tf.keras.layers.ReLU

Rectified Linear Unit activation function layer.

Inherits From: Layer, Operation

tf.keras.layers.ReLU(
    max_value=None, negative_slope=0.0, threshold=0.0, **kwargs
)

Used in the notebooks

Used in the guide	Used in the tutorials
Pruning for on-device inference w/ XNNPACK Sparse weights using structural pruning	pix2pix: Image-to-image translation with a conditional GAN

Formula:

f(x) = max(x,0)
f(x) = max_value if x >= max_value
f(x) = x if threshold <= x < max_value
f(x) = negative_slope * (x - threshold) otherwise

Example:

relu_layer = keras.layers.ReLU(
    max_value=10,
    negative_slope=0.5,
    threshold=0,
)
input = np.array([-10, -5, 0.0, 5, 10])
result = relu_layer(input)
# result = [-5. , -2.5,  0. ,  5. , 10.]

Args
max_value	Float >= 0. Maximum activation value. None means unlimited. Defaults to None.
negative_slope	Float >= 0. Negative slope coefficient. Defaults to 0.0.
threshold	Float >= 0. Threshold value for thresholded activation. Defaults to 0.0.
**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.

Methods

from_config

View source

@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.

Note: If you override this method, you might receive a serialized dtype config, which is a dict. You can deserialize it as follows:

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.