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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 = "plot_load_and_predict.html" > Load and predict with ONNX Runtime and a very 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 >
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< 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-common-errors-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 = "common-errors-with-onnxruntime" >
< span id = "l-example-common-error" > < / span > < span id = "sphx-glr-auto-examples-plot-common-errors-py" > < / span > < h1 > Common errors with onnxruntime< a class = "headerlink" href = "#common-errors-with-onnxruntime" title = "Permalink to this headline" > ¶< / a > < / h1 >
< p > This example looks into several common situations
in which < em > onnxruntime< / em > does not return the model
prediction but raises an exception instead.
It starts by loading the model trained in example
< a class = "reference internal" href = "plot_train_convert_predict.html#l-logreg-example" > < span class = "std std-ref" > Train, convert and predict with ONNX Runtime< / span > < / a > which produced a logistic regression
trained on < em > Iris< / em > datasets. The model takes
a vector of dimension 2 and returns a class among three.< / 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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< span class = "n" > example2< / span > < span class = "o" > =< / span > < span class = "n" > get_example< / span > < span class = "p" > (< / span > < span class = "s2" > " logreg_iris.onnx" < / 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 = "n" > example2< / span > < span class = "p" > )< / 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 = "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 >
< / pre > < / div >
< / div >
< p > The first example fails due to < em > bad types< / em > .
< em > onnxruntime< / em > only expects single floats (4 bytes)
and cannot handle any other kind of floats.< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "k" > try< / span > < span class = "p" > :< / 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" > array< / span > < span class = "p" > ([[< / span > < span class = "mf" > 1.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 2.0< / span > < span class = "p" > ],< / span > < span class = "p" > [< / span > < span class = "mf" > 3.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 4.0< / span > < span class = "p" > ],< / span > < span class = "p" > [< / span > < span class = "mf" > 5.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 6.0< / span > < span class = "p" > ]],< / span > < span class = "n" > dtype< / span > < span class = "o" > =< / span > < span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > float64< / span > < span class = "p" > )< / 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 >
< span class = "k" > except< / span > < span class = "ne" > Exception< / span > < span class = "k" > as< / span > < span class = "n" > e< / span > < span class = "p" > :< / span >
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< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " Unexpected type" < / span > < span class = "p" > )< / span >
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " < / span > < span class = "si" > {0}< / span > < span class = "s2" > : < / span > < span class = "si" > {1}< / span > < span class = "s2" > " < / span > < span class = "o" > .< / span > < span class = "n" > format< / span > < span class = "p" > (< / span > < span class = "nb" > type< / span > < span class = "p" > (< / span > < span class = "n" > e< / span > < span class = "p" > ),< / span > < span class = "n" > e< / 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 > Unexpected type
< class ' RuntimeError' > : Method run failed due to: [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Unexpected input data type. Actual: (class onnxruntime::NonOnnxType< double> ) , expected: (class onnxruntime::NonOnnxType< float> )
< / pre > < / div >
< / div >
< p > The model fails to return an output if the name
is misspelled.< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "k" > try< / span > < span class = "p" > :< / 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" > array< / span > < span class = "p" > ([[< / span > < span class = "mf" > 1.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 2.0< / span > < span class = "p" > ],< / span > < span class = "p" > [< / span > < span class = "mf" > 3.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 4.0< / span > < span class = "p" > ],< / span > < span class = "p" > [< / span > < span class = "mf" > 5.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 6.0< / span > < span class = "p" > ]],< / span > < span class = "n" > dtype< / span > < span class = "o" > =< / span > < span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > float32< / span > < span class = "p" > )< / span >
< span class = "n" > sess< / span > < span class = "o" > .< / span > < span class = "n" > run< / span > < span class = "p" > ([< / span > < span class = "s2" > " misspelled" < / 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 >
< span class = "k" > except< / span > < span class = "ne" > Exception< / span > < span class = "k" > as< / span > < span class = "n" > e< / span > < span class = "p" > :< / span >
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< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " Misspelled output name" < / span > < span class = "p" > )< / span >
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " < / span > < span class = "si" > {0}< / span > < span class = "s2" > : < / span > < span class = "si" > {1}< / span > < span class = "s2" > " < / span > < span class = "o" > .< / span > < span class = "n" > format< / span > < span class = "p" > (< / span > < span class = "nb" > type< / span > < span class = "p" > (< / span > < span class = "n" > e< / span > < span class = "p" > ),< / span > < span class = "n" > e< / 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 > Misspelled output name
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< class ' RuntimeError' > : Method run failed due to: [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Invalid Output Name:misspelled
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< / pre > < / div >
< / div >
< p > The output name is optional, it can be replaced by < em > None< / em >
and < em > onnxruntime< / em > will then return all the outputs.< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > x< / 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 = "mf" > 1.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 2.0< / span > < span class = "p" > ],< / span > < span class = "p" > [< / span > < span class = "mf" > 3.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 4.0< / span > < span class = "p" > ],< / span > < span class = "p" > [< / span > < span class = "mf" > 5.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 6.0< / span > < span class = "p" > ]],< / span > < span class = "n" > dtype< / span > < span class = "o" > =< / 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 = "kc" > None< / 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 >
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " All outputs" < / span > < span class = "p" > )< / span >
< 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 >
< div class = "sphx-glr-script-out highlight-none notranslate" > < div class = "highlight" > < pre > < span > < / span > All outputs
[array([0, 0, 0], dtype=int64), [{0: 0.950599730014801, 1: 0.027834169566631317, 2: 0.02156602405011654}, {0: 0.9974970817565918, 1: 5.6299926654901356e-05, 2: 0.0024466661270707846}, {0: 0.9997311234474182, 1: 1.1918064757310276e-07, 2: 0.00026869276189245284}]]
< / pre > < / div >
< / div >
< p > The same goes if the input name is misspelled.< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "k" > try< / span > < span class = "p" > :< / 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" > array< / span > < span class = "p" > ([[< / span > < span class = "mf" > 1.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 2.0< / span > < span class = "p" > ],< / span > < span class = "p" > [< / span > < span class = "mf" > 3.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 4.0< / span > < span class = "p" > ],< / span > < span class = "p" > [< / span > < span class = "mf" > 5.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 6.0< / span > < span class = "p" > ]],< / span > < span class = "n" > dtype< / span > < span class = "o" > =< / span > < span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > float32< / span > < span class = "p" > )< / 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 = "s2" > " misspelled" < / span > < span class = "p" > :< / span > < span class = "n" > x< / span > < span class = "p" > })< / span >
< span class = "k" > except< / span > < span class = "ne" > Exception< / span > < span class = "k" > as< / span > < span class = "n" > e< / span > < span class = "p" > :< / span >
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< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " Misspelled input name" < / span > < span class = "p" > )< / span >
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " < / span > < span class = "si" > {0}< / span > < span class = "s2" > : < / span > < span class = "si" > {1}< / span > < span class = "s2" > " < / span > < span class = "o" > .< / span > < span class = "n" > format< / span > < span class = "p" > (< / span > < span class = "nb" > type< / span > < span class = "p" > (< / span > < span class = "n" > e< / span > < span class = "p" > ),< / span > < span class = "n" > e< / 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 > Misspelled input name
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< class ' RuntimeError' > : Method run failed due to: [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Invalid Feed Input Name:misspelled
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< / pre > < / div >
< / div >
< p > < em > onnxruntime< / em > does not necessarily fail if the input
dimension is a multiple of the expected input dimension.< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "k" > for< / span > < span class = "n" > x< / span > < span class = "ow" > in< / span > < span class = "p" > [< / span >
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< span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > array< / span > < span class = "p" > ([< / span > < span class = "mf" > 1.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 2.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 3.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 4.0< / span > < span class = "p" > ],< / span > < span class = "n" > dtype< / span > < span class = "o" > =< / span > < span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > float32< / span > < span class = "p" > ),< / span >
< span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > array< / span > < span class = "p" > ([[< / span > < span class = "mf" > 1.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 2.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 3.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 4.0< / span > < span class = "p" > ]],< / span > < span class = "n" > dtype< / span > < span class = "o" > =< / span > < span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > float32< / span > < span class = "p" > ),< / span >
< span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > array< / span > < span class = "p" > ([[< / span > < span class = "mf" > 1.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 2.0< / span > < span class = "p" > ],< / span > < span class = "p" > [< / span > < span class = "mf" > 3.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 4.0< / span > < span class = "p" > ]],< / span > < span class = "n" > dtype< / span > < span class = "o" > =< / span > < span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > float32< / span > < span class = "p" > ),< / span >
< span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > array< / span > < span class = "p" > ([< / span > < span class = "mf" > 1.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 2.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 3.0< / span > < span class = "p" > ],< / span > < span class = "n" > dtype< / span > < span class = "o" > =< / span > < span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > float32< / span > < span class = "p" > ),< / span >
< span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > array< / span > < span class = "p" > ([[< / span > < span class = "mf" > 1.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 2.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 3.0< / span > < span class = "p" > ]],< / span > < span class = "n" > dtype< / span > < span class = "o" > =< / span > < span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > float32< / span > < span class = "p" > ),< / span >
< span class = "p" > ]:< / span >
< span class = "n" > r< / 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 = "s2" > " Shape=< / span > < span class = "si" > {0}< / span > < span class = "s2" > and predicted labels=< / span > < span class = "si" > {1}< / span > < span class = "s2" > " < / span > < span class = "o" > .< / span > < span class = "n" > format< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "o" > .< / span > < span class = "n" > shape< / span > < span class = "p" > ,< / span > < span class = "n" > r< / span > < span class = "p" > ))< / span >
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< span class = "k" > for< / span > < span class = "n" > x< / span > < span class = "ow" > in< / span > < span class = "p" > [< / span >
< span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > array< / span > < span class = "p" > ([< / span > < span class = "mf" > 1.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 2.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 3.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 4.0< / span > < span class = "p" > ],< / span > < span class = "n" > dtype< / span > < span class = "o" > =< / span > < span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > float32< / span > < span class = "p" > ),< / span >
< span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > array< / span > < span class = "p" > ([[< / span > < span class = "mf" > 1.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 2.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 3.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 4.0< / span > < span class = "p" > ]],< / span > < span class = "n" > dtype< / span > < span class = "o" > =< / span > < span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > float32< / span > < span class = "p" > ),< / span >
< span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > array< / span > < span class = "p" > ([[< / span > < span class = "mf" > 1.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 2.0< / span > < span class = "p" > ],< / span > < span class = "p" > [< / span > < span class = "mf" > 3.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 4.0< / span > < span class = "p" > ]],< / span > < span class = "n" > dtype< / span > < span class = "o" > =< / span > < span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > float32< / span > < span class = "p" > ),< / span >
< span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > array< / span > < span class = "p" > ([< / span > < span class = "mf" > 1.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 2.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 3.0< / span > < span class = "p" > ],< / span > < span class = "n" > dtype< / span > < span class = "o" > =< / span > < span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > float32< / span > < span class = "p" > ),< / span >
< span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > array< / span > < span class = "p" > ([[< / span > < span class = "mf" > 1.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 2.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 3.0< / span > < span class = "p" > ]],< / span > < span class = "n" > dtype< / span > < span class = "o" > =< / span > < span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > float32< / span > < span class = "p" > ),< / span >
< span class = "p" > ]:< / span >
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< span class = "n" > r< / 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 = "kc" > None< / 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 >
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " Shape=< / span > < span class = "si" > {0}< / span > < span class = "s2" > and predicted probabilities=< / span > < span class = "si" > {1}< / span > < span class = "s2" > " < / span > < span class = "o" > .< / span > < span class = "n" > format< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "o" > .< / span > < span class = "n" > shape< / span > < span class = "p" > ,< / span > < span class = "n" > r< / span > < span class = "p" > [< / span > < span class = "mi" > 1< / 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 > Shape=(4,) and predicted labels=[array([2], dtype=int64)]
Shape=(1, 4) and predicted labels=[array([2], dtype=int64)]
Shape=(2, 2) and predicted labels=[array([0, 0], dtype=int64)]
Shape=(3,) and predicted labels=[array([0], dtype=int64)]
Shape=(1, 3) and predicted labels=[array([0], dtype=int64)]
Shape=(4,) and predicted probabilities=[{0: 0.0009370420593768358, 1: 0.001740509644150734, 2: 0.9973224401473999}]
Shape=(1, 4) and predicted probabilities=[{0: 0.0009370420593768358, 1: 0.001740509644150734, 2: 0.9973224401473999}]
Shape=(2, 2) and predicted probabilities=[{0: 0.950599730014801, 1: 0.027834169566631317, 2: 0.02156602405011654}, {0: 0.9974970817565918, 1: 5.6299926654901356e-05, 2: 0.0024466661270707846}]
Shape=(3,) and predicted probabilities=[{0: 0.7892322540283203, 1: 0.20707039535045624, 2: 0.0036973499227315187}]
Shape=(1, 3) and predicted probabilities=[{0: 0.7892322540283203, 1: 0.20707039535045624, 2: 0.0036973499227315187}]
< / pre > < / div >
< / div >
< p > It does not fail either if the number of dimension
is higher than expects but produces a warning.< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "k" > for< / span > < span class = "n" > x< / span > < span class = "ow" > in< / span > < span class = "p" > [< / span >
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< span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > array< / span > < span class = "p" > ([[[< / span > < span class = "mf" > 1.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 2.0< / span > < span class = "p" > ],< / span > < span class = "p" > [< / span > < span class = "mf" > 3.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 4.0< / span > < span class = "p" > ]]],< / span > < span class = "n" > dtype< / span > < span class = "o" > =< / span > < span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > float32< / span > < span class = "p" > ),< / span >
< span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > array< / span > < span class = "p" > ([[[< / span > < span class = "mf" > 1.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 2.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 3.0< / span > < span class = "p" > ]]],< / span > < span class = "n" > dtype< / span > < span class = "o" > =< / span > < span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > float32< / span > < span class = "p" > ),< / span >
< span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > array< / span > < span class = "p" > ([[[< / span > < span class = "mf" > 1.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 2.0< / span > < span class = "p" > ]],< / span > < span class = "p" > [[< / span > < span class = "mf" > 3.0< / span > < span class = "p" > ,< / span > < span class = "mf" > 4.0< / span > < span class = "p" > ]]],< / span > < span class = "n" > dtype< / span > < span class = "o" > =< / span > < span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > float32< / span > < span class = "p" > ),< / span >
< span class = "p" > ]:< / span >
< span class = "n" > r< / 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 = "s2" > " Shape=< / span > < span class = "si" > {0}< / span > < span class = "s2" > and predicted labels=< / span > < span class = "si" > {1}< / span > < span class = "s2" > " < / span > < span class = "o" > .< / span > < span class = "n" > format< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "o" > .< / span > < span class = "n" > shape< / span > < span class = "p" > ,< / span > < span class = "n" > r< / 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 > Shape=(1, 2, 2) and predicted labels=[array([0], dtype=int64)]
Shape=(1, 1, 3) and predicted labels=[array([1], dtype=int64)]
Shape=(2, 1, 2) and predicted labels=[array([1, 1], dtype=int64)]
< / pre > < / div >
< / div >
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< p class = "sphx-glr-timing" > < strong > Total running time of the script:< / strong > ( 0 minutes 0.105 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-common-errors-py" >
< div class = "sphx-glr-download docutils container" >
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< p > < a class = "reference download internal" download = "" href = "../_downloads/f4c99d319f64c4c243bda22d3c439108/plot_common_errors.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_common_errors.py< / span > < / code > < / a > < / p >
< / div >
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< div class = "sphx-glr-download docutils container" >
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< p > < a class = "reference download internal" download = "" href = "../_downloads/9be21d2b106c483e4362baf1d7c1c256/plot_common_errors.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_common_errors.ipynb< / span > < / code > < / a > < / p >
< / div >
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< / div >
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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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< / div >
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< script src = "https://ajax.googleapis.com/ajax/libs/jquery/3.0.0/jquery.min.js"
integrity="sha384-THPy051/pYDQGanwU6poAc/hOdQxjnOEXzbT+OuUAFqNqFjL+4IGLBgCJC3ZOShY"
crossorigin="anonymous">< / script >
< script src = "https://cdnjs.cloudflare.com/ajax/libs/tether/1.2.0/js/tether.min.js"
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< script src = "https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0-alpha.3/js/bootstrap.min.js"
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< script src = 'https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' > < / script >
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