# Activation

## Abs

class `paddle.v2.activation.``Abs`

  Abs Activation.

  Forward: \(f(z) = abs(z)\)

  Derivative:

  \[\begin{split}1 &\quad if \quad z > 0 \\ -1 &\quad if \quad z < 0 \\ 0 &\quad if \quad z = 0\end{split}\]

## Exp

class `paddle.v2.activation.``Exp`

  Exponential Activation.

  \[f(z) = e^z.\]

## Identity

`paddle.v2.activation.``Identity`

  `Linear` 的别名

## Linear

class `paddle.v2.activation.``Linear`

  Identity Activation.

  Just do nothing for output both forward/backward.

## Log

class `paddle.v2.activation.``Log`

  Logarithm Activation.

  \[f(z) = log(z)\]

## Square

class `paddle.v2.activation.``Square`

  Square Activation.

  \[f(z) = z^2.\]

## Sigmoid

class `paddle.v2.activation.``Sigmoid`

  Sigmoid activation.

  \[f(z) = \frac{1}{1+exp(-z)}\]

## Softmax

class `paddle.v2.activation.``Softmax`

  Softmax activation for simple input

  \[P(y=j|x) = \frac{e^{x_j}} {\sum^K_{k=1} e^{x_j} }\]

## SequenceSoftmax

class `paddle.v2.activation.``SequenceSoftmax`

  Softmax activation for one sequence. The dimension of input feature must be 1 and a sequence.

      result = softmax(for each_feature_vector[0] in input_feature)
      for i, each_time_step_output in enumerate(output):
          each_time_step_output = result[i]

## Relu

class `paddle.v2.activation.``Relu`

  Relu activation.

  forward. \(y = max(0, z)\)

  derivative:

  \[\begin{split}1 &\quad if z > 0 \\ 0 &\quad\mathrm{otherwize}\end{split}\]

## BRelu

class `paddle.v2.activation.``BRelu`

  BRelu Activation.

  forward. \(y = min(24, max(0, z))\)

  derivative:

  \[\begin{split}1 &\quad if 0 < z < 24 \\ 0 &\quad \mathrm{otherwise}\end{split}\]

## SoftRelu

class `paddle.v2.activation.``SoftRelu`

  SoftRelu Activation.

## Tanh

class `paddle.v2.activation.``Tanh`

  Tanh activation.

  \[f(z)=tanh(z)=\frac{e^z-e^{-z}}{e^z+e^{-z}}\]

## STanh

class `paddle.v2.activation.``STanh`

  Scaled Tanh Activation.

  \[f(z) = 1.7159 * tanh(2/3*z)\]