CNN-BI pytorch
时间: 2023-09-30 21:11:45 浏览: 58
CNN-BI是指使用双向卷积神经网络进行图像分类的方法。PyTorch是一个基于Python的深度学习框架,可以用于构建、训练和部署神经网络模型。
在PyTorch中实现CNN-BI,需要定义模型结构、损失函数和优化器,并进行训练和测试。下面是一个简单的CNN-BI模型实现示例:
```python
import torch
import torch.nn as nn
class CNNBI(nn.Module):
def __init__(self):
super(CNNBI, self).__init__()
self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.fc1 = nn.Linear(64 * 8 * 8, 512)
self.fc2 = nn.Linear(512, 10)
def forward(self, x):
x = self.conv1(x)
x = nn.functional.relu(x)
x = self.pool(x)
x = self.conv2(x)
x = nn.functional.relu(x)
x = self.pool(x)
x = x.view(-1, 64 * 8 * 8)
x = self.fc1(x)
x = nn.functional.relu(x)
x = self.fc2(x)
return x
model = CNNBI()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
# 训练模型
for epoch in range(10):
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
inputs, labels = data
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 2000 == 1999:
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 2000))
running_loss = 0.0
# 测试模型
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
images, labels = data
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the network on the 10000 test images: %d %%' % (
100 * correct / total))
```
这是一个简单的CNN-BI模型,使用了两个卷积层、两个池化层和两个全连接层。训练过程使用了交叉熵损失函数和随机梯度下降优化器。在训练完模型后,使用测试集进行测试并计算准确率。
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