# rbm.load_params('rbm_trained.pkl')
# make an optimizer to train it (AdaDelta is a good default)
# optimizer = SGD(model=rbm, dataset=mnist, batch_size=20, learning_rate=0.1, lr_decay=False, nesterov_momentum=False, momentum=False)
optimizer = Optimizer(lr_decay=False, learning_rate=0.1, model=rbm, dataset=mnist, batch_size=20, save_frequency=1)
ll = Monitor('pseudo-log', rbm.get_monitors()['pseudo-log'])
# perform training!
optimizer.train(monitor_channels=ll)
# test it on some images!
test_data = mnist.getSubset(TEST)[0]
test_data = test_data[:25].eval()
# use the run function!
preds = rbm.run(test_data)
# Construct image from the test matrix
image = Image.fromarray(
tile_raster_images(
X=test_data,
img_shape=(28, 28),
tile_shape=(5, 5),
tile_spacing=(1, 1)
)
)
image.save('rbm_test.png')
# Construct image from the preds matrix
image = Image.fromarray(
tile_raster_images(
