【机器学习】基于mnist数据集的手写数字识别

：机器学习在头歌上发布的作业记录。dataReader函数有更新！记得函数返回return batch_reader不用加括号，否则会出现’XXX’ object is not callable的错误。

第1关：创建训练样本批量生成器

任务描述：
补充完成函数：参考任务生成器创建代码，为分类器提供批量样本，每个样本为(图片，标签)对。
1️⃣输入：def dataReader(imgfile, labelfile, batch_size, drop_last)
imgfile：为IDX图片文件；labelfile：为IDX标签文件，标签顺序与图片顺序相同；batch_size：为生成器提供的批量样本数量；drop_last：是否丢弃不足批量的剩余样本。
2️⃣返回：batch_size长度的列表，列表中元素为图像和标签对。
形式如下：[(图像1，标签1)，（图像2，标签2），（图像3，标签3）…]

❗注意：训练时样本顺序必须打乱，随机选取。
列表中的图像元素为28*28的矩阵，类型为
标签元素标量，类型为即不改变原文件中存储的数据元素类型。

########1.加载数据
import struct,random,numpy as np
code2type = {0x08: 'B', 0x09: 'b', 0x0B: 'h', 0x0c: 'i', 0x0D: 'f', 0x0E: 'd'}
def readMatrix(filename):
    with open(filename,'rb') as f:
        buff = f.read()
        offset = 0
        fmt = '>HBB'#格式定义，>表示高位在前，I表示4字节整数
        _,dcode,dimslen = struct.unpack_from(fmt,buff,offset)
        offset += struct.calcsize(fmt)

        fmt = '>{}I'.format(dimslen)
        shapes = struct.unpack_from(fmt,buff,offset)
        offset += struct.calcsize(fmt)

        fmt = '>'+ str(np.prod(shapes)) + code2type[dcode]
        matrix = struct.unpack_from(fmt,buff,offset)
        matrix = np.reshape(matrix,shapes).astype(code2type[dcode])

    return matrix

def dataReader(imgfile, labelfile, batch_size, drop_last):
    images = readMatrix(imgfile)
    labels = readMatrix(labelfile)
    buff = list(zip(images,labels))
    batchnum = int(len(images)/batch_size)
    random.shuffle(buff)
    def batch_reader():
        for i in range(batchnum):
            yield buff[(i)*batch_size:(i+1)*batch_size]
        if drop_last and len(images)%batch_size!=0:
            yield buff[batchnum*batch_size:]
    return batch_reader

第2关：创建卷积神经网络

任务描述：
利用pytorch框架构建卷积网络,用于手写数字的识别。按图创建卷积+全连接+分类器网络。可以尝试不同激活函数，汇聚层采用池化层，看看运行300组样本后的预测效果。网络结构如下：

import numpy as np
import torch
import torch.nn as nn,torch.nn.functional as F,torch.optim as optim
from loader import dataReader

#########2.定义卷积神经网络
class MnistNet(nn.Module):
    def __init__(self):
        super(MnistNet, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=1,out_channels=6,kernel_size=5,stride=1,padding=2)
        self.pool = nn.MaxPool2d(kernel_size=2,stride=2)
        self.conv2 = nn.Conv2d(6,16,5)
        self.fc1 = nn.Linear(16*5*5,120)
        self.fc2 = nn.Linear(120,84)
        self.fc3 = nn.Linear(84,10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x))) 
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1,16*5*5)     # view函数相当于numpy的reshape，-1表示一个不确定的数。
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x
 
# 3.训练网络
def train(loader):
    model = MnistNet()
    # 用分类交叉熵损失和带有动量的 SGD
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
    for epoch in range(1):
        running_loss = 0.0
        for i, data in enumerate(loader):
            inputs, labels = zip(*data)
            inputs = np.array(inputs).astype('float32')
            labels = np.array(labels).astype('int64')
            inputs = torch.from_numpy(inputs).unsqueeze(1)    # 扩展通道维度 NCHW
            labels = torch.from_numpy(labels)
            # zero the parameter gradients
            optimizer.zero_grad()
            # forward + backward + optimize
            outputs = model(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()
            # print statistics
            running_loss += loss.item()
            if i % 100 == 99:
                last_loss = running_loss / 100  # 每个batch的loss
                print('  batch {} loss: {}'.format(i + 1, last_loss))
                running_loss = 0.
            if i==199:
                break
    print('Finished Training')
    return model

# 4.测试网络
def test(PATH,loader):
    # 重新加载保存的模型
    model = MnistNet()
    model.load_state_dict(torch.load(PATH))
    correct = 0
    total = 0
    with torch.no_grad():
        for data in loader:
            images, labels = zip(*data)
            images = np.array(images).astype('float32')
            labels = np.array(labels).astype('int64')
            images = torch.from_numpy(images).unsqueeze(1)
            labels = torch.from_numpy(labels)
            outputs = model(images)
            _, predicted = torch.max(outputs, 1)#torch.argmax
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    print('Accuracy of the network on the {:d} test images: {:f}%'.format(total,100 * correct / total))
    return model

if __name__ == '__main__':
    BATCH_SIZE = 16
    PATH = '/data/workspace/myshixun/mnist/model/mnist_model.pth'
    train_loader = dataReader('/data/workspace/myshixun/mnist/data/train-images-idx3-ubyte', '/data/workspace/myshixun/mnist/data/train-labels-idx1-ubyte', BATCH_SIZE, True)
    test_loader = dataReader('/data/workspace/myshixun/mnist/data/t10k-images-idx3-ubyte', '/data/workspace/myshixun/mnist/data/t10k-labels-idx1-ubyte', BATCH_SIZE, False)
    model = train(train_loader)
    #快速保存我们训练过的模型：
    torch.save(model.state_dict(), PATH)
    test(PATH,test_loader)

：欢迎大家指出我的问题，一起讨论学习！