tf.keras.layers.ReLU

Rectified Linear Unit activation function.

Inherits From: Layer, Module

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

With default values, it returns element-wise max(x, 0).

Otherwise, it follows:

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

Usage:

layer = tf.keras.layers.ReLU()
output = layer([-3.0, -1.0, 0.0, 2.0])
list(output.numpy())
[0.0, 0.0, 0.0, 2.0]
layer = tf.keras.layers.ReLU(max_value=1.0)
output = layer([-3.0, -1.0, 0.0, 2.0])
list(output.numpy())
[0.0, 0.0, 0.0, 1.0]
layer = tf.keras.layers.ReLU(negative_slope=1.0)
output = layer([-3.0, -1.0, 0.0, 2.0])
list(output.numpy())
[-3.0, -1.0, 0.0, 2.0]
layer = tf.keras.layers.ReLU(threshold=1.5)
output = layer([-3.0, -1.0, 1.0, 2.0])
list(output.numpy())
[0.0, 0.0, 0.0, 2.0]

Arbitrary. Use the keyword argument input_shape (tuple of integers, does not include the batch axis) when using this layer as the first layer in a model.

Same shape as the input.

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