tf.keras.layers.Permute
Stay organized with collections
Save and categorize content based on your preferences.
Permutes the dimensions of the input according to a given pattern.
Inherits From: Layer
tf.keras.layers.Permute(
dims, **kwargs
)
Useful for e.g. connecting RNNs and convnets together.
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.
|
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.
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2020-10-01 UTC.
[null,null,["Last updated 2020-10-01 UTC."],[],[],null,["# tf.keras.layers.Permute\n\n\u003cbr /\u003e\n\n|------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------|\n| [TensorFlow 2 version](/api_docs/python/tf/keras/layers/Permute) | [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/keras/layers/core.py#L480-L534) |\n\nPermutes the dimensions of the input according to a given pattern.\n\nInherits From: [`Layer`](../../../tf/keras/layers/Layer)\n\n#### View aliases\n\n\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.keras.layers.Permute`](/api_docs/python/tf/keras/layers/Permute), \\`tf.compat.v2.keras.layers.Permute\\`\n\n\u003cbr /\u003e\n\n tf.keras.layers.Permute(\n dims, **kwargs\n )\n\nUseful for e.g. connecting RNNs and convnets together.\n\n#### Example:\n\n model = Sequential()\n model.add(Permute((2, 1), input_shape=(10, 64)))\n # now: model.output_shape == (None, 64, 10)\n # note: `None` is the batch dimension\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Arguments --------- ||\n|--------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `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. |\n\n\u003cbr /\u003e\n\n#### Input shape:\n\nArbitrary. Use the keyword argument `input_shape`\n(tuple of integers, does not include the samples axis)\nwhen using this layer as the first layer in a model.\n\n#### Output shape:\n\nSame as the input shape, but with the dimensions re-ordered according\nto the specified pattern."]]
TensorFlow 2 version
View source on GitHub