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< 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-l2" > < a class = "reference internal" href = "../api_summary.html#load-and-run-a-model" > Load and run a model< / a > < / li >
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< li class = "toctree-l2 current" > < a class = "current reference internal" href = "#" > 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 >
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< 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" > < a class = "reference internal" href = "plot_train_convert_predict.html" > 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-load-and-predict-py" > < span class = "std std-ref" > here< / span > < / a > to download the full example code< / p >
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< div class = "sphx-glr-example-title section" id = "load-and-predict-with-onnx-runtime-and-a-very-simple-model" >
< span id = "l-example-simple-usage" > < / span > < span id = "sphx-glr-auto-examples-plot-load-and-predict-py" > < / span > < h1 > Load and predict with ONNX Runtime and a very simple model< a class = "headerlink" href = "#load-and-predict-with-onnx-runtime-and-a-very-simple-model" title = "Permalink to this headline" > ¶< / a > < / h1 >
< p > This example demonstrates how to load a model and compute
the output for an input vector. It also shows how to
retrieve the definition of its inputs and outputs.< / 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 = "kn" > import< / span > < span class = "nn" > numpy< / span >
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< span class = "kn" > from< / span > < span class = "nn" > onnxruntime.datasets< / span > < span class = "k" > import< / span > < span class = "n" > get_example< / span >
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< / pre > < / div >
< / div >
< p > Let’
The model is available on github < a class = "reference external" href = "https://github.com/onnx/onnx/tree/master/onnx/backend/test/data/node/test_sigmoid" > onnx…test_sigmoid< / a > .< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > example1< / span > < span class = "o" > =< / span > < span class = "n" > get_example< / span > < span class = "p" > (< / span > < span class = "s2" > " sigmoid.onnx" < / span > < span class = "p" > )< / 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 = "n" > example1< / span > < span class = "p" > )< / span >
< / pre > < / div >
< / div >
< p > Let’ < / 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 >
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " input name" < / span > < span class = "p" > ,< / span > < span class = "n" > input_name< / span > < span class = "p" > )< / span >
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< span class = "n" > input_shape< / 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" > shape< / span >
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< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " input shape" < / span > < span class = "p" > ,< / span > < span class = "n" > input_shape< / span > < span class = "p" > )< / span >
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< span class = "n" > input_type< / 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" > type< / span >
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< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " input type" < / span > < span class = "p" > ,< / span > < span class = "n" > input_type< / 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 x
input shape [3, 4, 5]
input type tensor(float)
< / pre > < / div >
< / div >
< p > Let’ < / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > output_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 = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " output name" < / span > < span class = "p" > ,< / span > < span class = "n" > output_name< / span > < span class = "p" > )< / span >
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< span class = "n" > output_shape< / 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" > shape< / span >
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< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " output shape" < / span > < span class = "p" > ,< / span > < span class = "n" > output_shape< / span > < span class = "p" > )< / span >
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< span class = "n" > output_type< / 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" > type< / span >
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< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " output type" < / span > < span class = "p" > ,< / span > < span class = "n" > output_type< / 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 > output name y
output shape [3, 4, 5]
output type tensor(float)
< / pre > < / div >
< / div >
< p > Let’ < / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > import< / span > < span class = "nn" > numpy.random< / span >
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< span class = "n" > x< / span > < span class = "o" > =< / span > < span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > random< / span > < span class = "o" > .< / span > < span class = "n" > random< / span > < span class = "p" > ((< / span > < span class = "mi" > 3< / span > < span class = "p" > ,< / span > < span class = "mi" > 4< / span > < span class = "p" > ,< / span > < span class = "mi" > 5< / span > < span class = "p" > ))< / span >
< span class = "n" > x< / span > < span class = "o" > =< / 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 = "n" > res< / 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" > output_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 = "p" > })< / span >
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< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "n" > res< / 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 > [array([[[0.5360762 , 0.666669 , 0.6136168 , 0.6098715 , 0.6330898 ],
[0.70909226, 0.67855203, 0.5512253 , 0.6054007 , 0.5453689 ],
[0.67941666, 0.60376704, 0.59458643, 0.56611687, 0.567247 ],
[0.6243701 , 0.6456822 , 0.5711165 , 0.5364119 , 0.50267375]],
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[[0.72189057, 0.51031893, 0.508917 , 0.724934 , 0.6013869 ],
[0.5751356 , 0.63135314, 0.70504206, 0.66305155, 0.53833747],
[0.64060616, 0.5622595 , 0.6350931 , 0.64188236, 0.5740597 ],
[0.56608844, 0.65403676, 0.5818875 , 0.5041134 , 0.7170976 ]],
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[[0.61101925, 0.51149344, 0.7126139 , 0.64984477, 0.5554885 ],
[0.6957284 , 0.6517055 , 0.726743 , 0.66872954, 0.58586377],
[0.57124126, 0.505744 , 0.5355146 , 0.62422305, 0.72279936],
[0.6734552 , 0.70127356, 0.5607188 , 0.5609607 , 0.53647304]]],
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dtype=float32)]
< / pre > < / div >
< / div >
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< p class = "sphx-glr-timing" > < strong > Total running time of the script:< / strong > ( 0 minutes 0.059 seconds)< / p >
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< p > < a class = "reference download internal" download = "" href = "../_downloads/0ff83a030e1136eccaf6469eb8ef667f/plot_load_and_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_load_and_predict.py< / span > < / code > < / a > < / p >
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< p > < a class = "reference download internal" download = "" href = "../_downloads/8666f7a401ae67f53189bd407617ab0b/plot_load_and_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_load_and_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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