tf.keras.activations.relu

Applies the rectified linear unit activation function.

tf.keras.activations.relu(
    x, alpha=0.0, max_value=None, threshold=0
)

With default values, this returns the standard ReLU activation: max(x, 0), the element-wise maximum of 0 and the input tensor.

Modifying default parameters allows you to use non-zero thresholds, change the max value of the activation, and to use a non-zero multiple of the input for values below the threshold.

For example:

foo = tf.constant([-10, -5, 0.0, 5, 10], dtype = tf.float32)
tf.keras.activations.relu(foo).numpy()
array([ 0.,  0.,  0.,  5., 10.], dtype=float32)
tf.keras.activations.relu(foo, alpha=0.5).numpy()
array([-5. , -2.5,  0. ,  5. , 10. ], dtype=float32)
tf.keras.activations.relu(foo, max_value=5).numpy()
array([0., 0., 0., 5., 5.], dtype=float32)
tf.keras.activations.relu(foo, threshold=5).numpy()
array([-0., -0.,  0.,  0., 10.], dtype=float32)

x Input tensor or variable.
alpha A float that governs the slope for values lower than the threshold.
max_value A float that sets the saturation threshold (the largest value the function will return).
threshold A float giving the threshold value of the activation function below which values will be damped or set to zero.

A Tensor representing the input tensor, transformed by the relu activation function. Tensor will be of the same shape and dtype of input x.