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This commit is contained in:
Jean-Marc Valin 2018-07-23 17:05:21 -04:00
parent 211435f5d3
commit b6af21f31c
2 changed files with 17 additions and 5 deletions
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7
dnn/gatedconv.py
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@ -1,6 +1,6 @@
from keras import backend as K
from keras.engine.topology import Layer
from keras.layers import activations, initializers, regularizers, constraints, InputSpec, Conv1D
from keras.layers import activations, initializers, regularizers, constraints, InputSpec, Conv1D, Dense
import numpy as np
class GatedConv(Conv1D):
@ -42,13 +42,16 @@ class GatedConv(Conv1D):
self.out_dims = filters
self.nongate_activation = activations.get(activation)
def call(self, inputs, memory=None):
def call(self, inputs, cond=None, 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)
if cond is not None:
d = Dense(2*self.out_dims, use_bias=False, activation='linear')
ret = ret + d(cond)
ret = self.nongate_activation(ret[:, :, :self.out_dims]) * activations.sigmoid(ret[:, :, self.out_dims:])
if self.return_memory:
ret = ret, inputs[:, :self.mem_size, :]