tf.keras.layers.GaussianNoise
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Apply additive zero-centered Gaussian noise.
Inherits From: Layer
tf.keras.layers.GaussianNoise(
stddev, **kwargs
)
This is useful to mitigate overfitting
(you could see it as a form of random data augmentation).
Gaussian Noise (GS) is a natural choice as corruption process
for real valued inputs.
As it is a regularization layer, it is only active at training time.
Arguments |
|---|
stddev
|
Float, standard deviation of the noise distribution.
|
Call arguments:
inputs: Input tensor (of any rank).training: Python boolean indicating whether the layer should behave in
training mode (adding noise) or in inference mode (doing nothing).
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 input.
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Last updated 2020-10-01 UTC.
[null,null,["Last updated 2020-10-01 UTC."],[],[],null,["# tf.keras.layers.GaussianNoise\n\n\u003cbr /\u003e\n\n|------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------|\n| [TensorFlow 2 version](/api_docs/python/tf/keras/layers/GaussianNoise) | [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/keras/layers/noise.py#L32-L79) |\n\nApply additive zero-centered Gaussian noise.\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.GaussianNoise`](/api_docs/python/tf/keras/layers/GaussianNoise), \\`tf.compat.v2.keras.layers.GaussianNoise\\`\n\n\u003cbr /\u003e\n\n tf.keras.layers.GaussianNoise(\n stddev, **kwargs\n )\n\nThis is useful to mitigate overfitting\n(you could see it as a form of random data augmentation).\nGaussian Noise (GS) is a natural choice as corruption process\nfor real valued inputs.\n\nAs it is a regularization layer, it is only active at training time.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Arguments --------- ||\n|----------|------------------------------------------------------|\n| `stddev` | Float, standard deviation of the noise distribution. |\n\n\u003cbr /\u003e\n\n#### Call arguments:\n\n- **`inputs`**: Input tensor (of any rank).\n- **`training`**: Python boolean indicating whether the layer should behave in training mode (adding noise) or in inference mode (doing nothing).\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 input."]]
TensorFlow 2 version
View source on GitHub