HardTanHType< InputType, OutputType > Class Template Reference

The Hard Tanh activation function, defined by. More...

Inheritance diagram for HardTanHType< InputType, OutputType >:

Public Member Functions

 HardTanHType (const double maxValue=1, const double minValue=-1)
 Create the HardTanH object using the specified parameters. More...

 
void Backward (const InputType &input, const OutputType &gy, OutputType &g)
 Ordinary feed backward pass of a neural network, calculating the function f(x) by propagating x backwards through f. More...

 
HardTanHTypeClone () const
 Clone the HardTanHType object. This handles polymorphism correctly. More...

 
void Forward (const InputType &input, OutputType &output)
 Ordinary feed forward pass of a neural network, evaluating the function f(x) by propagating the activity forward through f. More...

 
double const & MaxValue () const
 Get the maximum value. More...

 
double & MaxValue ()
 Modify the maximum value. More...

 
double const & MinValue () const
 Get the minimum value. More...

 
double & MinValue ()
 Modify the minimum value. More...

 
template
<
typename
Archive
>
void serialize (Archive &ar, const uint32_t)
 Serialize the layer. More...

 
- Public Member Functions inherited from Layer< InputType, OutputType >
 Layer ()
 Default constructor. More...

 
 Layer (const Layer &layer)
 Copy constructor. This is not responsible for copying weights! More...

 
 Layer (Layer &&layer)
 Move constructor. This is not responsible for moving weights! More...

 
virtual ~Layer ()
 Default deconstructor. More...

 
virtual void Backward (const InputType &, const InputType &, InputType &)
 Performs a backpropagation step through the layer, with respect to the given input. More...

 
virtual void ComputeOutputDimensions ()
 Compute the output dimensions. More...

 
virtual void CustomInitialize (InputType &, const size_t)
 Override the weight matrix of the layer. More...

 
virtual void Forward (const InputType &, InputType &)
 Takes an input object, and computes the corresponding output of the layer. More...

 
virtual void Forward (const InputType &, const InputType &)
 Takes an input and output object, and computes the corresponding loss of the layer. More...

 
virtual void Gradient (const InputType &, const InputType &, InputType &)
 Computing the gradient of the layer with respect to its own input. More...

 
const std::vector< size_t > & InputDimensions () const
 Get the input dimensions. More...

 
std::vector< size_t > & InputDimensions ()
 Modify the input dimensions. More...

 
virtual double Loss ()
 Get the layer loss. More...

 
virtual Layeroperator= (const Layer &layer)
 Copy assignment operator. This is not responsible for copying weights! More...

 
virtual Layeroperator= (Layer &&layer)
 Move assignment operator. This is not responsible for moving weights! More...

 
const std::vector< size_t > & OutputDimensions ()
 Get the output dimensions. More...

 
virtual size_t OutputSize () final
 Get the number of elements in the output from this layer. More...

 
virtual const InputType & Parameters () const
 Get the parameters. More...

 
virtual InputType & Parameters ()
 Set the parameters. More...

 
void serialize (Archive &ar, const uint32_t)
 Serialize the layer. More...

 
virtual void SetWeights (typename InputType ::elem_type *)
 Reset the layer parameter. More...

 
virtual bool const & Training () const
 Get whether the layer is currently in training mode. More...

 
virtual bool & Training ()
 Modify whether the layer is currently in training mode. More...

 
virtual size_t WeightSize () const
 Get the total number of trainable weights in the layer. More...

 

Additional Inherited Members

- Protected Attributes inherited from Layer< InputType, OutputType >
std::vector< size_t > inputDimensions
 Logical input dimensions of each point. More...

 
std::vector< size_t > outputDimensions
 Logical output dimensions of each point. More...

 
bool training
 If true, the layer is in training mode; otherwise, it is in testing mode. More...

 
bool validOutputDimensions
 This is true if ComputeOutputDimensions() has been called, and outputDimensions can be considered to be up-to-date. More...

 

Detailed Description


template
<
typename
InputType
=
arma::mat
,
typename
OutputType
=
arma::mat
>

class mlpack::ann::HardTanHType< InputType, OutputType >

The Hard Tanh activation function, defined by.

\begin{eqnarray*} f(x) &=& \left\{ \begin{array}{lr} max & : x > maxValue \\ min & : x \le minValue \\ x & : otherwise \end{array} \right. \\ f'(x) &=& \left\{ \begin{array}{lr} 0 & : x > maxValue \\ 0 & : x \le minValue \\ 1 & : otherwise \end{array} \right. \end{eqnarray*}

Template Parameters
InputTypeThe type of the layer's inputs. The layer automatically cast inputs to this type (Default: arma::mat).
OutputTypeThe type of the computation which also causes the output to also be in this type. The type also allows the computation and weight type to differ from the input type (Default: arma::mat).

Definition at line 49 of file hard_tanh.hpp.

Constructor & Destructor Documentation

◆ HardTanHType()

HardTanHType ( const double  maxValue = 1,
const double  minValue = -1 
)

Create the HardTanH object using the specified parameters.

The range of the linear region can be adjusted by specifying the maxValue and minValue. Default (maxValue = 1, minValue = -1).

Parameters
maxValueRange of the linear region maximum value.
minValueRange of the linear region minimum value.

Referenced by HardTanHType< InputType, OutputType >::Clone().

Member Function Documentation

◆ Backward()

void Backward ( const InputType &  input,
const OutputType &  gy,
OutputType &  g 
)

Ordinary feed backward pass of a neural network, calculating the function f(x) by propagating x backwards through f.

Using the results from the feed forward pass.

Parameters
inputThe propagated input activation.
gyThe backpropagated error.
gThe calculated gradient.

Referenced by HardTanHType< InputType, OutputType >::Clone().

◆ Clone()

HardTanHType* Clone ( ) const
inlinevirtual

◆ Forward()

void Forward ( const InputType &  input,
OutputType &  output 
)

Ordinary feed forward pass of a neural network, evaluating the function f(x) by propagating the activity forward through f.

Parameters
inputInput data used for evaluating the specified function.
outputResulting output activation.

Referenced by HardTanHType< InputType, OutputType >::Clone().

◆ MaxValue() [1/2]

double const& MaxValue ( ) const
inline

Get the maximum value.

Definition at line 86 of file hard_tanh.hpp.

◆ MaxValue() [2/2]

double& MaxValue ( )
inline

Modify the maximum value.

Definition at line 88 of file hard_tanh.hpp.

◆ MinValue() [1/2]

double const& MinValue ( ) const
inline

Get the minimum value.

Definition at line 91 of file hard_tanh.hpp.

◆ MinValue() [2/2]

double& MinValue ( )
inline

Modify the minimum value.

Definition at line 93 of file hard_tanh.hpp.

References HardTanHType< InputType, OutputType >::serialize().

◆ serialize()

void serialize ( Archive &  ar,
const uint32_t   
)

Serialize the layer.

Referenced by HardTanHType< InputType, OutputType >::MinValue().


The documentation for this class was generated from the following file:
  • /home/jenkins-mlpack/mlpack.org/_src/mlpack-git/src/mlpack/methods/ann/layer/not_adapted/hard_tanh.hpp