def __init__(self):
self.mnist = MNIST.full() # first_10k()
datasets = load_data(dataset)
train_set_x, train_set_y = datasets[0]
test_set_x, test_set_y = datasets[2]
batch_size = 100 # size of the minibatch
# compute number of minibatches for training, validation and testing
n_train_batches = train_set_x.value.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 fot the persistent chain (state = hidden layer of chain)
persistent_chain = theano.shared(numpy.zeros((batch_size, 500)))
# construct the RBM class
self.rbm = RBM(input=x, n_visible=28 * 28, n_hidden=500, numpy_rng=rng, theano_rng=theano_rng)
# get the cost and the gradient corresponding to one step of CD
self.init_series()
def __init__(self, state):
self.state = state
if TEST_CONFIG:
self.mnist = MNIST.first_1k()
print "Test config, so loaded MNIST first 1000"
else:
self.mnist = MNIST.full()#first_10k()
print "Loaded MNIST full"
self.cp = ConvolutionParams( \
num_filters=state.num_filters,
num_input_planes=1,
height_filters=state.filter_size,
width_filters=state.filter_size)
self.image_size = (28,28)
self.minibatch_size = state.minibatch_size
self.lr = state.learning_rate
self.sparsity_lambda = state.sparsity_lambda
# about 1/num_filters, so only one filter active at a time
# 40 * 0.05 = ~2 filters active for any given pixel
self.sparsity_p = state.sparsity_p
self.crbm = CRBM( \
minibatch_size=self.minibatch_size,
image_size=self.image_size,
conv_params=self.cp,
learning_rate=self.lr,
sparsity_lambda=self.sparsity_lambda,
sparsity_p=self.sparsity_p)
self.num_epochs = state.num_epochs
self.init_series()
