layer_permute: Permute the dimensions of an input according to a given...
In keras: R Interface to 'Keras'

layer_permute

R Documentation

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.