mnist.NUM_CLASSES)
n = 54200
inputs = inputs[0:n]
targets = targets[0:n]
labels = labels[0:n]
# 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
# data
inputs = mnist.load_inputs(DATA_PATH, mnist.TRAIN_INPUTS)
labels = mnist.load_labels(DATA_PATH, mnist.TRAIN_LABELS)
inputs = data.balance_classes(inputs, labels, mnist.NUM_CLASSES).inputs
batches = data.BatchIterator(inputs[0:54200])
# IDEA: Have a bit of code which loads the current file (as text) and
# dumps and code between two specific comments into a meta.txt file in
# the output directory.
# meta
num_vis = inputs.shape[1]
num_hid = 49
epochs = 10
k = 1 # applies to cd only
initial_params = rbm.initial_params(num_hid, num_vis)
weight_constraint = None
# weight_decay = reg.l2(0.0002)
# weight_decay = None
# momentum = meta.linear(0.5, 0.9, 10)
momentum = 0
learning_rate = 0.1
def generate(params, n=1, start=1, step=20, count=100):
# Use the probability as pixel intensities.
# This means we can't use the very first sample from the chain, as
# it has v_mean == None.
assert start > 0
num_vis = params.W.shape[1]
# data
inputs = mnist.load_inputs(DATA_PATH, mnist.TRAIN_INPUTS)
labels = mnist.load_labels(DATA_PATH, mnist.TRAIN_LABELS)
inputs = data.balance_classes(inputs, labels, mnist.NUM_CLASSES).inputs
batches = data.BatchIterator(inputs[0:54200])
# IDEA: Have a bit of code which loads the current file (as text) and
# dumps and code between two specific comments into a meta.txt file in
# the output directory.
# meta
num_vis = inputs.shape[1]
num_hid = 49
epochs = 10
k = 1 # applies to cd only
initial_params = rbm.initial_params(num_hid, num_vis)
weight_constraint = None
# weight_decay = reg.l2(0.0002)
# weight_decay = None
# momentum = meta.linear(0.5, 0.9, 10)
momentum = 0
learning_rate = 0.1
def generate(params, n=1, start=1, step=20, count=100):
# Use the probability as pixel intensities.
# This means we can't use the very first sample from the chain, as
# it has v_mean == None.
assert start > 0
num_vis = params.W.shape[1]
#initial_v = np.zeros((n, num_vis))
inputs, targets, labels = data.balance_classes(inputs, labels, mnist.NUM_CLASSES)
n = 54200
inputs = inputs[0:n]
targets = targets[0:n]
labels = labels[0:n]
# 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.
