ref: 211435f5d3ad2afad1aca3dc5ef8d6dfee564ff0
parent: 0fa7150454740a0c2157d33b7ccf80d217684841
author: Jean-Marc Valin <jmvalin@jmvalin.ca>
date: Fri Jul 13 13:10:03 EDT 2018
Gated convolution
--- /dev/null
+++ b/dnn/gatedconv.py
@@ -1,0 +1,62 @@
+from keras import backend as K
+from keras.engine.topology import Layer
+from keras.layers import activations, initializers, regularizers, constraints, InputSpec, Conv1D
+import numpy as np
+
+class GatedConv(Conv1D):
+
+ def __init__(self, filters,
+ kernel_size,
+ dilation_rate=1,
+ activation='tanh',
+ use_bias=True,
+ kernel_initializer='glorot_uniform',
+ bias_initializer='zeros',
+ kernel_regularizer=None,
+ bias_regularizer=None,
+ activity_regularizer=None,
+ kernel_constraint=None,
+ bias_constraint=None,
+ return_memory=False,
+ **kwargs):
+
+ super(GatedConv, self).__init__(
+ filters=2*filters,
+ kernel_size=kernel_size,
+ strides=1,
+ padding='valid',
+ data_format='channels_last',
+ dilation_rate=dilation_rate,
+ activation='linear',
+ use_bias=use_bias,
+ kernel_initializer=kernel_initializer,
+ bias_initializer=bias_initializer,
+ kernel_regularizer=kernel_regularizer,
+ bias_regularizer=bias_regularizer,
+ activity_regularizer=activity_regularizer,
+ kernel_constraint=kernel_constraint,
+ bias_constraint=bias_constraint,
+ **kwargs)
+ self.mem_size = dilation_rate*(kernel_size-1)
+ self.return_memory = return_memory
+ self.out_dims = filters
+ self.nongate_activation = activations.get(activation)
+
+ def call(self, inputs, memory=None):
+ if memory is None:
+ mem = K.zeros((K.shape(inputs)[0], self.mem_size, K.shape(inputs)[-1]))
+ else:
+ mem = K.variable(K.cast_to_floatx(memory))
+ inputs = K.concatenate([mem, inputs], axis=1)
+ ret = super(GatedConv, self).call(inputs)
+ ret = self.nongate_activation(ret[:, :, :self.out_dims]) * activations.sigmoid(ret[:, :, self.out_dims:])
+ if self.return_memory:
+ ret = ret, inputs[:, :self.mem_size, :]
+ return ret
+
+ def compute_output_shape(self, input_shape):
+ assert input_shape and len(input_shape) >= 2
+ assert input_shape[-1]
+ output_shape = list(input_shape)
+ output_shape[-1] = self.out_dims
+ return tuple(output_shape)
--- a/dnn/wavenet.py
+++ b/dnn/wavenet.py
@@ -9,8 +9,9 @@
import h5py
import sys
from causalconv import CausalConv
+from gatedconv import GatedConv
-units=256
+units=128
pcm_bits = 8
pcm_levels = 2**pcm_bits
nb_used_features = 38
@@ -37,10 +38,8 @@
res = tmp
tmp = Concatenate()([tmp, rfeat])
dilation = 9-k if fftnet else k
- c1 = CausalConv(units, 2, dilation_rate=2**dilation, activation='tanh')
- c2 = CausalConv(units, 2, dilation_rate=2**dilation, activation='sigmoid')
- tmp = Multiply()([c1(tmp), c2(tmp)])
- tmp = Dense(units, activation='relu')(tmp)
+ c = GatedConv(units, 2, dilation_rate=2**dilation, activation='tanh')
+ tmp = Dense(units, activation='relu')(c(tmp))
if k != 0:
tmp = Add()([tmp, res])
--
⑨