Function test_rbm

code/rbm.py:363–542

Demonstrate how to train and afterwards sample from it using Theano. This is demonstrated on MNIST. :param learning_rate: learning rate used for training the RBM :param training_epochs: number of epochs used for training :param dataset: path the the pickled dataset :par

(learning_rate=0.1, training_epochs=15,
             dataset='mnist.pkl.gz', batch_size=20,
             n_chains=20, n_samples=10, output_folder='rbm_plots',
             n_hidden=500)

def test_rbm(learning_rate=0.1, training_epochs=15,
             dataset='mnist.pkl.gz', batch_size=20,
             n_chains=20, n_samples=10, output_folder='rbm_plots',
             n_hidden=500):
    """
    Demonstrate how to train and afterwards sample from it using Theano.

    This is demonstrated on MNIST.

    :param learning_rate: learning rate used for training the RBM

    :param training_epochs: number of epochs used for training

    :param dataset: path the the pickled dataset

    :param batch_size: size of a batch used to train the RBM

    :param n_chains: number of parallel Gibbs chains to be used for sampling

    :param n_samples: number of samples to plot for each chain

    """
    datasets = load_data(dataset)

    train_set_x, train_set_y = datasets[0]
    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] // 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

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

    # initialize storage for the persistent chain (state = hidden
    # layer of chain)
    persistent_chain = theano.shared(numpy.zeros((batch_size, n_hidden),
                                                 dtype=theano.config.floatX),
                                     borrow=True)

    # construct the RBM class
    rbm = RBM(input=x, n_visible=28 * 28,
              n_hidden=n_hidden, numpy_rng=rng, theano_rng=theano_rng)

    # get the cost and the gradient corresponding to one step of CD-15
    cost, updates = rbm.get_cost_updates(lr=learning_rate,
                                         persistent=persistent_chain, k=15)

    #################################
    #     Training the RBM          #
    #################################
    if not os.path.isdir(output_folder):
        os.makedirs(output_folder)
    os.chdir(output_folder)