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Added support for audio databases

Merged Pavel KORSHUNOV requested to merge audio-clean into master
1 file
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@@ -46,6 +46,7 @@ class FullyConnected(Layer):
@@ -46,6 +46,7 @@ class FullyConnected(Layer):
weights_initialization=Xavier(),
weights_initialization=Xavier(),
bias_initialization=Constant(),
bias_initialization=Constant(),
batch_norm=False,
batch_norm=False,
 
init_value=None,
use_gpu=False,
use_gpu=False,
):
):
@@ -61,11 +62,14 @@ class FullyConnected(Layer):
@@ -61,11 +62,14 @@ class FullyConnected(Layer):
self.W = None
self.W = None
self.b = None
self.b = None
self.shape = None
self.shape = None
 
self.init_value = init_value
def create_variables(self, input_layer):
def create_variables(self, input_layer):
self.input_layer = input_layer
self.input_layer = input_layer
if self.W is None:
if self.W is None:
input_dim = reduce(mul, self.input_layer.get_shape().as_list()[1:])
input_dim = reduce(mul, self.input_layer.get_shape().as_list()[1:])
 
if self.init_value is None:
 
self.init_value = input_dim
variable = "W_" + str(self.name)
variable = "W_" + str(self.name)
if self.get_varible_by_name(variable) is not None:
if self.get_varible_by_name(variable) is not None:
@@ -73,7 +77,8 @@ class FullyConnected(Layer):
@@ -73,7 +77,8 @@ class FullyConnected(Layer):
else:
else:
self.W = self.weights_initialization(shape=[input_dim, self.output_dim],
self.W = self.weights_initialization(shape=[input_dim, self.output_dim],
name="W_" + str(self.name),
name="W_" + str(self.name),
scope="W_" +str(self.name)
scope="W_" +str(self.name),
 
init_value=self.init_value
)
)
# if self.activation is not None:
# if self.activation is not None:
variable = "b_" + str(self.name)
variable = "b_" + str(self.name)
@@ -82,14 +87,15 @@ class FullyConnected(Layer):
@@ -82,14 +87,15 @@ class FullyConnected(Layer):
else:
else:
self.b = self.bias_initialization(shape=[self.output_dim],
self.b = self.bias_initialization(shape=[self.output_dim],
name="b_" + str(self.name),
name="b_" + str(self.name),
scope="b_" + str(self.name)
scope="b_" + str(self.name),
 
init_value=self.init_value
)
)
def get_graph(self, training_phase=True):
def get_graph(self, training_phase=True):
with tf.name_scope(str(self.name)):
with tf.name_scope(str(self.name)):
if len(self.input_layer.get_shape()) == 4:
if len(self.input_layer.get_shape()) == 4 or len(self.input_layer.get_shape()) == 3:
shape = self.input_layer.get_shape().as_list()
shape = self.input_layer.get_shape().as_list()
fc = tf.reshape(self.input_layer, [-1, numpy.prod(shape[1:])])
fc = tf.reshape(self.input_layer, [-1, numpy.prod(shape[1:])])
else:
else:
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