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,
last_block=True,
**kwargs):
# type: (int, int, int, str, str, float, str, 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.
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.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:
# TODO should be WeightNorm here, but using batchNorm instead
self._add_and_activate_layer(BatchNormalization())
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 = 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]
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=True,
dropout_rate=0.0,
return_sequences=False,
activation='linear',
kernel_initializer='he_normal',
use_batch_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
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)
def build(self, input_shape):
self.main_conv1D = Conv1D(filters=self.nb_filters,
kernel_size=1,
padding=self.padding,
kernel_initializer=self.kernel_initializer)
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 = list()
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,
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)
self.lambda_layer = Lambda(lambda tt: tt[:, -1, :])
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
x = self.main_conv1D(x)
skip_connections = list()
for layer in self.residual_blocks:
x, skip_out = layer(x, training=training)
skip_connections.append(skip_out)
if self.use_skip_connections:
x = add(skip_connections)
if not self.return_sequences:
x = self.lambda_layer(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['kernel_initializer'] = self.kernel_initializer
return config
class ClockworkRNN(Layer):
"""Clockwork RNN ([Koutnik et al., 2014](https://arxiv.org/abs/1402.3511)).
Constructs a CW-RNN from RNNs of a given type.
# Arguments
periods: List of positive integers. The periods of each internal RNN.
units_per_period: Positive integer or list of positive integers.
Number of units for each internal RNN. If list, it must have the
same length as `periods`.
input_shape: Shape of the input data.
output_units: Positive integer. Dimensionality of the output space.
output_activation: String or callable. Activation function to use. If
you don't specify anything, no activation is applied (i.e.,
"linear" activation: `a(x) = x`).
return_sequences: Boolean (default False). Whether to return the last
output in the output sequence, or the full sequence.
sort_ascending: Boolean (default False). Whether to sort the periods
in ascending or descending order (default, as in the original
paper).
include_top: Whether to include the fully-connected layer at the top
of the network.
dense_kwargs: Dictionary. Optional arguments for the trailing Dense
unit (`activation` and `units` keys will be ignored).
rnn_dtype: The type of RNN to use as clockwork layer. Can be a string
("SimpleRNN", "GRU", "LSTM", "CuDNNGRU", "CuDNNLSTM") or any RNN
subclass.
rnn_kwargs: Dictionary. Optional arguments for the internal RNNs
(`return_sequences` and `return_state` will be ignored).
"""
def __init__(self, periods,
units_per_period,
output_units,
output_activtion='linear',
return_sequences=False,
sort_ascending=False,
include_top=True,
dense_kwargs=None,
rnn_dtype="SimpleRNN",
rnn_kwargs=None,
**kwargs):
if type(rnn_dtype) is str:
self.rnn_dtype = getattr(layers, rnn_dtype)
else:
self.rnn_dtype = rnn_dtype
ClockworkRNN.__name__ = "Clockwork" + self.rnn_dtype.__name__
super(ClockworkRNN, self).__init__(**kwargs)
if type(units_per_period) is list:
self.units_per_period = units_per_period
else:
self.units_per_period = [units_per_period] * len(periods)
self.periods = periods
self.rnn_kwargs = rnn_kwargs or {}
self.rnn_kwargs['return_sequences'] = True
self.rnn_kwargs['return_state'] = False
self.rnn_kwargs.pop("units", True)
self.dense_kwargs = dense_kwargs or {}
self.dense_kwargs['activation'] = output_activtion
self.dense_kwargs['units'] = output_units
self.include_top = include_top
self.return_sequences = return_sequences
self.sort_ascending = sort_ascending
self.blocks = []
def build(self, input_shape):
last_shape = input_shape
output_shapes = []
for period, units in sorted(zip(self.periods, self.units_per_period),
reverse=not self.sort_ascending):
block, output_shape, last_shape = self._build_clockwork_block(
units, period, last_shape)
output_shapes.append(output_shape)
self.blocks.append(block)
self.concat_all = Concatenate()
self.concat_all.build(output_shapes)
last_shape = self.concat_all.compute_output_shape(output_shapes)
if not self.return_sequences:
self.lambda_last = Lambda(lambda x: x[:, -1])
self.lambda_last.build(last_shape)
last_shape = self.lambda_last.compute_output_shape(last_shape)
if self.include_top:
if self.return_sequences:
self.dense = TimeDistributed(Dense(**self.dense_kwargs))
else:
self.dense = Dense(**self.dense_kwargs)
self.dense.build(last_shape)
self._trainable_weights.extend(self.dense.trainable_weights)
last_shape = self.dense.compute_output_shape(last_shape)
super(ClockworkRNN, self).build(input_shape)
def call(self, x):
rnns = []
to_next_block = x
for block in self.blocks:
to_dense, to_next_block = self._call_clockwork_block(
to_next_block, *block)
rnns.append(to_dense)
out = self.concat_all(rnns)
if not self.return_sequences:
out = self.lambda_last(out)
if self.include_top:
out = self.dense(out)
return out
def compute_output_shape(self, input_shape):
if self.include_top:
out_dim = self.dense_kwargs['units']
else:
out_dim = np.sum(self.units_per_period)
if self.return_sequences:
return input_shape[:-1] + (out_dim,)
else:
return input_shape[:-2] + (out_dim,)
def _delay(self, x):
return K.temporal_padding(x, (1, 0))[:, :-1]
def _crop(self, x, timesteps):
return x[:, :K.cast(timesteps, "int32")]
def _build_clockwork_block(self, units, period, input_shape):
output_shape = input_shape[:-1] + (units,)
pool = MaxPooling1D(1, period)
rnn = self.rnn_dtype(units=units, **self.rnn_kwargs)
unpool = UpSampling1D(period)
crop = Lambda(lambda x: self._crop(x[0], x[1]),
output_shape=output_shape[1:])
delay = Lambda(lambda x: self._delay(x),
output_shape=output_shape[1:])
concat = Concatenate()
block = (pool, rnn, unpool, crop, delay, concat)
pool.build(input_shape)
pool_output_shape = pool.compute_output_shape(input_shape)
rnn.build(pool_output_shape)
self._trainable_weights.extend(rnn.trainable_weights)
rnn_output_shape = rnn.compute_output_shape(pool_output_shape)
unpool.build(rnn_output_shape)
crop.build([unpool.compute_output_shape(rnn_output_shape), ()])
delay.build(output_shape)
concat.build([input_shape, output_shape])
return block, output_shape, \
concat.compute_output_shape([input_shape, output_shape])
def _call_clockwork_block(self, x, pool, rnn, unpool, crop, delay, concat):
pooled = pool(x)
rnn_out = rnn(pooled)
unpooled = unpool(rnn_out)
to_dense = crop([unpooled, K.shape(x)[1]])
delayed = delay(to_dense)
to_next_block = concat([x, delayed])
return to_dense, to_next_block
def get_config(self):
config = super(ClockworkRNN, self).get_config()
config['units_per_period'] = self.units_per_period
config['periods'] = self.periods
config['rnn_dtype'] = self.rnn_dtype.__name__
config['rnn_kwargs'] = self.rnn_kwargs
config['dense_kwargs'] = self.dense_kwargs
config['include_top'] = self.include_top
config['return_sequences'] = self.return_sequences
config['sort_ascending'] = self.sort_ascending
return config