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<p><span style="float:right;"><a href="https://github.com/keras-team/keras/blob/master/keras/layers/advanced_activations.py#L19">[source]</a></span></p>
<h3 id="leakyrelu">LeakyReLU</h3>
<pre><code class="python">keras.layers.LeakyReLU(alpha=0.3)
</code></pre>
<p>Leaky version of a Rectified Linear Unit.</p>
<p>It allows a small gradient when the unit is not active:
<code>f(x) = alpha * x for x < 0</code>,
<code>f(x) = x for x >= 0</code>.</p>
<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 the input.</p>
<p><strong>Arguments</strong></p>
<ul>
<li><strong>alpha</strong>: float >= 0. Negative slope coefficient.</li>
</ul>
<p><strong>References</strong></p>
<ul>
<li><a href="https://ai.stanford.edu/~amaas/papers/relu_hybrid_icml2013_final.pdf">Rectifier Nonlinearities Improve Neural Network Acoustic Models</a></li>
</ul>
<hr />
<p><span style="float:right;"><a href="https://github.com/keras-team/keras/blob/master/keras/layers/advanced_activations.py#L59">[source]</a></span></p>
<h3 id="prelu">PReLU</h3>
<pre><code class="python">keras.layers.PReLU(alpha_initializer='zeros', alpha_regularizer=None, alpha_constraint=None, shared_axes=None)
</code></pre>
<p>Parametric Rectified Linear Unit.</p>
<p>It follows:
<code>f(x) = alpha * x for x < 0</code>,
<code>f(x) = x for x >= 0</code>,
where <code>alpha</code> is a learned array with the same shape as x.</p>
<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 the input.</p>
<p><strong>Arguments</strong></p>
<ul>
<li><strong>alpha_initializer</strong>: initializer function for the weights.</li>
<li><strong>alpha_regularizer</strong>: regularizer for the weights.</li>
<li><strong>alpha_constraint</strong>: constraint for the weights.</li>
<li><strong>shared_axes</strong>: the axes along which to share learnable
parameters for the activation function.
For example, if the incoming feature maps
are from a 2D convolution
with output shape <code>(batch, height, width, channels)</code>,
and you wish to share parameters across space
so that each filter only has one set of parameters,
set <code>shared_axes=[1, 2]</code>.</li>
</ul>
<p><strong>References</strong></p>
<ul>
<li><a href="https://arxiv.org/abs/1502.01852">Delving Deep into Rectifiers: Surpassing Human-Level Performance on
ImageNet Classification</a></li>
</ul>
<hr />
<p><span style="float:right;"><a href="https://github.com/keras-team/keras/blob/master/keras/layers/advanced_activations.py#L153">[source]</a></span></p>
<h3 id="elu">ELU</h3>
<pre><code class="python">keras.layers.ELU(alpha=1.0)
</code></pre>
<p>Exponential Linear Unit.</p>
<p>It follows:
<code>f(x) = alpha * (exp(x) - 1.) for x < 0</code>,
<code>f(x) = x for x >= 0</code>.</p>
<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 the input.</p>
<p><strong>Arguments</strong></p>
<ul>
<li><strong>alpha</strong>: scale for the negative factor.</li>
</ul>
<p><strong>References</strong></p>
<ul>
<li><a href="https://arxiv.org/abs/1511.07289v1">Fast and Accurate Deep Network Learning by Exponential Linear Units
(ELUs)</a></li>
</ul>
<hr />
<p><span style="float:right;"><a href="https://github.com/keras-team/keras/blob/master/keras/layers/advanced_activations.py#L193">[source]</a></span></p>
<h3 id="thresholdedrelu">ThresholdedReLU</h3>
<pre><code class="python">keras.layers.ThresholdedReLU(theta=1.0)
</code></pre>
<p>Thresholded Rectified Linear Unit.</p>
<p>It follows:
<code>f(x) = x for x > theta</code>,
<code>f(x) = 0 otherwise</code>.</p>
<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 the input.</p>
<p><strong>Arguments</strong></p>
<ul>
<li><strong>theta</strong>: float >= 0. Threshold location of activation.</li>
</ul>
<p><strong>References</strong></p>
<ul>
<li><a href="https://arxiv.org/abs/1402.3337">Zero-Bias Autoencoders and the Benefits of Co-Adapting Features</a></li>
</ul>
<hr />
<p><span style="float:right;"><a href="https://github.com/keras-team/keras/blob/master/keras/layers/advanced_activations.py#L233">[source]</a></span></p>
<h3 id="softmax">Softmax</h3>
<pre><code class="python">keras.layers.Softmax(axis=-1)
</code></pre>
<p>Softmax activation function.</p>
<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 the input.</p>
<p><strong>Arguments</strong></p>
<ul>
<li><strong>axis</strong>: Integer, axis along which the softmax normalization is applied.</li>
</ul>
<hr />
<p><span style="float:right;"><a href="https://github.com/keras-team/keras/blob/master/keras/layers/advanced_activations.py#L265">[source]</a></span></p>
<h3 id="relu">ReLU</h3>
<pre><code class="python">keras.layers.ReLU(max_value=None, negative_slope=0.0, threshold=0.0)
</code></pre>
<p>Rectified Linear Unit activation function.</p>
<p>With default values, it returns element-wise <code>max(x, 0)</code>.</p>
<p>Otherwise, it follows:
<code>f(x) = max_value</code> for <code>x >= max_value</code>,
<code>f(x) = x</code> for <code>threshold <= x < max_value</code>,
<code>f(x) = negative_slope * (x - threshold)</code> otherwise.</p>
<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 the input.</p>
<p><strong>Arguments</strong></p>
<ul>
<li><strong>max_value</strong>: float >= 0. Maximum activation value.</li>
<li><strong>negative_slope</strong>: float >= 0. Negative slope coefficient.</li>
<li><strong>threshold</strong>: float. Threshold value for thresholded activation.</li>
</ul>
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