euclidean layer update

[jacinto-ai/caffe-jacinto.git] / src / caffe / layers / loss_layer.cu

diff --git a/src/caffe/layers/multinomial_logistic_loss_layer.cu b/src/caffe/layers/loss_layer.cu
similarity index 54%
rename from src/caffe/layers/multinomial_logistic_loss_layer.cu
rename to src/caffe/layers/loss_layer.cu
index 5ffa4accc5029ce847afb66438a4aa4aae1c6e30..0c09a5dbe29f79d4d26cd5cbba0b2e939ff225a7 100644 (file)
--- a/src/caffe/layers/multinomial_logistic_loss_layer.cu
+++ b/src/caffe/layers/loss_layer.cu
@@ -44,9 +44,39 @@ Dtype MultinomialLogisticLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>
   return loss / num;
 }
 
-// TODO: implement the GPU version
+// TODO: implement the GPU version for multinomial loss
+
+template <typename Dtype>
+void EuclideanLossLayer<Dtype>::SetUp(
+  const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+  CHECK_EQ(bottom.size(), 2) << "Loss Layer takes two blobs as input.";
+  CHECK_EQ(top->size(), 0) << "Loss Layer takes no as output.";
+  CHECK_EQ(bottom[0]->num(), bottom[1]->num())
+      << "The data and label should have the same number.";
+  CHECK_EQ(bottom[0]->channels(), bottom[1]->channels());
+  CHECK_EQ(bottom[0]->height(), bottom[1]->height());
+  CHECK_EQ(bottom[0]->width(), bottom[1]->width());
+  difference_.Reshape(bottom[0]->num(), bottom[0]->channels(),
+      bottom[0]->height(), bottom[0]->width());
+}
+
+template <typename Dtype>
+Dtype EuclideanLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
+  int count = (*bottom)[0]->count();
+  int num = (*bottom)[0]->num();
+  caffe_sub(count, (*bottom)[0]->cpu_data(), (*bottom)[1]->cpu_data(),
+      difference_.mutable_cpu_data());
+  Dtype loss = caffe_cpu_dot(
+      count, difference_.cpu_data(), difference_.cpu_data()) / num / Dtype(2);
+  // Compute the gradient
+  caffe_axpby(count, Dtype(1) / num, difference_.cpu_data(), Dtype(0),
+      (*bottom)[0]->mutable_cpu_diff());
+  return loss;
+}
 
 INSTANTIATE_CLASS(MultinomialLogisticLossLayer);
+INSTANTIATE_CLASS(EuclideanLossLayer);
 
 
 }  // namespace caffe