labels = labels[0:n]
dataset = data.dataset(inputs, targets, labels)
batches = data.BatchIterator(dataset)
# meta
epochs = 50
learning_rate = 0.1
cd_k = 15
weight_decay = reg.l2(0.0002)
momentum = 0 #0.1
# Load and stack params from initial pre-training.
# initial_params = dbn.params_from_rbms([parameters.load(OUTPUT_PATH, t)
# for t in (1358445776, 1358451846, 1358456203)])
# Params after first 50 epochs of fine tuning. (lr 0.1, p 0.0, cd_k=10)
initial_params = parameters.load(OUTPUT_PATH, timestamp=1358541318)
# The sampling function used by the top-level RBM.
sample_v_softmax = functools.partial(rbm.sample_v_softmax, k=mnist.NUM_CLASSES)
# Optimization objective.
def f(params, data):
return contrastive_wake_sleep(params, data, weight_decay, cd_k)
output_dir = utils.make_output_directory(OUTPUT_PATH)
save_params = parameters.save_hook(output_dir)
params = optimize.sgd(f, initial_params, batches, epochs, learning_rate, momentum,
post_epoch=save_params)
sample_v_softmax_clamped = functools.partial(sample_v_softmax,
labels=np.eye(10))
# Perform an upward pass from the pixels to the visible units of
# the top-level RBM.
# initial_v = np.hstack((
# np.eye(10),
# up_pass(dbn_params, initial_pixels)))
initial_v = np.hstack(
(np.eye(10), np.random.random((10, dbn_params[-1].W.shape[1] - 10))))
# Initialize the gibbs chain.
gc = rbm.gibbs_chain(initial_v, dbn_params[-1], rbm.sample_h,
sample_v_softmax_clamped)
tile_2_by_5 = functools.partial(utils.tile, grid_shape=(2, 5))
gen = itertools.islice(gc, 1, None, 1)
gen = itertools.islice(gen, 2000)
gen = itertools.imap(operator.itemgetter(1), gen)
gen = itertools.imap(lambda v: down_pass(dbn_params, v), gen)
gen = itertools.imap(tile_2_by_5, gen)
# Save to disk.
utils.save_images(gen, tempfile.mkdtemp(dir=OUTPUT_PATH))
params = parameters.load(OUTPUT_PATH, timestamp=1358586160)
generate(params)
dataset = data.dataset(inputs, targets, labels)
batches = data.BatchIterator(dataset)
# meta
epochs = 50
learning_rate = 0.1
cd_k = 15
weight_decay = reg.l2(0.0002)
momentum = 0 #0.1
# Load and stack params from initial pre-training.
# initial_params = dbn.params_from_rbms([parameters.load(OUTPUT_PATH, t)
# for t in (1358445776, 1358451846, 1358456203)])
# Params after first 50 epochs of fine tuning. (lr 0.1, p 0.0, cd_k=10)
initial_params = parameters.load(OUTPUT_PATH, timestamp=1358541318)
# The sampling function used by the top-level RBM.
sample_v_softmax = functools.partial(rbm.sample_v_softmax, k=mnist.NUM_CLASSES)
# Optimization objective.
def f(params, data):
return contrastive_wake_sleep(params, data, weight_decay, cd_k)
output_dir = utils.make_output_directory(OUTPUT_PATH)
save_params = parameters.save_hook(output_dir)
params = optimize.sgd(f,
initial_params,
# Perform an upward pass from the pixels to the visible units of
# the top-level RBM.
# initial_v = np.hstack((
# np.eye(10),
# up_pass(dbn_params, initial_pixels)))
initial_v = np.hstack((
np.eye(10),
np.random.random((10, dbn_params[-1].W.shape[1] - 10))))
# Initialize the gibbs chain.
gc = rbm.gibbs_chain(initial_v,
dbn_params[-1],
rbm.sample_h,
sample_v_softmax_clamped)
tile_2_by_5 = functools.partial(utils.tile, grid_shape=(2, 5))
gen = itertools.islice(gc, 1, None, 1)
gen = itertools.islice(gen, 2000)
gen = itertools.imap(operator.itemgetter(1), gen)
gen = itertools.imap(lambda v: down_pass(dbn_params, v), gen)
gen = itertools.imap(tile_2_by_5, gen)
# Save to disk.
utils.save_images(gen, tempfile.mkdtemp(dir=OUTPUT_PATH))
params = parameters.load(OUTPUT_PATH, timestamp=1358586160)
generate(params)
