关于caffe的笔记

1、利用caffe训练期间，内存需要多少，内存中都保存了学什么？

内存需要保存一个训练周期中的所有 feature map 数据+网络参数数据
受bach_size 及网络模型大小关系较大。

2、利用训练好的模型文件.caffemodel如何初始化网络模型文件 .prototxt?

• matlab 接口:
  net=caffe.Net()
  net.copy_from()
• caffe 源码解释：
  调用函数：caffe_net.CopyTrainedLayersFrom(FLAGS_weights);
  利用 .caffemodel文件中与网络模型文件（.prototxt）网络层名称一致的参数进行赋值，与其网络名称不一致的不做处理，默认是随机初始化。

template <typename Dtype>
void Net**::CopyTrainedLayersFrom(const NetParameter& param)** {
  int num_source_layers = param.layer_size();
  for (int i = 0; i < num_source_layers; ++i) {
    const LayerParameter& source_layer = param.layer(i);
    const string& source_layer_name = source_layer.name();
    int target_layer_id = 0;
    while (target_layer_id != layer_names_.size() &&
        layer_names_[target_layer_id] != source_layer_name) {
      ++target_layer_id;
    }
    if (target_layer_id == layer_names_.size()) {
      LOG(INFO) << "Ignoring source layer " << source_layer_name;
      continue;
    }
    DLOG(INFO) << "Copying source layer " << source_layer_name;
    vector<shared_ptr > >& target_blobs =
        layers_[target_layer_id]->blobs();
    CHECK_EQ(target_blobs.size(), source_layer.blobs_size())
        << "Incompatible number of blobs for layer " << source_layer_name;
    for (int j = 0; j < target_blobs.size(); ++j) {
      if (!target_blobs[j]->ShapeEquals(source_layer.blobs(j))) {
        Blob source_blob;
        const bool kReshape = true;
        source_blob.FromProto(source_layer.blobs(j), kReshape);
        LOG(FATAL) << "Cannot copy param " << j << " weights from layer '"
            << source_layer_name << "'; shape mismatch.  Source param shape is "
            << source_blob.shape_string() << "; target param shape is "
            << target_blobs[j]->shape_string() << ". "
            << "To learn this layer's parameters from scratch rather than "
            << "copying from a saved net, rename the layer.";
      }
      const bool kReshape = false;
      target_blobs[j]->FromProto(source_layer.blobs(j), kReshape);
    }
  }
}

3、