.. only:: html

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

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

    .. _sphx_glr_auto_examples_plot_nn_training_curves.py:


=======================================
Plotting Neural Network Training Curves
=======================================

This is a basic example using a convolutional recurrent neural network to learn segments directly from time series data



.. code-block:: default

    # Author: David Burns
    # License: BSD

    import matplotlib.pyplot as plt
    import numpy as np
    from tensorflow.python.keras.layers import Dense, LSTM, Conv1D
    from tensorflow.python.keras.models import Sequential
    from tensorflow.python.keras.wrappers.scikit_learn import KerasClassifier
    from pandas import DataFrame
    from sklearn.model_selection import train_test_split

    from seglearn.datasets import load_watch
    from seglearn.pipe import Pype
    from seglearn.transform import Segment









Simple NN Model
#############################################


.. code-block:: default


    def crnn_model(width=100, n_vars=6, n_classes=7, conv_kernel_size=5,
                   conv_filters=3, lstm_units=3):
        input_shape = (width, n_vars)
        model = Sequential()
        model.add(Conv1D(filters=conv_filters, kernel_size=conv_kernel_size,
                         padding='valid', activation='relu', input_shape=input_shape))
        model.add(LSTM(units=lstm_units, dropout=0.1, recurrent_dropout=0.1))
        model.add(Dense(n_classes, activation="softmax"))

        model.compile(loss='categorical_crossentropy', optimizer='adam',
                      metrics=['accuracy'])

        return model









Setup
#############################################


.. code-block:: default


    # load the data
    data = load_watch()
    X = data['X']
    y = data['y']

    # split the data
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)

    # create a segment learning pipeline
    pipe = Pype([('seg', Segment(width=100, step=100, order='C')),
                 ('crnn', KerasClassifier(build_fn=crnn_model, epochs=4, batch_size=256,
                                          verbose=0, validation_split=0.2))])









Accessing training history
#############################################


.. code-block:: default


    # this is a bit of a hack, because history object is returned by the
    # keras wrapper when fit is called
    # this approach won't work with a more complex estimator pipeline, in which case
    # a callable class with the desired properties should be made passed to build_fn

    pipe.fit(X_train, y_train)
    history = pipe.history.history
    print(DataFrame(history))

    # depends on version
    if 'accuracy' in history:
        ac_train = history['accuracy']
        ac_val = history['val_accuracy']
    elif 'acc' in history:
        ac_train = history['acc']
        ac_val = history['val_acc']
    else:
        raise ValueError("History object doesn't contain accuracy record")

    epoch = np.arange(len(ac_train)) + 1





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

 Out:

 .. code-block:: none

           loss  accuracy  val_loss  val_accuracy
    0  1.957218  0.184432  1.948542      0.047619
    1  1.950467  0.192847  1.946699      0.036415
    2  1.943282  0.189341  1.944778      0.028011
    3  1.937308  0.205470  1.943990      0.022409




Training Curves
#############################################


.. code-block:: default


    plt.plot(epoch, ac_train, 'o', label="train")
    plt.plot(epoch, ac_val, '+', label="validation")
    plt.xlabel("Epoch")
    plt.ylabel("Accuracy")
    plt.legend()
    plt.show()



.. image:: /auto_examples/images/sphx_glr_plot_nn_training_curves_001.png
    :class: sphx-glr-single-img


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

 Out:

 .. code-block:: none

    /home/david/Code/seglearn/examples/plot_nn_training_curves.py:96: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
      plt.show()





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

   **Total running time of the script:** ( 0 minutes  3.716 seconds)


.. _sphx_glr_download_auto_examples_plot_nn_training_curves.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_nn_training_curves.py <plot_nn_training_curves.py>`



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

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


.. only:: html

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

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