Pytorch学习系列(7):循环神经网络(Recurrent Neural Network)
many to one 的形式解决 MINIST 数据集 手写数字分类问题。
导包
# 包
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
设置参数
# 设备配置
# Device configuration
torch.cuda.set_device(1) # 这句用来设置pytorch在哪块GPU上运行
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# 超参数设置
# Hyper-parameters
sequence_length = 28
input_size = 28
hidden_size = 128
num_layers = 2
num_classes = 10
batch_size = 100
num_epochs = 2
learning_rate = 0.01
MINIST 数据集
# 训练数据
train_dataset = torchvision.datasets.MNIST(root='./data/minist/',
train=True,
transform=transforms.ToTensor(),
download=True)
# 测试数据
test_dataset = torchvision.datasets.MNIST(root='./data/minist/',
train=False,
transform=transforms.ToTensor())
# 训练数据加载器
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=batch_size,
shuffle=True)
# 测试数据加载器
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
batch_size=batch_size,
shuffle=False)
循环神经网络搭建
class RNN(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, num_classes):
super(RNN, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True) # 选用LSTM RNN结构
self.fc = nn.Linear(hidden_size, num_classes) # 最后一层为全连接层,将隐状态转为分类
def forward(self, x):
# 初始化隐层状态和细胞状态
h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
# 前向传播LSTM
out, _ = self.lstm(x, (h0, c0)) # 输出大小 (batch_size, seq_length, hidden_size)
# 解码最后一个时刻的隐状态
out = self.fc(out[:, -1, :])
return out
# 实例化一个模型
# 注意输入维度,虽然我不懂将一幅图28x28拆成28个大小为28的序列有啥意义
model = RNN(input_size, hidden_size, num_layers, num_classes).to(device)
# 定义损失函数和优化器
# Adam: A Method for Stochastic Optimization
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
训练模型
total_step = len(train_loader)
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
images = images.reshape(-1, sequence_length, input_size).to(device) # 注意维度
labels = labels.to(device)
# 前向传播
outputs = model(images)
loss = criterion(outputs, labels)
# 反向传播和优化,注意梯度每次清零
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i+1) % 100 == 0:
print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
.format(epoch+1, num_epochs, i+1, total_step, loss.item()))
Epoch [1/2], Step [100/600], Loss: 0.7669
Epoch [1/2], Step [200/600], Loss: 0.2382
Epoch [1/2], Step [300/600], Loss: 0.2442
Epoch [1/2], Step [400/600], Loss: 0.2004
Epoch [1/2], Step [500/600], Loss: 0.1421
Epoch [1/2], Step [600/600], Loss: 0.1199
Epoch [2/2], Step [100/600], Loss: 0.0189
Epoch [2/2], Step [200/600], Loss: 0.0203
Epoch [2/2], Step [300/600], Loss: 0.1831
Epoch [2/2], Step [400/600], Loss: 0.0595
Epoch [2/2], Step [500/600], Loss: 0.0483
Epoch [2/2], Step [600/600], Loss: 0.0311
测试模型
# 测试集
with torch.no_grad():
correct = 0
total = 0
for images, labels in test_loader:
images = images.reshape(-1, sequence_length, input_size).to(device)
labels = labels.to(device)
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Test Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))
Test Accuracy of the model on the 10000 test images: 96.38 %
保存模型
# 保存模型
torch.save(model.state_dict(), 'model.ckpt')