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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) `
```

Arguments:

• `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.

Returns:

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