tf.keras.layers.Reshape

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Layer that reshapes inputs into the given shape.

Inherits From: Layer, Operation

tf.keras.layers.Reshape(
    target_shape, **kwargs
)

Used in the notebooks

Used in the guide	Used in the tutorials
Customizing what happens in `fit()` Using Counterfactual Logit Pairing with Keras	Time series forecasting Custom training loop with Keras and MultiWorkerMirroredStrategy Multi-worker training with Keras Intro to Autoencoders Convolutional Variational Autoencoder

Args
target_shape	Target shape. Tuple of integers, does not include the samples dimension (batch size).

Input shape
Arbitrary, although all dimensions in the input shape must be known/fixed. Use the keyword argument input_shape (tuple of integers, does not include the samples/batch size axis) when using this layer as the first layer in a model.

Output shape
(batch_size, *target_shape)

Example:

x = keras.Input(shape=(12,))
y = keras.layers.Reshape((3, 4))(x)
y.shape
(None, 3, 4)

# also supports shape inference using `-1` as dimension
y = keras.layers.Reshape((-1, 2, 2))(x)
y.shape
(None, 3, 2, 2)

Attributes
input	Retrieves the input tensor(s) of a symbolic operation. Only returns the tensor(s) corresponding to the first time the operation was called.
output	Retrieves the output tensor(s) of a layer. Only returns the tensor(s) corresponding to the first time the operation was called.

Methods

from_config

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@classmethod
from_config(
    config
)

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Args
config	A Python dictionary, typically the output of get_config.

Returns
A layer instance.

symbolic_call

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symbolic_call(
    *args, **kwargs
)