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TensorFlow 1 version
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View source on GitHub
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Rectified Linear Unit activation function.
tf.keras.layers.ReLU(
max_value=None, negative_slope=0, threshold=0, **kwargs
)
Used in the notebooks
| Used in the tutorials |
|---|
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. |