Function test_cA

code/cA.py:231–312

This demo is tested on MNIST :type learning_rate: float :param learning_rate: learning rate used for training the contracting AutoEncoder :type training_epochs: int :param training_epochs: number of epochs used for training :type dataset: string

(learning_rate=0.01, training_epochs=20,
            dataset='mnist.pkl.gz',
            batch_size=10, output_folder='cA_plots', contraction_level=.1)

def test_cA(learning_rate=0.01, training_epochs=20,
            dataset='mnist.pkl.gz',
            batch_size=10, output_folder='cA_plots', contraction_level=.1):
    """
    This demo is tested on MNIST

    :type learning_rate: float
    :param learning_rate: learning rate used for training the contracting
                          AutoEncoder

    :type training_epochs: int
    :param training_epochs: number of epochs used for training

    :type dataset: string
    :param dataset: path to the picked dataset

    """
    datasets = load_data(dataset)
    train_set_x, train_set_y = datasets[0]

    # compute number of minibatches for training, validation and testing
    n_train_batches = train_set_x.get_value(borrow=True).shape[0] // batch_size

    # allocate symbolic variables for the data
    index = T.lscalar()    # index to a [mini]batch
    x = T.matrix('x')  # the data is presented as rasterized images

    if not os.path.isdir(output_folder):
        os.makedirs(output_folder)
    os.chdir(output_folder)
    ####################################
    #        BUILDING THE MODEL        #
    ####################################

    rng = numpy.random.RandomState(123)

    ca = cA(numpy_rng=rng, input=x,
            n_visible=28 * 28, n_hidden=500, n_batchsize=batch_size)

    cost, updates = ca.get_cost_updates(contraction_level=contraction_level,
                                        learning_rate=learning_rate)

    train_ca = theano.function(
        [index],
        [T.mean(ca.L_rec), ca.L_jacob],
        updates=updates,
        givens={
            x: train_set_x[index * batch_size: (index + 1) * batch_size]
        }
    )

    start_time = timeit.default_timer()

    ############
    # TRAINING #
    ############

    # go through training epochs