BaseActivation¶

class paddle.trainer_config_helpers.activations.BaseActivation(name, support_hppl)

A mark for activation class. Each activation inherit BaseActivation, which has two parameters.

Parameters:	name (basestring) – activation name in paddle config. support_hppl (bool) – True if supported by hppl. HPPL is a library used by paddle internally. Currently, lstm layer can only use activations supported by hppl.

AbsActivation¶

class paddle.trainer_config_helpers.activations.AbsActivation

Abs Activation.

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

Derivative:

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

IdentityActivation¶

class paddle.trainer_config_helpers.activations.IdentityActivation

Identity Activation.

Just do nothing for output both forward/backward.

LinearActivation¶

paddle.trainer_config_helpers.activations.LinearActivation: alias of IdentityActivation

SquareActivation¶

class paddle.trainer_config_helpers.activations.SquareActivation: Square Activation.

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

SigmoidActivation¶

class paddle.trainer_config_helpers.activations.SigmoidActivation: Sigmoid activation.

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

SoftmaxActivation¶

class paddle.trainer_config_helpers.activations.SoftmaxActivation: Softmax activation for simple input

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

SequenceSoftmaxActivation¶

class paddle.trainer_config_helpers.activations.SequenceSoftmaxActivation

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]

ReluActivation¶

class paddle.trainer_config_helpers.activations.ReluActivation

Relu activation.

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

derivative:

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

BReluActivation¶

class paddle.trainer_config_helpers.activations.BReluActivation

BRelu Activation.

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

derivative:

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

SoftReluActivation¶

class paddle.trainer_config_helpers.activations.SoftReluActivation: SoftRelu Activation.

TanhActivation¶

class paddle.trainer_config_helpers.activations.TanhActivation: Tanh activation.

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

STanhActivation¶

class paddle.trainer_config_helpers.activations.STanhActivation: Scaled Tanh Activation.

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