基于Pytorch的MNIST手写数据集的RNN与CNN实现

基于Pytorch的MNIST手写数据集的RNN与CNN实现

LSTM处理手写数据集（分类问题）
LSTM通过sin预测cos（回归问题）

pytorch输入参数格式

input_size – 输入的特征维度，即词嵌入里是one-hot的长度，一个单词的长度是300，则input_size为300，输入图像的宽为28，则input_size为28

hidden_size – 隐状态的特征维度，可以自行设定

num_layers – 层数（和时序展开要区分开），一般为1或者2

bias – 如果为False，那么LSTM将不会使用$b_{ih},b_{hh}$，默认为True。

batch_first – 如果为True，那么输入和输出Tensor的形状为 (batch, time_step, input_size)，否则 (time_step batch, input_size)

dropout – 如果非零的话，将会在RNN的输出上加个dropout，最后一层除外。

bidirectional – 如果为True，将会变成一个双向RNN，默认为False。

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time_step:长度为一句话的长度，一句话包含的单词数量，
batch_size:分批次送入rnn的数量
以下代码来自lstm

lstm输入是input, (h_0, c_0)
input (time_step, batch, input_size) 如果设置了batch_first，则batch为第一维。

(h_0, c_0) 隐层状态
h0 shape：(num_layers * num_directions, batch, hidden_size) 
c0 shape：(num_layers * num_directions, batch, hidden_size)

lstm输出是output, (h_n, c_n)
output (time_step, batch, hidden_size * num_directions) 包含每一个时刻的输出特征，如果设置了batch_first，则batch为第一维
(h_n, c_n) 隐层状态 
h_n shape: (num_layers * num_directions, batch, hidden_size)
c_n shape: (num_layers * num_directions, batch, hidden_size)

在一些文档中，time_step和seq_len都表示时间步

class RNN(nn.Module):
    def __init__(self):
        super(RNN, self).__init__()

        self.rnn = nn.LSTM(         
            input_size=INPUT_SIZE,
            hidden_size=64,        
            num_layers=1,          
            batch_first=True,     #(batch, time_step, input_size)
        )

        self.out = nn.Linear(64, 10)  #Linear(num_layer*hidden_size,分类的个数)

    def forward(self, x):
        # x shape (batch, time_step, input_size)
        # r_out shape (batch, time_step, output_size)
        # h_n shape (n_layers, batch, hidden_size)
        # h_c shape (n_layers, batch, hidden_size)
        r_out, (h_n, h_c) = self.rnn(x, None)   # 初始状态为None
        out = self.out(r_out[:, -1, :])
        return out

其中 out = self.out(r_out[:, -1, :])这一句是取time_step为最后时刻的输出作为linera层的输入，因为输入一个xt和ht-1会产生一个r_out和(h_n, h_c)，产生的hn会继续送入rnn的输入继续产生下一个r_out，所以需要取最后时刻的输出作为输入

完整代码整理后发布
CNN实现手写数据集代码如下

import torch
import torch.nn
from torch import nn
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
import numpy as np
import matplotlib.pyplot as plt
from torchvision import datasets,transforms
import torch.optim as optim
import torch.nn.functional as F



BATCH_SIZE=512
EPOCHS=20
DEVICE=torch.device("cuda" if torch.cuda.is_available() else "cpu")


transform=transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.1307,), (0.3081,))
])
train_data=datasets.MNIST(root='./mnist',download=True,train=True,transform=transform)
test_data=datasets.MNIST(root='./mnist',download=True,train=False,transform=transform)

train_loader=DataLoader(train_data,batch_size=BATCH_SIZE,shuffle=True)
test_loader=DataLoader(test_data,batch_size=BATCH_SIZE,shuffle=True)







class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Sequential(
            nn.Conv2d(in_channels=1, out_channels=10, kernel_size=5),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2)

        )
        self.conv2 = nn.Sequential(
            nn.Conv2d(in_channels=10, out_channels=20, kernel_size=3),
            nn.ReLU(),
            nn.MaxPool2d(2)
        )
        self.out = nn.Linear(20 * 5 * 5, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = x.view(x.size(0), -1)
        output = self.out(x)
        return output


model = Net()  # 实例化网络net，再送入gpu训练
optimizer = optim.Adam(model.parameters())
criterion = nn.CrossEntropyLoss()


def train(model, device, train_loader, optimizer, epoch, criterion):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):

        output = model(data)

        # loss=criterion(output,target)

        optimizer.zero_grad()
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()

        if (batch_idx + 1) % 30 == 0:  # train_loader的长度为train_loader.dataset的长度除以batch_size
            print('Train Epoch:{} [{}/{} ({:.0f}%)]\tLoss:{:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset), 100. * batch_idx / len(train_loader),
                loss.item()
            ))


def test(model, device, test_loader):
    model.eval()
    test_loss = 0
    test_corr = 0
    with torch.no_grad():
        for img, label in test_loader:
            output = model(img)
            test_loss += criterion(output, label)
            pred = output.max(1, keepdim=True)[1]
            test_corr += pred.eq(label.view_as(pred)).sum().item()

        print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
            test_loss, test_corr, len(test_loader.dataset), 100. * (test_corr / len(test_loader.dataset))
        ))


for epoch in range(1, EPOCHS + 1):
    train(model, DEVICE, train_loader, optimizer, epoch, criterion)
    test(model, DEVICE, test_loader)

训练结果如下
Train Epoch:1 [14848/60000 (25%)] Loss:0.809119
Train Epoch:1 [30208/60000 (50%)] Loss:0.332066
Train Epoch:1 [45568/60000 (75%)] Loss:0.248601

Test set: Average loss: 3.3879, Accuracy: 9515/10000 (95%)

Train Epoch:2 [14848/60000 (25%)] Loss:0.200926
Train Epoch:2 [30208/60000 (50%)] Loss:0.167642
Train Epoch:2 [45568/60000 (75%)] Loss:0.129635

Test set: Average loss: 1.9960, Accuracy: 9700/10000 (97%)

Train Epoch:3 [14848/60000 (25%)] Loss:0.097073
Train Epoch:3 [30208/60000 (50%)] Loss:0.078300
Train Epoch:3 [45568/60000 (75%)] Loss:0.095262

Test set: Average loss: 1.5412, Accuracy: 9764/10000 (98%)

Train Epoch:4 [14848/60000 (25%)] Loss:0.067570
Train Epoch:4 [30208/60000 (50%)] Loss:0.091387
Train Epoch:4 [45568/60000 (75%)] Loss:0.058170

Test set: Average loss: 1.3722, Accuracy: 9795/10000 (98%)

Train Epoch:5 [14848/60000 (25%)] Loss:0.081385
Train Epoch:5 [30208/60000 (50%)] Loss:0.069488
Train Epoch:5 [45568/60000 (75%)] Loss:0.108909

Test set: Average loss: 1.1676, Accuracy: 9818/10000 (98%)

Train Epoch:6 [14848/60000 (25%)] Loss:0.060494
Train Epoch:6 [30208/60000 (50%)] Loss:0.070833
Train Epoch:6 [45568/60000 (75%)] Loss:0.085588

Test set: Average loss: 1.0887, Accuracy: 9833/10000 (98%)

Train Epoch:7 [14848/60000 (25%)] Loss:0.067081
Train Epoch:7 [30208/60000 (50%)] Loss:0.082414
Train Epoch:7 [45568/60000 (75%)] Loss:0.045014

Test set: Average loss: 1.0601, Accuracy: 9837/10000 (98%)

Train Epoch:8 [14848/60000 (25%)] Loss:0.062390
Train Epoch:8 [30208/60000 (50%)] Loss:0.048241
Train Epoch:8 [45568/60000 (75%)] Loss:0.042879

Test set: Average loss: 0.9528, Accuracy: 9836/10000 (98%)

Train Epoch:9 [14848/60000 (25%)] Loss:0.048539
Train Epoch:9 [30208/60000 (50%)] Loss:0.055073
Train Epoch:9 [45568/60000 (75%)] Loss:0.055796

Test set: Average loss: 0.8623, Accuracy: 9866/10000 (99%)

Train Epoch:10 [14848/60000 (25%)] Loss:0.051431
Train Epoch:10 [30208/60000 (50%)] Loss:0.045435
Train Epoch:10 [45568/60000 (75%)] Loss:0.075674

Test set: Average loss: 0.7783, Accuracy: 9874/10000 (99%)

Train Epoch:11 [14848/60000 (25%)] Loss:0.028392
Train Epoch:11 [30208/60000 (50%)] Loss:0.049267
Train Epoch:11 [45568/60000 (75%)] Loss:0.042472

Test set: Average loss: 0.8189, Accuracy: 9875/10000 (99%)

Train Epoch:12 [14848/60000 (25%)] Loss:0.058731
Train Epoch:12 [30208/60000 (50%)] Loss:0.025470
Train Epoch:12 [45568/60000 (75%)] Loss:0.029647

Test set: Average loss: 0.7829, Accuracy: 9871/10000 (99%)

Train Epoch:13 [14848/60000 (25%)] Loss:0.052567
Train Epoch:13 [30208/60000 (50%)] Loss:0.028609
Train Epoch:13 [45568/60000 (75%)] Loss:0.020649

Test set: Average loss: 0.7527, Accuracy: 9872/10000 (99%)

Train Epoch:14 [14848/60000 (25%)] Loss:0.039200
Train Epoch:14 [30208/60000 (50%)] Loss:0.019106
Train Epoch:14 [45568/60000 (75%)] Loss:0.067107

Test set: Average loss: 0.7386, Accuracy: 9886/10000 (99%)

Train Epoch:15 [14848/60000 (25%)] Loss:0.038181
Train Epoch:15 [30208/60000 (50%)] Loss:0.022419
Train Epoch:15 [45568/60000 (75%)] Loss:0.016036

Test set: Average loss: 0.7954, Accuracy: 9862/10000 (99%)

Train Epoch:16 [14848/60000 (25%)] Loss:0.018675
Train Epoch:16 [30208/60000 (50%)] Loss:0.039494
Train Epoch:16 [45568/60000 (75%)] Loss:0.017992

Test set: Average loss: 0.8029, Accuracy: 9859/10000 (99%)

Train Epoch:17 [14848/60000 (25%)] Loss:0.019442
Train Epoch:17 [30208/60000 (50%)] Loss:0.014947
Train Epoch:17 [45568/60000 (75%)] Loss:0.024432

Test set: Average loss: 0.6863, Accuracy: 9874/10000 (99%)

Train Epoch:18 [14848/60000 (25%)] Loss:0.013267
Train Epoch:18 [30208/60000 (50%)] Loss:0.022075
Train Epoch:18 [45568/60000 (75%)] Loss:0.024906

Test set: Average loss: 0.6707, Accuracy: 9887/10000 (99%)

Train Epoch:19 [14848/60000 (25%)] Loss:0.031900
Train Epoch:19 [30208/60000 (50%)] Loss:0.014791
Train Epoch:19 [45568/60000 (75%)] Loss:0.037303

Test set: Average loss: 0.7329, Accuracy: 9878/10000 (99%)

Train Epoch:20 [14848/60000 (25%)] Loss:0.030795
Train Epoch:20 [30208/60000 (50%)] Loss:0.016112
Train Epoch:20 [45568/60000 (75%)] Loss:0.020148

Test set: Average loss: 0.6894, Accuracy: 9884/10000 (99%)