.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/plot_onnx_diff.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_examples_plot_onnx_diff.py: .. _l-onnx-diff-example: Compares the conversions of the same model with different options ================================================================= The script compares two onnx models obtained with the same trained scikit-learn models but converted with different options. A model +++++++ .. GENERATED FROM PYTHON SOURCE LINES 14-28 .. code-block:: Python from sklearn.mixture import GaussianMixture from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from skl2onnx import to_onnx from onnx_array_api.reference import compare_onnx_execution from onnx_array_api.plotting.text_plot import onnx_simple_text_plot data = load_iris() X_train, X_test = train_test_split(data.data) model = GaussianMixture() model.fit(X_train) .. raw:: html

GaussianMixture()

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.. GENERATED FROM PYTHON SOURCE LINES 29-31 Conversion to onnx ++++++++++++++++++ .. GENERATED FROM PYTHON SOURCE LINES 31-38 .. code-block:: Python onx = to_onnx( model, X_train[:1], options={id(model): {"score_samples": True}}, target_opset=12 ) print(onnx_simple_text_plot(onx)) .. rst-class:: sphx-glr-script-out .. code-block:: none opset: domain='' version=12 input: name='X' type=dtype('float64') shape=['', 4] init: name='Ad_Addcst' type=float64 shape=(1,) -- array([7.35150827]) init: name='Ge_Gemmcst' type=float64 shape=(4, 4) init: name='Ge_Gemmcst1' type=float64 shape=(4,) -- array([-7.51792282, -7.89910213, 4.37651563, 3.02521798]) init: name='Mu_Mulcst' type=float64 shape=(1,) -- array([-0.5]) init: name='Ad_Addcst1' type=float64 shape=(1,) -- array([3.24465589]) init: name='Ad_Addcst2' type=float64 shape=(1,) -- array([0.]) Gemm(X, Ge_Gemmcst, Ge_Gemmcst1, alpha=1.00, beta=1.00) -> Ge_Y0 ReduceSumSquare(Ge_Y0, axes=[1], keepdims=1) -> Re_reduced0 Concat(Re_reduced0, axis=1) -> Co_concat_result0 Add(Ad_Addcst, Co_concat_result0) -> Ad_C02 Mul(Ad_C02, Mu_Mulcst) -> Mu_C0 Add(Mu_C0, Ad_Addcst1) -> Ad_C01 Add(Ad_C01, Ad_Addcst2) -> Ad_C0 ArgMax(Ad_C0, axis=1) -> label ReduceLogSumExp(Ad_C0, axes=[1], keepdims=1) -> score_samples Sub(Ad_C0, score_samples) -> Su_C0 Exp(Su_C0) -> probabilities output: name='label' type=dtype('int64') shape=['', 1] output: name='probabilities' type=dtype('float64') shape=['', 1] output: name='score_samples' type=dtype('float64') shape=['', 1] .. GENERATED FROM PYTHON SOURCE LINES 39-41 Conversion to onnx without ReduceLogSumExp ++++++++++++++++++++++++++++++++++++++++++ .. GENERATED FROM PYTHON SOURCE LINES 41-53 .. code-block:: Python onx2 = to_onnx( model, X_train[:1], options={id(model): {"score_samples": True}}, black_op={"ReduceLogSumExp"}, target_opset=12, ) print(onnx_simple_text_plot(onx2)) .. rst-class:: sphx-glr-script-out .. code-block:: none opset: domain='' version=12 input: name='X' type=dtype('float64') shape=['', 4] init: name='Ad_Addcst' type=float64 shape=(1,) -- array([7.35150827]) init: name='Ge_Gemmcst' type=float64 shape=(4, 4) init: name='Ge_Gemmcst1' type=float64 shape=(4,) -- array([-7.51792282, -7.89910213, 4.37651563, 3.02521798]) init: name='Mu_Mulcst' type=float64 shape=(1,) -- array([-0.5]) init: name='Ad_Addcst1' type=float64 shape=(1,) -- array([3.24465589]) init: name='Ad_Addcst2' type=float64 shape=(1,) -- array([0.]) Gemm(X, Ge_Gemmcst, Ge_Gemmcst1, alpha=1.00, beta=1.00) -> Ge_Y0 Mul(Ge_Y0, Ge_Y0) -> Mu_C01 ReduceSum(Mu_C01, axes=[1], keepdims=1) -> Re_reduced0 Concat(Re_reduced0, axis=1) -> Co_concat_result0 Add(Ad_Addcst, Co_concat_result0) -> Ad_C02 Mul(Ad_C02, Mu_Mulcst) -> Mu_C0 Add(Mu_C0, Ad_Addcst1) -> Ad_C01 Add(Ad_C01, Ad_Addcst2) -> Ad_C0 ArgMax(Ad_C0, axis=1) -> label ReduceMax(Ad_C0, axes=[1], keepdims=1) -> Re_reduced03 Sub(Ad_C0, Re_reduced03) -> Su_C01 Exp(Su_C01) -> Ex_output0 ReduceSum(Ex_output0, axes=[1], keepdims=1) -> Re_reduced02 Log(Re_reduced02) -> Lo_output0 Add(Lo_output0, Re_reduced03) -> score_samples Sub(Ad_C0, score_samples) -> Su_C0 Exp(Su_C0) -> probabilities output: name='label' type=dtype('int64') shape=['', 1] output: name='probabilities' type=dtype('float64') shape=['', 1] output: name='score_samples' type=dtype('float64') shape=['', 1] .. GENERATED FROM PYTHON SOURCE LINES 54-60 Differences +++++++++++ Function :func:`onnx_array_api.reference.compare_onnx_execution` compares the intermediate results of two onnx models. Then it finds the best alignmet between the two models using an edit distance. .. GENERATED FROM PYTHON SOURCE LINES 60-66 .. code-block:: Python res1, res2, align, dc = compare_onnx_execution(onx, onx2, verbose=1) print("------------") text = dc.to_str(res1, res2, align) print(text) .. rst-class:: sphx-glr-script-out .. code-block:: none [compare_onnx_execution] generate inputs [compare_onnx_execution] execute with 1 inputs [compare_onnx_execution] execute first model [compare_onnx_execution] got 21 results [compare_onnx_execution] execute second model [compare_onnx_execution] got 21 results (first model) [compare_onnx_execution] got 27 results (second model) [compare_onnx_execution] compute edit distance [compare_onnx_execution] got 27 pairs [compare_onnx_execution] done ------------ 001 = | INITIA float64 1:1 HAAA Ad | INITIA float64 1:1 HAAA Ad 002 = | INITIA float64 2:4x4 ADZF Ge | INITIA float64 2:4x4 ADZF Ge 003 = | INITIA float64 1:4 TTED Ge | INITIA float64 1:4 TTED Ge 004 = | INITIA float64 1:1 AAAA Mu | INITIA float64 1:1 AAAA Mu 005 = | INITIA float64 1:1 DAAA Ad | INITIA float64 1:1 DAAA Ad 006 = | INITIA float64 1:1 AAAA Ad | INITIA float64 1:1 AAAA Ad 007 = | INPUT float64 2:1x4 AAAA X | INPUT float64 2:1x4 AAAA X 008 = | RESULT float64 2:1x4 TTFF Gemm Ge | RESULT float64 2:1x4 TTFF Gemm Ge 009 + | | RESULT float64 2:1x4 EBFD Mul Mu 010 ~ | RESULT float64 2:1x1 PAAA ReduceSumSquare Re | RESULT float64 2:1x1 PAAA ReduceSum Re 011 = | RESULT float64 2:1x1 PAAA Concat Co | RESULT float64 2:1x1 PAAA Concat Co 012 = | RESULT float64 2:1x1 XAAA Add Ad | RESULT float64 2:1x1 XAAA Add Ad 013 = | RESULT float64 2:1x1 PAAA Mul Mu | RESULT float64 2:1x1 PAAA Mul Mu 014 = | RESULT float64 2:1x1 SAAA Add Ad | RESULT float64 2:1x1 SAAA Add Ad 015 = | RESULT float64 2:1x1 SAAA Add Ad | RESULT float64 2:1x1 SAAA Add Ad 016 = | RESULT int64 2:1x1 AAAA ArgMax la | RESULT int64 2:1x1 AAAA ArgMax la 017 + | | RESULT float64 2:1x1 SAAA ReduceMax Re 018 + | | RESULT float64 2:1x1 AAAA Sub Su 019 + | | RESULT float64 2:1x1 BAAA Exp Ex 020 + | | RESULT float64 2:1x1 BAAA ReduceSum Re 021 + | | RESULT float64 2:1x1 AAAA Log Lo 022 ~ | RESULT float64 2:1x1 SAAA ReduceLogSumExp sc | RESULT float64 2:1x1 SAAA Add sc 023 = | RESULT float64 2:1x1 AAAA Sub Su | RESULT float64 2:1x1 AAAA Sub Su 024 = | RESULT float64 2:1x1 BAAA Exp pr | RESULT float64 2:1x1 BAAA Exp pr 025 = | OUTPUT int64 2:1x1 AAAA la | OUTPUT int64 2:1x1 AAAA la 026 = | OUTPUT float64 2:1x1 BAAA pr | OUTPUT float64 2:1x1 BAAA pr 027 = | OUTPUT float64 2:1x1 SAAA sc | OUTPUT float64 2:1x1 SAAA sc .. GENERATED FROM PYTHON SOURCE LINES 67-70 See :ref:`l-long-output-compare_onnx_execution` for a better view. The display shows that ReduceSumSquare was replaced by Mul + ReduceSum, and ReduceLogSumExp by ReduceMax + Sub + Exp + Log + Add. .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 4.456 seconds) .. _sphx_glr_download_auto_examples_plot_onnx_diff.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_onnx_diff.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_onnx_diff.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_onnx_diff.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_