【问题解决方案】Keras手写数字识别-ConnectionResetError: [WinError 10054] 远程主机强迫关闭了一个现有的连接

参考：

• 台大李宏毅老师视频课程-Keras-Demo

• 调参博文1：深度学习入门实践_十行搭建手写数字识别神经网络

• 调参博文2：手写数字识别---demo（有小错误）

编程环境：

• 操作系统：win7 - CPU

• anaconda-Python3-jupyter notebook

• tersonFlow：1.10.0

• Keras：2.2.4

背景

• Keras实现手写数字识别，在载入数据阶段报错：

  • ConnectionResetError: [WinError 10054] 远程主机强迫关闭了一个现有的连接

  
  

问题解决步骤：

1-去官网下载数据集
2-编写独立的载入数据模块以便主程序引用
3-在主程序进行相应的修改
4-测试运行是否正常
5-数组过大的新问题与梯子解决

1-去官网下载数据集：

• 数据集网址（宝可梦大师课程里也有提到过）：http://yann.lecun.com/exdb/mnist/

2-编写独立的载入数据模块以便主程序引用

• 将如下代码另存为一个文件load_data.py，后面直接import使用（代码来自调参博文1）

• 数据集放在代码文件所在目录下

• 注意文件路径格式

# encoding: utf-8
"""
对MNIST手写数字数据文件转换为bmp图片文件格式。
数据集下载地址为http://yann.lecun.com/exdb/mnist。
相关格式转换见官网以及代码注释。

========================
关于IDX文件格式的解析规则：
========================
THE IDX FILE FORMAT

the IDX file format is a simple format for vectors and multidimensional matrices of various numerical types.
The basic format is

magic number
size in dimension 0
size in dimension 1
size in dimension 2
.....
size in dimension N
data

The magic number is an integer (MSB first). The first 2 bytes are always 0.

The third byte codes the type of the data:
0x08: unsigned byte
0x09: signed byte
0x0B: short (2 bytes)
0x0C: int (4 bytes)
0x0D: float (4 bytes)
0x0E: double (8 bytes)

The 4-th byte codes the number of dimensions of the vector/matrix: 1 for vectors, 2 for matrices....

The sizes in each dimension are 4-byte integers (MSB first, high endian, like in most non-Intel processors).

The data is stored like in a C array, i.e. the index in the last dimension changes the fastest.
"""

import numpy as np
import struct
import matplotlib.pyplot as plt

# 训练集文件
train_images_idx3_ubyte_file = './data/train-images-idx3-ubyte'
# 训练集标签文件
train_labels_idx1_ubyte_file = './data/train-labels-idx1-ubyte'

# 测试集文件
test_images_idx3_ubyte_file = './data/t10k-images-idx3-ubyte'
# 测试集标签文件
test_labels_idx1_ubyte_file = './data/t10k-labels-idx1-ubyte'


def decode_idx3_ubyte(idx3_ubyte_file):
    """
    解析idx3文件的通用函数
    :param idx3_ubyte_file: idx3文件路径
    :return: 数据集
    """
    # 读取二进制数据
    bin_data = open(idx3_ubyte_file, 'rb').read()

    # 解析文件头信息，依次为魔数、图片数量、每张图片高、每张图片宽
    offset = 0
    fmt_header = '>iiii'
    magic_number, num_images, num_rows, num_cols = struct.unpack_from(fmt_header, bin_data, offset)
    #print('魔数:%d, 图片数量: %d张, 图片大小: %d*%d' % (magic_number, num_images, num_rows, num_cols))

    # 解析数据集
    image_size = num_rows * num_cols
    offset += struct.calcsize(fmt_header)
    fmt_image = '>' + str(image_size) + 'B'
    images = np.empty((num_images, num_rows, num_cols))
    for i in range(num_images):
        #if (i + 1) % 10000 == 0:
            #print('已解析 %d' % (i + 1) + '张')
        images[i] = np.array(struct.unpack_from(fmt_image, bin_data, offset)).reshape((num_rows, num_cols))
        offset += struct.calcsize(fmt_image)
    return images


def decode_idx1_ubyte(idx1_ubyte_file):
    """
    解析idx1文件的通用函数
    :param idx1_ubyte_file: idx1文件路径
    :return: 数据集
    """
    # 读取二进制数据
    bin_data = open(idx1_ubyte_file, 'rb').read()

    # 解析文件头信息，依次为魔数和标签数
    offset = 0
    fmt_header = '>ii'
    magic_number, num_images = struct.unpack_from(fmt_header, bin_data, offset)
    #print('魔数:%d, 图片数量: %d张' % (magic_number, num_images))

    # 解析数据集
    offset += struct.calcsize(fmt_header)
    fmt_image = '>B'
    labels = np.empty(num_images)
    for i in range(num_images):
        #if (i + 1) % 10000 == 0:
        #    print('已解析 %d' % (i + 1) + '张')
        labels[i] = struct.unpack_from(fmt_image, bin_data, offset)[0]
        offset += struct.calcsize(fmt_image)
    return labels


def load_train_images(idx_ubyte_file=train_images_idx3_ubyte_file):
    """
    TRAINING SET IMAGE FILE (train-images-idx3-ubyte):
    [offset] [type]          [value]          [description]
    0000     32 bit integer  0x00000803(2051) magic number
    0004     32 bit integer  60000            number of images
    0008     32 bit integer  28               number of rows
    0012     32 bit integer  28               number of columns
    0016     unsigned byte   ??               pixel
    0017     unsigned byte   ??               pixel
    ........
    xxxx     unsigned byte   ??               pixel
    Pixels are organized row-wise. Pixel values are 0 to 255. 0 means background (white), 255 means foreground (black).

    :param idx_ubyte_file: idx文件路径
    :return: n*row*col维np.array对象，n为图片数量
    """
    return decode_idx3_ubyte(idx_ubyte_file)


def load_train_labels(idx_ubyte_file=train_labels_idx1_ubyte_file):
    """
    TRAINING SET LABEL FILE (train-labels-idx1-ubyte):
    [offset] [type]          [value]          [description]
    0000     32 bit integer  0x00000801(2049) magic number (MSB first)
    0004     32 bit integer  60000            number of items
    0008     unsigned byte   ??               label
    0009     unsigned byte   ??               label
    ........
    xxxx     unsigned byte   ??               label
    The labels values are 0 to 9.

    :param idx_ubyte_file: idx文件路径
    :return: n*1维np.array对象，n为图片数量
    """
    return decode_idx1_ubyte(idx_ubyte_file)


def load_test_images(idx_ubyte_file=test_images_idx3_ubyte_file):
    """
    TEST SET IMAGE FILE (t10k-images-idx3-ubyte):
    [offset] [type]          [value]          [description]
    0000     32 bit integer  0x00000803(2051) magic number
    0004     32 bit integer  10000            number of images
    0008     32 bit integer  28               number of rows
    0012     32 bit integer  28               number of columns
    0016     unsigned byte   ??               pixel
    0017     unsigned byte   ??               pixel
    ........
    xxxx     unsigned byte   ??               pixel
    Pixels are organized row-wise. Pixel values are 0 to 255. 0 means background (white), 255 means foreground (black).

    :param idx_ubyte_file: idx文件路径
    :return: n*row*col维np.array对象，n为图片数量
    """
    return decode_idx3_ubyte(idx_ubyte_file)


def load_test_labels(idx_ubyte_file=test_labels_idx1_ubyte_file):
    """
    TEST SET LABEL FILE (t10k-labels-idx1-ubyte):
    [offset] [type]          [value]          [description]
    0000     32 bit integer  0x00000801(2049) magic number (MSB first)
    0004     32 bit integer  10000            number of items
    0008     unsigned byte   ??               label
    0009     unsigned byte   ??               label
    ........
    xxxx     unsigned byte   ??               label
    The labels values are 0 to 9.

    :param idx_ubyte_file: idx文件路径
    :return: n*1维np.array对象，n为图片数量
    """
    return decode_idx1_ubyte(idx_ubyte_file)

def run():
    train_images = load_train_images()
    train_labels = load_train_labels()
    test_images = load_test_images()
    test_labels = load_test_labels()

    # 查看前十个数据及其标签以读取是否正确
    for i in range(10):
        print(train_labels[i])
        plt.imshow(train_images[i], cmap='gray')
        plt.show()
    print('done')

if __name__ == '__main__':
    run()

3-在主程序进行相应的修改

• 由原来的 from keras.datasets.. 修改为 from load_data import *

• 数据预处理部分相应的修改：

4-测试运行是否正常

• 报错：找不到文件路径

• 继续报错：ValueError: array is too big; `arr.size * arr.dtype.itemsize` is larger than the maximum possible size.

5-数组过大的问题搜了很多也解决不了，转而求助于梯子后用原方法载入数据

• 因为上面的路子已经卡死了，过大的问题解决不了进行不下去了，希望可以成功把数据载入...

• 成功了啊!!!! 感天动地!!!! 梯子万岁!!!!!

• 梯子心得：只要梯子没问题，可以多试几次，最终都会成功的，太赞啦~~

总结：

• 这个问题的本质是墙的问题，只有梯子够高够稳，其实不用以上这么麻烦

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