1 // Copyright 2013 Yangqing Jia
3 #include <cuda_runtime.h>
5 #include <google/protobuf/text_format.h>
9 #include "caffe/blob.hpp"
10 #include "caffe/common.hpp"
11 #include "caffe/net.hpp"
12 #include "caffe/filler.hpp"
13 #include "caffe/proto/caffe.pb.h"
14 #include "caffe/util/io.hpp"
15 #include "caffe/optimization/solver.hpp"
17 using namespace caffe;
19 int main(int argc, char** argv) {
21 Caffe::set_mode(Caffe::GPU);
22 Caffe::set_phase(Caffe::TRAIN);
24 NetParameter net_param;
25 ReadProtoFromTextFile("data/lenet.prototxt",
27 vector<Blob<float>*> bottom_vec;
28 Net<float> caffe_net(net_param, bottom_vec);
30 // Run the network without training.
31 LOG(ERROR) << "Performing Forward";
32 caffe_net.Forward(bottom_vec);
33 LOG(ERROR) << "Performing Backward";
34 LOG(ERROR) << "Initial loss: " << caffe_net.Backward();
36 SolverParameter solver_param;
37 solver_param.set_base_lr(0.01);
38 solver_param.set_display(100);
39 solver_param.set_max_iter(6000);
40 solver_param.set_lr_policy("inv");
41 solver_param.set_gamma(0.0001);
42 solver_param.set_power(0.75);
43 solver_param.set_momentum(0.9);
44 solver_param.set_weight_decay(0.0005);
46 LOG(ERROR) << "Starting Optimization";
47 SGDSolver<float> solver(solver_param);
48 solver.Solve(&caffe_net);
49 LOG(ERROR) << "Optimization Done.";
51 // Run the network after training.
52 LOG(ERROR) << "Performing Forward";
53 caffe_net.Forward(bottom_vec);
54 LOG(ERROR) << "Performing Backward";
55 float loss = caffe_net.Backward();
56 LOG(ERROR) << "Final loss: " << loss;
58 NetParameter trained_net_param;
59 caffe_net.ToProto(&trained_net_param);
61 NetParameter traintest_net_param;
62 ReadProtoFromTextFile("data/lenet_traintest.prototxt",
63 &traintest_net_param);
64 Net<float> caffe_traintest_net(traintest_net_param, bottom_vec);
65 caffe_traintest_net.CopyTrainedLayersFrom(trained_net_param);
67 Caffe::set_phase(Caffe::TEST);
70 double train_accuracy = 0;
71 int batch_size = traintest_net_param.layers(0).layer().batchsize();
72 for (int i = 0; i < 60000 / batch_size; ++i) {
73 const vector<Blob<float>*>& result =
74 caffe_traintest_net.Forward(bottom_vec);
75 train_accuracy += result[0]->cpu_data()[0];
77 train_accuracy /= 60000 / batch_size;
78 LOG(ERROR) << "Train accuracy:" << train_accuracy;
80 NetParameter test_net_param;
81 ReadProtoFromTextFile("data/lenet_test.prototxt", &test_net_param);
82 Net<float> caffe_test_net(test_net_param, bottom_vec);
83 caffe_test_net.CopyTrainedLayersFrom(trained_net_param);
86 double test_accuracy = 0;
87 batch_size = test_net_param.layers(0).layer().batchsize();
88 for (int i = 0; i < 10000 / batch_size; ++i) {
89 const vector<Blob<float>*>& result =
90 caffe_test_net.Forward(bottom_vec);
91 test_accuracy += result[0]->cpu_data()[0];
93 test_accuracy /= 10000 / batch_size;
94 LOG(ERROR) << "Test accuracy:" << test_accuracy;