:384: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
" freq = pd.tseries.frequencies.to_offset(freq)\n",
"INFO:pytorch_lightning.utilities.rank_zero:Trainer already configured with model summary callbacks: []. Skipping setting a default `ModelSummary` callback.\n",
"INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
"INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
"INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
"INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
]
},
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"Predicting: | | 0/? [00:00, ?it/s]"
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"text": [
":58: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations.\n",
" plt.tight_layout()\n"
]
},
{
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""
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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"### Visualization FEDformer (oho)\n"
],
"metadata": {
"id": "UYohFr6cKNDo"
}
},
{
"cell_type": "code",
"source": [
"# Plot the results\n",
"#import matplotlib.pyplot as plt\n",
"#\n",
"#plt.figure(figsize=(10, 5))\n",
"#for unique_id in Y_hat_insample.index.unique():\n",
"# stock_data = Y_hat_insample.loc[unique_id]\n",
"#\n",
"# # Plot true values and forecast\n",
"# plt.plot(stock_data['ds'], stock_data['y'], label=f'True ({unique_id})')\n",
"# plt.plot(stock_data['ds'], stock_data['PatchTST'],\n",
"# label=f'Forecast ({unique_id})', linestyle='--')\n",
"#\n",
"# # Mark the train-val split (assuming the last 'horizon' points are val)\n",
"# if len(stock_data) > horizon:\n",
"# plt.axvline(stock_data['ds'].iloc[-horizon], color='black',\n",
"# linestyle='dotted', alpha=0.7)\n",
"#\n",
"#plt.xlabel('Timestamp [t]')\n",
"#plt.ylabel('Stock value')\n",
"#plt.title('True vs. Forecast Values per Stock with PatchTST')\n",
"#plt.grid(alpha=0.4)\n",
"#\n",
"## Adjust legend to fit better\n",
"#plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
"#\n",
"#plt.tight_layout()\n",
"#plt.show()"
],
"metadata": {
"id": "tnglFH4oKbMy"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Informer (oho)"
],
"metadata": {
"id": "6qQKj8kFZ94_"
}
},
{
"cell_type": "code",
"source": [
"# Informer Example\n",
"# Similar to the Autoformer, FEDformer, or PatchTST snippets, but uses Informer instead.\n",
"\n",
"from neuralforecast.models import Informer\n",
"from neuralforecast import NeuralForecast\n",
"\n",
"# Define the forecast horizon (how many steps ahead to predict)\n",
"horizon = 12 # adjust as needed for your data\n",
"\n",
"# Instantiate the Informer model with some basic hyperparameters\n",
"models = [\n",
" Informer(\n",
" h=horizon, # Forecast horizon\n",
" input_size=horizon, # Length of input sequence\n",
" max_steps=1000, # Number of training steps\n",
" val_check_steps=100,# Check validation loss every X steps\n",
" early_stop_patience_steps=3,\n",
" hidden_size=128 # e.g., hidden dimension, adjust to taste\n",
" )\n",
"]\n",
"\n",
"# Create the NeuralForecast pipeline\n",
"# 'hist' should be your DataFrame with columns: [unique_id, ds, y, (exogenous...)]\n",
"nf = NeuralForecast(models=models, freq='M') # freq='M' is monthly; adjust as needed\n",
"\n",
"# Train the Informer model\n",
"# 'val_size' is how many of the last data points to use as a validation set\n",
"nf.fit(df=hist, val_size=horizon)\n",
"\n",
"# Get in-sample predictions\n",
"Y_hat_insample = nf.predict_insample(step_size=horizon)\n",
"\n"
],
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},
"execution_count": null,
"outputs": [
{
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"name": "stderr",
"text": [
"INFO:lightning_fabric.utilities.seed:Seed set to 1\n",
"INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
"INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
"INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
"INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
"INFO:pytorch_lightning.callbacks.model_summary:\n",
" | Name | Type | Params | Mode \n",
"--------------------------------------------------------\n",
"0 | loss | MAE | 0 | train\n",
"1 | padder_train | ConstantPad1d | 0 | train\n",
"2 | scaler | TemporalNorm | 0 | train\n",
"3 | enc_embedding | DataEmbedding | 384 | train\n",
"4 | dec_embedding | DataEmbedding | 384 | train\n",
"5 | encoder | TransEncoder | 199 K | train\n",
"6 | decoder | TransDecoder | 141 K | train\n",
"--------------------------------------------------------\n",
"341 K Trainable params\n",
"0 Non-trainable params\n",
"341 K Total params\n",
"1.368 Total estimated model params size (MB)\n",
"73 Modules in train mode\n",
"0 Modules in eval mode\n"
]
},
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"INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_steps=1000` reached.\n",
"/usr/local/lib/python3.10/dist-packages/utilsforecast/processing.py:384: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
" freq = pd.tseries.frequencies.to_offset(freq)\n",
"INFO:pytorch_lightning.utilities.rank_zero:Trainer already configured with model summary callbacks: []. Skipping setting a default `ModelSummary` callback.\n",
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]
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":58: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations.\n",
" plt.tight_layout()\n"
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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"### Visualization Informer"
],
"metadata": {
"id": "WAynZjHoKjXa"
}
},
{
"cell_type": "code",
"source": [
"# Plot the results\n",
"#import matplotlib.pyplot as plt\n",
"#\n",
"#plt.figure(figsize=(10, 5))\n",
"#for unique_id in Y_hat_insample.index.unique():\n",
"# stock_data = Y_hat_insample.loc[unique_id]\n",
"#\n",
"# # Plot true values and forecast\n",
"# plt.plot(stock_data['ds'], stock_data['y'], label=f'True ({unique_id})')\n",
"# plt.plot(stock_data['ds'], stock_data['Informer'],\n",
"# label=f'Forecast ({unique_id})', linestyle='--')\n",
"#\n",
"# # Mark the train-val split (assuming the last 'horizon' points are for validation)\n",
"# if len(stock_data) > horizon:\n",
"# plt.axvline(stock_data['ds'].iloc[-horizon], color='black',\n",
"# linestyle='dotted', alpha=0.7)\n",
"#\n",
"#plt.xlabel('Timestamp [t]')\n",
"#plt.ylabel('Stock value')\n",
"#plt.title('True vs. Forecast Values per Stock with Informer')\n",
"#plt.grid(alpha=0.4)\n",
"#\n",
"## Adjust legend to fit better\n",
"#plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
"#\n",
"#plt.tight_layout()\n",
"#plt.show()"
],
"metadata": {
"id": "3zHEZqMiKmmP"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Comparison some transformer models - m6 spearman"
],
"metadata": {
"id": "9XXsSJLpf6Rw"
}
},
{
"cell_type": "code",
"source": [
"# Compare performance of Informer, Autoformer, PatchTST, and FEDformer\n",
"# with respect to M6 goals (e.g., rank-based correlation).\n",
"# This cell assumes you already have DataFrames like:\n",
"# Y_hat_informer -> columns: [unique_id, ds, y, \"Informer\"]\n",
"# Y_hat_autoformer -> columns: [unique_id, ds, y, \"Autoformer\"]\n",
"# Y_hat_patchtst -> columns: [unique_id, ds, y, \"PatchTST\"]\n",
"# Y_hat_fedformer -> columns: [unique_id, ds, y, \"FEDformer\"]\n",
"#\n",
"# Each DataFrame holds the model's in-sample or out-of-sample predictions plus the true y.\n",
"# We'll illustrate an M6-like measure using Spearman rank correlation as a placeholder.\n",
"\n",
"import pandas as pd\n",
"import numpy as np\n",
"from scipy.stats import spearmanr\n",
"\n",
"def m6_score(true_values, predictions):\n",
" \"\"\"\n",
" Example M6 measure using Spearman rank correlation.\n",
" If you have a more accurate M6 formula, replace this with the official scoring logic.\n",
" \"\"\"\n",
" # Make sure inputs are numpy arrays\n",
" t = np.array(true_values)\n",
" p = np.array(predictions)\n",
" corr, _ = spearmanr(t, p)\n",
" return corr\n",
"\n",
"# List of (model_name, dataframe, prediction_col) for easy iteration\n",
"model_data = [\n",
" (\"Informer\", Y_hat_informer, \"Informer\"),\n",
" (\"Autoformer\", Y_hat_autoformer, \"Autoformer\"),\n",
" (\"PatchTST\", Y_hat_patchtst, \"PatchTST\"),\n",
" (\"FEDformer\", Y_hat_fedformer, \"FEDformer\"),\n",
"]\n",
"\n",
"# We'll create a comparison DataFrame\n",
"comparison = []\n",
"\n",
"for model_name, df_model, pred_col in model_data:\n",
" # Merge or ensure df_model has columns: [unique_id, ds, 'y', pred_col]\n",
" # If needed, reset_index\n",
" if isinstance(df_model.index, pd.MultiIndex):\n",
" df_model = df_model.reset_index()\n",
"\n",
" # Filter rows if necessary (e.g., removing NaN predictions)\n",
" df_valid = df_model.dropna(subset=['y', pred_col])\n",
" if df_valid.empty:\n",
" comparison.append({\n",
" \"Model\": model_name,\n",
" \"M6_Score\": np.nan,\n",
" \"RMSE\": np.nan,\n",
" \"Count\": 0\n",
" })\n",
" continue\n",
"\n",
" # Calculate M6 score (rank correlation)\n",
" m6_val_score = m6_score(df_valid['y'], df_valid[pred_col])\n",
"\n",
" # Calculate a standard metric, e.g. RMSE\n",
" rmse_val = np.sqrt(np.mean((df_valid['y'] - df_valid[pred_col])**2))\n",
"\n",
" # Summarize\n",
" comparison.append({\n",
" \"Model\": model_name,\n",
" \"M6_Score\": m6_val_score,\n",
" \"RMSE\": rmse_val,\n",
" \"Count\": len(df_valid)\n",
" })\n",
"\n",
"# Build a result table\n",
"comparison_df = pd.DataFrame(comparison)\n",
"print(\"Comparison of Informer, Autoformer, PatchTST, and FEDformer:\")\n",
"print(comparison_df)\n",
"\n",
"# Example usage:\n",
"# Model M6_Score RMSE Count\n",
"# 0 Informer 0.55 10.2 350\n",
"# 1 Autoformer 0.57 9.8 350\n",
"# 2 PatchTST 0.62 9.1 350\n",
"# 3 FEDformer 0.59 9.4 350\n",
"#\n",
"# (These numbers are illustrative placeholders.)"
],
"metadata": {
"id": "Kr6c6O6XgCbo",
"outputId": "f2823446-dbc8-49b3-a18d-2cd783332460",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 216
}
},
"execution_count": null,
"outputs": [
{
"output_type": "error",
"ename": "NameError",
"evalue": "name 'Y_hat_informer' is not defined",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[0;31m# List of (model_name, dataframe, prediction_col) for easy iteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 28\u001b[0m model_data = [\n\u001b[0;32m---> 29\u001b[0;31m \u001b[0;34m(\u001b[0m\u001b[0;34m\"Informer\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_hat_informer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Informer\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 30\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"Autoformer\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_hat_autoformer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Autoformer\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"PatchTST\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_hat_patchtst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"PatchTST\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'Y_hat_informer' is not defined"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Compare Informer, Autoformer, PatchTST, and FEDformer in one cell,\n",
"# using code analogous to your existing \"snippet style\" for each model.\n",
"# We’ll train each model separately on the same 'hist' DataFrame,\n",
"# collect their in-sample predictions, and then compute M6-like scores\n",
"# (e.g., Spearman rank correlation) plus a standard RMSE.\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from scipy.stats import spearmanr\n",
"\n",
"from neuralforecast import NeuralForecast\n",
"from neuralforecast.models import Informer, Autoformer, PatchTST, FEDformer\n",
"\n",
"# Example M6 measure: Spearman rank correlation\n",
"def m6_score(true_vals, pred_vals):\n",
" t = np.array(true_vals)\n",
" p = np.array(pred_vals)\n",
" corr, _ = spearmanr(t, p)\n",
" return corr\n",
"\n",
"# Forecast horizon\n",
"horizon = 12\n",
"\n",
"# Your historical DataFrame 'hist' must have columns: ['unique_id', 'ds', 'y'] (+ any exogenous)\n",
"# freq='M' for monthly, adjust if your data is daily ('D'), etc.\n",
"\n",
"# Define one instance of each model with basic hyperparams\n",
"model_definitions = {\n",
" 'Informer': Informer(\n",
" h=horizon,\n",
" input_size=horizon,\n",
" max_steps=500,\n",
" val_check_steps=100,\n",
" early_stop_patience_steps=3,\n",
" hidden_size=128\n",
" ),\n",
" 'Autoformer': Autoformer(\n",
" h=horizon,\n",
" input_size=horizon,\n",
" max_steps=500,\n",
" val_check_steps=100,\n",
" early_stop_patience_steps=3\n",
" ),\n",
" 'PatchTST': PatchTST(\n",
" h=horizon,\n",
" input_size=horizon,\n",
" max_steps=500,\n",
" val_check_steps=100,\n",
" early_stop_patience_steps=3,\n",
" hidden_size=128\n",
" ),\n",
" 'FEDformer': FEDformer(\n",
" h=horizon,\n",
" input_size=horizon,\n",
" max_steps=500,\n",
" val_check_steps=100,\n",
" early_stop_patience_steps=3,\n",
" hidden_size=128\n",
" ),\n",
"}\n",
"\n",
"# Dictionary to store each model's in-sample predictions\n",
"insample_preds = {}\n",
"\n",
"# Train each model and collect predictions\n",
"for model_name, model_inst in model_definitions.items():\n",
" print(f\"\\nTraining {model_name} ...\")\n",
" nf = NeuralForecast(models=[model_inst], freq='M') # adjust freq if needed\n",
" nf.fit(df=hist, val_size=horizon)\n",
"\n",
" Y_hat_insample = nf.predict_insample(step_size=horizon)\n",
" # We'll store it so we can compare later.\n",
" insample_preds[model_name] = Y_hat_insample.copy()\n",
"\n",
"# Now compare the models' performance\n",
"results = []\n",
"for model_name, df_pred in insample_preds.items():\n",
" # Ensure we have 'y' and the model's column in the DataFrame\n",
" # If needed, reset MultiIndex\n",
" if isinstance(df_pred.index, pd.MultiIndex):\n",
" df_pred = df_pred.reset_index()\n",
"\n",
" if model_name not in df_pred.columns:\n",
" print(f\"Warning: column '{model_name}' not found in {model_name}'s dataframe.\")\n",
" continue\n",
"\n",
" # Drop any rows with missing predictions\n",
" df_pred = df_pred.dropna(subset=['y', model_name])\n",
" if df_pred.empty:\n",
" print(f\"No valid data for {model_name}\")\n",
" continue\n",
"\n",
" # Compute rank-based correlation (M6 proxy) + RMSE\n",
" m6_val_score = m6_score(df_pred['y'], df_pred[model_name])\n",
" rmse_val = np.sqrt(np.mean((df_pred['y'] - df_pred[model_name])**2))\n",
"\n",
" results.append({\n",
" 'Model': model_name,\n",
" 'M6_Score': m6_val_score,\n",
" 'RMSE': rmse_val,\n",
" 'Count': len(df_pred)\n",
" })\n",
"\n",
"results_df = pd.DataFrame(results)\n",
"print(\"\\n=== Model Comparison (M6 Score & RMSE) ===\")\n",
"print(results_df)\n",
"\n",
"# Optionally, plot a quick side-by-side bar chart of M6 scores\n",
"# (just as an example visualization)\n",
"plt.figure(figsize=(6, 4))\n",
"plt.bar(results_df['Model'], results_df['M6_Score'], color='skyblue')\n",
"plt.title('M6 Score Comparison')\n",
"plt.ylabel('Spearman Correlation')\n",
"plt.show()"
],
"metadata": {
"id": "UY5OJpCQh6Uw",
"outputId": "bc530970-dd22-4cb9-cb92-327b0e6ead4e",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000,
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"INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
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"INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
"INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
"INFO:pytorch_lightning.callbacks.model_summary:\n",
" | Name | Type | Params | Mode \n",
"--------------------------------------------------------\n",
"0 | loss | MAE | 0 | train\n",
"1 | padder_train | ConstantPad1d | 0 | train\n",
"2 | scaler | TemporalNorm | 0 | train\n",
"3 | decomp | SeriesDecomp | 0 | train\n",
"4 | enc_embedding | DataEmbedding | 384 | train\n",
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"6 | encoder | Encoder | 161 K | train\n",
"7 | decoder | Decoder | 177 K | train\n",
"--------------------------------------------------------\n",
"339 K Trainable params\n",
"0 Non-trainable params\n",
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"Training FEDformer ...\n"
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"/usr/local/lib/python3.10/dist-packages/utilsforecast/processing.py:384: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
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"text": [
"No valid data for FEDformer\n",
"\n",
"=== Model Comparison (M6 Score & RMSE) ===\n",
" Model M6_Score RMSE Count\n",
"0 Informer 0.051820 113.125923 6408\n",
"1 Autoformer 0.995597 6.561259 6408\n",
"2 PatchTST 0.997048 5.739539 6408\n"
]
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""
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