cohen裁剪算法Python代码
时间: 2023-07-05 18:06:41 浏览: 111
以下是使用PyTorch实现Cohen剪枝算法的示例代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
def cohen_prune(model, prune_percent):
"""
Perform Cohen's weight pruning on the model, removing prune_percent % of the weights with the smallest magnitude.
"""
parameters_to_prune = []
for module in model.modules():
if isinstance(module, nn.Conv2d) or isinstance(module, nn.Linear):
parameters_to_prune.append((module, 'weight'))
prune.global_unstructured(
parameters_to_prune,
pruning_method=prune.L1Unstructured,
amount=prune_percent/100
)
# Define your model
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 32, kernel_size=3)
self.conv2 = nn.Conv2d(32, 64, kernel_size=3)
self.fc1 = nn.Linear(1600, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = nn.functional.relu(self.conv1(x))
x = nn.functional.relu(self.conv2(x))
x = x.view(-1, 1600)
x = nn.functional.relu(self.fc1(x))
x = self.fc2(x)
return x
model = Net()
# Train your model
optimizer = optim.Adam(model.parameters(), lr=0.01)
loss_fn = nn.CrossEntropyLoss()
for epoch in range(10):
for batch_idx, (data, target) in enumerate(train_loader):
optimizer.zero_grad()
output = model(data)
loss = loss_fn(output, target)
loss.backward()
optimizer.step()
# Prune the model
cohen_prune(model, 50)
# Evaluate the pruned model
correct = 0
total = 0
with torch.no_grad():
for data, target in test_loader:
output = model(data)
_, predicted = torch.max(output.data, 1)
total += target.size(0)
correct += (predicted == target).sum().item()
print('Accuracy of the pruned model: %d %%' % (
100 * correct / total))
```
这里我们定义了一个简单的模型,使用MNIST数据集进行训练和测试,然后使用`cohen_prune`函数进行剪枝。在这个例子中,我们将剪去50%的权重。