Caffe
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Computes the hinge loss for a one-of-many classification task. More...
#include <hinge_loss_layer.hpp>
Public Member Functions | |
HingeLossLayer (const LayerParameter ¶m) | |
virtual const char * | type () const |
Returns the layer type. | |
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LossLayer (const LayerParameter ¶m) | |
virtual void | LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top) |
Does layer-specific setup: your layer should implement this function as well as Reshape. More... | |
virtual void | Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top) |
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More... | |
virtual int | ExactNumBottomBlobs () const |
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More... | |
virtual bool | AutoTopBlobs () const |
For convenience and backwards compatibility, instruct the Net to automatically allocate a single top Blob for LossLayers, into which they output their singleton loss, (even if the user didn't specify one in the prototxt, etc.). | |
virtual int | ExactNumTopBlobs () const |
Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More... | |
virtual bool | AllowForceBackward (const int bottom_index) const |
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Layer (const LayerParameter ¶m) | |
void | SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top) |
Implements common layer setup functionality. More... | |
virtual bool | ShareInParallel () const |
Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism. | |
bool | IsShared () const |
Return whether this layer is actually shared by other nets. If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then this function is expected return true. | |
void | SetShared (bool is_shared) |
Set whether this layer is actually shared by other nets If ShareInParallel() is true and using more than one GPU and the net has TRAIN phase, then is_shared should be set true. | |
Dtype | Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top) |
Given the bottom blobs, compute the top blobs and the loss. More... | |
void | Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom) |
Given the top blob error gradients, compute the bottom blob error gradients. More... | |
vector< shared_ptr< Blob< Dtype > > > & | blobs () |
Returns the vector of learnable parameter blobs. | |
const LayerParameter & | layer_param () const |
Returns the layer parameter. | |
virtual void | ToProto (LayerParameter *param, bool write_diff=false) |
Writes the layer parameter to a protocol buffer. | |
Dtype | loss (const int top_index) const |
Returns the scalar loss associated with a top blob at a given index. | |
void | set_loss (const int top_index, const Dtype value) |
Sets the loss associated with a top blob at a given index. | |
virtual int | MinBottomBlobs () const |
Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More... | |
virtual int | MaxBottomBlobs () const |
Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More... | |
virtual int | MinTopBlobs () const |
Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More... | |
virtual int | MaxTopBlobs () const |
Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More... | |
virtual bool | EqualNumBottomTopBlobs () const |
Returns true if the layer requires an equal number of bottom and top blobs. More... | |
bool | param_propagate_down (const int param_id) |
Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More... | |
void | set_param_propagate_down (const int param_id, const bool value) |
Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. | |
Protected Member Functions | |
virtual void | Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top) |
Computes the hinge loss for a one-of-many classification task. More... | |
virtual void | Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom) |
Computes the hinge loss error gradient w.r.t. the predictions. More... | |
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virtual void | Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top) |
Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable. | |
virtual void | Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom) |
Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable. | |
virtual void | CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top) |
void | SetLossWeights (const vector< Blob< Dtype > *> &top) |
Additional Inherited Members | |
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LayerParameter | layer_param_ |
Phase | phase_ |
vector< shared_ptr< Blob< Dtype > > > | blobs_ |
vector< bool > | param_propagate_down_ |
vector< Dtype > | loss_ |
Computes the hinge loss for a one-of-many classification task.
bottom | input Blob vector (length 2)
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top | output Blob vector (length 1)
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In an SVM, is the result of taking the inner product
of the features
and the learned hyperplane parameters
. So, a Net with just an InnerProductLayer (with num_output =
) providing predictions to a HingeLossLayer is equivalent to an SVM (assuming it has no other learned outside the InnerProductLayer and no other losses outside the HingeLossLayer).
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protectedvirtual |
Computes the hinge loss error gradient w.r.t. the predictions.
Gradients cannot be computed with respect to the label inputs (bottom[1]), so this method ignores bottom[1] and requires !propagate_down[1], crashing if propagate_down[1] is set.
top | output Blob vector (length 1), providing the error gradient with respect to the outputs |
propagate_down | see Layer::Backward. propagate_down[1] must be false as we can't compute gradients with respect to the labels. |
bottom | input Blob vector (length 2)
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Implements caffe::Layer< Dtype >.
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protectedvirtual |
Computes the hinge loss for a one-of-many classification task.
bottom | input Blob vector (length 2)
|
top | output Blob vector (length 1)
|
In an SVM, is the result of taking the inner product
of the features
and the learned hyperplane parameters
. So, a Net with just an InnerProductLayer (with num_output =
) providing predictions to a HingeLossLayer is equivalent to an SVM (assuming it has no other learned outside the InnerProductLayer and no other losses outside the HingeLossLayer).
Implements caffe::Layer< Dtype >.