ReLU layer
ReLU class
tf_keras.layers.ReLU(max_value=None, negative_slope=0.0, threshold=0.0, **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. 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..