def __init__(self, shapes, queue, noise=None):
# Communication queue for the log
self.queue = queue
# Semantic variables for the input, corruption level
# and learning rate
X = T.matrix('X')
self.inputs = X
cl = T.scalar(dtype=theano.config.floatX,
name='corruption level')
self.cl = cl
lr = T.scalar(dtype=theano.config.floatX,
name='learning rate')
self.lr = lr
# Random number generators used for the noise
np_rng = np.random.RandomState()
theano_rng = RandomStreams(np_rng.randint(2**30))
# Layers initialisation, cast the shape
# and fill the layers list.
self.layers = []
self.mask = []
self.shapes = shapes
(nv,_,_) = shapes[0]
self.params = []
self.params_ft = []
output = X
sample_up = X
# Compute the droupout training function
p_do = 0.5
self.p_do = p_do
dropout_out =X
rec_do = X
# Build the layers, linking each one to the next
# Fill the param list
for i, s in enumerate(shapes[1:]):
lay = RBM(nv, s[0], output, v_unit=s[2],
unit_type=s[1])
self.layers.append(lay)
self.params += lay.params
self.params_ft += lay.params_ft
nv = s[0]
output = lay.up(output)
sample_up = lay.sample_h_given_v(sample_up)
if i != 0:
mask = theano_rng.binomial(size=dropout_out.shape, n=1, p=p_do)
dropout_out *= mask
rec_do *= p_do
dropout_out = lay.up(dropout_out)
rec_do = lay.up(rec_do)
# Define the up functions
self.code = output
self.sample_up = sample_up
# Prepare the variables to decode
self.N = len(self.layers)
recstr = output
decode = X
sample_down = X
sample = sample_up
# Add noise to the output for the fine tuning part
self.noise = noise
if self.noise == 'MASK':
fine_tune = T.clip(output * \
theano_rng.binomial(
size=output.shape,
n=1, p=1-cl),0.,1.)
elif self.noise == 'GAUSS':
fine_tune = T.clip(output +\
theano_rng.normal(
size=output.shape,
std=cl), 0.,1.)
else:
fine_tune = output
# Down sample every variable
for i in range(1, self.N+1):
lay = self.layers[self.N-i]
recstr = lay.down(recstr)
decode = lay.down(decode)
fine_tune = lay.down(fine_tune)
sample_dowm = lay.sample_v_given_h(sample_down)
sample = lay.sample_v_given_h(sample)
if i!= self.N:
rec_do *= p_do
mask = theano_rng.binomial(size=dropout_out.shape,
n=1, p=p_do)
dropout_out *= mask
dropout_out = lay.down(dropout_out)
rec_do = lay.down(rec_do)
#define the sampeling and decoding functions
self.recstr = recstr
self.decode = decode
self.ft = fine_tune
self.do = dropout_out
self.sample_down = sample_down
self.sample = sample
self.compile()
def __init__(self, shapes, queue, noise=None):
# Communication queue for the log
self.queue = queue
# Semantic variables for the input, corruption level
# and learning rate
X = T.matrix('X')
self.inputs = X
cl = T.scalar(dtype=theano.config.floatX, name='corruption level')
self.cl = cl
lr = T.scalar(dtype=theano.config.floatX, name='learning rate')
self.lr = lr
# Random number generators used for the noise
np_rng = np.random.RandomState()
theano_rng = RandomStreams(np_rng.randint(2**30))
# Layers initialisation, cast the shape
# and fill the layers list.
self.layers = []
self.mask = []
self.shapes = shapes
(nv, _, _) = shapes[0]
self.params = []
self.params_ft = []
output = X
sample_up = X
# Compute the droupout training function
p_do = 0.5
self.p_do = p_do
dropout_out = X
rec_do = X
# Build the layers, linking each one to the next
# Fill the param list
for i, s in enumerate(shapes[1:]):
lay = RBM(nv, s[0], output, v_unit=s[2], unit_type=s[1])
self.layers.append(lay)
self.params += lay.params
self.params_ft += lay.params_ft
nv = s[0]
output = lay.up(output)
sample_up = lay.sample_h_given_v(sample_up)
if i != 0:
mask = theano_rng.binomial(size=dropout_out.shape, n=1, p=p_do)
dropout_out *= mask
rec_do *= p_do
dropout_out = lay.up(dropout_out)
rec_do = lay.up(rec_do)
# Define the up functions
self.code = output
self.sample_up = sample_up
# Prepare the variables to decode
self.N = len(self.layers)
recstr = output
decode = X
sample_down = X
sample = sample_up
# Add noise to the output for the fine tuning part
self.noise = noise
if self.noise == 'MASK':
fine_tune = T.clip(output * \
theano_rng.binomial(
size=output.shape,
n=1, p=1-cl),0.,1.)
elif self.noise == 'GAUSS':
fine_tune = T.clip(output +\
theano_rng.normal(
size=output.shape,
std=cl), 0.,1.)
else:
fine_tune = output
# Down sample every variable
for i in range(1, self.N + 1):
lay = self.layers[self.N - i]
recstr = lay.down(recstr)
decode = lay.down(decode)
fine_tune = lay.down(fine_tune)
sample_dowm = lay.sample_v_given_h(sample_down)
sample = lay.sample_v_given_h(sample)
if i != self.N:
rec_do *= p_do
mask = theano_rng.binomial(size=dropout_out.shape, n=1, p=p_do)
dropout_out *= mask
dropout_out = lay.down(dropout_out)
rec_do = lay.down(rec_do)
#define the sampeling and decoding functions
self.recstr = recstr
self.decode = decode
self.ft = fine_tune
self.do = dropout_out
self.sample_down = sample_down
self.sample = sample
self.compile()
