如何使用 Python 堆实现卷积神经网络算法？

import heapq

def init_heap(input_map, kernel_map, output_map):
    input_heap = []
    kernel_heap = []
    output_heap = []

    for row in input_map:
        heapq.heappush(input_heap, row)

    for row in kernel_map:
        heapq.heappush(kernel_heap, row)

    for row in output_map:
        heapq.heappush(output_heap, row)

    return input_heap, kernel_heap, output_heap

def convolution(input_heap, kernel_heap, output_heap, kernel_size, output_map_size):
    # 计算卷积核和特征图相乘的结果
    for i in range(output_map_size):
        for j in range(output_map_size):
            sum = 0
            for k in range(0, kernel_size):
                for l in range(0, kernel_size):
                    input_row = heapq.heappop(input_heap)
                    kernel_row = heapq.heappop(kernel_heap)
                    sum += input_row[j + l] * kernel_row[k]
                    heapq.heappush(input_heap, input_row) # 放回堆中
                    heapq.heappush(kernel_heap, kernel_row) # 放回堆中
            output_row = heapq.heappop(output_heap)
            output_row[j] += sum
            heapq.heappush(output_heap, output_row) # 放回堆中
    return output_heap

def heap_to_matrix(heap, output_map_size):
    matrix = []
    for i in range(output_map_size):
        row = heapq.heappop(heap)
        matrix.append(row)
    return matrix

import heapq

def init_heap(input_map, kernel_map, output_map):
    input_heap = []
    kernel_heap = []
    output_heap = []

    for row in input_map:
        heapq.heappush(input_heap, row)

    for row in kernel_map:
        heapq.heappush(kernel_heap, row)

    for row in output_map:
        heapq.heappush(output_heap, row)

    return input_heap, kernel_heap, output_heap

def convolution(input_heap, kernel_heap, output_heap, kernel_size, output_map_size):
    # 计算卷积核和特征图相乘的结果
    for i in range(output_map_size):
        for j in range(output_map_size):
            sum = 0
            for k in range(0, kernel_size):
                for l in range(0, kernel_size):
                    input_row = heapq.heappop(input_heap)
                    kernel_row = heapq.heappop(kernel_heap)
                    sum += input_row[j + l] * kernel_row[k]
                    heapq.heappush(input_heap, input_row) # 放回堆中
                    heapq.heappush(kernel_heap, kernel_row) # 放回堆中
            output_row = heapq.heappop(output_heap)
            output_row[j] += sum
            heapq.heappush(output_heap, output_row) # 放回堆中
    return output_heap

def heap_to_matrix(heap, output_map_size):
    matrix = []
    for i in range(output_map_size):
        row = heapq.heappop(heap)
        matrix.append(row)
    return matrix

if __name__ == '__main__':
    input_map = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
    kernel_map = [[1, 2], [3, 4]]
    output_map_size = (len(input_map) - len(kernel_map)) + 1
    kernel_size = len(kernel_map)

    output_map = [[0 for i in range(output_map_size)] for j in range(output_map_size)]

    input_heap, kernel_heap, output_heap = init_heap(input_map, kernel_map, output_map)

    output_heap = convolution(input_heap, kernel_heap, output_heap, kernel_size, output_map_size)

    output_matrix = heap_to_matrix(output_heap, output_map_size)

    print(output_matrix)