def _build_model_residual_block(self, x, i, s):
original_x = x
# TODO: initalization, regularization?
tanh_out = CausalDilatedConv1D(self.filters, 2, atrous_rate=2 ** i, border_mode='valid', causal=True, bias=self.use_bias, name='dilated_conv_%d_tanh_s%d' % (2 ** i, s), activation='tanh', W_regularizer=l2(self.res_l2))(x)
sigm_out = CausalDilatedConv1D(self.filters, 2, atrous_rate=2 ** i, border_mode='valid', causal=True, bias=self.use_bias, name='dilated_conv_%d_sigm_s%d' % (2 ** i, s), activation='sigmoid', W_regularizer=l2(self.res_l2))(x)
x = layers.Multiply()([tanh_out, sigm_out])
res_x = layers.Conv1D(self.filters, 1, padding='same', use_bias=self.use_bias, kernel_regularizer=l2(self.res_l2))(x)
skip_x = layers.Conv1D(self.filters, 1, padding='same', use_bias=self.use_bias, kernel_regularizer=l2(self.res_l2))(x)
res_x = layers.Add()([original_x, res_x])
return res_x, skip_x
def residual_block(x):
original_x = x
# TODO: initalization, regularization?
# Note: The AtrousConvolution1D with the 'causal' flag is implemented in github.com/basveeling/keras#@wavenet.
tanh_out = CausalDilatedConv1D(nb_filters,
2,
atrous_rate=2**i,
border_mode='valid',
causal=True,
bias=use_bias,
name='dilated_conv_%d_tanh_s%d' %
(2**i, s),
activation='tanh',
W_regularizer=l2(res_l2))(x)
sigm_out = CausalDilatedConv1D(nb_filters,
2,
atrous_rate=2**i,
border_mode='valid',
causal=True,
bias=use_bias,
name='dilated_conv_%d_sigm_s%d' %
(2**i, s),
activation='sigmoid',
W_regularizer=l2(res_l2))(x)
x = layers.Multiply()([tanh_out, sigm_out])
res_x = layers.Conv1D(nb_filters,
1,
padding='same',
use_bias=use_bias,
kernel_regularizer=l2(res_l2))(x)
skip_x = layers.Conv1D(nb_filters,
1,
padding='same',
use_bias=use_bias,
kernel_regularizer=l2(res_l2))(x)
res_x = layers.Add()([original_x, res_x])
return res_x, skip_x
def _build_model(self):
input_shape = Input(shape=(self.fragment_length, self.output_bins),
name='input_part')
out = input_shape
skip_connections = []
out = CausalDilatedConv1D(self.filters,
2,
atrous_rate=1,
border_mode='valid',
causal=True,
name='initial_causal_conv')(out)
for s in range(self.stacks):
for i in range(0, self.dilation_depth + 1):
out, skip_out = self._build_model_residual_block(out, i, s)
skip_connections.append(skip_out)
if self.use_skip_connections:
out = layers.Add()(skip_connections)
out = layers.Activation('relu')(out)
out = layers.Conv1D(self.output_bins,
1,
padding='same',
kernel_regularizer=l2(self.final_l2))(out)
out = layers.Activation('relu')(out)
out = layers.Conv1D(self.output_bins, 1, padding='same')(out)
if not self.learn_all_outputs:
raise DeprecationWarning(
'Learning on just all outputs is wasteful, now learning only inside receptive field.'
)
out = layers.Lambda(
lambda x: x[:, -1, :], output_shape=(out._keras_shape[-1], ))(
out) # Based on gif in deepmind blog: take last output?
out = layers.Activation('softmax', name="output_softmax")(out)
model = Model(input_shape, out)
self.receptive_field, self.receptive_field_ms = self._compute_receptive_field(
)
return model
def build_model(fragment_length, nb_filters, nb_output_bins, dilation_depth,
nb_stacks, use_skip_connections, learn_all_outputs, _log,
desired_sample_rate, use_bias, res_l2, final_l2):
def residual_block(x):
original_x = x
# TODO: initalization, regularization?
# Note: The AtrousConvolution1D with the 'causal' flag is implemented in github.com/basveeling/keras#@wavenet.
tanh_out = CausalDilatedConv1D(nb_filters,
2,
atrous_rate=2**i,
border_mode='valid',
causal=True,
bias=use_bias,
name='dilated_conv_%d_tanh_s%d' %
(2**i, s),
activation='tanh',
W_regularizer=l2(res_l2))(x)
sigm_out = CausalDilatedConv1D(nb_filters,
2,
atrous_rate=2**i,
border_mode='valid',
causal=True,
bias=use_bias,
name='dilated_conv_%d_sigm_s%d' %
(2**i, s),
activation='sigmoid',
W_regularizer=l2(res_l2))(x)
x = layers.Multiply()([tanh_out, sigm_out])
res_x = layers.Conv1D(nb_filters,
1,
padding='same',
use_bias=use_bias,
kernel_regularizer=l2(res_l2))(x)
skip_x = layers.Conv1D(nb_filters,
1,
padding='same',
use_bias=use_bias,
kernel_regularizer=l2(res_l2))(x)
res_x = layers.Add()([original_x, res_x])
return res_x, skip_x
input_shape = Input(shape=(fragment_length, nb_output_bins),
name='input_part')
out = input_shape
skip_connections = []
out = CausalDilatedConv1D(nb_filters,
2,
atrous_rate=1,
border_mode='valid',
causal=True,
name='initial_causal_conv')(out)
for s in range(nb_stacks):
for i in range(0, dilation_depth + 1):
out, skip_out = residual_block(out)
skip_connections.append(skip_out)
if use_skip_connections:
out = layers.Add()(skip_connections)
out = layers.Activation('relu')(out)
out = layers.Conv1D(nb_output_bins,
1,
padding='same',
kernel_regularizer=l2(final_l2))(out)
out = layers.Activation('relu')(out)
out = layers.Conv1D(nb_output_bins, 1, padding='same')(out)
if not learn_all_outputs:
raise DeprecationWarning(
'Learning on just all outputs is wasteful, now learning only inside receptive field.'
)
out = layers.Lambda(
lambda x: x[:, -1, :], output_shape=(out._keras_shape[-1], ))(
out) # Based on gif in deepmind blog: take last output?
out = layers.Activation('softmax', name="output_softmax")(out)
model = Model(input_shape, out)
receptive_field, receptive_field_ms = compute_receptive_field()
_log.info('Receptive Field: %d (%dms)' %
(receptive_field, int(receptive_field_ms)))
return model