}
// go through the bottom and parameter blobs
// LOG(ERROR) << "Checking " << blobs_to_check.size() << " blobs.";
- for (int blobid = 0; blobid < blobs_to_check.size(); ++blobid) {
- Blob<Dtype>* current_blob = blobs_to_check[blobid];
- // LOG(ERROR) << "Blob " << blobid << ": checking " << current_blob->count()
- // << " parameters.";
+ for (int blob_id = 0; blob_id < blobs_to_check.size(); ++blob_id) {
+ Blob<Dtype>* current_blob = blobs_to_check[blob_id];
+ // LOG(ERROR) << "Blob " << blob_id << ": checking "
+ // << current_blob->count() << " parameters.";
// go through the values
for (int feat_id = 0; feat_id < current_blob->count(); ++feat_id) {
// First, obtain the original data
max(fabs(computed_gradient), fabs(estimated_gradient)), 1.);
EXPECT_NEAR(computed_gradient, estimated_gradient, threshold_ * scale)
<< "debug: (top_id, top_data_id, blob_id, feat_id)="
- << top_id << "," << top_data_id << "," << blobid << "," << feat_id;
+ << top_id << "," << top_data_id << "," << blob_id << "," << feat_id;
}
// LOG(ERROR) << "Feature: " << current_blob->cpu_data()[feat_id];
// LOG(ERROR) << "computed gradient: " << computed_gradient