1 // Copyright 2013 Yangqing Jia
3 #ifndef CAFFE_NET_HPP_
4 #define CAFFE_NET_HPP_
6 #include <map>
7 #include <string>
8 #include <vector>
10 #include "caffe/blob.hpp"
11 #include "caffe/common.hpp"
12 #include "caffe/proto/caffe.pb.h"
14 using std::map;
15 using std::vector;
16 using std::string;
18 namespace caffe {
20 template <typename Dtype>
21 class Net {
22 public:
23 Net(const NetParameter& param,
24 const vector<Blob<Dtype>* >& bottom);
25 ~Net() {}
26 void Forward(const vector<Blob<Dtype>* > & bottom,
27 vector<Blob<Dtype>*>* top);
28 // The network backward should take no input and output, since it solely
29 // computes the gradient w.r.t the parameters, and the data has already
30 // been provided during the forward pass.
31 Dtype Backward();
33 // For an already initialized net, CopyTrainedLayersFrom() copies the already
34 // trained layers from another net parameter instance.
35 void CopyTrainedLayersFrom(const NetParameter& param);
36 // Writes the net to a proto.
37 void ToProto(NetParameter* param, bool write_diff = false);
39 // returns the network name.
40 const string& name() { return name_; }
42 protected:
43 // Individual layers in the net
44 vector<shared_ptr<Layer<Dtype> > > layers_;
45 vector<string> layer_names_;
46 // blobs stores the blobs that store intermediate results between the
47 // layers.
48 vector<Blob<Dtype> > blobs_;
49 vector<string> blob_names_;
50 // bottom_vecs stores the vectors containing the input for each layer, except
51 // for the first layer whose bottom vec is provided by the network's input.
52 vector<vector<Blob<Dtype>*> > bottom_vecs_;
53 // top_vecs stores the vectors containing the output for each layer, except
54 // for the last layer (likewise)
55 vector<vector<Blob<Dtype>*> > top_vecs_;
56 // blob indices for the input and the output of the net.
57 vector<int> net_input_blob_indices_;
58 vector<int> net_output_blob_indices_;
59 string name_;
60 };
63 } // namespace caffe
65 #endif // CAFFE_NET_HPP_