Function test_SdA

code/SdA.py:329–487

Demonstrates how to train and test a stochastic denoising autoencoder. This is demonstrated on MNIST. :type learning_rate: float :param learning_rate: learning rate used in the finetune stage (factor for the stochastic gradient) :type pretraining_epochs: int :param pr

(finetune_lr=0.1, pretraining_epochs=15,
             pretrain_lr=0.001, training_epochs=1000,
             dataset='mnist.pkl.gz', batch_size=1)

def test_SdA(finetune_lr=0.1, pretraining_epochs=15,
             pretrain_lr=0.001, training_epochs=1000,
             dataset='mnist.pkl.gz', batch_size=1):
    """
    Demonstrates how to train and test a stochastic denoising autoencoder.

    This is demonstrated on MNIST.

    :type learning_rate: float
    :param learning_rate: learning rate used in the finetune stage
    (factor for the stochastic gradient)

    :type pretraining_epochs: int
    :param pretraining_epochs: number of epoch to do pretraining

    :type pretrain_lr: float
    :param pretrain_lr: learning rate to be used during pre-training

    :type n_iter: int
    :param n_iter: maximal number of iterations ot run the optimizer

    :type dataset: string
    :param dataset: path the the pickled dataset

    """

    datasets = load_data(dataset)

    train_set_x, train_set_y = datasets[0]
    valid_set_x, valid_set_y = datasets[1]
    test_set_x, test_set_y = datasets[2]

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

    # numpy random generator
    # start-snippet-3
    numpy_rng = numpy.random.RandomState(89677)
    print('... building the model')
    # construct the stacked denoising autoencoder class
    sda = SdA(
        numpy_rng=numpy_rng,
        n_ins=28 * 28,
        hidden_layers_sizes=[1000, 1000, 1000],
        n_outs=10
    )
    # end-snippet-3 start-snippet-4
    #########################
    # PRETRAINING THE MODEL #
    #########################
    print('... getting the pretraining functions')
    pretraining_fns = sda.pretraining_functions(train_set_x=train_set_x,
                                                batch_size=batch_size)

    print('... pre-training the model')
    start_time = timeit.default_timer()
    ## Pre-train layer-wise