rng = numpy.random.RandomState(1234)
mrg = theano.tensor.shared_randomstreams.RandomStreams(rng.randint(2**30))
config_args = {
'input_size': 28 * 28,
'hidden_size': 500,
'k': 15,
'weights_init': 'uniform',
'weights_interval': 4 * numpy.sqrt(6. / 28 * 28 + 500),
'mrg': mrg
}
rbm = RBM(**config_args)
# rbm.load_params('rbm_trained.pkl')
optimizer = Optimizer(learning_rate=0.1,
model=rbm,
dataset=mnist,
batch_size=20,
epochs=15)
ll = Monitor('pseudo-log', rbm.get_monitors()['pseudo-log'])
# perform training!
optimizer.train(monitor_channels=ll)
# test it on some images!
test_data = mnist.test_inputs[:25]
# use the run function!
preds = rbm.run(test_data)
# Construct image from the test matrix
image = Image.fromarray(
tile_raster_images(X=test_data,
