tf.keras.layers.Reshape

Layer that reshapes inputs into the given shape.

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

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). One element of the `target_shape` can be -1 in which case the missing value is inferred from the size of the array and remaining dimensions.

Input shape
Arbitrary, but required to be compatible with `target_shape`.

Output shape
`(batch_size, *target_shape)`

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

# another example with 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.

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.

@classmethod
from_config(
    config
)

Creates an operation from its config.

This method is the reverse of get_config, capable of instantiating the same operation from the config dictionary.

if "dtype" in config and isinstance(config["dtype"], dict):
    policy = dtype_policies.deserialize(config["dtype"])

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

Returns
An operation instance.