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<p><span style="float:right;"><a href="https://github.com/keras-team/keras/blob/master/keras/layers/noise.py#L14">[source]</a></span></p>
<h3 id="gaussiannoise">GaussianNoise</h3>
<pre><code class="python">keras.layers.GaussianNoise(stddev)
</code></pre>
<p>Apply additive zero-centered Gaussian noise.</p>
<p>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.</p>
<p>As it is a regularization layer, it is only active at training time.</p>
<p><strong>Arguments</strong></p>
<ul>
<li><strong>stddev</strong>: float, standard deviation of the noise distribution.</li>
</ul>
<p><strong>Input shape</strong></p>
<p>Arbitrary. Use the keyword argument <code>input_shape</code>
(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.</p>
<p><strong>Output shape</strong></p>
<p>Same shape as input.</p>
<hr />
<p><span style="float:right;"><a href="https://github.com/keras-team/keras/blob/master/keras/layers/noise.py#L58">[source]</a></span></p>
<h3 id="gaussiandropout">GaussianDropout</h3>
<pre><code class="python">keras.layers.GaussianDropout(rate)
</code></pre>
<p>Apply multiplicative 1-centered Gaussian noise.</p>
<p>As it is a regularization layer, it is only active at training time.</p>
<p><strong>Arguments</strong></p>
<ul>
<li><strong>rate</strong>: float, drop probability (as with <code>Dropout</code>).
The multiplicative noise will have
standard deviation <code>sqrt(rate / (1 - rate))</code>.</li>
</ul>
<p><strong>Input shape</strong></p>
<p>Arbitrary. Use the keyword argument <code>input_shape</code>
(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.</p>
<p><strong>Output shape</strong></p>
<p>Same shape as input.</p>
<p><strong>References</strong></p>
<ul>
<li><a href="http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf">Dropout: A Simple Way to Prevent Neural Networks from Overfitting</a></li>
</ul>
<hr />
<p><span style="float:right;"><a href="https://github.com/keras-team/keras/blob/master/keras/layers/noise.py#L106">[source]</a></span></p>
<h3 id="alphadropout">AlphaDropout</h3>
<pre><code class="python">keras.layers.AlphaDropout(rate, noise_shape=None, seed=None)
</code></pre>
<p>Applies Alpha Dropout to the input.</p>
<p>Alpha Dropout is a <code>Dropout</code> that keeps mean and variance of inputs
to their original values, in order to ensure the self-normalizing property
even after this dropout.
Alpha Dropout fits well to Scaled Exponential Linear Units
by randomly setting activations to the negative saturation value.</p>
<p><strong>Arguments</strong></p>
<ul>
<li><strong>rate</strong>: float, drop probability (as with <code>Dropout</code>).
The multiplicative noise will have
standard deviation <code>sqrt(rate / (1 - rate))</code>.</li>
<li><strong>noise_shape</strong>: A 1-D <code>Tensor</code> of type <code>int32</code>, representing the
shape for randomly generated keep/drop flags.</li>
<li><strong>seed</strong>: A Python integer to use as random seed.</li>
</ul>
<p><strong>Input shape</strong></p>
<p>Arbitrary. Use the keyword argument <code>input_shape</code>
(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.</p>
<p><strong>Output shape</strong></p>
<p>Same shape as input.</p>
<p><strong>References</strong></p>
<ul>
<li><a href="https://arxiv.org/abs/1706.02515">Self-Normalizing Neural Networks</a></li>
</ul>
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