def call(self, inputs, training=None, mask=None):
kwargs = {}
func_args = inspect.getargspec(self.layer.call).args
if 'training' in func_args:
kwargs['training'] = training
if 'mask' in func_args:
kwargs['mask'] = mask
y = self.forward_layer.call(inputs, **kwargs)
y_rev = self.backward_layer.call(inputs, **kwargs)
if self.return_sequences:
y_rev = K.reverse(y_rev, 1)
if self.merge_mode == 'concat':
output = K.concatenate([y, y_rev])
elif self.merge_mode == 'sum':
output = y + y_rev
elif self.merge_mode == 'ave':
output = (y + y_rev) / 2
elif self.merge_mode == 'mul':
output = y * y_rev
elif self.merge_mode is None:
output = [y, y_rev]
# Properly set learning phase
if 0 < self.layer.dropout + self.layer.recurrent_dropout:
if self.merge_mode is None:
for out in output:
out._uses_learning_phase = True
else:
output._uses_learning_phase = True
return output
def call(self, inputs, training=None, mask=None):
kwargs = {}
func_args = tf_inspect.getargspec(self.layer.call).args
if 'training' in func_args:
kwargs['training'] = training
if 'mask' in func_args:
kwargs['mask'] = mask
y = self.forward_layer.call(inputs, **kwargs)
y_rev = self.backward_layer.call(inputs, **kwargs)
if self.return_sequences:
y_rev = K.reverse(y_rev, 1)
if self.merge_mode == 'concat':
output = K.concatenate([y, y_rev])
elif self.merge_mode == 'sum':
output = y + y_rev
elif self.merge_mode == 'ave':
output = (y + y_rev) / 2
elif self.merge_mode == 'mul':
output = y * y_rev
elif self.merge_mode is None:
output = [y, y_rev]
# Properly set learning phase
if 0 < self.layer.dropout + self.layer.recurrent_dropout:
if self.merge_mode is None:
for out in output:
out._uses_learning_phase = True
else:
output._uses_learning_phase = True
return output
def call(self, inputs, mask=None):
y = self.forward_layer.call(inputs, mask)
y_rev = self.backward_layer.call(inputs, mask)
if self.return_sequences:
y_rev = K.reverse(y_rev, 1)
if self.merge_mode == 'concat':
return K.concatenate([y, y_rev])
elif self.merge_mode == 'sum':
return y + y_rev
elif self.merge_mode == 'ave':
return (y + y_rev) / 2
elif self.merge_mode == 'mul':
return y * y_rev
elif self.merge_mode is None:
return [y, y_rev]