tf.keras.layers.LeakyReLU

Leaky version of a Rectified Linear Unit activation layer.

Inherits From: Layer, Operation

tf.keras.layers.LeakyReLU(
    negative_slope=0.3, **kwargs
)

Used in the notebooks

Used in the guide	Used in the tutorials
Customizing what happens in `fit()`	Deep Convolutional Generative Adversarial Network pix2pix: Image-to-image translation with a conditional GAN

This layer allows a small gradient when the unit is not active.

Formula:

f(x) = alpha * x if x < 0
f(x) = x if x >= 0

Example:

leaky_relu_layer = LeakyReLU(negative_slope=0.5)
input = np.array([-10, -5, 0.0, 5, 10])
result = leaky_relu_layer(input)
# result = [-5. , -2.5,  0. ,  5. , 10.]

Args
`negative_slope`	Float >= 0.0. Negative slope coefficient. Defaults to `0.3`.
`**kwargs`	Base layer keyword arguments, such as `name` and `dtype`.

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`