def _link(self, input_, **kwargs):
name = 'flatten' if self.output_shape is None else 'reshape'
self.abbreviation = name
input_shape = input_.get_shape().as_list()
output_shape = ([-1, np.prod(input_shape[1:])]
if self.output_shape is None else [-1] +
list(self.output_shape))
output = tf.reshape(input_, output_shape, name=name)
self.neuron_scale = get_scale(output)
return output
def _link(self, *args, **kwargs):
# This method is only accessible by Function.__call__ thus a None will
# be given as input
assert len(args) == 0 and len(kwargs) == 0
input_ = tf.placeholder(dtype=self.dtype,
shape=self.input_shape,
name=self.name)
# Update neuron scale
self.neuron_scale = get_scale(input_)
# Add input to default collection
tf.add_to_collection(pedia.default_feed_dict, input_)
# Return placeholder
self.place_holder = input_
return input_