'weights_init': 'uniform',
'weights_interval': 4*numpy.sqrt(6./28*28+500),
'mrg': mrg
}
rbm = RBM(**config_args)
# rbm.load_params('outputs/rbm/trained_epoch_15.pkl')
optimizer = Optimizer(learning_rate=0.1, model=rbm, dataset=mnist, batch_size=20, epochs=15)
# perform training!
optimizer.train()
# 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,
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(
# 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,
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(X=preds,
img_shape=(28, 28),
tile_shape=(5, 5),
tile_spacing=(1, 1)))
