def _create_variables_no_pretrain(self, n_features):
""" Create model variables (no previous unsupervised pretraining)
:param n_features: number of features
:return: self
"""
if not self.xavier_init[0]:
xinit = 1
else:
xinit = self.xavier_init[0]
self.encoding_w_ = []
self.encoding_b_ = []
for l, layer in enumerate(self.layers):
if l == 0:
self.encoding_w_.append(
tf.Variable(
utilities.xavier_init(n_features, self.layers[l],
xinit)))
self.encoding_b_.append(
tf.Variable(
tf.truncated_normal([self.layers[l]], stddev=0.01)))
else:
self.encoding_w_.append(
tf.Variable(
utilities.xavier_init(self.layers[l - 1],
self.layers[l], xinit)))
self.encoding_b_.append(
tf.Variable(
tf.truncated_normal([self.layers[l]], stddev=0.01)))
def _create_variables(self, n_features):
""" Create the TensorFlow variables for the model.
:return: self
"""
self.W_ = tf.Variable(utilities.xavier_init(n_features, self.n_components, self.xavier_init), name='enc-w')
self.bh_ = tf.Variable(tf.zeros([self.n_components]), name='hidden-bias')
self.bv_ = tf.Variable(tf.zeros([n_features]), name='visible-bias')
def _create_variables(self, n_features, W_=None, bh_=None, bv_=None):
""" Create the TensorFlow variables for the model.
:return: self
"""
if W_:
self.W_ = tf.Variable(W_, name='enc-w')
else:
self.W_ = tf.Variable(utilities.xavier_init(
n_features, self.n_components, self.xavier_init),
name='enc-w')
if bh_:
self.bh_ = tf.Variable(bh_, name='hidden-bias')
else:
self.bh_ = tf.Variable(tf.constant(0.01,
shape=[self.n_components]),
name='hidden-bias')
if bv_:
self.bv_ = tf.Variable(bv_, name='visible-bias')
else:
self.bv_ = tf.Variable(tf.constant(0.01, shape=[n_features]),
name='visible-bias')