class ResidualBlock(Layer):
def __init__(self,
dilation_rate: int,
nb_filters: int,
kernel_size: int,
padding: str,
activation: str = 'relu',
dropout_rate: float = 0,
kernel_initializer: str = 'he_normal',
use_batch_norm: bool = False,
use_layer_norm: bool = False,
use_weight_norm: bool = False,
**kwargs):
"""Defines the residual block for the WaveNet TCN
Args:
x: The previous layer in the model
training: boolean indicating whether the layer should behave in training mode or in inference mode
dilation_rate: The dilation power of 2 we are using for this residual block
nb_filters: The number of convolutional filters to use in this block
kernel_size: The size of the convolutional kernel
padding: The padding used in the convolutional layers, 'same' or 'causal'.
activation: The final activation used in o = Activation(x + F(x))
dropout_rate: Float between 0 and 1. Fraction of the input units to drop.
kernel_initializer: Initializer for the kernel weights matrix (Conv1D).
use_batch_norm: Whether to use batch normalization in the residual layers or not.
use_layer_norm: Whether to use layer normalization in the residual layers or not.
use_weight_norm: Whether to use weight normalization in the residual layers or not.
kwargs: Any initializers for Layer class.
"""
self.dilation_rate = dilation_rate
self.nb_filters = nb_filters
self.kernel_size = kernel_size
self.padding = padding
self.activation = activation
self.dropout_rate = dropout_rate
self.use_batch_norm = use_batch_norm
self.use_layer_norm = use_layer_norm
self.use_weight_norm = use_weight_norm
self.kernel_initializer = kernel_initializer
self.layers = []
self.layers_outputs = []
self.shape_match_conv = None
self.res_output_shape = None
self.final_activation = None
super(ResidualBlock, self).__init__(**kwargs)
def _build_layer(self, layer):
"""Helper function for building layer
Args:
layer: Appends layer to internal layer list and builds it based on the current output
shape of ResidualBlocK. Updates current output shape.
"""
self.layers.append(layer)
self.layers[-1].build(self.res_output_shape)
self.res_output_shape = self.layers[-1].compute_output_shape(
self.res_output_shape)
def build(self, input_shape):
with K.name_scope(
self.name
): # name scope used to make sure weights get unique names
self.layers = []
self.res_output_shape = input_shape
for k in range(2):
name = 'conv1D_{}'.format(k)
with K.name_scope(
name
): # name scope used to make sure weights get unique names
conv = Conv1D(filters=self.nb_filters,
kernel_size=self.kernel_size,
dilation_rate=self.dilation_rate,
padding=self.padding,
name=name,
kernel_initializer=self.kernel_initializer)
if self.use_weight_norm:
from tensorflow_addons.layers import WeightNormalization
# wrap it. WeightNormalization API is different than BatchNormalization or LayerNormalization.
with K.name_scope('norm_{}'.format(k)):
conv = WeightNormalization(conv)
self._build_layer(conv)
with K.name_scope('norm_{}'.format(k)):
if self.use_batch_norm:
self._build_layer(BatchNormalization())
elif self.use_layer_norm:
self._build_layer(LayerNormalization())
elif self.use_weight_norm:
pass # done above.
self._build_layer(Activation(self.activation))
self._build_layer(SpatialDropout1D(rate=self.dropout_rate))
if self.nb_filters != input_shape[-1]:
# 1x1 conv to match the shapes (channel dimension).
name = 'matching_conv1D'
with K.name_scope(name):
# make and build this layer separately because it directly uses input_shape
self.shape_match_conv = Conv1D(
filters=self.nb_filters,
kernel_size=1,
padding='same',
name=name,
kernel_initializer=self.kernel_initializer)
else:
name = 'matching_identity'
self.shape_match_conv = Lambda(lambda x: x, name=name)
with K.name_scope(name):
self.shape_match_conv.build(input_shape)
self.res_output_shape = self.shape_match_conv.compute_output_shape(
input_shape)
self._build_layer(Activation(self.activation))
self.final_activation = Activation(self.activation)
self.final_activation.build(
self.res_output_shape) # probably isn't necessary
# this is done to force Keras to add the layers in the list to self._layers
for layer in self.layers:
self.__setattr__(layer.name, layer)
self.__setattr__(self.shape_match_conv.name, self.shape_match_conv)
self.__setattr__(self.final_activation.name, self.final_activation)
super(ResidualBlock, self).build(
input_shape) # done to make sure self.built is set True
def call(self, inputs, training=None):
"""
Returns: A tuple where the first element is the residual model tensor, and the second
is the skip connection tensor.
"""
x = inputs
self.layers_outputs = [x]
for layer in self.layers:
training_flag = 'training' in dict(
inspect.signature(layer.call).parameters)
x = layer(x, training=training) if training_flag else layer(x)
self.layers_outputs.append(x)
x2 = self.shape_match_conv(inputs)
self.layers_outputs.append(x2)
res_x = layers.add([x2, x])
self.layers_outputs.append(res_x)
res_act_x = self.final_activation(res_x)
self.layers_outputs.append(res_act_x)
return [res_act_x, x]
def compute_output_shape(self, input_shape):
return [self.res_output_shape, self.res_output_shape]
class ResidualBlock(Layer):
def __init__(self,
dilation_rate: int,
nb_filters: int,
kernel_size: int,
padding: str,
activation: str = 'relu',
dropout_rate: float = 0,
kernel_initializer: str = 'he_normal',
use_batch_norm: bool = False,
use_layer_norm: bool = False,
use_weight_norm: bool = False,
**kwargs):
self.dilation_rate = dilation_rate
self.nb_filters = nb_filters
self.kernel_size = kernel_size
self.padding = padding
self.activation = activation
self.dropout_rate = dropout_rate
self.use_batch_norm = use_batch_norm
self.use_layer_norm = use_layer_norm
self.use_weight_norm = use_weight_norm
self.kernel_initializer = kernel_initializer
self.layers = []
self.layers_outputs = []
self.shape_match_conv = None
self.res_output_shape = None
self.final_activation = None
super(ResidualBlock, self).__init__(**kwargs)
def _build_layer(self, layer):
self.layers.append(layer)
self.layers[-1].build(self.res_output_shape)
self.res_output_shape = self.layers[-1].compute_output_shape(
self.res_output_shape)
def build(self, input_shape):
with K.name_scope(
self.name
): # name scope used to make sure weights get unique names
self.layers = []
self.res_output_shape = input_shape
for k in range(2):
name = 'conv1D_{}'.format(k)
with K.name_scope(
name
): # name scope used to make sure weights get unique names
conv = Conv1D(filters=self.nb_filters,
kernel_size=self.kernel_size,
dilation_rate=self.dilation_rate,
padding=self.padding,
name=name,
kernel_initializer=self.kernel_initializer)
if self.use_weight_norm:
from tensorflow_addons.layers import WeightNormalization
# wrap it. WeightNormalization API is different than BatchNormalization or LayerNormalization.
with K.name_scope('norm_{}'.format(k)):
conv = WeightNormalization(conv)
self._build_layer(conv)
with K.name_scope('norm_{}'.format(k)):
if self.use_batch_norm:
self._build_layer(BatchNormalization())
elif self.use_layer_norm:
self._build_layer(LayerNormalization())
elif self.use_weight_norm:
pass # done above.
self._build_layer(Activation(self.activation))
self._build_layer(SpatialDropout1D(rate=self.dropout_rate))
if self.nb_filters != input_shape[-1]:
# 1x1 conv to match the shapes (channel dimension).
name = 'matching_conv1D'
with K.name_scope(name):
# make and build this layer separately because it directly uses input_shape
self.shape_match_conv = Conv1D(
filters=self.nb_filters,
kernel_size=1,
padding='same',
name=name,
kernel_initializer=self.kernel_initializer)
else:
name = 'matching_identity'
self.shape_match_conv = Lambda(lambda x: x, name=name)
with K.name_scope(name):
self.shape_match_conv.build(input_shape)
self.res_output_shape = self.shape_match_conv.compute_output_shape(
input_shape)
self._build_layer(Activation(self.activation))
self.final_activation = Activation(self.activation)
self.final_activation.build(
self.res_output_shape) # probably isn't necessary
# this is done to force Keras to add the layers in the list to self._layers
for layer in self.layers:
self.__setattr__(layer.name, layer)
self.__setattr__(self.shape_match_conv.name, self.shape_match_conv)
self.__setattr__(self.final_activation.name, self.final_activation)
super(ResidualBlock, self).build(
input_shape) # done to make sure self.built is set True
def call(self, inputs, training=None):
x = inputs
self.layers_outputs = [x]
for layer in self.layers:
training_flag = 'training' in dict(
inspect.signature(layer.call).parameters)
x = layer(x, training=training) if training_flag else layer(x)
self.layers_outputs.append(x)
x2 = self.shape_match_conv(inputs)
self.layers_outputs.append(x2)
res_x = layers.add([x2, x])
self.layers_outputs.append(res_x)
res_act_x = self.final_activation(res_x)
self.layers_outputs.append(res_act_x)
return [res_act_x, x]
def compute_output_shape(self, input_shape):
return [self.res_output_shape, self.res_output_shape]
class TCN(Layer):
"""Creates a TCN layer.
Input shape:
A tensor of shape (batch_size, timesteps, input_dim).
Args:
nb_filters: The number of filters to use in the convolutional layers.
kernel_size: The size of the kernel to use in each convolutional layer.
dilations: The list of the dilations. Example is: [1, 2, 4, 8, 16, 32, 64].
nb_stacks : The number of stacks of residual blocks to use.
padding: The padding to use in the convolutional layers, 'causal' or 'same'.
use_skip_connections: Boolean. If we want to add skip connections from input to each residual blocK.
return_sequences: Boolean. Whether to return the last output in the output sequence, or the full sequence.
activation: The activation used in the residual blocks o = Activation(x + F(x)).
dropout_rate: Float between 0 and 1. Fraction of the input units to drop.
kernel_initializer: Initializer for the kernel weights matrix (Conv1D).
use_batch_norm: Whether to use batch normalization in the residual layers or not.
kwargs: Any other arguments for configuring parent class Layer. For example "name=str", Name of the model.
Use unique names when using multiple TCN.
Returns:
A TCN layer.
"""
def __init__(self,
nb_filters=64,
kernel_size=2,
nb_stacks=1,
dilations=(1, 2, 4, 8, 16, 32),
padding='causal',
use_skip_connections=False,
dropout_rate=0.0,
return_sequences=False,
activation='relu',
kernel_initializer='he_normal',
use_batch_norm=False,
use_layer_norm=False,
**kwargs):
self.return_sequences = return_sequences
self.dropout_rate = dropout_rate
self.use_skip_connections = use_skip_connections
self.dilations = dilations
self.nb_stacks = nb_stacks
self.kernel_size = kernel_size
self.nb_filters = nb_filters
self.activation = activation
self.padding = padding
self.kernel_initializer = kernel_initializer
self.use_batch_norm = use_batch_norm
self.use_layer_norm = use_layer_norm
self.skip_connections = []
self.residual_blocks = []
self.layers_outputs = []
self.build_output_shape = None
self.lambda_layer = None
self.lambda_ouput_shape = None
if padding != 'causal' and padding != 'same':
raise ValueError(
"Only 'causal' or 'same' padding are compatible for this layer."
)
if not isinstance(nb_filters, int):
print('An interface change occurred after the version 2.1.2.')
print('Before: tcn.TCN(x, return_sequences=False, ...)')
print('Now should be: tcn.TCN(return_sequences=False, ...)(x)')
print(
'The alternative is to downgrade to 2.1.2 (pip install keras-tcn==2.1.2).'
)
raise Exception()
# initialize parent class
super(TCN, self).__init__(**kwargs)
@property
def receptive_field(self):
assert_msg = 'The receptive field formula works only with power of two dilations.'
assert all([is_power_of_two(i) for i in self.dilations]), assert_msg
return self.kernel_size * self.nb_stacks * self.dilations[-1]
def build(self, input_shape):
# member to hold current output shape of the layer for building purposes
self.build_output_shape = input_shape
# list to hold all the member ResidualBlocks
self.residual_blocks = []
total_num_blocks = self.nb_stacks * len(self.dilations)
if not self.use_skip_connections:
total_num_blocks += 1 # cheap way to do a false case for below
for s in range(self.nb_stacks):
for d in self.dilations:
self.residual_blocks.append(
ResidualBlock(dilation_rate=d,
nb_filters=self.nb_filters,
kernel_size=self.kernel_size,
padding=self.padding,
activation=self.activation,
dropout_rate=self.dropout_rate,
use_batch_norm=self.use_batch_norm,
use_layer_norm=self.use_layer_norm,
kernel_initializer=self.kernel_initializer,
name='residual_block_{}'.format(
len(self.residual_blocks))))
# build newest residual block
self.residual_blocks[-1].build(self.build_output_shape)
self.build_output_shape = self.residual_blocks[
-1].res_output_shape
# this is done to force keras to add the layers in the list to self._layers
for layer in self.residual_blocks:
self.__setattr__(layer.name, layer)
# Author: @karolbadowski.
output_slice_index = int(self.build_output_shape.as_list()[1] /
2) if self.padding == 'same' else -1
self.lambda_layer = Lambda(lambda tt: tt[:, output_slice_index, :])
self.lambda_ouput_shape = self.lambda_layer.compute_output_shape(
self.build_output_shape)
def compute_output_shape(self, input_shape):
"""
Overridden in case keras uses it somewhere... no idea. Just trying to avoid future errors.
"""
if not self.built:
self.build(input_shape)
if not self.return_sequences:
return self.lambda_ouput_shape
else:
return self.build_output_shape
def call(self, inputs, training=None):
x = inputs
self.layers_outputs = [x]
self.skip_connections = []
for layer in self.residual_blocks:
x, skip_out = layer(x, training=training)
self.skip_connections.append(skip_out)
self.layers_outputs.append(x)
if self.use_skip_connections:
x = layers.add(self.skip_connections)
self.layers_outputs.append(x)
if not self.return_sequences:
x = self.lambda_layer(x)
self.layers_outputs.append(x)
return x
def get_config(self):
"""
Returns the config of a the layer. This is used for saving and loading from a model
:return: python dictionary with specs to rebuild layer
"""
config = super(TCN, self).get_config()
config['nb_filters'] = self.nb_filters
config['kernel_size'] = self.kernel_size
config['nb_stacks'] = self.nb_stacks
config['dilations'] = self.dilations
config['padding'] = self.padding
config['use_skip_connections'] = self.use_skip_connections
config['dropout_rate'] = self.dropout_rate
config['return_sequences'] = self.return_sequences
config['activation'] = self.activation
config['use_batch_norm'] = self.use_batch_norm
config['use_layer_norm'] = self.use_layer_norm
config['kernel_initializer'] = self.kernel_initializer
return config
class ResidualBlock(Layer):
def __init__(self,
dilation_rate, # dilation power of 2
nb_filters, # number of conv filters
kernel_size, # conv kernel size
padding, # valid(no padding), same(inputlen=outputlen), causal
activation='relu', # o = Activation(x + F(x))
dropout_rate=0, # [0, 1]
kernel_initializer='he_normal',
use_batch_norm=True,
use_layer_norm=False,
last_block=True,
**kwargs): # key, value
self.dilation_rate = dilation_rate
self.nb_filters = nb_filters
self.kernel_size = kernel_size
self.padding = padding
self.activation = activation
self.dropout_rate = dropout_rate
self.kernel_initializer = kernel_initializer
self.use_batch_norm = use_batch_norm
self.use_layer_norm = use_layer_norm
self.last_block = last_block
self.layers = []
self.layers_outputs = []
self.shape_match_conv = None
self.res_output_shape = None
self.final_activation = None
super(ResidualBlock, self).__init__(**kwargs)
def _add_and_activate_layer(self, layer): # append layer to internal layer list
self.layers.append(layer)
self.layers[-1].build(self.res_output_shape)
self.res_output_shape = self.layers[-1].compute_output_shape(self.res_output_shape)
def build(self, input_shape):
with K.name_scope(self.name): # name scope used to make sure weights get unique names
self.layers = []
self.res_output_shape = input_shape
for k in range(2):
with K.name_scope('conv1D_{}'.format(k)): # conv1D_0 or conv1D_1
self._add_and_activate_layer(Conv1D(filters=self.nb_filters,
kernel_size=self.kernel_size,
dilation_rate=self.dilation_rate,
padding=self.padding,
name='conv1D_{}'.format(k),
kernel_initializer=self.kernel_initializer))
#self._add_and_activate_layer(MaxPooling1D(pool_size=3))
with K.name_scope('norm_{}'.format(k)): # norm_0 or norm_1
if self.use_batch_norm:
self._add_and_activate_layer(BatchNormalization())
print('use batch_norm')
elif self.use_layer_norm:
self._add_and_activate_layer(LayerNormalization())
print('use layer_norm')
self._add_and_activate_layer(Activation('relu'))
self._add_and_activate_layer(SpatialDropout1D(rate=self.dropout_rate))
if not self.last_block:
# 1x1 conv to match the shapes (channel dimension).
name = 'conv1D_{}'.format(k + 1)
with K.name_scope(name):
# make and build this layer separately because it directly uses input_shape
self.shape_match_conv = Conv1D(filters=self.nb_filters,
kernel_size=1,
padding='same', # len(input) == len(output)
name=name,
kernel_initializer=self.kernel_initializer)
else:
self.shape_match_conv = Lambda(lambda x: x, name='identity') # Lambda : Layer로 감싸줌
self.shape_match_conv.build(input_shape)
self.res_output_shape = self.shape_match_conv.compute_output_shape(input_shape)
self.final_activation = Activation(self.activation)
self.final_activation.build(self.res_output_shape) # probably isn't necessary
# this is done to force Keras to add the layers in the list to self._layers
for layer in self.layers:
self.__setattr__(layer.name, layer)
super(ResidualBlock, self).build(input_shape) # done to make sure self.built is set True
def call(self, inputs, training=None): # training : boolean whether it's training mode or not
x = inputs
self.layers_outputs = [x]
for layer in self.layers:
training_flag = 'training' in dict(inspect.signature(layer.call).parameters)
x = layer(x, training=training) if training_flag else layer(x)
# training_flag == False -> x = layer(x)
self.layers_outputs.append(x)
x2 = self.shape_match_conv(inputs)
self.layers_outputs.append(x2)
res_x = layers.add([x2, x])
self.layers_outputs.append(res_x)
res_act_x = self.final_activation(res_x)
self.layers_outputs.append(res_act_x)
return [res_act_x, x] # residual model tensor, skip connection tensor
def compute_output_shape(self, input_shape):
return [self.res_output_shape, self.res_output_shape]
class TCN(Layer): # input shape (batch_size, timesteps, input_dim), return TCN layer
def __init__(self,
nb_filters=64,
kernel_size=16,
nb_stacks=1,
dilations=(1, 2, 4, 8, 16, 32),
padding='causal',
use_skip_connections=True,
dropout_rate=0.0,
return_sequences=False,
activation='linear',
kernel_initializer='he_normal',
use_batch_norm=False,
use_layer_norm=False,
**kwargs): # to configure parent class layer
#print('__init__\n')
self.return_sequences = return_sequences
self.dropout_rate = dropout_rate
self.use_skip_connections = use_skip_connections
self.dilations = dilations
self.nb_stacks = nb_stacks
self.kernel_size = kernel_size
self.nb_filters = nb_filters
self.activation = activation
self.padding = padding
self.kernel_initializer = kernel_initializer
self.use_batch_norm = use_batch_norm
self.use_layer_norm = use_layer_norm
self.skip_connections = []
self.residual_blocks = []
self.layers_outputs = []
self.main_conv1D = None
self.build_output_shape = None
self.lambda_layer = None
self.lambda_ouput_shape = None
if padding != 'causal' and padding != 'same': # Must use padding
raise ValueError("Only 'causal' or 'same' padding are compatible for this layer.")
if not isinstance(nb_filters, int): # check whether nb_filters(64) is integer or not
print('An interface change occurred after the version 2.1.2.')
print('Before: tcn.TCN(x, return_sequences=False, ...)')
print('Now should be: tcn.TCN(return_sequences=False, ...)(x)')
print('The alternative is to downgrade to 2.1.2 (pip install keras-tcn==2.1.2).')
raise Exception()
# initialize parent class
super(TCN, self).__init__(**kwargs)
@property # getter
def receptive_field(self):
assert_msg = 'The receptive field formula works only with power of two dilations.'
assert all([is_power_of_two(i) for i in self.dilations]), assert_msg
# check dilation numbers are power of two or not / if not true, raise assert error
return self.kernel_size * self.nb_stacks * self.dilations[-1]
def build(self, input_shape):
#print('build\n')
self.main_conv1D = Conv1D(filters=64,#self.nb_filters, # of filters
kernel_size=10, # same with Dense layer
padding=self.padding, # causal
kernel_initializer=self.kernel_initializer) # he_normal
'''
block = Conv1D(filters=64,#self.nb_filters, # of filters
kernel_size=10, # same with Dense layer
padding=self.padding, # causal
kernel_initializer=self.kernel_initializer) # he_normal
block = MaxPooling1D(pool_size=3)(block)
self.main_conv1D = block
'''
self.main_conv1D.build(input_shape)
# member to hold current output shape of the layer for building purposes
self.build_output_shape = self.main_conv1D.compute_output_shape(input_shape)
# list to hold all the member ResidualBlocks
self.residual_blocks = []
total_num_blocks = self.nb_stacks * len(self.dilations) # block = layer, total_num_blocks = 6
if not self.use_skip_connections:
total_num_blocks += 1 # cheap way to do a false case for below
for s in range(self.nb_stacks):
for d in self.dilations:
self.residual_blocks.append(ResidualBlock(dilation_rate=d,
nb_filters=self.nb_filters,
kernel_size=self.kernel_size,
padding=self.padding,
activation=self.activation,
dropout_rate=self.dropout_rate,
use_batch_norm=self.use_batch_norm,
use_layer_norm=self.use_layer_norm,
kernel_initializer=self.kernel_initializer,
last_block=len(self.residual_blocks) + 1 == total_num_blocks,
name='residual_block_{}'.format(len(self.residual_blocks))))
# build newest residual block
self.residual_blocks[-1].build(self.build_output_shape)
self.build_output_shape = self.residual_blocks[-1].res_output_shape
# this is done to force keras to add the layers in the list to self._layers
for layer in self.residual_blocks:
self.__setattr__(layer.name, layer)
# Author: @karolbadowski.
output_slice_index = int(self.build_output_shape.as_list()[1] / 2) if self.padding == 'same' else -1
#print(self.build_output_shape.as_list()) #(None, 10000, nb_filters)
#print('output slice index : ', output_slice_index) # -1
self.lambda_layer = Lambda(lambda tt: tt[:, output_slice_index, :])
self.lambda_ouput_shape = self.lambda_layer.compute_output_shape(self.build_output_shape)
def compute_output_shape(self, input_shape):
if not self.built:
self.build(input_shape)
if not self.return_sequences:
return self.lambda_ouput_shape
else:
return self.build_output_shape
def call(self, inputs, training=None):
#print('call\n')
x = inputs
self.layers_outputs = [x]
try:
x = self.main_conv1D(x)
self.layers_outputs.append(x)
except AttributeError:
print('The backend of keras-tcn>2.8.3 has changed from keras to tensorflow.keras.')
print('Either update your imports:\n- From "from keras.layers import <LayerName>" '
'\n- To "from tensorflow.keras.layers import <LayerName>"')
print('Or downgrade to 2.8.3 by running "pip install keras-tcn==2.8.3"')
import sys
sys.exit(0)
self.skip_connections = []
for layer in self.residual_blocks:
x, skip_out = layer(x, training=training)
self.skip_connections.append(skip_out)
self.layers_outputs.append(x)
if self.use_skip_connections:
x = layers.add(self.skip_connections)
self.layers_outputs.append(x)
if not self.return_sequences:
x = self.lambda_layer(x)
self.layers_outputs.append(x)
return x
def get_config(self):
config = super(TCN, self).get_config()
config['nb_filters'] = self.nb_filters
config['kernel_size'] = self.kernel_size
config['nb_stacks'] = self.nb_stacks
config['dilations'] = self.dilations
config['padding'] = self.padding
config['use_skip_connections'] = self.use_skip_connections
config['dropout_rate'] = self.dropout_rate
config['return_sequences'] = self.return_sequences
config['activation'] = self.activation
config['use_batch_norm'] = self.use_batch_norm
config['use_layer_norm'] = self.use_layer_norm
config['kernel_initializer'] = self.kernel_initializer
return config
class ResidualBlock(Layer):
def __init__(self,
dilation_rate,
nb_filters,
kernel_size,
padding,
activation='relu',
dropout_rate=0,
kernel_initializer='he_normal',
use_batch_norm=False,
use_layer_norm=False,
last_block=True,
**kwargs):
# type: (int, int, int, str, str, float, str, bool, bool, dict) -> None
"""Defines the residual block for the WaveNet TCN
Args:
x: The previous layer in the model
training: boolean indicating whether the layer should behave in training mode or in inference mode
dilation_rate: The dilation power of 2 we are using for this residual block
nb_filters: The number of convolutional filters to use in this block
kernel_size: The size of the convolutional kernel
padding: The padding used in the convolutional layers, 'same' or 'causal'.
activation: The final activation used in o = Activation(x + F(x))
dropout_rate: Float between 0 and 1. Fraction of the input units to drop.
kernel_initializer: Initializer for the kernel weights matrix (Conv1D).
use_batch_norm: Whether to use batch normalization in the residual layers or not.
use_layer_norm: Whether to use layer normalization in the residual layers or not.
kwargs: Any initializers for Layer class.
"""
self.dilation_rate = dilation_rate
self.nb_filters = nb_filters
self.kernel_size = kernel_size
self.padding = padding
self.activation = activation
self.dropout_rate = dropout_rate
self.use_batch_norm = use_batch_norm
self.use_layer_norm = use_layer_norm
self.kernel_initializer = kernel_initializer
self.last_block = last_block
super(ResidualBlock, self).__init__(**kwargs)
def _add_and_activate_layer(self, layer):
"""Helper function for building layer
Args:
layer: Appends layer to internal layer list and builds it based on the current output
shape of ResidualBlocK. Updates current output shape.
"""
self.residual_layers.append(layer)
self.residual_layers[-1].build(self.res_output_shape)
self.res_output_shape = self.residual_layers[-1].compute_output_shape(
self.res_output_shape)
def build(self, input_shape):
with K.name_scope(
self.name
): # name scope used to make sure weights get unique names
self.residual_layers = list()
self.res_output_shape = input_shape
for k in range(2):
name = 'conv1D_{}'.format(k)
with K.name_scope(
name
): # name scope used to make sure weights get unique names
self._add_and_activate_layer(
Conv1D(filters=self.nb_filters,
kernel_size=self.kernel_size,
dilation_rate=self.dilation_rate,
padding=self.padding,
name=name,
kernel_initializer=self.kernel_initializer))
if self.use_batch_norm:
self._add_and_activate_layer(BatchNormalization())
elif self.use_layer_norm:
self._add_and_activate_layer(LayerNormalization())
self._add_and_activate_layer(Activation('relu'))
self._add_and_activate_layer(
SpatialDropout1D(rate=self.dropout_rate))
if not self.last_block:
# 1x1 conv to match the shapes (channel dimension).
name = 'conv1D_{}'.format(k + 1)
with K.name_scope(name):
# make and build this layer separately because it directly uses input_shape
self.shape_match_conv = Conv1D(
filters=self.nb_filters,
kernel_size=1,
padding='same',
name=name,
kernel_initializer=self.kernel_initializer)
else:
self.shape_match_conv = Lambda(lambda x: x, name='identity')
self.shape_match_conv.build(input_shape)
self.res_output_shape = self.shape_match_conv.compute_output_shape(
input_shape)
self.final_activation = Activation(self.activation)
self.final_activation.build(
self.res_output_shape) # probably isn't necessary
# this is done to force keras to add the layers in the list to self._layers
for layer in self.residual_layers:
self.__setattr__(layer.name, layer)
super(ResidualBlock, self).build(
input_shape) # done to make sure self.built is set True
def call(self, inputs, training=None):
"""
Returns: A tuple where the first element is the residual model tensor, and the second
is the skip connection tensor.
"""
x = inputs
for layer in self.residual_layers:
if isinstance(layer, SpatialDropout1D):
x = layer(x, training=training)
else:
x = layer(x)
x2 = self.shape_match_conv(inputs)
res_x = layers.add([x2, x])
return [self.final_activation(res_x), x]
def compute_output_shape(self, input_shape):
return [self.res_output_shape, self.res_output_shape]
