【deep learning学习笔记】注释yusugomori的LR代码 --- 模型测试

测试部分代码：

void test_lr() {  srand(0);    double learning_rate = 0.1;  double n_epochs = 500;  int train_N = 6;  int test_N = 2;  int n_in = 6;  int n_out = 2;  // int **train_X;  // int **train_Y;  // int **test_X;  // double **test_Y;  // train_X = new int*[train_N];  // train_Y = new int*[train_N];  // for(i=0; i<train_N; i++){  //   train_X[i] = new int[n_in];  //   train_Y[i] = new int[n_out];  // };  // test_X = new int*[test_N];  // test_Y = new double*[test_N];  // for(i=0; i<test_N; i++){  //   test_X[i] = new int[n_in];  //   test_Y[i] = new double[n_out];  // }  // training data  int train_X[6][6] = {    {1, 1, 1, 0, 0, 0},    {1, 0, 1, 0, 0, 0},    {1, 1, 1, 0, 0, 0},    {0, 0, 1, 1, 1, 0},    {0, 0, 1, 1, 0, 0},    {0, 0, 1, 1, 1, 0}  };  int train_Y[6][2] = {    {1, 0},    {1, 0},    {1, 0},    {0, 1},    {0, 1},    {0, 1}  };  // construct LogisticRegression  LogisticRegression classifier(train_N, n_in, n_out);  // i wonder that we should set the N value to 1 as training online  //LogisticRegression classifier(1, n_in, n_out);  // train online  for(int epoch=0; epoch<n_epochs; epoch++) {    for(int i=0; i<train_N; i++) {      classifier.train(train_X[i], train_Y[i], learning_rate);    }    // learning_rate *= 0.95;  }  // test data  int test_X[2][6] = {    {1, 0, 1, 0, 0, 0},    {0, 0, 1, 1, 1, 0}  };  double test_Y[2][2];  // test  for(int i=0; i<test_N; i++) {    classifier.predict(test_X[i], test_Y[i]);    for(int j=0; j<n_out; j++) {      cout << test_Y[i][j] << " ";    }    cout << endl;  }}int main() {  test_lr();  getchar();  return 0;}

测试数据实际上是在训练集合中的，分别是第二个和第四个训练数据，也就是说，这是“封闭测试”。测试结果如下所示：

不过总感觉这个调用

“

LogisticRegression classifier(train_N, n_in, n_out);

”

不对。在线训练，是单个样本为单位的训练，train_N的值应该设置为1。将这一句改成
“

LogisticRegression classifier(1, n_in, n_out);

”

运行结果如下：

与上面的结果差别不大。恐怕要到实际应用中检验了。