tf.keras.layers.ReLU

TensorFlow 1 version View source on GitHub

Rectified Linear Unit activation function.

Inherits From: Layer

tf.keras.layers.ReLU(
    max_value=None, negative_slope=0, threshold=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]

Input shape:

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.

Output shape:

Same shape as the input.

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