# ReLU layer

### `ReLU` class

``````tf.keras.layers.ReLU(max_value=None, negative_slope=0, threshold=0, **kwargs)
``````

Rectified Linear Unit activation function.

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.

Arguments

• 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.