def _link(self, inputs, **kwargs):
if isinstance(inputs, tf.Tensor):
inputs = [inputs]
# Avoid that insert operation below changes the original list
inputs = inputs.copy()
assert isinstance(inputs, list)
if not self._companions is None:
for tensor in self._companions.keys():
assert isinstance(tensor, tf.Tensor)
inputs.insert(self._companions[tensor], tensor)
# Check inputs
if len(inputs) < 2:
raise ValueError('inputs to concatenate layer must have a length'
' larger than 1')
# Prepare inputs for concatenation
assert isinstance(inputs[0], tf.Tensor)
leader_shape_tensor = tf.shape(inputs[0])
leader_shape = inputs[0].get_shape().as_list()
for i in range(1, len(inputs)):
assert isinstance(inputs[i], tf.Tensor)
shape = inputs[i].get_shape().as_list()
shape_tensor = tf.shape(inputs[i])
ones = tf.ones([leader_shape_tensor[0]] + leader_shape[1:-1] +
[shape[-1]])
target_shape = ([shape_tensor[0]] + [1] * (len(leader_shape) - 2) +
[shape[-1]])
reshaped = tf.reshape(inputs[i], target_shape)
inputs[i] = reshaped * ones
result = tf.concat(inputs, axis=len(leader_shape) - 1)
self.neuron_scale = get_scale(result)
return result
def __call__(self, input_=None, **kwargs): assert isinstance(input_, tf.Tensor) output = MaxPool1D_.__call__(self, input_, scope=self.full_name) self.neuron_scale = get_scale(output) return output
def __call__(self, input_=None, **kwargs): assert isinstance(input_, tf.Tensor) # TODO: too violent ? output = super(Function, self).__call__(input_, scope=self.full_name) self.neuron_scale = get_scale(output) return output