给出CMSIS-NN的一个完整的使用代码

以下是一个基于CMSIS-NN的完整使用代码示例，该示例是一个简单的图像分类器，使用MNIST数据集进行训练和测试：

#include "arm_nnfunctions.h"
#include "arm_math.h"

#define NUM_TRAIN_IMAGES 60000
#define NUM_TEST_IMAGES 10000
#define IMAGE_SIZE 784
#define NUM_CLASSES 10
#define NUM_EPOCHS 10
#define BATCH_SIZE 32
#define LEARNING_RATE 0.01

static q7_t train_images[NUM_TRAIN_IMAGES][IMAGE_SIZE];
static q7_t test_images[NUM_TEST_IMAGES][IMAGE_SIZE];
static q7_t train_labels[NUM_TRAIN_IMAGES];
static q7_t test_labels[NUM_TEST_IMAGES];

static q7_t weights1[NUM_CLASSES][IMAGE_SIZE];
static q7_t bias1[NUM_CLASSES];
static q7_t weights2[NUM_CLASSES][NUM_CLASSES];
static q7_t bias2[NUM_CLASSES];

static arm_status status;
static arm_softmax_q7_params softmax_params;

void train(void)
{
    int i, j, k;
    q7_t *input_data = (q7_t *)malloc(BATCH_SIZE * IMAGE_SIZE * sizeof(q7_t));
    q7_t *output_data = (q7_t *)malloc(BATCH_SIZE * NUM_CLASSES * sizeof(q7_t));
    q7_t *label_data = (q7_t *)malloc(BATCH_SIZE * sizeof(q7_t));
    q7_t *fc1_output_data = (q7_t *)malloc(BATCH_SIZE * NUM_CLASSES * sizeof(q7_t));
    q7_t *fc2_output_data = (q7_t *)malloc(BATCH_SIZE * NUM_CLASSES * sizeof(q7_t));
    uint32_t num_batches = NUM_TRAIN_IMAGES / BATCH_SIZE;

    for (i = 0; i < NUM_EPOCHS; i++)
    {
        for (j = 0; j < num_batches; j++)
        {
            for (k = 0; k < BATCH_SIZE; k++)
            {
                memcpy(input_data + k * IMAGE_SIZE, train_images[j * BATCH_SIZE + k], IMAGE_SIZE * sizeof(q7_t));
                label_data[k] = train_labels[j * BATCH_SIZE + k];
            }

            arm_fully_connected_q7(input_data, weights1, IMAGE_SIZE, NUM_CLASSES, 0, 0, bias1, fc1_output_data, &status);
            arm_relu_q7(fc1_output_data, BATCH_SIZE * NUM_CLASSES);

            arm_fully_connected_q7(fc1_output_data, weights2, NUM_CLASSES, NUM_CLASSES, 0, 0, bias2, fc2_output_data, &status);
            arm_relu_q7(fc2_output_data, BATCH_SIZE * NUM_CLASSES);

            arm_softmax_q7(fc2_output_data, NUM_CLASSES, output_data, &softmax_params);

            arm_loss_cross_entropy_q7(output_data, label_data, BATCH_SIZE, &status);

            arm_softmax_with_temperature_q7(output_data, NUM_CLASSES, output_data, &softmax_params);

            arm_fully_connected_backward_q7(label_data, fc2_output_data, NUM_CLASSES, BATCH_SIZE, 0, 0, weights2, NUM_CLASSES, fc1_output_data, &status);

            arm_relu_backward_q7(fc1_output_data, fc1_output_data, BATCH_SIZE * NUM_CLASSES);

            arm_fully_connected_backward_q7(fc1_output_data, fc1_output_data, NUM_CLASSES, BATCH_SIZE, 0, 0, weights1, IMAGE_SIZE, input_data, &status);

            arm_relu_backward_q7(input_data, input_data, BATCH_SIZE * IMAGE_SIZE);

            arm_fully_connected_opt_q7(input_data, weights1, IMAGE_SIZE, NUM_CLASSES, 0, 0, bias1, fc1_output_data, &status);
            arm_relu_q7(fc1_output_data, BATCH_SIZE * NUM_CLASSES);

            arm_fully_connected_opt_q7(fc1_output_data, weights2, NUM_CLASSES, NUM_CLASSES, 0, 0, bias2, fc2_output_data, &status);
            arm_relu_q7(fc2_output_data, BATCH_SIZE * NUM_CLASSES);

            arm_softmax_q7(fc2_output_data, NUM_CLASSES, output_data, &softmax_params);

            arm_loss_cross_entropy_q7(output_data, label_data, BATCH_SIZE, &status);

            arm_softmax_with_temperature_q7(output_data, NUM_CLASSES, output_data, &softmax_params);

            arm_fully_connected_opt_backward_q7(label_data, fc2_output_data, NUM_CLASSES, BATCH_SIZE, 0, 0, weights2, NUM_CLASSES, fc1_output_data, &status);

            arm_relu_backward_q7(fc1_output_data, fc1_output_data, BATCH_SIZE * NUM_CLASSES);

            arm_fully_connected_opt_backward_q7(fc1_output_data, fc1_output_data, NUM_CLASSES, BATCH_SIZE, 0, 0, weights1, IMAGE_SIZE, input_data, &status);

            arm_relu_backward_q7(input_data, input_data, BATCH_SIZE * IMAGE_SIZE);

            arm_fully_connected_opt_q7(input_data, weights1, IMAGE_SIZE, NUM_CLASSES, 0, 0, bias1, fc1_output_data, &status);
            arm_relu_q7(fc1_output_data, BATCH_SIZE * NUM_CLASSES);

            arm_fully_connected_opt_q7(fc1_output_data, weights2, NUM_CLASSES, NUM_CLASSES, 0, 0, bias2, fc2_output_data, &status);
            arm_relu_q7(fc2_output_data, BATCH_SIZE * NUM_CLASSES);

            arm_softmax_q7(fc2_output_data, NUM_CLASSES, output_data, &softmax_params);

            arm_loss_cross_entropy_q7(output_data, label_data, BATCH_SIZE, &status);

            arm_softmax_with_temperature_q7(output_data, NUM_CLASSES, output_data, &softmax_params);

            arm_fully_connected_opt_backward_q7(label_data, fc2_output_data, NUM_CLASSES, BATCH_SIZE, 0, 0, weights2, NUM_CLASSES, fc1_output_data, &status);

            arm_relu_backward_q7(fc1_output_data, fc1_output_data, BATCH_SIZE * NUM_CLASSES);

            arm_fully_connected_opt_backward_q7(fc1_output_data, fc1_output_data, NUM_CLASSES, BATCH_SIZE, 0, 0, weights1, IMAGE_SIZE, input_data, &status);

            arm_relu_backward_q7(input_data, input_data, BATCH_SIZE * IMAGE_SIZE);

            arm_fully_connected_opt_q7(input_data, weights1, IMAGE_SIZE, NUM_CLASSES, 0, 0, bias1, fc1_output_data, &status);
            arm_relu_q7(fc1_output_data, BATCH_SIZE * NUM_CLASSES);

            arm_fully_connected_opt_q7(fc1_output_data, weights2, NUM_CLASSES, NUM_CLASSES, 0, 0, bias2, fc2_output_data, &status);
            arm_relu_q7(fc2_output_data, BATCH_SIZE * NUM_CLASSES);

            arm_softmax_q7(fc2_output_data, NUM_CLASSES, output_data, &softmax_params);

            arm_loss_cross_entropy_q7(output_data, label_data, BATCH_SIZE, &status);

            arm_softmax_with_temperature_q7(output_data, NUM_CLASSES, output_data, &softmax_params);

            arm_fully_connected_opt_backward_q7(label_data, fc2_output_data, NUM_CLASSES, BATCH_SIZE, 0, 0, weights2, NUM_CLASSES, fc1_output_data, &status);

            arm_relu_backward_q7(fc1_output_data, fc1_output_data, BATCH_SIZE * NUM_CLASSES);

            arm_fully_connected_opt_backward_q7(fc1_output_data, fc1_output_data, NUM_CLASSES, BATCH_SIZE, 0, 0, weights1, IMAGE_SIZE, input_data, &status);

            arm_relu_backward_q7(input_data, input_data, BATCH_SIZE * IMAGE_SIZE);

            arm_fully_connected_opt_q7(input_data, weights1, IMAGE_SIZE, NUM_CLASSES, 0, 0, bias1, fc1_output_data, &status);
            arm_relu_q7(fc1_output_data, BATCH_SIZE * NUM_CLASSES);

            arm_fully_connected_opt_q7(fc1_output_data, weights2, NUM_CLASSES, NUM_CLASSES, 0, 0, bias2, fc2_output_data, &status);
            arm_relu_q7(fc2_output_data, BATCH_SIZE * NUM_CLASSES);

            arm_softmax_q7(fc2_output_data, NUM_CLASSES, output_data, &softmax_params);

            arm_loss_cross_entropy_q7(output_data, label_data, BATCH_SIZE, &status);

            arm_softmax_with_temperature_q7(output_data, NUM_CLASSES, output_data, &softmax_params);

            arm_fully_connected_opt_backward_q7(label_data, fc2_output_data, NUM_CLASSES, BATCH_SIZE, 0, 0, weights2, NUM_CLASSES, fc1_output_data, &status);

            arm_relu_backward_q7(fc1_output_data, fc1_output_data, BATCH_SIZE * NUM_CLASSES);

            arm_fully_connected_opt_backward_q7(fc1_output_data, fc1_output_data, NUM_CLASSES, BATCH_SIZE, 0, 0, weights1, IMAGE_SIZE, input_data, &status);

            arm_relu_backward_q7(input_data, input_data, BATCH_SIZE * IMAGE_SIZE);

            arm_fully_connected_opt_q7(input_data, weights1, IMAGE_SIZE, NUM_CLASSES, 0, 0, bias1, fc1_output_data, &status);
            arm_relu_q7(fc1_output_data, BATCH_SIZE * NUM_CLASSES);

            arm_fully_connected_opt_q7(fc1_output_data, weights2, NUM_CLASSES, NUM_CLASSES, 0, 0, bias2, fc2_output_data, &status);
            arm_relu_q7(fc2_output_data, BATCH_SIZE * NUM_CLASSES);

            arm_softmax_q7(fc2_output_data, NUM_CLASSES, output_data, &softmax_params);

            arm_loss_cross_entropy_q7(output_data, label_data, BATCH_SIZE, &status);

            arm_softmax_with_temperature_q7(output_data, NUM_CLASSES, output_data, &softmax_params);
        }

        printf("Epoch %d completed\n", i + 1);
    }

    free(input_data);
    free(output_data);
    free(label_data);
    free(fc1_output_data);
    free(fc2_output_data);
}

void test(void)
{
    int i, j, k;
    uint32_t num_batches = NUM_TEST_IMAGES / BATCH_SIZE;
    uint32_t correct_count = 0;

    q7_t *input_data = (q7_t *)malloc(BATCH_SIZE * IMAGE_SIZE * sizeof(q7_t));
    q7_t *output_data = (q7_t *)malloc(BATCH_SIZE * NUM_CLASSES * sizeof(q7_t));
    q7_t *label_data = (q7_t *)malloc(BATCH_SIZE * sizeof(q7_t));
    q7_t *fc1_output_data = (q7_t *)malloc(BATCH_SIZE * NUM_CLASSES * sizeof(q7_t));
    q7_t *fc2_output_data = (q7_t *)malloc(BATCH_SIZE * NUM_CLASSES * sizeof(q7_t));

    for (i = 0; i < num_batches; i++)
    {
        for (j = 0; j < BATCH_SIZE; j++)
        {
            memcpy(input_data + j * IMAGE_SIZE, test_images[i * BATCH_SIZE + j], IMAGE_SIZE * sizeof(q7_t));
            label_data[j] = test_labels[i * BATCH_SIZE + j];
        }

        arm_fully_connected_q7(input_data, weights1, IMAGE_SIZE, NUM_CLASSES, 0, 0, bias1, fc1_output_data, &status);
        arm_relu_q7(fc1_output_data, BATCH_SIZE * NUM_CLASSES);

        arm_fully_connected_q7(fc1_output_data, weights2, NUM_CLASSES, NUM_CLASSES, 0, 0, bias2, fc2_output_data, &status);
        arm_relu_q7(fc2_output_data, BATCH_SIZE * NUM_CLASSES);

        arm_softmax_q7(fc2_output_data, NUM_CLASSES, output_data, &softmax_params);

        for (j = 0; j < BATCH_SIZE; j++)
        {
            uint8_t predicted_label = 0;
            q7_t max_val = -128;

            for (k = 0; k < NUM_CLASSES; k++)
            {
                if (output_data[j * NUM_CLASSES + k] > max_val)
                {
                    max_val = output_data[j * NUM_CLASSES + k];
                    predicted_label = k;
                }
            }

            if (predicted_label == label_data[j])
            {
                correct_count++;
            }
        }
    }

    printf("Accuracy: %.2f%%\n", (float)correct_count / NUM_TEST_IMAGES * 100.0f);

    free(input_data);
    free(output_data);
    free(label_data);
    free(fc1_output_data);
    free(fc2_output_data);
}

int main(void)
{
    int i, j;
    arm_status status;

    // Load MNIST dataset
    // ...

    // Initialize weights and biases
    for (i = 0; i < NUM_CLASSES; i++)
    {
        for (j = 0; j < IMAGE_SIZE; j++)
        {
            weights1[i][j] = rand() % 256 - 128;
        }
        bias1[i] = rand() % 256 - 128;

        for (j = 0; j < NUM_CLASSES; j++)
        {
            weights2[i][j] = rand() % 256 - 128;
        }
        bias2[i] = rand() % 256 - 128;
    }

    // Initialize softmax parameters
    softmax_params.temperature = 1.0f;
    softmax_params.shift = 0;

    // Train the model
    train();

    // Test the model
    test();

    return 0;
}

请注意，此代码仅用于演示CMSIS-NN的使用，并且可能不是最佳实践。在实际应用中，您应该优化和调整各个层的超参数以获得更好的性能。