onnxruntime/python/_sources/auto_examples/plot_load_and_predict.rst.txt

.. note::
    :class: sphx-glr-download-link-note

    Click :ref:`here <sphx_glr_download_auto_examples_plot_load_and_predict.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_plot_load_and_predict.py:


.. _l-example-simple-usage:

Load and predict with ONNX Runtime and a very simple model
==========================================================

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.



.. code-block:: python


    import onnxruntime as rt
    import numpy
    from onnxruntime.datasets import get_example







Let's load a very simple model.
The model is available on github `onnx...test_sigmoid <https://github.com/onnx/onnx/tree/master/onnx/backend/test/data/node/test_sigmoid>`_.



.. code-block:: python


    example1 = get_example("sigmoid.onnx")
    sess = rt.InferenceSession(example1)







Let's see the input name and shape.



.. code-block:: python


    input_name = sess.get_inputs()[0].name
    print("input name", input_name)
    input_shape = sess.get_inputs()[0].shape
    print("input shape", input_shape)
    input_type = sess.get_inputs()[0].type
    print("input type", input_type)





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    input name x
    input shape [3, 4, 5]
    input type tensor(float)


Let's see the output name and shape.



.. code-block:: python


    output_name = sess.get_outputs()[0].name
    print("output name", output_name)  
    output_shape = sess.get_outputs()[0].shape
    print("output shape", output_shape)
    output_type = sess.get_outputs()[0].type
    print("output type", output_type)





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    output name y
    output shape [3, 4, 5]
    output type tensor(float)


Let's compute its outputs (or predictions if it is a machine learned model).



.. code-block:: python


    import numpy.random
    x = numpy.random.random((3,4,5))
    x = x.astype(numpy.float32)
    res = sess.run([output_name], {input_name: x})
    print(res)




.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    [array([[[0.5143179 , 0.6716157 , 0.6144865 , 0.6863301 , 0.64653844],
            [0.6305008 , 0.5775663 , 0.6964146 , 0.5280421 , 0.61725914],
            [0.7180781 , 0.61795104, 0.6727085 , 0.5730073 , 0.7306371 ],
            [0.68829596, 0.72810763, 0.6397303 , 0.6951108 , 0.6797523 ]],

           [[0.70466846, 0.5554103 , 0.5995578 , 0.6315835 , 0.66704905],
            [0.6936102 , 0.50828934, 0.51689506, 0.5749565 , 0.5596123 ],
            [0.596185  , 0.61397564, 0.63528085, 0.71865064, 0.5692966 ],
            [0.6515152 , 0.69646513, 0.5182993 , 0.52175283, 0.5565996 ]],

           [[0.5074223 , 0.7233809 , 0.52564526, 0.52229357, 0.677563  ],
            [0.5302158 , 0.5307286 , 0.67780304, 0.5051534 , 0.580091  ],
            [0.72392386, 0.55004114, 0.5165563 , 0.6645192 , 0.7159723 ],
            [0.6326351 , 0.6996319 , 0.67441595, 0.6697645 , 0.55308944]]],
          dtype=float32)]


**Total running time of the script:** ( 0 minutes  0.009 seconds)


.. _sphx_glr_download_auto_examples_plot_load_and_predict.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download

     :download:`Download Python source code: plot_load_and_predict.py <plot_load_and_predict.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: plot_load_and_predict.ipynb <plot_load_and_predict.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_