基于Paddle的手写数字识别模型

　　百度飞桨(paddlepaddle)是百度的开源深度学习平台，今天就利用paddle来编写入门级的手写数字模型．

一，准备数据

1. 下载数据集，这里我们使用的是MNIST数据集

   # 下载原始的 MNIST 数据集并进行解压
   wget https://paddle-imagenet-models-name.bj.bcebos.com/data/mnist.tar
   tar -xf mnist.tar
   

   数据集的目录格式如下：

   mnist.
   ├── train
   │   └── imgs
   │       ├── 0
   │       ├── 1
   │       ├── 2
   │       ├── 3
   │       ├── 4
   │       ├── 5
   │       ├── 6
   │       ├── 7
   │       ├── 8
   │       └── 9
   └── val
      └── imgs
           ├── 0
           ├── 1
           ├── 2
           ├── 3
           ├── 4
           ├── 5
           ├── 6
           ├── 7
           ├── 8
           └── 9
   

   train和val目录下均有一个标签文件label.txt

2. 定义数据加载模块，这里使用百度paddle提供paddle.io.Dataset来实现自定义的MyDataSet:

   class MyDataSet(Dataset):
    
       def __init__(self, data_dir, label_path, tansform=None):
           super(MyDataSet, self).__init__()
           self.data_list = []
    
           with open(label_path, encoding='utf-8') as f:
    
               for line in f.readlines():
                   image_path, label = line.split('\t')
                   image_path = os.path.join(data_dir, image_path)
                   self.data_list.append([image_path, label])
    
           self.tansform = tansform
    
       def __getitem__(self, index):
    
           image_path, label = self.data_list[index]
           image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
           image = image.astype('float32')
    
           # 应用图像变换
           if self.tansform is not None:
               self.tansform(image)
    
           label = int(label)
           return image, label
    
       def __len__(self):
           return len(self.data_list)
   

二，模型实现

这里我们参照LeNet进行实现，下面看一下LeNet的网络结构

 定义一个MyNet:

class MyNet(nn.Layer):
    def __init__(self, num_classes=10):
        super().__init__()
        self.num_classes = num_classes
 
        # 定义
        self.conv1 = nn.Conv2D(1, 6, 3, stride=1, padding=1)
        self.conv2 = nn.Conv2D(6, 16, 5, stride=1, padding=0)
        self.relu = nn.ReLU()
 
        self.features = nn.Sequential(
            self.conv1,
            self.relu,
            nn.MaxPool2D(2, 2),
            self.conv2,
            self.relu,
            nn.MaxPool2D(2, 2))
 
        if num_classes > 0:
            self.linear = nn.Sequential(
                nn.Linear(400, 120),
                nn.Linear(120, 84),
                nn.Linear(84, num_classes)
            )
 
    def forward(self, x):
        x = self.features(x)
        if self.num_classes > 0:
            x = paddle.flatten(x, 1)
            x = self.linear(x)
        return x

三，模型训练

# 定义一个网络
model = MyNet()
# 可视化模型组网结构和参数
params_info = paddle.summary(model, (1, 1, 28, 28))
print(params_info)

这里定义一个MyNet的网络，然后查看网络的结构，下面开始进行模型训练

total_epoch = 5
    batch_size = 16
 
    # transform = F.normalize(mean=[127.5], std=[127.5], data_format=['CHW'])
    transform = Normalize(mean=[127.5], std=[127.5], data_format=['CHW'])
 
    # 训练集
    data_dir_train = './mnist/train'
    label_path_train = './mnist/train/label.txt'
    
 
    # 加载数据
    train_dataset = MyDataSet(data_dir_train, label_path_train, transform)
    val_dataset = MyDataSet(data_dir_val, label_path_val, transform)
    print(f'训练图片张数：{len(train_dataset)} 测试集图张数:{len(val_dataset)}')
    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
 
    optim = paddle.optimizer.Adam(parameters=model.parameters())
    # 设置损失函数
    loss_fn = paddle.nn.CrossEntropyLoss()
    for epoch in range(total_epoch):
        for batch_id, data in enumerate(train_loader):
 
            x_data = data[0]  # 训练数据
            y_data = data[1]  # 训练数据标签
            # print(y_data)
            # print(y_data.shape)
            # 增加维度
            x_data = paddle.unsqueeze(x_data, axis=1)
            predicts = model(x_data)  # 预测结果
            # print(f'predicts:{predicts} predicts.shape={predicts.shape}')
            y_data = paddle.unsqueeze(y_data, axis=1)
            # 计算损失 等价于 prepare 中loss的设置
            loss = loss_fn(predicts, y_data)
 
            # 计算准确率 等价于 prepare 中metrics的设置
            acc = paddle.metric.accuracy(predicts, y_data)
 
            # 下面的反向传播、打印训练信息、更新参数、梯度清零都被封装到 Model.fit() 中
            # 反向传播
            loss.backward()
            # print("epoch: {}, batch_id: {}, loss is: {}, acc is: {}".format(epoch, batch_id + 1, loss.numpy(),
            #                                                                 acc.numpy()))
 
            if (batch_id + 1) % 100 == 0:
                print("epoch: {}, batch_id: {}, loss is: {}, acc is: {}".format(epoch, batch_id + 1, loss.numpy(),
                                                                                acc.numpy()))
                write_to_log("epoch: {}, batch_id: {}, loss is: {}, acc is: {}".format(epoch, batch_id + 1, loss.numpy(),
                                                                                acc.numpy()))
            # 更新参数
            optim.step()
            # 梯度清零
            optim.clear_grad()
        paddle.save(model.state_dict(), f'./mynet/mynet.ep{epoch}.pdparams')

注意输入的数据的维度要与网络结构保持一致 

四，模型推理

下面来看一下模型的效果：

输出结果：