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< title > Train, convert and predict with ONNX Runtime< / title >
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< li class = "toctree-l1" > < a class = "reference internal" href = "../tutorial.html" > Tutorial< / a > < ul >
< li class = "toctree-l2" > < a class = "reference internal" href = "../tutorial.html#step-1-train-a-model-using-your-favorite-framework" > Step 1: Train a model using your favorite framework< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "../tutorial.html#step-2-convert-or-export-the-model-into-onnx-format" > Step 2: Convert or export the model into ONNX format< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "../tutorial.html#step-3-load-and-run-the-model-using-onnx-runtime" > Step 3: Load and run the model using ONNX Runtime< / a > < / li >
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< li class = "toctree-l1" > < a class = "reference internal" href = "../api_summary.html" > API Summary< / a > < ul >
< li class = "toctree-l2" > < a class = "reference internal" href = "../api_summary.html#device" > Device< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "../api_summary.html#examples-and-datasets" > Examples and datasets< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "../api_summary.html#load-and-run-a-model" > Load and run a model< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "../api_summary.html#backend" > Backend< / a > < / li >
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< li class = "toctree-l1 current" > < a class = "reference internal" href = "index.html" > Gallery of examples< / a > < ul class = "current" >
< li class = "toctree-l2" > < a class = "reference internal" href = "plot_backend.html" > ONNX Runtime Backend for ONNX< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "plot_pipeline.html" > Draw a pipeline< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "plot_load_and_predict.html" > Load and predict with ONNX Runtime and a very simple model< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "plot_profiling.html" > Profile the execution of a simple model< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "plot_metadata.html" > Metadata< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "plot_dl_keras.html" > ONNX Runtime for Keras< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "plot_convert_pipeline_vectorizer.html" > Train, convert and predict with ONNX Runtime< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "plot_common_errors.html" > Common errors with onnxruntime< / a > < / li >
< li class = "toctree-l2 current" > < a class = "current reference internal" href = "#" > Train, convert and predict with ONNX Runtime< / a > < / li >
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< div class = "sphx-glr-download-link-note admonition note" >
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< p class = "admonition-title" > Note< / p >
< p > Click < a class = "reference internal" href = "#sphx-glr-download-auto-examples-plot-train-convert-predict-py" > < span class = "std std-ref" > here< / span > < / a > to download the full example code< / p >
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< / div >
< div class = "sphx-glr-example-title section" id = "train-convert-and-predict-with-onnx-runtime" >
< span id = "l-logreg-example" > < / span > < span id = "sphx-glr-auto-examples-plot-train-convert-predict-py" > < / span > < h1 > Train, convert and predict with ONNX Runtime< a class = "headerlink" href = "#train-convert-and-predict-with-onnx-runtime" title = "Permalink to this headline" > ¶< / a > < / h1 >
< p > This example demonstrates an end to end scenario
starting with the training of a machine learned model
to its use in its converted from.< / p >
< div class = "contents local topic" id = "contents" >
< ul class = "simple" >
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< li > < p > < a class = "reference internal" href = "#train-a-logistic-regression" id = "id1" > Train a logistic regression< / a > < / p > < / li >
< li > < p > < a class = "reference internal" href = "#conversion-to-onnx-format" id = "id2" > Conversion to ONNX format< / a > < / p > < / li >
< li > < p > < a class = "reference internal" href = "#probabilities" id = "id3" > Probabilities< / a > < / p > < / li >
< li > < p > < a class = "reference internal" href = "#benchmark-with-randomforest" id = "id4" > Benchmark with RandomForest< / a > < / p > < / li >
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< / ul >
< / div >
< div class = "section" id = "train-a-logistic-regression" >
< h2 > < a class = "toc-backref" href = "#id1" > Train a logistic regression< / a > < a class = "headerlink" href = "#train-a-logistic-regression" title = "Permalink to this headline" > ¶< / a > < / h2 >
< p > The first step consists in retrieving the iris datset.< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > from< / span > < span class = "nn" > sklearn.datasets< / span > < span class = "k" > import< / span > < span class = "n" > load_iris< / span >
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< span class = "n" > iris< / span > < span class = "o" > =< / span > < span class = "n" > load_iris< / span > < span class = "p" > ()< / span >
< span class = "n" > X< / span > < span class = "p" > ,< / span > < span class = "n" > y< / span > < span class = "o" > =< / span > < span class = "n" > iris< / span > < span class = "o" > .< / span > < span class = "n" > data< / span > < span class = "p" > ,< / span > < span class = "n" > iris< / span > < span class = "o" > .< / span > < span class = "n" > target< / span >
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< span class = "kn" > from< / span > < span class = "nn" > sklearn.model_selection< / span > < span class = "k" > import< / span > < span class = "n" > train_test_split< / span >
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< span class = "n" > X_train< / span > < span class = "p" > ,< / span > < span class = "n" > X_test< / span > < span class = "p" > ,< / span > < span class = "n" > y_train< / span > < span class = "p" > ,< / span > < span class = "n" > y_test< / span > < span class = "o" > =< / span > < span class = "n" > train_test_split< / span > < span class = "p" > (< / span > < span class = "n" > X< / span > < span class = "p" > ,< / span > < span class = "n" > y< / span > < span class = "p" > )< / span >
< / pre > < / div >
< / div >
< p > Then we fit a model.< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > from< / span > < span class = "nn" > sklearn.linear_model< / span > < span class = "k" > import< / span > < span class = "n" > LogisticRegression< / span >
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< span class = "n" > clr< / span > < span class = "o" > =< / span > < span class = "n" > LogisticRegression< / span > < span class = "p" > ()< / span >
< span class = "n" > clr< / span > < span class = "o" > .< / span > < span class = "n" > fit< / span > < span class = "p" > (< / span > < span class = "n" > X_train< / span > < span class = "p" > ,< / span > < span class = "n" > y_train< / span > < span class = "p" > )< / span >
< / pre > < / div >
< / div >
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< p class = "sphx-glr-script-out" > Out:< / p >
< div class = "sphx-glr-script-out highlight-none notranslate" > < div class = "highlight" > < pre > < span > < / span > c:\users\hasesh\appdata\local\programs\python\python36\lib\site-packages\sklearn\linear_model\logistic.py:432: FutureWarning: Default solver will be changed to ' lbfgs' in 0.22. Specify a solver to silence this warning.
FutureWarning)
c:\users\hasesh\appdata\local\programs\python\python36\lib\site-packages\sklearn\linear_model\logistic.py:469: FutureWarning: Default multi_class will be changed to ' auto' in 0.22. Specify the multi_class option to silence this warning.
" this warning." , FutureWarning)
< / pre > < / div >
< / div >
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< p > We compute the prediction on the test set
and we show the confusion matrix.< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > from< / span > < span class = "nn" > sklearn.metrics< / span > < span class = "k" > import< / span > < span class = "n" > confusion_matrix< / span >
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< span class = "n" > pred< / span > < span class = "o" > =< / span > < span class = "n" > clr< / span > < span class = "o" > .< / span > < span class = "n" > predict< / span > < span class = "p" > (< / span > < span class = "n" > X_test< / span > < span class = "p" > )< / span >
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< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "n" > confusion_matrix< / span > < span class = "p" > (< / span > < span class = "n" > y_test< / span > < span class = "p" > ,< / span > < span class = "n" > pred< / span > < span class = "p" > ))< / span >
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< / pre > < / div >
< / div >
< p class = "sphx-glr-script-out" > Out:< / p >
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< div class = "sphx-glr-script-out highlight-none notranslate" > < div class = "highlight" > < pre > < span > < / span > [[14 0 0]
[ 0 9 4]
[ 0 1 10]]
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< / pre > < / div >
< / div >
< / div >
< div class = "section" id = "conversion-to-onnx-format" >
< h2 > < a class = "toc-backref" href = "#id2" > Conversion to ONNX format< / a > < a class = "headerlink" href = "#conversion-to-onnx-format" title = "Permalink to this headline" > ¶< / a > < / h2 >
< p > We use module
< a class = "reference external" href = "https://github.com/onnx/sklearn-onnx" > sklearn-onnx< / a >
to convert the model into ONNX format.< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > from< / span > < span class = "nn" > skl2onnx< / span > < span class = "k" > import< / span > < span class = "n" > convert_sklearn< / span >
< span class = "kn" > from< / span > < span class = "nn" > skl2onnx.common.data_types< / span > < span class = "k" > import< / span > < span class = "n" > FloatTensorType< / span >
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< span class = "n" > initial_type< / span > < span class = "o" > =< / span > < span class = "p" > [(< / span > < span class = "s1" > ' float_input' < / span > < span class = "p" > ,< / span > < span class = "n" > FloatTensorType< / span > < span class = "p" > ([< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span > < span class = "mi" > 4< / span > < span class = "p" > ]))]< / span >
< span class = "n" > onx< / span > < span class = "o" > =< / span > < span class = "n" > convert_sklearn< / span > < span class = "p" > (< / span > < span class = "n" > clr< / span > < span class = "p" > ,< / span > < span class = "n" > initial_types< / span > < span class = "o" > =< / span > < span class = "n" > initial_type< / span > < span class = "p" > )< / span >
< span class = "k" > with< / span > < span class = "nb" > open< / span > < span class = "p" > (< / span > < span class = "s2" > " logreg_iris.onnx" < / span > < span class = "p" > ,< / span > < span class = "s2" > " wb" < / span > < span class = "p" > )< / span > < span class = "k" > as< / span > < span class = "n" > f< / span > < span class = "p" > :< / span >
< span class = "n" > f< / span > < span class = "o" > .< / span > < span class = "n" > write< / span > < span class = "p" > (< / span > < span class = "n" > onx< / span > < span class = "o" > .< / span > < span class = "n" > SerializeToString< / span > < span class = "p" > ())< / span >
< / pre > < / div >
< / div >
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< p class = "sphx-glr-script-out" > Out:< / p >
< div class = "sphx-glr-script-out highlight-none notranslate" > < div class = "highlight" > < pre > < span > < / span > The maximum opset needed by this model is only 9.
The maximum opset needed by this model is only 1.
< / pre > < / div >
< / div >
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< p > We load the model with ONNX Runtime and look at
its input and output.< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > import< / span > < span class = "nn" > onnxruntime< / span > < span class = "k" > as< / span > < span class = "nn" > rt< / span >
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< span class = "n" > sess< / span > < span class = "o" > =< / span > < span class = "n" > rt< / span > < span class = "o" > .< / span > < span class = "n" > InferenceSession< / span > < span class = "p" > (< / span > < span class = "s2" > " logreg_iris.onnx" < / span > < span class = "p" > )< / span >
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< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " input name=' < / span > < span class = "si" > {}< / span > < span class = "s2" > ' and shape=< / span > < span class = "si" > {}< / span > < span class = "s2" > " < / span > < span class = "o" > .< / span > < span class = "n" > format< / span > < span class = "p" > (< / span > < span class = "n" > sess< / span > < span class = "o" > .< / span > < span class = "n" > get_inputs< / span > < span class = "p" > ()[< / span > < span class = "mi" > 0< / span > < span class = "p" > ]< / span > < span class = "o" > .< / span > < span class = "n" > name< / span > < span class = "p" > ,< / span > < span class = "n" > sess< / span > < span class = "o" > .< / span > < span class = "n" > get_inputs< / span > < span class = "p" > ()[< / span > < span class = "mi" > 0< / span > < span class = "p" > ]< / span > < span class = "o" > .< / span > < span class = "n" > shape< / span > < span class = "p" > ))< / span >
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " output name=' < / span > < span class = "si" > {}< / span > < span class = "s2" > ' and shape=< / span > < span class = "si" > {}< / span > < span class = "s2" > " < / span > < span class = "o" > .< / span > < span class = "n" > format< / span > < span class = "p" > (< / span > < span class = "n" > sess< / span > < span class = "o" > .< / span > < span class = "n" > get_outputs< / span > < span class = "p" > ()[< / span > < span class = "mi" > 0< / span > < span class = "p" > ]< / span > < span class = "o" > .< / span > < span class = "n" > name< / span > < span class = "p" > ,< / span > < span class = "n" > sess< / span > < span class = "o" > .< / span > < span class = "n" > get_outputs< / span > < span class = "p" > ()[< / span > < span class = "mi" > 0< / span > < span class = "p" > ]< / span > < span class = "o" > .< / span > < span class = "n" > shape< / span > < span class = "p" > ))< / span >
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< / pre > < / div >
< / div >
< p class = "sphx-glr-script-out" > Out:< / p >
< div class = "sphx-glr-script-out highlight-none notranslate" > < div class = "highlight" > < pre > < span > < / span > input name=' float_input' and shape=[1, 4]
output name=' output_label' and shape=[1]
< / pre > < / div >
< / div >
< p > We compute the predictions.< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > input_name< / span > < span class = "o" > =< / span > < span class = "n" > sess< / span > < span class = "o" > .< / span > < span class = "n" > get_inputs< / span > < span class = "p" > ()[< / span > < span class = "mi" > 0< / span > < span class = "p" > ]< / span > < span class = "o" > .< / span > < span class = "n" > name< / span >
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< span class = "n" > label_name< / span > < span class = "o" > =< / span > < span class = "n" > sess< / span > < span class = "o" > .< / span > < span class = "n" > get_outputs< / span > < span class = "p" > ()[< / span > < span class = "mi" > 0< / span > < span class = "p" > ]< / span > < span class = "o" > .< / span > < span class = "n" > name< / span >
< span class = "kn" > import< / span > < span class = "nn" > numpy< / span >
< span class = "n" > pred_onx< / span > < span class = "o" > =< / span > < span class = "n" > sess< / span > < span class = "o" > .< / span > < span class = "n" > run< / span > < span class = "p" > ([< / span > < span class = "n" > label_name< / span > < span class = "p" > ],< / span > < span class = "p" > {< / span > < span class = "n" > input_name< / span > < span class = "p" > :< / span > < span class = "n" > X_test< / span > < span class = "o" > .< / span > < span class = "n" > astype< / span > < span class = "p" > (< / span > < span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > float32< / span > < span class = "p" > )})[< / span > < span class = "mi" > 0< / span > < span class = "p" > ]< / span >
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< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "n" > confusion_matrix< / span > < span class = "p" > (< / span > < span class = "n" > pred< / span > < span class = "p" > ,< / span > < span class = "n" > pred_onx< / span > < span class = "p" > ))< / span >
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< / pre > < / div >
< / div >
< p class = "sphx-glr-script-out" > Out:< / p >
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< div class = "sphx-glr-script-out highlight-none notranslate" > < div class = "highlight" > < pre > < span > < / span > [[14 0 0]
[ 0 10 0]
[ 0 0 14]]
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< / pre > < / div >
< / div >
< p > The prediction are perfectly identical.< / p >
< / div >
< div class = "section" id = "probabilities" >
< h2 > < a class = "toc-backref" href = "#id3" > Probabilities< / a > < a class = "headerlink" href = "#probabilities" title = "Permalink to this headline" > ¶< / a > < / h2 >
< p > Probabilities are needed to compute other
relevant metrics such as the ROC Curve.
Let’
scikit-learn.< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > prob_sklearn< / span > < span class = "o" > =< / span > < span class = "n" > clr< / span > < span class = "o" > .< / span > < span class = "n" > predict_proba< / span > < span class = "p" > (< / span > < span class = "n" > X_test< / span > < span class = "p" > )< / span >
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "n" > prob_sklearn< / span > < span class = "p" > [:< / span > < span class = "mi" > 3< / span > < span class = "p" > ])< / span >
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< / pre > < / div >
< / div >
< p class = "sphx-glr-script-out" > Out:< / p >
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< div class = "sphx-glr-script-out highlight-none notranslate" > < div class = "highlight" > < pre > < span > < / span > [[0.02503056 0.43689584 0.53807361]
[0.00202039 0.19895737 0.79902224]
[0.01142149 0.64908707 0.33949145]]
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< / pre > < / div >
< / div >
< p > And then with ONNX Runtime.
The probabilies appear to be< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > prob_name< / span > < span class = "o" > =< / span > < span class = "n" > sess< / span > < span class = "o" > .< / span > < span class = "n" > get_outputs< / span > < span class = "p" > ()[< / span > < span class = "mi" > 1< / span > < span class = "p" > ]< / span > < span class = "o" > .< / span > < span class = "n" > name< / span >
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< span class = "n" > prob_rt< / span > < span class = "o" > =< / span > < span class = "n" > sess< / span > < span class = "o" > .< / span > < span class = "n" > run< / span > < span class = "p" > ([< / span > < span class = "n" > prob_name< / span > < span class = "p" > ],< / span > < span class = "p" > {< / span > < span class = "n" > input_name< / span > < span class = "p" > :< / span > < span class = "n" > X_test< / span > < span class = "o" > .< / span > < span class = "n" > astype< / span > < span class = "p" > (< / span > < span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > float32< / span > < span class = "p" > )})[< / span > < span class = "mi" > 0< / span > < span class = "p" > ]< / span >
< span class = "kn" > import< / span > < span class = "nn" > pprint< / span >
< span class = "n" > pprint< / span > < span class = "o" > .< / span > < span class = "n" > pprint< / span > < span class = "p" > (< / span > < span class = "n" > prob_rt< / span > < span class = "p" > [< / span > < span class = "mi" > 0< / span > < span class = "p" > :< / span > < span class = "mi" > 3< / span > < span class = "p" > ])< / span >
< / pre > < / div >
< / div >
< p class = "sphx-glr-script-out" > Out:< / p >
2019-08-02 01:12:59 +00:00
< div class = "sphx-glr-script-out highlight-none notranslate" > < div class = "highlight" > < pre > < span > < / span > [{0: 0.025030435994267464, 1: 0.4368962347507477, 2: 0.5380733609199524},
{0: 0.002020390471443534, 1: 0.1989573985338211, 2: 0.7990221977233887},
{0: 0.011421487666666508, 1: 0.6490871906280518, 2: 0.3394913375377655}]
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< / pre > < / div >
< / div >
< p > Let’ < / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > from< / span > < span class = "nn" > timeit< / span > < span class = "k" > import< / span > < span class = "n" > Timer< / span >
2019-01-11 19:08:33 +00:00
< span class = "k" > def< / span > < span class = "nf" > speed< / span > < span class = "p" > (< / span > < span class = "n" > inst< / span > < span class = "p" > ,< / span > < span class = "n" > number< / span > < span class = "o" > =< / span > < span class = "mi" > 10< / span > < span class = "p" > ,< / span > < span class = "n" > repeat< / span > < span class = "o" > =< / span > < span class = "mi" > 20< / span > < span class = "p" > ):< / span >
< span class = "n" > timer< / span > < span class = "o" > =< / span > < span class = "n" > Timer< / span > < span class = "p" > (< / span > < span class = "n" > inst< / span > < span class = "p" > ,< / span > < span class = "nb" > globals< / span > < span class = "o" > =< / span > < span class = "nb" > globals< / span > < span class = "p" > ())< / span >
< span class = "n" > raw< / span > < span class = "o" > =< / span > < span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > array< / span > < span class = "p" > (< / span > < span class = "n" > timer< / span > < span class = "o" > .< / span > < span class = "n" > repeat< / span > < span class = "p" > (< / span > < span class = "n" > repeat< / span > < span class = "p" > ,< / span > < span class = "n" > number< / span > < span class = "o" > =< / span > < span class = "n" > number< / span > < span class = "p" > ))< / span >
< span class = "n" > ave< / span > < span class = "o" > =< / span > < span class = "n" > raw< / span > < span class = "o" > .< / span > < span class = "n" > sum< / span > < span class = "p" > ()< / span > < span class = "o" > /< / span > < span class = "nb" > len< / span > < span class = "p" > (< / span > < span class = "n" > raw< / span > < span class = "p" > )< / span > < span class = "o" > /< / span > < span class = "n" > number< / span >
< span class = "n" > mi< / span > < span class = "p" > ,< / span > < span class = "n" > ma< / span > < span class = "o" > =< / span > < span class = "n" > raw< / span > < span class = "o" > .< / span > < span class = "n" > min< / span > < span class = "p" > ()< / span > < span class = "o" > /< / span > < span class = "n" > number< / span > < span class = "p" > ,< / span > < span class = "n" > raw< / span > < span class = "o" > .< / span > < span class = "n" > max< / span > < span class = "p" > ()< / span > < span class = "o" > /< / span > < span class = "n" > number< / span >
2019-08-02 01:12:59 +00:00
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " Average < / span > < span class = "si" > %1.3g< / span > < span class = "s2" > min=< / span > < span class = "si" > %1.3g< / span > < span class = "s2" > max=< / span > < span class = "si" > %1.3g< / span > < span class = "s2" > " < / span > < span class = "o" > %< / span > < span class = "p" > (< / span > < span class = "n" > ave< / span > < span class = "p" > ,< / span > < span class = "n" > mi< / span > < span class = "p" > ,< / span > < span class = "n" > ma< / span > < span class = "p" > ))< / span >
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< span class = "k" > return< / span > < span class = "n" > ave< / span >
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< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " Execution time for clr.predict" < / span > < span class = "p" > )< / span >
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< span class = "n" > speed< / span > < span class = "p" > (< / span > < span class = "s2" > " clr.predict(X_test)" < / span > < span class = "p" > )< / span >
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< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " Execution time for ONNX Runtime" < / span > < span class = "p" > )< / span >
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< span class = "n" > speed< / span > < span class = "p" > (< / span > < span class = "s2" > " sess.run([label_name], {input_name: X_test.astype(numpy.float32)})[0]" < / span > < span class = "p" > )< / span >
< / pre > < / div >
< / div >
< p class = "sphx-glr-script-out" > Out:< / p >
< div class = "sphx-glr-script-out highlight-none notranslate" > < div class = "highlight" > < pre > < span > < / span > Execution time for clr.predict
2019-08-02 01:12:59 +00:00
Average 4.86e-05 min=4.49e-05 max=8.59e-05
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Execution time for ONNX Runtime
2019-08-02 01:12:59 +00:00
Average 0.00163 min=0.00118 max=0.0024
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< / pre > < / div >
< / div >
< p > Let’
experiences: the model has to do one prediction at a time
as opposed to a batch of prediction.< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "k" > def< / span > < span class = "nf" > loop< / span > < span class = "p" > (< / span > < span class = "n" > X_test< / span > < span class = "p" > ,< / span > < span class = "n" > fct< / span > < span class = "p" > ,< / span > < span class = "n" > n< / span > < span class = "o" > =< / span > < span class = "kc" > None< / span > < span class = "p" > ):< / span >
2019-01-11 19:08:33 +00:00
< span class = "n" > nrow< / span > < span class = "o" > =< / span > < span class = "n" > X_test< / span > < span class = "o" > .< / span > < span class = "n" > shape< / span > < span class = "p" > [< / span > < span class = "mi" > 0< / span > < span class = "p" > ]< / span >
2019-08-02 01:12:59 +00:00
< span class = "k" > if< / span > < span class = "n" > n< / span > < span class = "ow" > is< / span > < span class = "kc" > None< / span > < span class = "p" > :< / span >
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< span class = "n" > n< / span > < span class = "o" > =< / span > < span class = "n" > nrow< / span >
< span class = "k" > for< / span > < span class = "n" > i< / span > < span class = "ow" > in< / span > < span class = "nb" > range< / span > < span class = "p" > (< / span > < span class = "mi" > 0< / span > < span class = "p" > ,< / span > < span class = "n" > n< / span > < span class = "p" > ):< / span >
< span class = "n" > im< / span > < span class = "o" > =< / span > < span class = "n" > i< / span > < span class = "o" > %< / span > < span class = "n" > nrow< / span >
< span class = "n" > fct< / span > < span class = "p" > (< / span > < span class = "n" > X_test< / span > < span class = "p" > [< / span > < span class = "n" > im< / span > < span class = "p" > :< / span > < span class = "n" > im< / span > < span class = "o" > +< / span > < span class = "mi" > 1< / span > < span class = "p" > ])< / span >
2019-08-02 01:12:59 +00:00
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " Execution time for clr.predict" < / span > < span class = "p" > )< / span >
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< span class = "n" > speed< / span > < span class = "p" > (< / span > < span class = "s2" > " loop(X_test, clr.predict, 100)" < / span > < span class = "p" > )< / span >
< span class = "k" > def< / span > < span class = "nf" > sess_predict< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "p" > ):< / span >
< span class = "k" > return< / span > < span class = "n" > sess< / span > < span class = "o" > .< / span > < span class = "n" > run< / span > < span class = "p" > ([< / span > < span class = "n" > label_name< / span > < span class = "p" > ],< / span > < span class = "p" > {< / span > < span class = "n" > input_name< / span > < span class = "p" > :< / span > < span class = "n" > x< / span > < span class = "o" > .< / span > < span class = "n" > astype< / span > < span class = "p" > (< / span > < span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > float32< / span > < span class = "p" > )})[< / span > < span class = "mi" > 0< / span > < span class = "p" > ]< / span >
2019-08-02 01:12:59 +00:00
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " Execution time for sess_predict" < / span > < span class = "p" > )< / span >
2019-01-11 19:08:33 +00:00
< span class = "n" > speed< / span > < span class = "p" > (< / span > < span class = "s2" > " loop(X_test, sess_predict, 100)" < / span > < span class = "p" > )< / span >
< / pre > < / div >
< / div >
< p class = "sphx-glr-script-out" > Out:< / p >
< div class = "sphx-glr-script-out highlight-none notranslate" > < div class = "highlight" > < pre > < span > < / span > Execution time for clr.predict
2019-08-02 01:12:59 +00:00
Average 0.00537 min=0.00411 max=0.017
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Execution time for sess_predict
2019-08-02 01:12:59 +00:00
Average 0.00241 min=0.0015 max=0.00453
2019-01-11 19:08:33 +00:00
< / pre > < / div >
< / div >
< p > Let’ < / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " Execution time for predict_proba" < / span > < span class = "p" > )< / span >
2019-01-11 19:08:33 +00:00
< span class = "n" > speed< / span > < span class = "p" > (< / span > < span class = "s2" > " loop(X_test, clr.predict_proba, 100)" < / span > < span class = "p" > )< / span >
< span class = "k" > def< / span > < span class = "nf" > sess_predict_proba< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "p" > ):< / span >
< span class = "k" > return< / span > < span class = "n" > sess< / span > < span class = "o" > .< / span > < span class = "n" > run< / span > < span class = "p" > ([< / span > < span class = "n" > prob_name< / span > < span class = "p" > ],< / span > < span class = "p" > {< / span > < span class = "n" > input_name< / span > < span class = "p" > :< / span > < span class = "n" > x< / span > < span class = "o" > .< / span > < span class = "n" > astype< / span > < span class = "p" > (< / span > < span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > float32< / span > < span class = "p" > )})[< / span > < span class = "mi" > 0< / span > < span class = "p" > ]< / span >
2019-08-02 01:12:59 +00:00
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " Execution time for sess_predict_proba" < / span > < span class = "p" > )< / span >
2019-01-11 19:08:33 +00:00
< span class = "n" > speed< / span > < span class = "p" > (< / span > < span class = "s2" > " loop(X_test, sess_predict_proba, 100)" < / span > < span class = "p" > )< / span >
< / pre > < / div >
< / div >
< p class = "sphx-glr-script-out" > Out:< / p >
< div class = "sphx-glr-script-out highlight-none notranslate" > < div class = "highlight" > < pre > < span > < / span > Execution time for predict_proba
2019-08-02 01:12:59 +00:00
Average 0.00673 min=0.00536 max=0.0101
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Execution time for sess_predict_proba
2019-08-02 01:12:59 +00:00
Average 0.00159 min=0.00148 max=0.00184
2019-01-11 19:08:33 +00:00
< / pre > < / div >
< / div >
< p > This second comparison is better as
ONNX Runtime, in this experience,
computes the label and the probabilities
in every case.< / p >
< / div >
< div class = "section" id = "benchmark-with-randomforest" >
< h2 > < a class = "toc-backref" href = "#id4" > Benchmark with RandomForest< / a > < a class = "headerlink" href = "#benchmark-with-randomforest" title = "Permalink to this headline" > ¶< / a > < / h2 >
< p > We first train and save a model in ONNX format.< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > from< / span > < span class = "nn" > sklearn.ensemble< / span > < span class = "k" > import< / span > < span class = "n" > RandomForestClassifier< / span >
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< span class = "n" > rf< / span > < span class = "o" > =< / span > < span class = "n" > RandomForestClassifier< / span > < span class = "p" > ()< / span >
< span class = "n" > rf< / span > < span class = "o" > .< / span > < span class = "n" > fit< / span > < span class = "p" > (< / span > < span class = "n" > X_train< / span > < span class = "p" > ,< / span > < span class = "n" > y_train< / span > < span class = "p" > )< / span >
< span class = "n" > initial_type< / span > < span class = "o" > =< / span > < span class = "p" > [(< / span > < span class = "s1" > ' float_input' < / span > < span class = "p" > ,< / span > < span class = "n" > FloatTensorType< / span > < span class = "p" > ([< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span > < span class = "mi" > 4< / span > < span class = "p" > ]))]< / span >
< span class = "n" > onx< / span > < span class = "o" > =< / span > < span class = "n" > convert_sklearn< / span > < span class = "p" > (< / span > < span class = "n" > rf< / span > < span class = "p" > ,< / span > < span class = "n" > initial_types< / span > < span class = "o" > =< / span > < span class = "n" > initial_type< / span > < span class = "p" > )< / span >
< span class = "k" > with< / span > < span class = "nb" > open< / span > < span class = "p" > (< / span > < span class = "s2" > " rf_iris.onnx" < / span > < span class = "p" > ,< / span > < span class = "s2" > " wb" < / span > < span class = "p" > )< / span > < span class = "k" > as< / span > < span class = "n" > f< / span > < span class = "p" > :< / span >
< span class = "n" > f< / span > < span class = "o" > .< / span > < span class = "n" > write< / span > < span class = "p" > (< / span > < span class = "n" > onx< / span > < span class = "o" > .< / span > < span class = "n" > SerializeToString< / span > < span class = "p" > ())< / span >
< / pre > < / div >
< / div >
2019-08-02 01:12:59 +00:00
< p class = "sphx-glr-script-out" > Out:< / p >
< div class = "sphx-glr-script-out highlight-none notranslate" > < div class = "highlight" > < pre > < span > < / span > c:\users\hasesh\appdata\local\programs\python\python36\lib\site-packages\sklearn\ensemble\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.
" 10 in version 0.20 to 100 in 0.22." , FutureWarning)
The maximum opset needed by this model is only 9.
The maximum opset needed by this model is only 1.
< / pre > < / div >
< / div >
2019-01-11 19:08:33 +00:00
< p > We compare.< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > sess< / span > < span class = "o" > =< / span > < span class = "n" > rt< / span > < span class = "o" > .< / span > < span class = "n" > InferenceSession< / span > < span class = "p" > (< / span > < span class = "s2" > " rf_iris.onnx" < / span > < span class = "p" > )< / span >
2019-01-11 19:08:33 +00:00
< span class = "k" > def< / span > < span class = "nf" > sess_predict_proba_rf< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "p" > ):< / span >
< span class = "k" > return< / span > < span class = "n" > sess< / span > < span class = "o" > .< / span > < span class = "n" > run< / span > < span class = "p" > ([< / span > < span class = "n" > prob_name< / span > < span class = "p" > ],< / span > < span class = "p" > {< / span > < span class = "n" > input_name< / span > < span class = "p" > :< / span > < span class = "n" > x< / span > < span class = "o" > .< / span > < span class = "n" > astype< / span > < span class = "p" > (< / span > < span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > float32< / span > < span class = "p" > )})[< / span > < span class = "mi" > 0< / span > < span class = "p" > ]< / span >
2019-08-02 01:12:59 +00:00
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " Execution time for predict_proba" < / span > < span class = "p" > )< / span >
2019-01-11 19:08:33 +00:00
< span class = "n" > speed< / span > < span class = "p" > (< / span > < span class = "s2" > " loop(X_test, rf.predict_proba, 100)" < / span > < span class = "p" > )< / span >
2019-08-02 01:12:59 +00:00
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " Execution time for sess_predict_proba" < / span > < span class = "p" > )< / span >
2019-01-11 19:08:33 +00:00
< span class = "n" > speed< / span > < span class = "p" > (< / span > < span class = "s2" > " loop(X_test, sess_predict_proba_rf, 100)" < / span > < span class = "p" > )< / span >
< / pre > < / div >
< / div >
< p class = "sphx-glr-script-out" > Out:< / p >
< div class = "sphx-glr-script-out highlight-none notranslate" > < div class = "highlight" > < pre > < span > < / span > Execution time for predict_proba
2019-08-02 01:12:59 +00:00
Average 0.0881 min=0.0836 max=0.111
2019-01-11 19:08:33 +00:00
Execution time for sess_predict_proba
2019-08-02 01:12:59 +00:00
Average 0.00222 min=0.0016 max=0.00379
2019-01-11 19:08:33 +00:00
< / pre > < / div >
< / div >
< p > Let’ < / p >
2019-08-02 01:12:59 +00:00
< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > measures< / span > < span class = "o" > =< / span > < span class = "p" > []< / span >
2019-01-11 19:08:33 +00:00
< span class = "k" > for< / span > < span class = "n" > n_trees< / span > < span class = "ow" > in< / span > < span class = "nb" > range< / span > < span class = "p" > (< / span > < span class = "mi" > 5< / span > < span class = "p" > ,< / span > < span class = "mi" > 51< / span > < span class = "p" > ,< / span > < span class = "mi" > 5< / span > < span class = "p" > ):< / span >
2019-08-02 01:12:59 +00:00
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "n" > n_trees< / span > < span class = "p" > )< / span >
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< span class = "n" > rf< / span > < span class = "o" > =< / span > < span class = "n" > RandomForestClassifier< / span > < span class = "p" > (< / span > < span class = "n" > n_estimators< / span > < span class = "o" > =< / span > < span class = "n" > n_trees< / span > < span class = "p" > )< / span >
< span class = "n" > rf< / span > < span class = "o" > .< / span > < span class = "n" > fit< / span > < span class = "p" > (< / span > < span class = "n" > X_train< / span > < span class = "p" > ,< / span > < span class = "n" > y_train< / span > < span class = "p" > )< / span >
< span class = "n" > initial_type< / span > < span class = "o" > =< / span > < span class = "p" > [(< / span > < span class = "s1" > ' float_input' < / span > < span class = "p" > ,< / span > < span class = "n" > FloatTensorType< / span > < span class = "p" > ([< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span > < span class = "mi" > 4< / span > < span class = "p" > ]))]< / span >
< span class = "n" > onx< / span > < span class = "o" > =< / span > < span class = "n" > convert_sklearn< / span > < span class = "p" > (< / span > < span class = "n" > rf< / span > < span class = "p" > ,< / span > < span class = "n" > initial_types< / span > < span class = "o" > =< / span > < span class = "n" > initial_type< / span > < span class = "p" > )< / span >
< span class = "k" > with< / span > < span class = "nb" > open< / span > < span class = "p" > (< / span > < span class = "s2" > " rf_iris_< / span > < span class = "si" > %d< / span > < span class = "s2" > .onnx" < / span > < span class = "o" > %< / span > < span class = "n" > n_trees< / span > < span class = "p" > ,< / span > < span class = "s2" > " wb" < / span > < span class = "p" > )< / span > < span class = "k" > as< / span > < span class = "n" > f< / span > < span class = "p" > :< / span >
< span class = "n" > f< / span > < span class = "o" > .< / span > < span class = "n" > write< / span > < span class = "p" > (< / span > < span class = "n" > onx< / span > < span class = "o" > .< / span > < span class = "n" > SerializeToString< / span > < span class = "p" > ())< / span >
< span class = "n" > sess< / span > < span class = "o" > =< / span > < span class = "n" > rt< / span > < span class = "o" > .< / span > < span class = "n" > InferenceSession< / span > < span class = "p" > (< / span > < span class = "s2" > " rf_iris_< / span > < span class = "si" > %d< / span > < span class = "s2" > .onnx" < / span > < span class = "o" > %< / span > < span class = "n" > n_trees< / span > < span class = "p" > )< / span >
< span class = "k" > def< / span > < span class = "nf" > sess_predict_proba_loop< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "p" > ):< / span >
< span class = "k" > return< / span > < span class = "n" > sess< / span > < span class = "o" > .< / span > < span class = "n" > run< / span > < span class = "p" > ([< / span > < span class = "n" > prob_name< / span > < span class = "p" > ],< / span > < span class = "p" > {< / span > < span class = "n" > input_name< / span > < span class = "p" > :< / span > < span class = "n" > x< / span > < span class = "o" > .< / span > < span class = "n" > astype< / span > < span class = "p" > (< / span > < span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > float32< / span > < span class = "p" > )})[< / span > < span class = "mi" > 0< / span > < span class = "p" > ]< / span >
< span class = "n" > tsk< / span > < span class = "o" > =< / span > < span class = "n" > speed< / span > < span class = "p" > (< / span > < span class = "s2" > " loop(X_test, rf.predict_proba, 100)" < / span > < span class = "p" > ,< / span > < span class = "n" > number< / span > < span class = "o" > =< / span > < span class = "mi" > 5< / span > < span class = "p" > ,< / span > < span class = "n" > repeat< / span > < span class = "o" > =< / span > < span class = "mi" > 5< / span > < span class = "p" > )< / span >
< span class = "n" > trt< / span > < span class = "o" > =< / span > < span class = "n" > speed< / span > < span class = "p" > (< / span > < span class = "s2" > " loop(X_test, sess_predict_proba_loop, 100)" < / span > < span class = "p" > ,< / span > < span class = "n" > number< / span > < span class = "o" > =< / span > < span class = "mi" > 5< / span > < span class = "p" > ,< / span > < span class = "n" > repeat< / span > < span class = "o" > =< / span > < span class = "mi" > 5< / span > < span class = "p" > )< / span >
< span class = "n" > measures< / span > < span class = "o" > .< / span > < span class = "n" > append< / span > < span class = "p" > ({< / span > < span class = "s1" > ' n_trees' < / span > < span class = "p" > :< / span > < span class = "n" > n_trees< / span > < span class = "p" > ,< / span > < span class = "s1" > ' sklearn' < / span > < span class = "p" > :< / span > < span class = "n" > tsk< / span > < span class = "p" > ,< / span > < span class = "s1" > ' rt' < / span > < span class = "p" > :< / span > < span class = "n" > trt< / span > < span class = "p" > })< / span >
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< span class = "kn" > from< / span > < span class = "nn" > pandas< / span > < span class = "k" > import< / span > < span class = "n" > DataFrame< / span >
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< span class = "n" > df< / span > < span class = "o" > =< / span > < span class = "n" > DataFrame< / span > < span class = "p" > (< / span > < span class = "n" > measures< / span > < span class = "p" > )< / span >
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< span class = "n" > ax< / span > < span class = "o" > =< / span > < span class = "n" > df< / span > < span class = "o" > .< / span > < span class = "n" > plot< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "o" > =< / span > < span class = "s2" > " n_trees" < / span > < span class = "p" > ,< / span > < span class = "n" > y< / span > < span class = "o" > =< / span > < span class = "s2" > " sklearn" < / span > < span class = "p" > ,< / span > < span class = "n" > label< / span > < span class = "o" > =< / span > < span class = "s2" > " scikit-learn" < / span > < span class = "p" > ,< / span > < span class = "n" > c< / span > < span class = "o" > =< / span > < span class = "s2" > " blue" < / span > < span class = "p" > ,< / span > < span class = "n" > logy< / span > < span class = "o" > =< / span > < span class = "kc" > True< / span > < span class = "p" > )< / span >
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< span class = "n" > df< / span > < span class = "o" > .< / span > < span class = "n" > plot< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "o" > =< / span > < span class = "s2" > " n_trees" < / span > < span class = "p" > ,< / span > < span class = "n" > y< / span > < span class = "o" > =< / span > < span class = "s2" > " rt" < / span > < span class = "p" > ,< / span > < span class = "n" > label< / span > < span class = "o" > =< / span > < span class = "s2" > " onnxruntime" < / span > < span class = "p" > ,< / span >
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< span class = "n" > ax< / span > < span class = "o" > =< / span > < span class = "n" > ax< / span > < span class = "p" > ,< / span > < span class = "n" > c< / span > < span class = "o" > =< / span > < span class = "s2" > " green" < / span > < span class = "p" > ,< / span > < span class = "n" > logy< / span > < span class = "o" > =< / span > < span class = "kc" > True< / span > < span class = "p" > )< / span >
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< span class = "n" > ax< / span > < span class = "o" > .< / span > < span class = "n" > set_xlabel< / span > < span class = "p" > (< / span > < span class = "s2" > " Number of trees" < / span > < span class = "p" > )< / span >
< span class = "n" > ax< / span > < span class = "o" > .< / span > < span class = "n" > set_ylabel< / span > < span class = "p" > (< / span > < span class = "s2" > " Prediction time (s)" < / span > < span class = "p" > )< / span >
< span class = "n" > ax< / span > < span class = "o" > .< / span > < span class = "n" > set_title< / span > < span class = "p" > (< / span > < span class = "s2" > " Speed comparison between scikit-learn and ONNX Runtime< / span > < span class = "se" > \n< / span > < span class = "s2" > For a random forest on Iris dataset" < / span > < span class = "p" > )< / span >
< span class = "n" > ax< / span > < span class = "o" > .< / span > < span class = "n" > legend< / span > < span class = "p" > ()< / span >
< / pre > < / div >
< / div >
< img alt = "../_images/sphx_glr_plot_train_convert_predict_001.png" class = "sphx-glr-single-img" src = "../_images/sphx_glr_plot_train_convert_predict_001.png" / >
< p class = "sphx-glr-script-out" > Out:< / p >
< div class = "sphx-glr-script-out highlight-none notranslate" > < div class = "highlight" > < pre > < span > < / span > 5
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The maximum opset needed by this model is only 9.
The maximum opset needed by this model is only 1.
Average 0.0557 min=0.054 max=0.06
Average 0.00168 min=0.00152 max=0.00199
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10
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The maximum opset needed by this model is only 9.
The maximum opset needed by this model is only 1.
Average 0.0966 min=0.0881 max=0.106
Average 0.00167 min=0.00158 max=0.00185
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15
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The maximum opset needed by this model is only 9.
The maximum opset needed by this model is only 1.
Average 0.121 min=0.119 max=0.126
Average 0.00199 min=0.00172 max=0.00227
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20
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The maximum opset needed by this model is only 9.
The maximum opset needed by this model is only 1.
Average 0.156 min=0.15 max=0.164
Average 0.00227 min=0.00172 max=0.00346
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25
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The maximum opset needed by this model is only 9.
The maximum opset needed by this model is only 1.
Average 0.189 min=0.184 max=0.192
Average 0.00191 min=0.0018 max=0.00217
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30
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The maximum opset needed by this model is only 9.
The maximum opset needed by this model is only 1.
Average 0.212 min=0.202 max=0.238
Average 0.00191 min=0.00181 max=0.00196
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35
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The maximum opset needed by this model is only 9.
The maximum opset needed by this model is only 1.
Average 0.245 min=0.232 max=0.26
Average 0.002 min=0.00194 max=0.00212
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40
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The maximum opset needed by this model is only 9.
The maximum opset needed by this model is only 1.
Average 0.318 min=0.28 max=0.427
Average 0.00287 min=0.00252 max=0.00322
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45
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The maximum opset needed by this model is only 9.
The maximum opset needed by this model is only 1.
Average 0.36 min=0.308 max=0.484
Average 0.00481 min=0.00458 max=0.00519
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50
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The maximum opset needed by this model is only 9.
The maximum opset needed by this model is only 1.
Average 0.491 min=0.428 max=0.539
Average 0.00233 min=0.00206 max=0.00259
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< / pre > < / div >
< / div >
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< p class = "sphx-glr-timing" > < strong > Total running time of the script:< / strong > ( 1 minutes 20.485 seconds)< / p >
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< div class = "sphx-glr-footer class sphx-glr-footer-example docutils container" id = "sphx-glr-download-auto-examples-plot-train-convert-predict-py" >
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< p > < a class = "reference download internal" download = "" href = "../_downloads/3e3e28694833fba4efa1a2b909ae73b8/plot_train_convert_predict.py" > < code class = "xref download docutils literal notranslate" > < span class = "pre" > Download< / span > < span class = "pre" > Python< / span > < span class = "pre" > source< / span > < span class = "pre" > code:< / span > < span class = "pre" > plot_train_convert_predict.py< / span > < / code > < / a > < / p >
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< p > < a class = "reference download internal" download = "" href = "../_downloads/c3f83d30b2e208d485300a7cc4e93419/plot_train_convert_predict.ipynb" > < code class = "xref download docutils literal notranslate" > < span class = "pre" > Download< / span > < span class = "pre" > Jupyter< / span > < span class = "pre" > notebook:< / span > < span class = "pre" > plot_train_convert_predict.ipynb< / span > < / code > < / a > < / p >
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< p class = "sphx-glr-signature" > < a class = "reference external" href = "https://sphinx-gallery.github.io" > Gallery generated by Sphinx-Gallery< / a > < / p >
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