# 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.

$f(z) = 1.7159 * tanh(2/3*z)$