.. only:: html

    .. 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:: default


    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:: default


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








Let's see the input name and shape.


.. code-block:: default


    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:: default


    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:: default


    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([[[0.51158583, 0.71759593, 0.58291364, 0.7120014 , 0.6782218 ],
            [0.6614846 , 0.666275  , 0.5892761 , 0.6977681 , 0.62876207],
            [0.6731731 , 0.504596  , 0.68911326, 0.64759874, 0.55347925],
            [0.68378323, 0.6788985 , 0.70521903, 0.7012743 , 0.6849997 ]],

           [[0.69679904, 0.6268262 , 0.635523  , 0.705201  , 0.6575402 ],
            [0.5726959 , 0.585034  , 0.61183447, 0.6313906 , 0.5761279 ],
            [0.5608245 , 0.6542765 , 0.62418705, 0.68640214, 0.6531157 ],
            [0.60396266, 0.51947266, 0.64402723, 0.7152903 , 0.67965746]],

           [[0.5545499 , 0.6871018 , 0.69852173, 0.53383857, 0.5307777 ],
            [0.5215983 , 0.7091785 , 0.50127536, 0.6032124 , 0.6328076 ],
            [0.6385152 , 0.6556017 , 0.56685936, 0.5005025 , 0.56599593],
            [0.56378585, 0.6028171 , 0.54956913, 0.65903753, 0.64652383]]],
          dtype=float32)]





.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.083 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 sphx-glr-download-python

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



  .. container:: sphx-glr-download sphx-glr-download-jupyter

     :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.github.io>`_