用resnet50进行语音数据集分类的源码
时间: 2023-09-03 20:13:43 浏览: 404
下面是一个使用ResNet-50进行语音数据集分类的示例源代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torchvision.datasets import SpeechCommands
# 设置超参数
num_epochs = 10
batch_size = 32
learning_rate = 0.001
# 数据预处理
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.5], std=[0.5])
])
# 加载数据集
train_dataset = SpeechCommands(root='./data', subset='training', download=True, transform=transform)
test_dataset = SpeechCommands(root='./data', subset='testing', download=True, transform=transform)
# 创建数据加载器
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
# 定义ResNet-50模型
class ResNet50(nn.Module):
def __init__(self, num_classes):
super(ResNet50, self).__init__()
self.resnet = torch.hub.load('pytorch/vision:v0.6.0', 'resnet50', pretrained=True)
self.fc = nn.Linear(1000, num_classes)
def forward(self, x):
x = self.resnet(x)
x = self.fc(x)
return x
# 创建模型实例
model = ResNet50(num_classes=len(train_dataset.classes))
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
# 训练模型
total_step = len(train_loader)
for epoch in range(num_epochs):
for i, (inputs, labels) in enumerate(train_loader):
# 前向传播
outputs = model(inputs)
loss = criterion(outputs, labels)
# 反向传播和优化
optimizer.zero_grad()
loss.backward()
optimizer.step()
# 打印训练信息
if (i+1) % 100 == 0:
print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
.format(epoch+1, num_epochs, i+1, total_step, loss.item()))
# 在测试集上评估模型
model.eval()
with torch.no_grad():
correct = 0
total = 0
for inputs, labels in test_loader:
outputs = model(inputs)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Test Accuracy: {} %'.format(100 * correct / total))
```
请注意,此代码假设您已经安装了相关的库,并且已经下载并准备好了SpeechCommands数据集。您可以根据需要进行修改和调整。希望这可以帮助到您!
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