|TensorFlow 2 version||View source on GitHub|
Permutes the dimensions of the input according to a given pattern.
Useful for e.g. connecting RNNs and convnets together.
model = Sequential() model.add(Permute((2, 1), input_shape=(10, 64))) # now: model.output_shape == (None, 64, 10) # note: `None` is the batch dimension
dims: Tuple of integers. Permutation pattern, does not include the samples dimension. Indexing starts at 1. For instance,
(2, 1)permutes the first and second dimensions of the input.
Arbitrary. Use the keyword argument
(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.
Same as the input shape, but with the dimensions re-ordered according to the specified pattern.
__init__( dims, **kwargs )