.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "_examples/shap/plot_main08.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        Click :ref:`here <sphx_glr_download__examples_shap_plot_main08.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr__examples_shap_plot_main08.py:


08. Basic example
=================

.. GENERATED FROM PYTHON SOURCE LINES 6-54




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

 Out:

 .. code-block:: none


    "\nSAMPLES_CNT = 1000\n\ntrain_x = np.random.rand(SAMPLES_CNT,5,4)\ntrain_y = np.vectorize(lambda x: int(round(x)))(np.random.rand(SAMPLES_CNT))\n\nval_x = np.random.rand(int(SAMPLES_CNT * 0.1),5,4)\nval_y = np.vectorize(lambda x: int(round(x)))(np.random.rand(int(SAMPLES_CNT * 0.1)))\n\n# Train model\n\nmodel = Sequential()\nmodel.add(LSTM(32,input_shape=train_x.shape[1:], return_sequences=False, stateful=False))\nmodel.add(Dense(1, activation='sigmoid'))\n\nmodel.compile(optimizer=tf.keras.optimizers.Adam(lr=0.001),\n              loss='binary_crossentropy',metrics=['accuracy'])\n\nfit = model.fit(train_x, train_y, batch_size=64, epochs=2,\n                validation_data=(val_x, val_y), shuffle=False)\n\nexplainer = shap.DeepExplainer(model, train_x[:10])\nshap_vals = explainer.shap_values(val_x[:10][:, 0, :])\n"





|

.. code-block:: default
   :lineno-start: 7


    import numpy as np
    import tensorflow as tf
    from tensorflow.keras.models import Sequential
    from tensorflow.keras.layers import Dense, Dropout, BatchNormalization, LSTM
    import shap

    # Create random training values.
    #
    # train_x is [
    #   [
    #        [0.3, 0.54 ... 0.8],
    #        [0.4, 0.6 ... 0.55],
    #        ...
    #   ],
    #   [
    #        [0.3, 0.54 ... 0.8],
    #        [0.4, 0.6 ... 0.55],
    #        ...
    #   ],
    #   ...
    # ]
    #
    # train_y is corresponding classification of train_x sequences, always 0 or 1
    # [0, 1, 0, 1, 0, ... 0]
    """
    SAMPLES_CNT = 1000

    train_x = np.random.rand(SAMPLES_CNT,5,4)
    train_y = np.vectorize(lambda x: int(round(x)))(np.random.rand(SAMPLES_CNT))

    val_x = np.random.rand(int(SAMPLES_CNT * 0.1),5,4)
    val_y = np.vectorize(lambda x: int(round(x)))(np.random.rand(int(SAMPLES_CNT * 0.1)))

    # Train model

    model = Sequential()
    model.add(LSTM(32,input_shape=train_x.shape[1:], return_sequences=False, stateful=False))
    model.add(Dense(1, activation='sigmoid'))

    model.compile(optimizer=tf.keras.optimizers.Adam(lr=0.001),
                  loss='binary_crossentropy',metrics=['accuracy'])

    fit = model.fit(train_x, train_y, batch_size=64, epochs=2,
                    validation_data=(val_x, val_y), shuffle=False)

    explainer = shap.DeepExplainer(model, train_x[:10])
    shap_vals = explainer.shap_values(val_x[:10][:, 0, :])
    """

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

   **Total running time of the script:** ( 0 minutes  0.001 seconds)


.. _sphx_glr_download__examples_shap_plot_main08.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_main08.py <plot_main08.py>`



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

     :download:`Download Jupyter notebook: plot_main08.ipynb <plot_main08.ipynb>`


.. only:: html

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

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