alphabetize classes in vision_layers.hpp

diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index d80809b95a8238eda997c0b0e814c15feeaaf7db..079537e9aa05a0f5a0a7131b6fac5fce5c4bb9b5 100644 (file)
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -34,11 +34,10 @@ class NeuronLayer : public Layer<Dtype> {
       vector<Blob<Dtype>*>* top);
 };
 
-
 template <typename Dtype>
-class ReLULayer : public NeuronLayer<Dtype> {
+class BNLLLayer : public NeuronLayer<Dtype> {
  public:
-  explicit ReLULayer(const LayerParameter& param)
+  explicit BNLLLayer(const LayerParameter& param)
       : NeuronLayer<Dtype>(param) {}
 
  protected:
@@ -46,7 +45,6 @@ class ReLULayer : public NeuronLayer<Dtype> {
       vector<Blob<Dtype>*>* top);
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
-
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const bool propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
@@ -54,10 +52,12 @@ class ReLULayer : public NeuronLayer<Dtype> {
 };
 
 template <typename Dtype>
-class TanHLayer : public NeuronLayer<Dtype> {
+class DropoutLayer : public NeuronLayer<Dtype> {
  public:
-  explicit TanHLayer(const LayerParameter& param)
+  explicit DropoutLayer(const LayerParameter& param)
       : NeuronLayer<Dtype>(param) {}
+  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
 
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
@@ -68,12 +68,17 @@ class TanHLayer : public NeuronLayer<Dtype> {
       const bool propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+
+  shared_ptr<SyncedMemory> rand_vec_;
+  float threshold_;
+  float scale_;
+  unsigned int uint_thres_;
 };
 
 template <typename Dtype>
-class SigmoidLayer : public NeuronLayer<Dtype> {
+class ReLULayer : public NeuronLayer<Dtype> {
  public:
-  explicit SigmoidLayer(const LayerParameter& param)
+  explicit ReLULayer(const LayerParameter& param)
       : NeuronLayer<Dtype>(param) {}
 
  protected:
@@ -81,17 +86,17 @@ class SigmoidLayer : public NeuronLayer<Dtype> {
       vector<Blob<Dtype>*>* top);
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const bool propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const bool propagate_down, vector<Blob<Dtype>*>* bottom);
 };
 
-
 template <typename Dtype>
-class BNLLLayer : public NeuronLayer<Dtype> {
+class SigmoidLayer : public NeuronLayer<Dtype> {
  public:
-  explicit BNLLLayer(const LayerParameter& param)
+  explicit SigmoidLayer(const LayerParameter& param)
       : NeuronLayer<Dtype>(param) {}
 
  protected:
@@ -105,14 +110,11 @@ class BNLLLayer : public NeuronLayer<Dtype> {
       const bool propagate_down, vector<Blob<Dtype>*>* bottom);
 };
 
-
 template <typename Dtype>
-class DropoutLayer : public NeuronLayer<Dtype> {
+class TanHLayer : public NeuronLayer<Dtype> {
  public:
-  explicit DropoutLayer(const LayerParameter& param)
+  explicit TanHLayer(const LayerParameter& param)
       : NeuronLayer<Dtype>(param) {}
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
 
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
@@ -123,18 +125,13 @@ class DropoutLayer : public NeuronLayer<Dtype> {
       const bool propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const bool propagate_down, vector<Blob<Dtype>*>* bottom);
-
-  shared_ptr<SyncedMemory> rand_vec_;
-  float threshold_;
-  float scale_;
-  unsigned int uint_thres_;
 };
 
 
 template <typename Dtype>
-class SplitLayer : public Layer<Dtype> {
+class AccuracyLayer : public Layer<Dtype> {
  public:
-  explicit SplitLayer(const LayerParameter& param)
+  explicit AccuracyLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -142,21 +139,17 @@ class SplitLayer : public Layer<Dtype> {
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+  // The accuracy layer should not be used to compute backward operations.
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
-
-  int count_;
+      const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
+    NOT_IMPLEMENTED;
+  }
 };
 
-
 template <typename Dtype>
-class FlattenLayer : public Layer<Dtype> {
+class ConcatLayer : public Layer<Dtype> {
  public:
-  explicit FlattenLayer(const LayerParameter& param)
+  explicit ConcatLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -171,14 +164,19 @@ class FlattenLayer : public Layer<Dtype> {
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const bool propagate_down, vector<Blob<Dtype>*>* bottom);
 
+  Blob<Dtype> col_bob_;
   int count_;
+  int num_;
+  int channels_;
+  int height_;
+  int width_;
+  int concat_dim_;
 };
 
-
 template <typename Dtype>
-class InnerProductLayer : public Layer<Dtype> {
+class ConvolutionLayer : public Layer<Dtype> {
  public:
-  explicit InnerProductLayer(const LayerParameter& param)
+  explicit ConvolutionLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -188,25 +186,41 @@ class InnerProductLayer : public Layer<Dtype> {
       vector<Blob<Dtype>*>* top);
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
-
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const bool propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const bool propagate_down, vector<Blob<Dtype>*>* bottom);
 
+  int kernel_size_;
+  int stride_;
+  int num_;
+  int channels_;
+  int pad_;
+  int height_;
+  int width_;
+  int num_output_;
+  int group_;
+  Blob<Dtype> col_buffer_;
+  shared_ptr<SyncedMemory> bias_multiplier_;
+  bool bias_term_;
   int M_;
   int K_;
   int N_;
-  bool bias_term_;
-  shared_ptr<SyncedMemory> bias_multiplier_;
 };
 
+// This function is used to create a pthread that prefetches the data.
+template <typename Dtype>
+void* DataLayerPrefetch(void* layer_pointer);
 
 template <typename Dtype>
-class LRNLayer : public Layer<Dtype> {
+class DataLayer : public Layer<Dtype> {
+  // The function used to perform prefetching.
+  friend void* DataLayerPrefetch<Dtype>(void* layer_pointer);
+
  public:
-  explicit LRNLayer(const LayerParameter& param)
+  explicit DataLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
+  virtual ~DataLayer();
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
@@ -216,53 +230,49 @@ class LRNLayer : public Layer<Dtype> {
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const bool propagate_down, vector<Blob<Dtype>*>* bottom) { return; }
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const bool propagate_down, vector<Blob<Dtype>*>* bottom) { return; }
 
-  // scale_ stores the intermediate summing results
-  Blob<Dtype> scale_;
-  int size_;
-  int pre_pad_;
-  Dtype alpha_;
-  Dtype beta_;
-  int num_;
-  int channels_;
-  int height_;
-  int width_;
+  shared_ptr<leveldb::DB> db_;
+  shared_ptr<leveldb::Iterator> iter_;
+  int datum_channels_;
+  int datum_height_;
+  int datum_width_;
+  int datum_size_;
+  pthread_t thread_;
+  shared_ptr<Blob<Dtype> > prefetch_data_;
+  shared_ptr<Blob<Dtype> > prefetch_label_;
+  Blob<Dtype> data_mean_;
 };
 
-
 template <typename Dtype>
-class Im2colLayer : public Layer<Dtype> {
+class EuclideanLossLayer : public Layer<Dtype> {
  public:
-  explicit Im2colLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
+  explicit EuclideanLossLayer(const LayerParameter& param)
+      : Layer<Dtype>(param), difference_() {}
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
  protected:
+  // The loss layer will do nothing during forward - all computation are
+  // carried out in the backward pass.
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+  // virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+  //     vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const bool propagate_down, vector<Blob<Dtype>*>* bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+  // virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+  //     const bool propagate_down, vector<Blob<Dtype>*>* bottom);
 
-  int kernel_size_;
-  int stride_;
-  int channels_;
-  int height_;
-  int width_;
-  int pad_;
+  Blob<Dtype> difference_;
 };
 
 template <typename Dtype>
-class PoolingLayer : public Layer<Dtype> {
+class FlattenLayer : public Layer<Dtype> {
  public:
-  explicit PoolingLayer(const LayerParameter& param)
+  explicit FlattenLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -277,22 +287,15 @@ class PoolingLayer : public Layer<Dtype> {
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const bool propagate_down, vector<Blob<Dtype>*>* bottom);
 
-  int kernel_size_;
-  int stride_;
-  int channels_;
-  int height_;
-  int width_;
-  int pooled_height_;
-  int pooled_width_;
-  Blob<float> rand_idx_;
+  int count_;
 };
 
-
 template <typename Dtype>
-class ConvolutionLayer : public Layer<Dtype> {
+class HDF5DataLayer : public Layer<Dtype> {
  public:
-  explicit ConvolutionLayer(const LayerParameter& param)
+  explicit HDF5DataLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
+  virtual ~HDF5DataLayer();
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
@@ -305,28 +308,20 @@ class ConvolutionLayer : public Layer<Dtype> {
       const bool propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+  virtual void load_hdf5_file_data(const char* filename);
 
-  int kernel_size_;
-  int stride_;
-  int num_;
-  int channels_;
-  int pad_;
-  int height_;
-  int width_;
-  int num_output_;
-  int group_;
-  Blob<Dtype> col_buffer_;
-  shared_ptr<SyncedMemory> bias_multiplier_;
-  bool bias_term_;
-  int M_;
-  int K_;
-  int N_;
+  std::vector<std::string> hdf_filenames_;
+  unsigned int num_files_;
+  unsigned int current_file_;
+  hsize_t current_row_;
+  Blob<Dtype> data_blob_;
+  Blob<Dtype> label_blob_;
 };
 
 template <typename Dtype>
-class ConcatLayer : public Layer<Dtype> {
+class Im2colLayer : public Layer<Dtype> {
  public:
-  explicit ConcatLayer(const LayerParameter& param)
+  explicit Im2colLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -341,28 +336,27 @@ class ConcatLayer : public Layer<Dtype> {
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const bool propagate_down, vector<Blob<Dtype>*>* bottom);
 
-  Blob<Dtype> col_bob_;
-  int count_;
-  int num_;
+  int kernel_size_;
+  int stride_;
   int channels_;
   int height_;
   int width_;
-  int concat_dim_;
+  int pad_;
 };
 
 // This function is used to create a pthread that prefetches the data.
 template <typename Dtype>
-void* DataLayerPrefetch(void* layer_pointer);
+void* ImagesLayerPrefetch(void* layer_pointer);
 
 template <typename Dtype>
-class DataLayer : public Layer<Dtype> {
+class ImagesLayer : public Layer<Dtype> {
   // The function used to perform prefetching.
-  friend void* DataLayerPrefetch<Dtype>(void* layer_pointer);
+  friend void* ImagesLayerPrefetch<Dtype>(void* layer_pointer);
 
  public:
-  explicit DataLayer(const LayerParameter& param)
+  explicit ImagesLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
-  virtual ~DataLayer();
+  virtual ~ImagesLayer();
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
@@ -376,8 +370,8 @@ class DataLayer : public Layer<Dtype> {
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const bool propagate_down, vector<Blob<Dtype>*>* bottom) { return; }
 
-  shared_ptr<leveldb::DB> db_;
-  shared_ptr<leveldb::Iterator> iter_;
+  vector<std::pair<std::string, int> > lines_;
+  int lines_id_;
   int datum_channels_;
   int datum_height_;
   int datum_width_;
@@ -388,51 +382,34 @@ class DataLayer : public Layer<Dtype> {
   Blob<Dtype> data_mean_;
 };
 
-// This function is used to create a pthread that prefetches the data.
-template <typename Dtype>
-void* ImagesLayerPrefetch(void* layer_pointer);
-
 template <typename Dtype>
-class ImagesLayer : public Layer<Dtype> {
-  // The function used to perform prefetching.
-  friend void* ImagesLayerPrefetch<Dtype>(void* layer_pointer);
-
+class InfogainLossLayer : public Layer<Dtype> {
  public:
-  explicit ImagesLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual ~ImagesLayer();
+  explicit InfogainLossLayer(const LayerParameter& param)
+      : Layer<Dtype>(param), infogain_() {}
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
  protected:
+  // The loss layer will do nothing during forward - all computation are
+  // carried out in the backward pass.
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+  // virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+  //     vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) { return; }
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) { return; }
+      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+  // virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+  //     const bool propagate_down, vector<Blob<Dtype>*>* bottom);
 
-  vector<std::pair<std::string, int> > lines_;
-  int lines_id_;
-  int datum_channels_;
-  int datum_height_;
-  int datum_width_;
-  int datum_size_;
-  pthread_t thread_;
-  shared_ptr<Blob<Dtype> > prefetch_data_;
-  shared_ptr<Blob<Dtype> > prefetch_label_;
-  Blob<Dtype> data_mean_;
+  Blob<Dtype> infogain_;
 };
 
-
 template <typename Dtype>
-class HDF5DataLayer : public Layer<Dtype> {
+class InnerProductLayer : public Layer<Dtype> {
  public:
-  explicit HDF5DataLayer(const LayerParameter& param)
+  explicit InnerProductLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
-  virtual ~HDF5DataLayer();
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
@@ -445,17 +422,14 @@ class HDF5DataLayer : public Layer<Dtype> {
       const bool propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const bool propagate_down, vector<Blob<Dtype>*>* bottom);
-  virtual void load_hdf5_file_data(const char* filename);
 
-  std::vector<std::string> hdf_filenames_;
-  unsigned int num_files_;
-  unsigned int current_file_;
-  hsize_t current_row_;
-  Blob<Dtype> data_blob_;
-  Blob<Dtype> label_blob_;
+  int M_;
+  int K_;
+  int N_;
+  bool bias_term_;
+  shared_ptr<SyncedMemory> bias_multiplier_;
 };
 
-
 template <typename Dtype>
 class HDF5OutputLayer : public Layer<Dtype> {
  public:
@@ -482,11 +456,10 @@ class HDF5OutputLayer : public Layer<Dtype> {
   Blob<Dtype> label_blob_;
 };
 
-
 template <typename Dtype>
-class SoftmaxLayer : public Layer<Dtype> {
+class LRNLayer : public Layer<Dtype> {
  public:
-  explicit SoftmaxLayer(const LayerParameter& param)
+  explicit LRNLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -499,15 +472,20 @@ class SoftmaxLayer : public Layer<Dtype> {
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const bool propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-     const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
 
-  // sum_multiplier is just used to carry out sum using blas
-  Blob<Dtype> sum_multiplier_;
-  // scale is an intermediate blob to hold temporary results.
+  // scale_ stores the intermediate summing results
   Blob<Dtype> scale_;
+  int size_;
+  int pre_pad_;
+  Dtype alpha_;
+  Dtype beta_;
+  int num_;
+  int channels_;
+  int height_;
+  int width_;
 };
 
-
 template <typename Dtype>
 class MultinomialLogisticLossLayer : public Layer<Dtype> {
  public:
@@ -530,28 +508,56 @@ class MultinomialLogisticLossLayer : public Layer<Dtype> {
 };
 
 template <typename Dtype>
-class InfogainLossLayer : public Layer<Dtype> {
+class PoolingLayer : public Layer<Dtype> {
  public:
-  explicit InfogainLossLayer(const LayerParameter& param)
-      : Layer<Dtype>(param), infogain_() {}
+  explicit PoolingLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
  protected:
-  // The loss layer will do nothing during forward - all computation are
-  // carried out in the backward pass.
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
-  // virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-  //     vector<Blob<Dtype>*>* top);
+  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const bool propagate_down, vector<Blob<Dtype>*>* bottom);
-  // virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-  //     const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
 
-  Blob<Dtype> infogain_;
+  int kernel_size_;
+  int stride_;
+  int channels_;
+  int height_;
+  int width_;
+  int pooled_height_;
+  int pooled_width_;
+  Blob<float> rand_idx_;
 };
 
+template <typename Dtype>
+class SoftmaxLayer : public Layer<Dtype> {
+ public:
+  explicit SoftmaxLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+
+ protected:
+  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+     const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+
+  // sum_multiplier is just used to carry out sum using blas
+  Blob<Dtype> sum_multiplier_;
+  // scale is an intermediate blob to hold temporary results.
+  Blob<Dtype> scale_;
+};
 
 // SoftmaxWithLossLayer is a layer that implements softmax and then computes
 // the loss - it is preferred over softmax + multinomiallogisticloss in the
@@ -584,47 +590,25 @@ class SoftmaxWithLossLayer : public Layer<Dtype> {
   vector<Blob<Dtype>*> softmax_top_vec_;
 };
 
-
 template <typename Dtype>
-class EuclideanLossLayer : public Layer<Dtype> {
+class SplitLayer : public Layer<Dtype> {
  public:
-  explicit EuclideanLossLayer(const LayerParameter& param)
-      : Layer<Dtype>(param), difference_() {}
+  explicit SplitLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
  protected:
-  // The loss layer will do nothing during forward - all computation are
-  // carried out in the backward pass.
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
-  // virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-  //     vector<Blob<Dtype>*>* top);
+  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const bool propagate_down, vector<Blob<Dtype>*>* bottom);
-  // virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-  //     const bool propagate_down, vector<Blob<Dtype>*>* bottom);
-
-  Blob<Dtype> difference_;
-};
-
-
-template <typename Dtype>
-class AccuracyLayer : public Layer<Dtype> {
- public:
-  explicit AccuracyLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
 
- protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  // The accuracy layer should not be used to compute backward operations.
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
-    NOT_IMPLEMENTED;
-  }
+  int count_;
 };
 
 // This function is used to create a pthread that prefetches the window data.