layer_permute {keras}

Permute the dimensions of an input according to a given pattern

Description

Permute the dimensions of an input according to a given pattern

Usage

layer_permute(
  object,
  dims,
  input_shape = NULL,
  batch_input_shape = NULL,
  batch_size = NULL,
  dtype = NULL,
  name = NULL,
  trainable = NULL,
  weights = NULL
)

Arguments

object

What to compose the new Layer instance with. Typically a Sequential model or a Tensor (e.g., as returned by layer_input()). The return value depends on object. If object is:

• missing or NULL, the Layer instance is returned.

• a Sequential model, the model with an additional layer is returned.

• a Tensor, the output tensor from layer_instance(object) is returned.

dims

List of integers. Permutation pattern, does not include the samples dimension. Indexing starts at 1. For instance, ⁠(2, 1)⁠ permutes the first and second dimension of the input.

input_shape

Input shape (list of integers, does not include the samples axis) which is required when using this layer as the first layer in a model.

batch_input_shape

Shapes, including the batch size. For instance, batch_input_shape=c(10, 32) indicates that the expected input will be batches of 10 32-dimensional vectors. batch_input_shape=list(NULL, 32) indicates batches of an arbitrary number of 32-dimensional vectors.

batch_size

Fixed batch size for layer

dtype

The data type expected by the input, as a string (float32, float64, int32...)

name

An optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn't provided.

trainable

Whether the layer weights will be updated during training.

weights

Initial weights for layer.

Input and Output Shapes

Input shape: Arbitrary

Output shape: Same as the input shape, but with the dimensions re-ordered according to the specified pattern.

Note

Useful for e.g. connecting RNNs and convnets together.

See Also

Other core layers: layer_activation(), layer_activity_regularization(), layer_attention(), layer_dense(), layer_dense_features(), layer_dropout(), layer_flatten(), layer_input(), layer_lambda(), layer_masking(), layer_repeat_vector(), layer_reshape()