def sample_hidden_from_visible(self, visible):
""" Sample the hidden units from the visible units.
This is the Positive phase of the Contrastive Divergence algorithm.
:param visible: activations of the visible units
:return: tuple(hidden probabilities, hidden binary states)
"""
hprobs = tf.nn.sigmoid(tf.matmul(visible, self.W) + self.bh_)
hstates = utilities.sample_prob(hprobs, self.hrand)
return hprobs, hstates
def _create_decoding_layers(self, decode_input):
""" Create the decoding layres of the model.
:param decode_input: output of the last encoding layer
:return: output of the last decoding layer
"""
decode_output = decode_input
for l in reversed(range(self.n_layers - 1)):
vprobs = tf.nn.sigmoid(
tf.matmul(decode_output, self.W_vars_t[l]) + self.bv_vars[l])
vstates = utilities.sample_prob(vprobs, self.vrand[l])
decode_output = vstates
return decode_output
def _create_encoding_layers(self):
""" Create the encoding layers of the model.
:return: output of the last encoding layer
"""
next_layer_feed = self.x
for l in range(self.n_layers - 1):
hprobs = tf.nn.dropout(
tf.nn.sigmoid(
tf.matmul(next_layer_feed, self.W_vars[l]) +
self.bh_vars[l]), self.keep_prob)
hstates = utilities.sample_prob(hprobs, self.hrand[l])
next_layer_feed = hstates
return next_layer_feed