def build_model(self, n_features, W_=None, bh_=None, bv_=None):
"""Create the computational graph.
Parameters
----------
n_features : int
Number of units in the input layer.
W_ : array_like, optional (default = None)
Weight matrix np array.
bh_ : array_like, optional (default = None)
Hidden bias np array.
bv_ : array_like, optional (default = None)
Visible bias np array.
Returns
-------
self
"""
self._create_placeholders(n_features)
self._create_variables(n_features, W_, bh_, bv_)
self._create_encode_layer()
self._create_decode_layer()
variables = [self.W_, self.bh_, self.bv_]
regterm = Layers.regularization(variables, self.regtype, self.regcoef)
self.cost = self.loss.compile(
self.reconstruction, self.input_data_orig, regterm=regterm)
self.train_step = self.trainer.compile(self.cost)
def build_model(self, n_features, regtype='none',
encoding_w=None, encoding_b=None):
"""Create the computational graph for the reconstruction task.
:param n_features: Number of features
:param regtype: regularization type
:param encoding_w: list of weights for the encoding layers.
:param encoding_b: list of biases for the encoding layers.
:return: self
"""
self._create_placeholders(n_features, n_features)
if encoding_w and encoding_b:
self.encoding_w_ = encoding_w
self.encoding_b_ = encoding_b
else:
self._create_variables(n_features)
self._create_encoding_layers()
self._create_decoding_layers()
variables = []
variables.extend(self.encoding_w_)
variables.extend(self.encoding_b_)
regterm = Layers.regularization(variables, self.regtype, self.regcoef)
self.cost = self.loss.compile(
self.reconstruction, self.input_labels, regterm=regterm)
self.train_step = self.trainer.compile(self.cost)
def build_model(self, n_features, encoding_w=None, encoding_b=None):
"""Create the computational graph for the reconstruction task.
:param n_features: Number of features
:param encoding_w: list of weights for the encoding layers.
:param encoding_b: list of biases for the encoding layers.
:return: self
"""
self._create_placeholders(n_features, n_features)
if encoding_w and encoding_b:
self.encoding_w_ = encoding_w
self.encoding_b_ = encoding_b
else:
self._create_variables(n_features)
self._create_encoding_layers()
self._create_decoding_layers()
variables = []
variables.extend(self.encoding_w_)
variables.extend(self.encoding_b_)
regterm = Layers.regularization(variables, self.regtype, self.regcoef)
self.cost = self.loss.compile(self.reconstruction,
self.input_labels,
regterm=regterm)
self.train_step = self.trainer.compile(self.cost)
def build_model(self, n_features, regtype='none'):
"""Build the Restricted Boltzmann Machine model in TensorFlow.
:param n_features: number of features
:param regtype: regularization type
:return: self
"""
self._create_placeholders(n_features)
self._create_variables(n_features)
self.encode = self.sample_hidden_from_visible(self.input_data)[0]
self.reconstruction = self.sample_visible_from_hidden(
self.encode, n_features)
hprob0, hstate0, vprob, hprob1, hstate1 = self.gibbs_sampling_step(
self.input_data, n_features)
positive = self.compute_positive_association(self.input_data, hprob0,
hstate0)
nn_input = vprob
for step in range(self.gibbs_sampling_steps - 1):
hprob, hstate, vprob, hprob1, hstate1 = self.gibbs_sampling_step(
nn_input, n_features)
nn_input = vprob
negative = tf.matmul(tf.transpose(vprob), hprob1)
self.w_upd8 = self.W.assign_add(
self.learning_rate * (positive - negative) / self.batch_size)
self.bh_upd8 = self.bh_.assign_add(
tf.multiply(self.learning_rate,
tf.reduce_mean(tf.subtract(hprob0, hprob1), 0)))
self.bv_upd8 = self.bv_.assign_add(
tf.multiply(self.learning_rate,
tf.reduce_mean(tf.subtract(self.input_data, vprob),
0)))
variables = [self.W, self.bh_, self.bv_]
regterm = Layers.regularization(variables, self.regtype, self.regcoef)
self.cost = self.loss.compile(vprob, self.input_data, regterm=regterm)
def build_model(self, n_features, regtype='none'):
"""Build the Restricted Boltzmann Machine model in TensorFlow.
:param n_features: number of features
:param regtype: regularization type
:return: self
"""
self._create_placeholders(n_features)
self._create_variables(n_features)
self.encode = self.sample_hidden_from_visible(self.input_data)[0]
self.reconstruction = self.sample_visible_from_hidden(
self.encode, n_features)
hprob0, hstate0, vprob, hprob1, hstate1 = self.gibbs_sampling_step(
self.input_data, n_features)
positive = self.compute_positive_association(self.input_data,
hprob0, hstate0)
nn_input = vprob
for step in range(self.gibbs_sampling_steps - 1):
hprob, hstate, vprob, hprob1, hstate1 = self.gibbs_sampling_step(
nn_input, n_features)
nn_input = vprob
negative = tf.matmul(tf.transpose(vprob), hprob1)
self.w_upd8 = self.W.assign_add(
self.learning_rate * (positive - negative) / self.batch_size)
self.bh_upd8 = self.bh_.assign_add(tf.multiply(self.learning_rate, tf.reduce_mean(
tf.subtract(hprob0, hprob1), 0)))
self.bv_upd8 = self.bv_.assign_add(tf.multiply(self.learning_rate, tf.reduce_mean(
tf.subtract(self.input_data, vprob), 0)))
variables = [self.W, self.bh_, self.bv_]
regterm = Layers.regularization(variables, self.regtype, self.regcoef)
self.cost = self.loss.compile(vprob, self.input_data, regterm=regterm)