基于pytorch实现RNN和LSTM对手写数字集分类的完整实例

admin • 2022-02-01 08:13 • 人工智能

RNN

# coding: utf-8
import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision
import numpy as np
import matplotlib.pyplot as plt

trainsets = datasets.MNIST(root="./data", train=True, download=True, transform=transforms.ToTensor())
testsets = datasets.MNIST(root="./data", train=False, transform=transforms.ToTensor())

class_names = trainsets.classes
BATCH_SIZE = 32  # 每批读取的数据大小
EPOCHS = 10  # 训练10轮

train_loader = torch.utils.data.DataLoader(dataset=trainsets, batch_size=BATCH_SIZE, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=testsets, batch_size=BATCH_SIZE, shuffle=True)

# 查看一批batch的数据
images, labels = next(iter(test_loader))

print(images.shape)
print(labels.shape)


def imshow(inp, title=None):
    inp = inp.numpy().transpose((1, 2, 0))
    mean = np.array([0.485, 0.456, 0.406])
    std = np.array([0.229, 0.224, 0.225])
    inp = std * inp + mean
    inp = np.clip(inp, 0, 1)
    plt.imshow(inp)
    if title is not None:
        plt.title(title)
    plt.pause(0.001)
    plt.show()


# 网格显示
out = torchvision.utils.make_grid(images)
imshow(out)


class RNN_Model(nn.Module):
    def __init__(self, input_dim, hidden_dim, layer_dim, output_dim):
        super(RNN_Model, self).__init__()
        self.hidden_dim = hidden_dim
        self.layer_dim = layer_dim
        self.rnn = nn.RNN(input_dim, hidden_dim, layer_dim, batch_first=True, nonlinearity='relu')
        # 全连接层
        self.fc = nn.Linear(hidden_dim, output_dim)

    def forward(self, x):
        # layer_dim, batch_size, hidden_dim
        h0 = torch.zeros(self.layer_dim, x.size(0), self.hidden_dim).requires_grad_().to(device)
        # 分离隐藏状态，避免梯度爆炸
        out, hn = self.rnn(x, h0.detach())
        out = self.fc(out[:, -1, ])
        return out

    
input_dim = 28  # 输入维度
hidden_dim = 100  # 隐层的维度
layer_dim = 2  # 2层RNN
output_dim = 10  # 输出维度

model = RNN_Model(input_dim, hidden_dim, layer_dim, output_dim)

device = torch.device("cuda:0" if torch.cuda.is_available() else 'cpu')

# 损失函数
criterion = nn.CrossEntropyLoss()

# 优化器
learning_rate = 0.01
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)

# 模型参数
l = len(list(model.parameters()))

for i in range(l):
    print("参数：%d" % (i + 1))
    print(list((model.parameters()))[i].size())

# 模型训练
sequence_dim = 28  # 序列长度
loss_list = []
accuracy_list = []
iteration_list = []  # 迭代次数

iter = 0
for epoch in range(EPOCHS):
    for i, (images, labels) in enumerate(train_loader):
        model.train()
        # 一个batch的数据转换为RNN的输入维度
        images = images.view(-1, sequence_dim, input_dim).requires_grad_().to(device)
        labels = labels.to(device)
        optimizer.zero_grad()
        # 前向传播
        outputs = model(images)
        # 计算损失
        loss = criterion(outputs, labels)
        # 反向传播
        loss.backward()
        # 更新参数
        optimizer.step()
        # 计数器加1
        iter += 1
        # 模型验证
        if iter % 500 == 0:
            model.eval()  # 声明
            # 计算验证的accuracy
            correct = 0.0
            total = 0.0
            # 迭代测试集，获取数据、预测
            for images, labels in test_loader:
                images = images.view(-1, sequence_dim, input_dim).to(device)
                # 模型预测
                outputs = model(images)
                # 获取预测概率最大值的下标
                predict = torch.max(outputs.data, 1)[1]
                # 统计测试集的大小
                total += labels.size(0)
                # 统计判断预测正确的数量
                if torch.cuda.is_available():
                    correct += (predict.gpu() == labels.gpu()).sum()
                else:
                    correct += (predict == labels).sum()

                # 计算accuracy
            accuracy = correct / total * 100
            loss_list.append(loss.data)
            accuracy_list.append(accuracy)
            iteration_list.append(iter)
            # 打印信息
            print("loos:{}, Loss:{}, Accuracy:{}".format(iter, loss.item(), accuracy))


plt.plot(iteration_list, loss_list)
plt.xlabel("Number of Iteration")
plt.ylabel("Loss")
plt.title("RNN")
plt.show()


plt.plot(iteration_list, accuracy_list, color='r')
plt.xlabel("Number of iteration")
plt.ylabel("Accuracy")
plt.title("RNN")
plt.show()

LSTM

# coding: utf-8
import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import matplotlib.pyplot as plt

input_dim = 28  # 输入维度
hidden_dim = 100  # 隐层的维度
layer_dim = 1  # 1层LSTM
output_dim = 10  # 输出维度
BATCH_SIZE = 32  # 每批读取的
EPOCHS = 10  # 训练10轮


trainsets = datasets.MNIST(root="./data", train=True, download=True, transform=transforms.ToTensor())
testsets = datasets.MNIST(root="./data", train=False, transform=transforms.ToTensor())

train_loader = torch.utils.data.DataLoader(dataset=trainsets, batch_size=BATCH_SIZE, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=testsets, batch_size=BATCH_SIZE, shuffle=True)


class LSTM_Model(nn.Module):
    def __init__(self, input_dim, hidden_dim, layer_dim, output_dim):
        super(LSTM_Model, self).__init__()
        self.hidden_dim = hidden_dim
        self.layer_dim = layer_dim
        self.lstm = nn.LSTM(input_dim, hidden_dim, layer_dim, batch_first=True)
        # 全连接层
        self.fc = nn.Linear(hidden_dim, output_dim)

    def forward(self, x):
        # layer_dim, batch_size, hidden_dim
        print("x", x.shape)
        print(x.size(0))
        h0 = torch.zeros(self.layer_dim, x.size(0), self.hidden_dim).requires_grad_().to(device)
        # 初始化cell, state
        c0 = torch.zeros(self.layer_dim, x.size(0), self.hidden_dim).requires_grad_().to(device)
        # 分离隐藏状态，避免梯度爆炸
        out, (hn, cn) = self.lstm(x, (h0.detach(), c0.detach()))
        print(out.shape)
        print(out[:, -1].shape)
        out = self.fc(out[:, -1])
        print("out-----", out.shape)
        return out


model = LSTM_Model(input_dim, hidden_dim, layer_dim, output_dim)

device = torch.device("cuda:0" if torch.cuda.is_available() else 'cpu')

# 损失函数
criterion = nn.CrossEntropyLoss()

# 优化器
learning_rate = 0.01
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)

# 模型训练
sequence_dim = 28  # 序列长度
loss_list = []
accuracy_list = []
iteration_list = []  # 迭代次数

iter = 0
for epoch in range(EPOCHS):
    for i, (images, labels) in enumerate(train_loader):
        model.train()
        # 一个batch的数据转换为RNN的输入维度
        images = images.view(-1, sequence_dim, input_dim).requires_grad_().to(device)
        labels = labels.to(device)
        optimizer.zero_grad()
        # 前向传播
        print(images.shape)
        print(labels.shape)
        outputs = model(images)
        # 计算损失
        loss = criterion(outputs, labels)
        # 反向传播
        loss.backward()
        # 更新参数
        optimizer.step()
        # 计数器加1
        iter += 1
        # 模型验证
        if iter % 500 == 0:
            model.eval()  # 声明
            # 计算验证的accuracy
            correct = 0.0
            total = 0.0
            # 迭代测试集，获取数据、预测
            for images, labels in test_loader:
                images = images.view(-1, sequence_dim, input_dim).to(device)
                # 模型预测
                outputs = model(images)
                # 获取预测概率最大值的下标
                predict = torch.max(outputs.data, 1)[1]
                # 统计测试集的大小
                total += labels.size(0)
                # 统计判断预测正确的数量
                if torch.cuda.is_available():
                    correct += (predict.gpu() == labels.gpu()).sum()
                else:
                    correct += (predict == labels).sum()

                    # 计算accuracy
            accuracy = correct / total * 100
            loss_list.append(loss.data)
            accuracy_list.append(accuracy)
            iteration_list.append(iter)
            # 打印信息
            print("loos:{}, Loss:{}, Accuracy:{}".format(iter, loss.item(), accuracy))

plt.plot(iteration_list, loss_list)
plt.xlabel("Number of Iteration")
plt.ylabel("Loss")
plt.title("LSTM")
plt.show()


plt.plot(iteration_list, accuracy_list, color='r')
plt.xlabel("Number of iteration")
plt.ylabel("Accuracy")
plt.title("LSTM")
plt.show()

本文参考https://www.bilibili.com/video/BV1n54y117KK/?spm_id_from=333.788
讲的还不错吧，可以听一听

本图文内容来源于网友网络收集整理提供，作为学习参考使用，版权属于原作者。

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