# Permute layer

[source]

`Permute`

class

```
tf.keras.layers.Permute(dims, **kwargs)
```

Permutes the dimensions of the input according to a given pattern.

Useful e.g. connecting RNNs and convnets.

**Example**

```
model = Sequential()
model.add(Permute((2, 1), input_shape=(10, 64)))
# now: model.output_shape == (None, 64, 10)
# note: `None` is the batch dimension
```

**Arguments**

**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.

**Input shape**

Arbitrary. Use the keyword argument `input_shape`

(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.

**Output shape**

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