写文件编辑部分 - 卷积神经网络(CNN)的简单介绍及代码实现

int width_image_input = width_image_input_CNN;
int height_image_input = height_image_input_CNN;
int width_image_C1 = width_image_C1_CNN;
int height_image_C1 = height_image_C1_CNN;
int width_image_S2 = width_image_S2_CNN;
int height_image_S2 = height_image_S2_CNN;
int width_image_C3 = width_image_C3_CNN;
int height_image_C3 = height_image_C3_CNN;
int width_image_S4 = width_image_S4_CNN;
int height_image_S4 = height_image_S4_CNN;
int width_image_C5 = width_image_C5_CNN;
int height_image_C5 = height_image_C5_CNN;
int width_image_output = width_image_output_CNN;
int height_image_output = height_image_output_CNN;

int width_kernel_conv = width_kernel_conv_CNN;
int height_kernel_conv = height_kernel_conv_CNN;
int width_kernel_pooling = width_kernel_pooling_CNN;
int height_kernel_pooling = height_kernel_pooling_CNN;

int num_map_input = num_map_input_CNN;
int num_map_C1 = num_map_C1_CNN;
int num_map_S2 = num_map_S2_CNN;
int num_map_C3 = num_map_C3_CNN;
int num_map_S4 = num_map_S4_CNN;
int num_map_C5 = num_map_C5_CNN;
int num_map_output = num_map_output_CNN;

int len_weight_C1 = len_weight_C1_CNN;
int len_bias_C1 = len_bias_C1_CNN;
int len_weight_S2 = len_weight_S2_CNN;
int len_bias_S2 = len_bias_S2_CNN;
int len_weight_C3 = len_weight_C3_CNN;
int len_bias_C3 = len_bias_C3_CNN;
int len_weight_S4 = len_weight_S4_CNN;
int len_bias_S4 = len_bias_S4_CNN;
int len_weight_C5 = len_weight_C5_CNN;
int len_bias_C5 = len_bias_C5_CNN;
int len_weight_output = len_weight_output_CNN;
int len_bias_output = len_bias_output_CNN;

int num_neuron_input = num_neuron_input_CNN;
int num_neuron_C1 = num_neuron_C1_CNN;
int num_neuron_S2 = num_neuron_S2_CNN;
int num_neuron_C3 = num_neuron_C3_CNN;
int num_neuron_S4 = num_neuron_S4_CNN;
int num_neuron_C5 = num_neuron_C5_CNN;
int num_neuron_output = num_neuron_output_CNN;

fwrite(&width_image_input, sizeof(int), 1, fp);
fwrite(&height_image_input, sizeof(int), 1, fp);
fwrite(&width_image_C1, sizeof(int), 1, fp);
fwrite(&height_image_C1, sizeof(int), 1, fp);
fwrite(&width_image_S2, sizeof(int), 1, fp);
fwrite(&height_image_S2, sizeof(int), 1, fp);
fwrite(&width_image_C3, sizeof(int), 1, fp);
fwrite(&height_image_C3, sizeof(int), 1, fp);
fwrite(&width_image_S4, sizeof(int), 1, fp);
fwrite(&height_image_S4, sizeof(int), 1, fp);
fwrite(&width_image_C5, sizeof(int), 1, fp);
fwrite(&height_image_C5, sizeof(int), 1, fp);
fwrite(&width_image_output, sizeof(int), 1, fp);
fwrite(&height_image_output, sizeof(int), 1, fp);

fwrite(&width_kernel_conv, sizeof(int), 1, fp);
fwrite(&height_kernel_conv, sizeof(int), 1, fp);
fwrite(&width_kernel_pooling, sizeof(int), 1, fp);
fwrite(&height_kernel_pooling, sizeof(int), 1, fp);

fwrite(&num_map_input, sizeof(int), 1, fp);
fwrite(&num_map_C1, sizeof(int), 1, fp);
fwrite(&num_map_S2, sizeof(int), 1, fp);
fwrite(&num_map_C3, sizeof(int), 1, fp);
fwrite(&num_map_S4, sizeof(int), 1, fp);
fwrite(&num_map_C5, sizeof(int), 1, fp);
fwrite(&num_map_output, sizeof(int), 1, fp);

fwrite(&len_weight_C1, sizeof(int), 1, fp);
fwrite(&len_bias_C1, sizeof(int), 1, fp);
fwrite(&len_weight_S2, sizeof(int), 1, fp);
fwrite(&len_bias_S2, sizeof(int), 1, fp);
fwrite(&len_weight_C3, sizeof(int), 1, fp);
fwrite(&len_bias_C3, sizeof(int), 1, fp);
fwrite(&len_weight_S4, sizeof(int), 1, fp);
fwrite(&len_bias_S4, sizeof(int), 1, fp);
fwrite(&len_weight_C5, sizeof(int), 1, fp);
fwrite(&len_bias_C5, sizeof(int), 1, fp);
fwrite(&len_weight_output, sizeof(int), 1, fp);
fwrite(&len_bias_output, sizeof(int), 1, fp);

fwrite(&num_neuron_input, sizeof(int), 1, fp);
fwrite(&num_neuron_C1, sizeof(int), 1, fp);
fwrite(&num_neuron_S2, sizeof(int), 1, fp);
fwrite(&num_neuron_C3, sizeof(int), 1, fp);
fwrite(&num_neuron_S4, sizeof(int), 1, fp);
fwrite(&num_neuron_C5, sizeof(int), 1, fp);
fwrite(&num_neuron_output, sizeof(int), 1, fp);

fwrite(weight_C1, sizeof(weight_C1), 1, fp);
fwrite(bias_C1, sizeof(bias_C1), 1, fp);
fwrite(weight_S2, sizeof(weight_S2), 1, fp);
fwrite(bias_S2, sizeof(bias_S2), 1, fp);
fwrite(weight_C3, sizeof(weight_C3), 1, fp);
fwrite(bias_C3, sizeof(bias_C3), 1, fp);
fwrite(weight_S4, sizeof(weight_S4), 1, fp);
fwrite(bias_S4, sizeof(bias_S4), 1, fp);
fwrite(weight_C5, sizeof(weight_C5), 1, fp);
fwrite(bias_C5, sizeof(bias_C5), 1, fp);
fwrite(weight_output, sizeof(weight_output), 1, fp);
fwrite(bias_output, sizeof(bias_output), 1, fp);

fflush(fp);
fclose(fp);

return true;
}

double CNN::test()
{
int count_accuracy = 0;