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|rss_image|  **2021-08 - 1/1** :ref:`Blog <ap-main-0>` :ref:`benchmark (4) <ap-cat-benchmark-0>` :ref:`onnx (8) <ap-cat-onnx-0>`

.. |rss_image| image:: feed-icon-16x16.png
    :target: ../_downloads/rss.xml
    :alt: RSS

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.. index:: 2021-08


.. _ap-month-2021-08-0:

2021-08 - 1/1
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.. blogpostagg::
    :title: A few tricks for tf2onnx
    :date: 2021-08-12
    :keywords: tips,tensorflow,tensorflow-onnx
    :categories: tf2onnx
    :rawfile: 2021/2021-08-12_tf2onnx.rst

    A few things I tend to forget. To run a specific
    test on a specific opset.
    

    ...




.. blogpostagg::
    :title: Decompose einsum into numpy operators
    :date: 2021-08-11
    :keywords: einsum
    :categories: onnx
    :rawfile: 2021/2021-08-11_einsum.rst

    Notebook :ref:`einsumdecompositionrst` what function :epkg:`numpy:einsum`
    does and how it can be decomposed into a series of basic operations,
    all available in ONNX. That's the purpose of function
    Function :func:`decompose_einsum_equation
    <mlprodict.testing.einsum.einsum_impl.decompose_einsum_equation>`.
    With function :func:`export2numpy
    <mlprodict.onnx_tools.onnx_export.export2numpy>`, it is possible to
    convert back this ONNX graph into a series of numpy operations.
    

    ...




.. blogpostagg::
    :title: onnxruntime shape [] != None
    :date: 2021-08-10
    :keywords: onnxruntime
    :categories: onnx
    :rawfile: 2021/2021-08-10_shape.rst

    `None` is the undefined shape, `[]` is an empty shape.
    And when shapes do not fit the results, the outputs can
    be suprising. The following example shows what :epkg:`onnxruntime`
    produces for the same graph except input and output shapes
    when defined as `None` and `[]`.
    

    ...





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|rss_image|  **2021-08 - 1/1** :ref:`2021-08 (3) <ap-month-2021-08-0>` :ref:`2022-02 (1) <ap-month-2022-02-0>` :ref:`2022-05 (1) <ap-month-2022-05-0>` :ref:`2022-06 (1) <ap-month-2022-06-0>` :ref:`2022-11 (1) <ap-month-2022-11-0>`