Function test_dA

code/dA.py:263–416

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

(learning_rate=0.1, training_epochs=15,
            dataset='mnist.pkl.gz',
            batch_size=20, output_folder='dA_plots')

def test_dA(learning_rate=0.1, training_epochs=15,
            dataset='mnist.pkl.gz',
            batch_size=20, output_folder='dA_plots'):

    """
    This demo is tested on MNIST

    :type learning_rate: float
    :param learning_rate: learning rate used for training the DeNosing
                          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

    # start-snippet-2
    # 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
    # end-snippet-2

    if not os.path.isdir(output_folder):
        os.makedirs(output_folder)
    os.chdir(output_folder)

    ####################################
    # BUILDING THE MODEL NO CORRUPTION #
    ####################################

    rng = numpy.random.RandomState(123)
    theano_rng = RandomStreams(rng.randint(2 ** 30))

    da = dA(
        numpy_rng=rng,
        theano_rng=theano_rng,
        input=x,
        n_visible=28 * 28,
        n_hidden=500
    )

    cost, updates = da.get_cost_updates(
        corruption_level=0.,
        learning_rate=learning_rate
    )

    train_da = theano.function(
        [index],
        cost,
        updates=updates,