基于python实现的CNN卷积神经网络手写数字识别实验源码+数据集（高分毕业设计）.zip

import os import struct import numpy as np import csv # 读入数据，加载下载的MNIST数据是，需要这样读出 def load_mnist(path, kind='train'): """Load MNIST data from `path`""" labels_path = os.path.join(path, '%s-labels.idx1-ubyte' % kind) images_path = os.path.join(path, '%s-images.idx3-ubyte' % kind) with open(labels_path, 'rb') as lbpath: magic, n = struct.unpack('>II', lbpath.read(8)) labels = np.fromfile(lbpath, dtype=np.uint8) with open(images_path, 'rb') as imgpath: magic, num, rows, cols = struct.unpack('>IIII', imgpath.read(16)) images = np.fromfile(imgpath, dtype=np.uint8).reshape(len(labels), 28, 28) return images, labels # image的大小是60000*28*28 # 将数据从原来的数据中读出，并写入CSV，分为6个CSV写入 # 把一个数据集拆分为6个 # 数据集的格式为：一张图片：28*28 + 1 # 返回数据集名称张量 def makeCSVFiles(path, kind="train"): data_train_path = os.path.join(path, "data_train") labels_path = os.path.join(path, '%s-labels.idx1-ubyte' % kind) images_path = os.path.join(path, '%s-images.idx3-ubyte' % kind) with open(labels_path, 'rb') as lbpath: magic, n = struct.unpack('>II', lbpath.read(8)) labels = np.fromfile(lbpath, dtype=np.uint8) with open(images_path, 'rb') as imgpath: magic, num, rows, cols = struct.unpack('>IIII', imgpath.read(16)) images = np.fromfile(imgpath, dtype=np.uint8).reshape(len(labels), 784) #打开第一个CSV file = open(os.path.join(data_train_path, ('mnist_batch0' + ".csv")),'w',newline='') for i in range(len(labels)): example = [] example.extend(images[i]) example.append(labels[i]) # with file as f: writer = csv.writer(file) writer.writerow(example) # print("example:"+str(example)) if i % 10000 == 0: print("i:"+str(i)) index = int(i / 10000) file.close() # 存入文件中，第10000-20000个存入另一个CSV file = open(os.path.join(data_train_path, ('mnist_batch' + str(index) + ".csv")),'w',newline='') file.close() # makeCSVFiles(path="data")