Rectified Linear Unit activation function.
Inherits From: Layer
, Module
tf.keras.layers.ReLU(
max_value=None, negative_slope=0.0, threshold=0.0, **kwargs
)
Used in the notebooks
Used in the guide |
Used in the tutorials |
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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]
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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.
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Output shape |
Same shape as the input.
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Args |
max_value
|
Float >= 0. Maximum activation value. None means unlimited.
Defaults to None .
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negative_slope
|
Float >= 0. Negative slope coefficient.
Defaults to 0. .
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threshold
|
Float >= 0. Threshold value for thresholded activation.
Defaults to 0. .
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