src/caffe/test/test_solver_linear_regression.cpp

   1 // Copyright 2013 Yangqing Jia
   2
   3 #include <cuda_runtime.h>
   4 #include <fcntl.h>
   5 #include <google/protobuf/text_format.h>
   6 #include <google/protobuf/io/zero_copy_stream_impl.h>
   7 #include <gtest/gtest.h>
   8
   9 #include <cstring>
  10
  11 #include "caffe/blob.hpp"
  12 #include "caffe/common.hpp"
  13 #include "caffe/net.hpp"
  14 #include "caffe/filler.hpp"
  15 #include "caffe/proto/caffe.pb.h"
  16 #include "caffe/util/io.hpp"
  17 #include "caffe/optimization/solver.hpp"
  18
  19 #include "caffe/test/test_caffe_main.hpp"
  20
  21 namespace caffe {
  22
  23 template <typename Dtype>
  24 class SolverTest : public ::testing::Test {};
  25
  26 typedef ::testing::Types<float, double> Dtypes;
  27 TYPED_TEST_CASE(SolverTest, Dtypes);
  28
  29 TYPED_TEST(SolverTest, TestSolveGPU) {
  30   Caffe::set_mode(Caffe::GPU);
  31
  32   NetParameter net_param;
  33   ReadProtoFromTextFile("data/linear_regression.prototxt",
  34       &net_param);
  35   // check if things are right
  36   EXPECT_EQ(net_param.layers_size(), 3);
  37   EXPECT_EQ(net_param.input_size(), 0);
  38   vector<Blob<TypeParam>*> bottom_vec;
  39   Net<TypeParam> caffe_net(net_param, bottom_vec);
  40   EXPECT_EQ(caffe_net.layer_names().size(), 3);
  41   EXPECT_EQ(caffe_net.blob_names().size(), 3);
  42
  43   // Run the network without training.
  44   LOG(INFO) << "Performing Forward";
  45   caffe_net.Forward(bottom_vec);
  46   LOG(INFO) << "Performing Backward";
  47   LOG(INFO) << "Initial loss: " << caffe_net.Backward();
  48
  49   SolverParameter solver_param;
  50   solver_param.set_base_lr(0.1);
  51   solver_param.set_display(0);
  52   solver_param.set_max_iter(100);
  53   solver_param.set_lr_policy("inv");
  54   solver_param.set_gamma(1.);
  55   solver_param.set_power(0.75);
  56   solver_param.set_momentum(0.9);
  57
  58   LOG(INFO) << "Starting Optimization";
  59   SGDSolver<TypeParam> solver(solver_param);
  60   solver.Solve(&caffe_net);
  61   LOG(INFO) << "Optimization Done.";
  62   LOG(INFO) << "Weight: " << caffe_net.params()[0]->cpu_data()[0] << ", "
  63       << caffe_net.params()[0]->cpu_data()[1];
  64   LOG(INFO) << "Bias: " << caffe_net.params()[1]->cpu_data()[0];
  65
  66   EXPECT_GE(caffe_net.params()[0]->cpu_data()[0], 0.3);
  67   EXPECT_LE(caffe_net.params()[0]->cpu_data()[0], 0.35);
  68
  69   EXPECT_GE(caffe_net.params()[0]->cpu_data()[1], 0.3);
  70   EXPECT_LE(caffe_net.params()[0]->cpu_data()[1], 0.35);
  71
  72   EXPECT_GE(caffe_net.params()[1]->cpu_data()[0], -0.01);
  73   EXPECT_LE(caffe_net.params()[1]->cpu_data()[0], 0.01);
  74 }
  75
  76
  77
  78 TYPED_TEST(SolverTest, TestSolveCPU) {
  79   Caffe::set_mode(Caffe::CPU);
  80
  81   NetParameter net_param;
  82   ReadProtoFromTextFile("data/linear_regression.prototxt",
  83       &net_param);
  84   // check if things are right
  85   EXPECT_EQ(net_param.layers_size(), 3);
  86   EXPECT_EQ(net_param.input_size(), 0);
  87   vector<Blob<TypeParam>*> bottom_vec;
  88   Net<TypeParam> caffe_net(net_param, bottom_vec);
  89   EXPECT_EQ(caffe_net.layer_names().size(), 3);
  90   EXPECT_EQ(caffe_net.blob_names().size(), 3);
  91
  92   // Run the network without training.
  93   LOG(INFO) << "Performing Forward";
  94   caffe_net.Forward(bottom_vec);
  95   LOG(INFO) << "Performing Backward";
  96   LOG(INFO) << "Initial loss: " << caffe_net.Backward();
  97
  98   SolverParameter solver_param;
  99   solver_param.set_base_lr(0.1);
 100   solver_param.set_display(0);
 101   solver_param.set_max_iter(100);
 102   solver_param.set_lr_policy("inv");
 103   solver_param.set_gamma(1.);
 104   solver_param.set_power(0.75);
 105   solver_param.set_momentum(0.9);
 106
 107   LOG(INFO) << "Starting Optimization";
 108   SGDSolver<TypeParam> solver(solver_param);
 109   solver.Solve(&caffe_net);
 110   LOG(INFO) << "Optimization Done.";
 111   LOG(INFO) << "Weight: " << caffe_net.params()[0]->cpu_data()[0] << ", "
 112       << caffe_net.params()[0]->cpu_data()[1];
 113   LOG(INFO) << "Bias: " << caffe_net.params()[1]->cpu_data()[0];
 114
 115   EXPECT_GE(caffe_net.params()[0]->cpu_data()[0], 0.3);
 116   EXPECT_LE(caffe_net.params()[0]->cpu_data()[0], 0.35);
 117
 118   EXPECT_GE(caffe_net.params()[0]->cpu_data()[1], 0.3);
 119   EXPECT_LE(caffe_net.params()[0]->cpu_data()[1], 0.35);
 120
 121   EXPECT_GE(caffe_net.params()[1]->cpu_data()[0], -0.01);
 122   EXPECT_LE(caffe_net.params()[1]->cpu_data()[0], 0.01);
 123 }
 124
 125 }  // namespace caffe