# motivation is to avoid having a square weight matrix between hidden
# layers to avoid matrix transpose errors.
num_vis = inputs.shape[1]
num_hid1 = 529 # 23^2
num_hid2 = 484 # 22^2
num_top = 1936 # 44^2
batches = data.BatchIterator(inputs)
initial_params = rbm.initial_params(num_hid1, num_vis)
params = sgd(rbm_obj, initial_params, batches, momentum)
inputs = logistic(inputs.dot(params.W.T) + params.h_bias)
batches = data.BatchIterator(inputs)
initial_params = rbm.initial_params(num_hid2, num_hid1)
params = sgd(rbm_obj, initial_params, batches, momentum)
inputs = logistic(inputs.dot(params.W.T) + params.h_bias)
batches = data.BatchIterator(np.hstack((targets, inputs)))
initial_params = rbm.initial_params(num_top, num_hid2 + mnist.NUM_CLASSES)
def post_epoch(*args):
print 'Mean hidden activation prob. is %.2f' % pcd.q
# Optimization objective for the top-level RBM.
pcd = rbm.pcd(rbm.sample_h, sample_v_softmax, rbm.neg_free_energy_grad,
weight_decay)
params = sgd(pcd, initial_params, batches, 0, post_epoch)
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,
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)
# These layers differ slightly from those in the paper. My main
# motivation is to avoid having a square weight matrix between hidden
# layers to avoid matrix transpose errors.
num_vis = inputs.shape[1]
num_hid1 = 529 # 23^2
num_hid2 = 484 # 22^2
num_top = 1936 # 44^2
batches = data.BatchIterator(inputs)
initial_params = rbm.initial_params(num_hid1, num_vis)
params = sgd(rbm_obj, initial_params, batches, momentum)
inputs = logistic(inputs.dot(params.W.T) + params.h_bias)
batches = data.BatchIterator(inputs)
initial_params = rbm.initial_params(num_hid2, num_hid1)
params = sgd(rbm_obj, initial_params, batches, momentum)
inputs = logistic(inputs.dot(params.W.T) + params.h_bias)
batches = data.BatchIterator(np.hstack((targets, inputs)))
initial_params = rbm.initial_params(num_top, num_hid2 + mnist.NUM_CLASSES)
def post_epoch(*args):
print 'Mean hidden activation prob. is %.2f' % pcd.q
# Optimization objective for the top-level RBM.
pcd = rbm.pcd(rbm.sample_h, sample_v_softmax,
rbm.neg_free_energy_grad, weight_decay)
params = sgd(pcd, initial_params, batches, 0, post_epoch)
