.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_tutorial/plot_gconverting.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_tutorial_plot_gconverting.py: Modify the ONNX graph ===================== This example shows how to change the default ONNX graph such as renaming the inputs or outputs names. .. contents:: :local: Basic example +++++++++++++ .. GENERATED FROM PYTHON SOURCE LINES 17-44 .. code-block:: default import numpy from onnxruntime import InferenceSession from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from skl2onnx.common.data_types import FloatTensorType, Int64TensorType from skl2onnx import to_onnx iris = load_iris() X, y = iris.data, iris.target X = X.astype(numpy.float32) X_train, X_test, y_train, y_test = train_test_split(X, y) clr = LogisticRegression(solver="liblinear") clr.fit(X_train, y_train) onx = to_onnx(clr, X, options={'zipmap': False}, target_opset=15) sess = InferenceSession(onx.SerializeToString()) input_names = [i.name for i in sess.get_inputs()] output_names = [o.name for o in sess.get_outputs()] print("inputs=%r, outputs=%r" % (input_names, output_names)) print(sess.run(None, {input_names[0]: X_test[:2]})) .. rst-class:: sphx-glr-script-out .. code-block:: none inputs=['X'], outputs=['label', 'probabilities'] [array([2, 0], dtype=int64), array([[5.0940731e-04, 3.0720213e-01, 6.9228852e-01], [9.4576669e-01, 5.4222260e-02, 1.1153538e-05]], dtype=float32)] .. GENERATED FROM PYTHON SOURCE LINES 45-51 Changes the input names +++++++++++++++++++++++ It is possible to change the input name by using the parameter *initial_types*. However, the user must specify the input types as well. .. GENERATED FROM PYTHON SOURCE LINES 51-63 .. code-block:: default onx = to_onnx(clr, X, options={'zipmap': False}, initial_types=[('X56', FloatTensorType([None, X.shape[1]]))], target_opset=15) sess = InferenceSession(onx.SerializeToString()) input_names = [i.name for i in sess.get_inputs()] output_names = [o.name for o in sess.get_outputs()] print("inputs=%r, outputs=%r" % (input_names, output_names)) print(sess.run(None, {input_names[0]: X_test[:2]})) .. rst-class:: sphx-glr-script-out .. code-block:: none inputs=['X56'], outputs=['label', 'probabilities'] [array([2, 0], dtype=int64), array([[5.0940731e-04, 3.0720213e-01, 6.9228852e-01], [9.4576669e-01, 5.4222260e-02, 1.1153538e-05]], dtype=float32)] .. GENERATED FROM PYTHON SOURCE LINES 64-69 Changes the output names ++++++++++++++++++++++++ It is possible to change the input name by using the parameter *final_types*. .. GENERATED FROM PYTHON SOURCE LINES 69-81 .. code-block:: default onx = to_onnx(clr, X, options={'zipmap': False}, final_types=[('L', Int64TensorType([None])), ('P', FloatTensorType([None, 3]))], target_opset=15) sess = InferenceSession(onx.SerializeToString()) input_names = [i.name for i in sess.get_inputs()] output_names = [o.name for o in sess.get_outputs()] print("inputs=%r, outputs=%r" % (input_names, output_names)) print(sess.run(None, {input_names[0]: X_test[:2]})) .. rst-class:: sphx-glr-script-out .. code-block:: none inputs=['X'], outputs=['L', 'P'] [array([2, 0], dtype=int64), array([[5.0940731e-04, 3.0720213e-01, 6.9228852e-01], [9.4576669e-01, 5.4222260e-02, 1.1153538e-05]], dtype=float32)] .. GENERATED FROM PYTHON SOURCE LINES 82-88 Renaming intermediate results +++++++++++++++++++++++++++++ It is possible to rename intermediate results by using a prefix or by using a function. The result will be post-processed in order to unique names. It does not impact the graph inputs or outputs. .. GENERATED FROM PYTHON SOURCE LINES 88-106 .. code-block:: default def rename_results(proposed_name, existing_names): result = "_" + proposed_name.upper() while result in existing_names: result += "A" print("changed %r into %r." % (proposed_name, result)) return result onx = to_onnx(clr, X, options={'zipmap': False}, naming=rename_results, target_opset=15) sess = InferenceSession(onx.SerializeToString()) input_names = [i.name for i in sess.get_inputs()] output_names = [o.name for o in sess.get_outputs()] print("inputs=%r, outputs=%r" % (input_names, output_names)) print(sess.run(None, {input_names[0]: X_test[:2]})) .. rst-class:: sphx-glr-script-out .. code-block:: none changed 'SklearnLinearClassifier' into '_SKLEARNLINEARCLASSIFIER'. changed 'label' into '_LABEL'. changed 'probabilities' into '_PROBABILITIES'. changed 'LinearClassifier' into '_LINEARCLASSIFIER'. changed 'probability_tensor' into '_PROBABILITY_TENSOR'. changed 'Normalizer' into '_NORMALIZER'. inputs=['X'], outputs=['label', 'probabilities'] [array([2, 0], dtype=int64), array([[5.0940731e-04, 3.0720213e-01, 6.9228852e-01], [9.4576669e-01, 5.4222260e-02, 1.1153538e-05]], dtype=float32)] .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 0.269 seconds) .. _sphx_glr_download_auto_tutorial_plot_gconverting.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_gconverting.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_gconverting.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_