# Permute the dimensions of an input according to a given pattern

Permute the dimensions of an input according to a given pattern

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

## Arguments

 object Model or layer object 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.

## Note

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

## 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.

Other core layers: layer_activation, layer_activity_regularization, layer_dense, layer_dropout, layer_flatten, layer_input, layer_lambda, layer_masking, layer_repeat_vector, layer_reshape