|TensorFlow 1 version||View source on GitHub|
Reshapes an output to a certain shape.
Used in the guide:
Used in the tutorials:
target_shape: Target shape. Tuple of integers, does not include the samples dimension (batch size).
Arbitrary, although all dimensions in the input shaped must be fixed.
Use the keyword argument
(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.
(batch_size,) + target_shape
# as first layer in a Sequential model model = Sequential() model.add(Reshape((3, 4), input_shape=(12,))) # now: model.output_shape == (None, 3, 4) # note: `None` is the batch dimension # as intermediate layer in a Sequential model model.add(Reshape((6, 2))) # now: model.output_shape == (None, 6, 2) # also supports shape inference using `-1` as dimension model.add(Reshape((-1, 2, 2))) # now: model.output_shape == (None, None, 2, 2)
__init__( target_shape, **kwargs )