.. 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([[[0.5723026 , 0.63803464, 0.6668191 , 0.5958905 , 0.6193227 ],
            [0.72006834, 0.6733471 , 0.69727564, 0.677417  , 0.54019606],
            [0.6529879 , 0.6253395 , 0.6622766 , 0.7127938 , 0.5429604 ],
            [0.604758  , 0.7297679 , 0.5023199 , 0.6422848 , 0.72463864]],

           [[0.7272017 , 0.6749091 , 0.6320263 , 0.53652936, 0.5730977 ],
            [0.5092271 , 0.6188758 , 0.7302063 , 0.6986053 , 0.681966  ],
            [0.71297586, 0.5980871 , 0.50415754, 0.5037554 , 0.555519  ],
            [0.66070724, 0.5136699 , 0.61995924, 0.62644744, 0.53362054]],

           [[0.71763974, 0.6305131 , 0.67285264, 0.61491245, 0.62528753],
            [0.6300376 , 0.5060302 , 0.6701227 , 0.6823867 , 0.6090256 ],
            [0.6845094 , 0.69262683, 0.5350911 , 0.7162322 , 0.6441792 ],
            [0.51676244, 0.6735578 , 0.54448766, 0.64972466, 0.66511655]]],
          dtype=float32)]





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

   **Total running time of the script:** ( 0 minutes  0.050 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>`_