tf.keras.layers.ReLU
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Rectified Linear Unit activation function.
Inherits From: Layer
tf.keras.layers.ReLU(
max_value=None, negative_slope=0, threshold=0, **kwargs
)
With default values, it returns element-wise max(x, 0)
.
Otherwise, it follows:
f(x) = max_value
for x >= max_value
,
f(x) = x
for threshold <= x < max_value
,
f(x) = negative_slope * (x - threshold)
otherwise.
Arbitrary. Use the keyword argument input_shape
(tuple of integers, does not include the samples 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.
|
negative_slope
|
Float >= 0. Negative slope coefficient.
|
threshold
|
Float. Threshold value for thresholded activation.
|
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Last updated 2020-10-01 UTC.
[null,null,["Last updated 2020-10-01 UTC."],[],[],null,["# tf.keras.layers.ReLU\n\n\u003cbr /\u003e\n\n|---------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------|\n| [TensorFlow 2 version](/api_docs/python/tf/keras/layers/ReLU) | [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/keras/layers/advanced_activations.py#L273-L332) |\n\nRectified Linear Unit activation function.\n\nInherits From: [`Layer`](../../../tf/keras/layers/Layer)\n\n#### View aliases\n\n\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.keras.layers.ReLU`](/api_docs/python/tf/keras/layers/ReLU), \\`tf.compat.v2.keras.layers.ReLU\\`\n\n\u003cbr /\u003e\n\n tf.keras.layers.ReLU(\n max_value=None, negative_slope=0, threshold=0, **kwargs\n )\n\nWith default values, it returns element-wise `max(x, 0)`.\n\nOtherwise, it follows:\n`f(x) = max_value` for `x \u003e= max_value`,\n`f(x) = x` for `threshold \u003c= x \u003c max_value`,\n`f(x) = negative_slope * (x - threshold)` otherwise.\n\n#### Input shape:\n\nArbitrary. Use the keyword argument `input_shape`\n(tuple of integers, does not include the samples axis)\nwhen using this layer as the first layer in a model.\n\n#### Output shape:\n\nSame shape as the input.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Arguments --------- ||\n|------------------|----------------------------------------------------|\n| `max_value` | Float \\\u003e= 0. Maximum activation value. |\n| `negative_slope` | Float \\\u003e= 0. Negative slope coefficient. |\n| `threshold` | Float. Threshold value for thresholded activation. |\n\n\u003cbr /\u003e"]]