A fully connected layer in a network definition. This layer expects an input tensor of three or more non-batch dimensions. The input is automatically reshaped into an MxV tensor X, where V is a product of the last three dimensions and M is a product of the remaining dimensions (where the product over 0 dimensions is defined as 1). For example: More...

#include <NvInfer.h>

Inheritance diagram for nvinfer1::IFullyConnectedLayer:

Public Member Functions
virtual void	setNbOutputChannels (int nbOutputs)=0
	Set the number of output channels `K` from the fully connected layer. More...

virtual int	getNbOutputChannels () const =0
	Get the number of output channels `K` from the fully connected layer. More...

virtual void	setKernelWeights (Weights weights)=0
	Set the kernel weights, given as a `KxC` matrix in row-major order. More...

virtual Weights	getKernelWeights () const =0
	Get the kernel weights. More...

virtual void	setBiasWeights (Weights weights)=0
	Set the bias weights. More...

virtual Weights	getBiasWeights () const =0
	Get the bias weights. More...

Public Member Functions inherited from nvinfer1::ILayer
virtual LayerType	getType () const =0
	Return the type of a layer. More...

virtual void	setName (const char *name)=0
	Set the name of a layer. More...

virtual const char *	getName () const =0
	Return the name of a layer. More...

virtual int	getNbInputs () const =0
	Get the number of inputs of a layer.

virtual ITensor *	getInput (int index) const =0
	Get the layer input corresponding to the given index. More...

virtual int	getNbOutputs () const =0
	Get the number of outputs of a layer.

virtual ITensor *	getOutput (int index) const =0
	Get the layer output corresponding to the given index. More...

Detailed Description

A fully connected layer in a network definition. This layer expects an input tensor of three or more non-batch dimensions. The input is automatically reshaped into an MxV tensor X, where V is a product of the last three dimensions and M is a product of the remaining dimensions (where the product over 0 dimensions is defined as 1). For example:

If the input tensor has shape {C, H, W}, then the tensor is reshaped into {1, C*H*W}.
If the input tensor has shape {P, C, H, W}, then the tensor is reshaped into {P, C*H*W}.

The layer then performs the following operation:

Y := matmul(X, W^T) + bias

Where X is the MxV tensor defined above, W is the KxV weight tensor of the layer, and bias is a row vector size K that is broadcasted to MxK. K is the number of output channels, and configurable via setNbOutputChannels(). If bias is not specified, it is implicitly 0.

The MxK result Y is then reshaped such that the last three dimensions are {K, 1, 1} and the remaining dimensions match the dimensions of the input tensor. For example:

If the input tensor has shape {C, H, W}, then the output tensor will have shape {K, 1, 1}.
If the input tensor has shape {P, C, H, W}, then the output tensor will have shape {P, K, 1, 1}.