.. only:: html

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

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

    .. _sphx_glr_auto_examples_keras_mnist_mlp.py:


Running Keras models with Tensorboard
=====================================

Lab integrates into a typical keras workflow.

WARNING: model persistence in Keras can be complicated, especially when
working with complext models. It is recommended to checkpoint each training
epoch independently from Lab's ``log_model`` API.

Bering by creating a new Lab Project:

    >>> echo "keras" > requirements.txt
    >>> lab init --name simple-keras


.. code-block:: default


    import keras
    from keras.datasets import mnist
    from keras.models import Sequential
    from keras.layers import Dense, Dropout
    from keras.optimizers import RMSprop
    from keras.callbacks import TensorBoard

    import tempfile

    from sklearn.metrics import accuracy_score, precision_score

    from lab.experiment import Experiment

    batch_size = 128
    num_classes = 10
    epochs = 20

    # the data, split between train and test sets
    (x_train, y_train), (x_test, y_test) = mnist.load_data()

    x_train = x_train.reshape(60000, 784)
    x_test = x_test.reshape(10000, 784)
    x_train = x_train.astype('float32')
    x_test = x_test.astype('float32')
    x_train /= 255
    x_test /= 255
    print(x_train.shape[0], 'train samples')
    print(x_test.shape[0], 'test samples')

    # convert class vectors to binary class matrices
    y_train = keras.utils.to_categorical(y_train, num_classes)
    y_test = keras.utils.to_categorical(y_test, num_classes)

    model = Sequential()
    model.add(Dense(512, activation='relu', input_shape=(784,)))
    model.add(Dropout(0.2))
    model.add(Dense(512, activation='relu'))
    model.add(Dropout(0.2))
    model.add(Dense(num_classes, activation='softmax'))

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

    e = Experiment()


    @e.start_run
    def train():

        # Create a temporary directory for tensorboard logs
        output_dir = tempfile.mkdtemp()
        print("Writing TensorBoard events locally to %s\n" % output_dir)
        tensorboard = TensorBoard(log_dir=output_dir)

        # During Experiment execution, tensorboard can be viewed through:
        # tensorboard --logdir=[output_dir]

        model.fit(x_train, y_train,
                  batch_size=batch_size,
                  epochs=epochs,
                  verbose=1,
                  validation_data=(x_test, y_test),
                  callbacks=[tensorboard])

        y_prob = model.predict(x_test)
        y_classes = y_prob.argmax(axis=-1)
        actual = y_test.argmax(axis=-1)

        accuracy = accuracy_score(y_true=actual, y_pred=y_classes)
        precision = precision_score(y_true=actual, y_pred=y_classes,
                                    average='macro')

        # Log tensorboard
        e.log_artifacts('tensorboard', output_dir)

        # Log all metrics
        e.log_metric('accuracy_score', accuracy)
        e.log_metric('precision_score', precision)

        # Log parameters
        e.log_parameter('batch_size', batch_size)

        # Save model
        e.log_model('mnist-mlp', model)


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

   **Total running time of the script:** ( 0 minutes  0.000 seconds)


.. _sphx_glr_download_auto_examples_keras_mnist_mlp.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: keras_mnist_mlp.py <keras_mnist_mlp.py>`



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

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


.. only:: html

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

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