def sgd(f, initial_params, batches, momentum, hook=None):
output_dir = utils.make_output_directory(OUTPUT_PATH)
save_params = parameters.save_hook(output_dir)
def post_epoch(*args):
save_params(*args)
if hook is not None:
hook(*args)
return optimize.sgd(f, initial_params, batches,
epochs, learning_rate, momentum,
post_epoch=post_epoch)
def ex(inputs):
inputs = utils.remove_dc(inputs)
inputs, zca = utils.zca_white(inputs, 0.1)
batches = data.BatchIterator(inputs, 100)
num_vis = inputs.shape[1]
num_hid = 400
epochs = 100
momentum = 0
initial_params = grbm.initial_params(num_hid, num_vis, 0.001, 1.0)
neg_free_energy_grad = functools.partial(grbm.neg_free_energy_grad,
learn_sigma=False)
def f(params, inputs):
return rbm.cd(params, inputs,
grbm.sample_h_noisy_relu, grbm.sample_v,
neg_free_energy_grad)
learning_rate = 0.005
output_dir = utils.make_output_directory(OUTPUT_PATH)
save_params = parameters.save_hook(output_dir)
error_history = []
sparsity_history = []
def post_epoch(*args):
W_norm = utils.rescale(args[0].W)
utils.save_image(utils.tile(W_norm),
os.path.join(output_dir, ('w%i.png' % args[1])))
# Estimate sparsity from subset of data.
h_mean = grbm.sample_h_noisy_relu(args[0], inputs[0:5000], True)[1]
mean_activation = np.mean(h_mean > 0)
print 'approx mean activation: %f' % mean_activation
# The callback from optimize.sgd needs modifying so that it
# passes the reconstrcution error as an argument to make this
# work. (This was used when I did the original experiments.)
# error_history.append(args[2])
sparsity_history.append(mean_activation)
save_params(args[0], args[1])
params = optimize.sgd(f, initial_params, batches,
epochs, learning_rate,
momentum,
post_epoch=post_epoch)
with(open(os.path.join(output_dir, 'history.pickle'), 'wb')) as f:
pickle.dump(error_history, f, -1)
pickle.dump(sparsity_history, f, -1)
return params, error_history, sparsity_history
def sgd(f, initial_params, batches, momentum, hook=None):
output_dir = utils.make_output_directory(OUTPUT_PATH)
save_params = parameters.save_hook(output_dir)
def post_epoch(*args):
save_params(*args)
if hook is not None:
hook(*args)
return optimize.sgd(f,
initial_params,
batches,
epochs,
learning_rate,
momentum,
post_epoch=post_epoch)
def ex(inputs):
inputs = zero_mean(inputs)
inputs, zca = utils.zca_white(inputs, 0.1)
batches = data.BatchIterator(inputs, 100)
num_vis = inputs.shape[1]
num_hid = 400
epochs = 100
momentum = 0
initial_params = grbm.initial_params(num_hid, num_vis, 0.001, 0.4)
neg_free_energy_grad = functools.partial(grbm.neg_free_energy_grad,
learn_sigma=False)
def f(params, inputs):
return rbm.cd(params, inputs, grbm.sample_h_noisy_relu, grbm.sample_v,
neg_free_energy_grad)
learning_rate = 0.005
output_dir = utils.make_output_directory(OUTPUT_PATH)
save_params = parameters.save_hook(output_dir)
def post_epoch(*args):
save_params(*args)
# Save visualization weights.
W_norm = utils.rescale(args[0].W)
img = Image.fromarray(
np.uint8(utils.tile(W_norm, channel_count=3) * 255))
img.save(os.path.join(output_dir, ('w%i.png' % args[1])))
# Estimate sparsity from subset of data.
h_mean = grbm.sample_h_noisy_relu(args[0], inputs[0:5000], True)[1]
mean_activation = np.mean(h_mean > 0)
print 'approx mean activation: %f' % mean_activation
return optimize.sgd(f,
initial_params,
batches,
epochs,
learning_rate,
momentum,
post_epoch=post_epoch)
def ex(inputs):
inputs = zero_mean(inputs)
inputs, zca = utils.zca_white(inputs, 0.1)
batches = data.BatchIterator(inputs, 100)
num_vis = inputs.shape[1]
num_hid = 400
epochs = 100
momentum = 0
initial_params = grbm.initial_params(num_hid, num_vis, 0.001, 0.4)
neg_free_energy_grad = functools.partial(grbm.neg_free_energy_grad,
learn_sigma=False)
def f(params, inputs):
return rbm.cd(params, inputs,
grbm.sample_h_noisy_relu, grbm.sample_v,
neg_free_energy_grad)
learning_rate = 0.005
output_dir = utils.make_output_directory(OUTPUT_PATH)
save_params = parameters.save_hook(output_dir)
def post_epoch(*args):
save_params(*args)
# Save visualization weights.
W_norm = utils.rescale(args[0].W)
img = Image.fromarray(np.uint8(utils.tile(W_norm, channel_count=3) * 255))
img.save(os.path.join(output_dir, ('w%i.png' % args[1])))
# Estimate sparsity from subset of data.
h_mean = grbm.sample_h_noisy_relu(args[0], inputs[0:5000], True)[1]
mean_activation = np.mean(h_mean > 0)
print 'approx mean activation: %f' % mean_activation
return optimize.sgd(f, initial_params, batches,
epochs, learning_rate,
momentum,
post_epoch=post_epoch)
g = itertools.islice(gc, start, None, step)
g = itertools.islice(g, count)
g = itertools.imap(operator.itemgetter(1), g)
g = itertools.imap(utils.tile, g)
utils.save_images(g, tempfile.mkdtemp(dir=OUTPUT_PATH))
# def f(params, inputs):
# return rbm.cd(params, inputs,
# rbm.sample_h,
# rbm.sample_v,
# rbm.neg_free_energy_grad,
# weight_decay=weight_decay,
# k=k)
output_dir = utils.make_output_directory(OUTPUT_PATH)
save_params = parameters.save_hook(output_dir)
f = rbm.pcd(rbm.sample_h, rbm.sample_v,
rbm.neg_free_energy_grad, weight_decay)
def post_epoch(*args):
#save_params(*args)
#print 'Mean hidden activation prob. is %f.' % f.q
pass
params = optimize.sgd(f, initial_params, batches, epochs,
learning_rate,
momentum,
weight_constraint=weight_constraint,
post_epoch=post_epoch)
g = itertools.islice(gc, start, None, step)
g = itertools.islice(g, count)
g = itertools.imap(operator.itemgetter(1), g)
g = itertools.imap(utils.tile, g)
utils.save_images(g, tempfile.mkdtemp(dir=OUTPUT_PATH))
# def f(params, inputs):
# return rbm.cd(params, inputs,
# rbm.sample_h,
# rbm.sample_v,
# rbm.neg_free_energy_grad,
# weight_decay=weight_decay,
# k=k)
output_dir = utils.make_output_directory(OUTPUT_PATH)
save_params = parameters.save_hook(output_dir)
f = rbm.pcd(rbm.sample_h, rbm.sample_v, rbm.neg_free_energy_grad, weight_decay)
def post_epoch(*args):
#save_params(*args)
#print 'Mean hidden activation prob. is %f.' % f.q
pass
params = optimize.sgd(f,
initial_params,
batches,
epochs,