tf.keras.layers.Maximum
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Computes element-wise maximum on a list of inputs.

Inherits From: Layer, Operation

tf.keras.layers.Maximum(
    **kwargs
)

It takes as input a list of tensors, all of the same shape, and returns a single tensor (also of the same shape).

Examples:

input_shape = (2, 3, 4)
x1 = np.random.rand(*input_shape)
x2 = np.random.rand(*input_shape)
y = keras.layers.Maximum()([x1, x2])

Usage in a Keras model:

input1 = keras.layers.Input(shape=(16,))
x1 = keras.layers.Dense(8, activation='relu')(input1)
input2 = keras.layers.Input(shape=(32,))
x2 = keras.layers.Dense(8, activation='relu')(input2)
# equivalent to `y = keras.layers.maximum([x1, x2])`
y = keras.layers.Maximum()([x1, x2])
out = keras.layers.Dense(4)(y)
model = keras.models.Model(inputs=[input1, input2], outputs=out)

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.

Methods

`from_config`

View source

@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`