for (int num = 0; num < num_patterns_test_CNN; num++) {
data_single_image = data_input_test + num * num_neuron_input_CNN;
data_single_label = data_output_test + num * num_neuron_output_CNN;

Forward_C1();
Forward_S2();
Forward_C3();
Forward_S4();
Forward_C5();
Forward_output();

int pos_t = -1;
int pos_y = -2;
double max_value_t = -9999.0;
double max_value_y = -9999.0;

for (int i = 0; i < num_neuron_output_CNN; i++) {
if (neuron_output[i] > max_value_y) {
max_value_y = neuron_output[i];
pos_y = i;
}

if (data_single_label[i] > max_value_t) {
max_value_t = data_single_label[i];
pos_t = i;
}
}

if (pos_y == pos_t) {
++count_accuracy;
}

Sleep(1);
}

return (count_accuracy * 1.0 / num_patterns_test_CNN);
}

}

測(cè)試代碼如下：
int test_CNN_train()
{
ANN::CNN cnn1;
cnn1.init();
cnn1.train();

return 0;
}

int test_CNN_predict()
{
ANN::CNN cnn2;
bool flag = cnn2.readModelFile("E:/GitCode/NN_Test/data/cnn.model");
if (!flag) {
std::cout << "read cnn model error" << std::endl;
return -1;
}

int width{ 32 }, height{ 32 };
std::vector target{ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 };
std::string image_path{ "E:/GitCode/NN_Test/data/images/" };

for (auto i : target) {
std::string str = std::to_string(i);
str += ".png";
str = image_path + str;

cv::Mat src = cv::imread(str, 0);
if (src.data == nullptr) {
fprintf(stderr, "read image error: %s\n", str.c_str());
return -1;
}

cv::Mat tmp(src.rows, src.cols, CV_8UC1, cv::Scalar::all(255));
cv::subtract(tmp, src, tmp);

cv::resize(tmp, tmp, cv::Size(width, height));

auto ret = cnn2.predict(tmp.data, width, height);

fprintf(stdout, "the actual digit is: %d, correct digit is: %d\n", ret, i);
}

return 0;
}

通過(guò)執(zhí)行test_CNN_train()函數(shù)可生成cnn model文件，執(zhí)行結(jié)果如下：

通過(guò)執(zhí)行test_CNN_predict()函數(shù)來(lái)測(cè)試CNN的準(zhǔn)確率，通過(guò)畫(huà)圖工具，每個(gè)數(shù)字生成一張圖像，共10幅，如下圖：

測(cè)試結(jié)果如下：