layer_permute
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
| Arguments | Description |
|---|---|
| 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. |
Section
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_features(), layer_dense(), layer_dropout(), layer_flatten(), layer_input(), layer_lambda(), layer_masking(), layer_repeat_vector(), layer_reshape()