【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。