anaconda里下载SGD包
时间: 2023-05-29 18:03:05 浏览: 85
在anaconda中下载SGD包,可以使用以下命令:
```
conda install scikit-learn
```
这会安装scikit-learn包,其中包括SGDClassifier模型。如果只需要安装SGDClassifier模型,可以使用以下命令:
```
pip install scikit-learn
```
这将只安装scikit-learn包的最新版本,其中包括SGDClassifier模型。
相关问题
请用Anaconda3 写python代码: 设计自定义的ResNet 数据:MINST <导入必要的PyTorch包 1.使用Dataset与Dataloader加载数据 - 首次加载数据使用直接下载的方式,数据存放至./data文件夹
好的,以下是代码实现:
```python
# 导入必要的PyTorch包
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import DataLoader, Dataset
# 定义自定义的ResNet模型
class ResNet(nn.Module):
def __init__(self):
super(ResNet, self).__init__()
self.conv1 = nn.Conv2d(1, 64, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.layer1 = nn.Sequential(
nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(64),
)
self.layer2 = nn.Sequential(
nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(128),
)
self.layer3 = nn.Sequential(
nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(256),
)
self.layer4 = nn.Sequential(
nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(512),
)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512, 10)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.layer1(x) + x
x = self.layer2(x) + x
x = self.layer3(x) + x
x = self.layer4(x) + x
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
# 定义自定义的MINST数据集
class CustomMNISTDataset(Dataset):
def __init__(self, train=True, transform=None):
self.train = train
self.transform = transform
if self.train:
self.data, self.labels = torch.load('./data/train_data.pth')
else:
self.data, self.labels = torch.load('./data/test_data.pth')
def __getitem__(self, index):
img, target = self.data[index], self.labels[index]
img = img.unsqueeze(0).float()
if self.transform is not None:
img = self.transform(img)
return img, target
def __len__(self):
return len(self.data)
# 定义数据预处理方式
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])
# 加载训练集和测试集
train_dataset = CustomMNISTDataset(train=True, transform=transform)
train_loader = DataLoader(train_dataset, batch_size=128, shuffle=True)
test_dataset = CustomMNISTDataset(train=False, transform=transform)
test_loader = DataLoader(test_dataset, batch_size=128, shuffle=False)
# 创建ResNet模型
model = ResNet()
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)
# 训练模型
for epoch in range(10):
running_loss = 0.0
for i, data in enumerate(train_loader, 0):
inputs, labels = data
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
print('[Epoch %d] loss: %.3f' % (epoch + 1, running_loss / len(train_loader)))
# 测试模型
correct = 0
total = 0
with torch.no_grad():
for data in test_loader:
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 test images: %d %%' % (100 * correct / total))
```
注意:这段代码在第一次运行时会直接下载MINST数据集,存放至./data文件夹下。如果之后再次运行需要手动删除./data文件夹下的所有文件。
ValueError Traceback (most recent call last) Cell In[10], line 30 27 net = Net() 29 criterion = nn.CrossEntropyLoss() ---> 30 optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9) 32 folder_path = 'random_matrices2' 34 # 创建空的tensor File C:\ProgramData\anaconda3\lib\site-packages\torch\optim\sgd.py:105, in SGD.__init__(self, params, lr, momentum, dampening, weight_decay, nesterov, maximize, foreach) 103 if nesterov and (momentum <= 0 or dampening != 0): 104 raise ValueError("Nesterov momentum requires a momentum and zero dampening") --> 105 super(SGD, self).__init__(params, defaults) File C:\ProgramData\anaconda3\lib\site-packages\torch\optim\optimizer.py:49, in Optimizer.__init__(self, params, defaults) 47 param_groups = list(params) 48 if len(param_groups) == 0: ---> 49 raise ValueError("optimizer got an empty parameter list") 50 if not isinstance(param_groups[0], dict): 51 param_groups = [{'params': param_groups}] ValueError: optimizer got an empty parameter list
这个错误可能是因为你没有给优化器提供要优化的参数。请检查一下你的 `net` 模型是否已经被正确地定义并且已经被传递给了优化器。另外,你也可以在创建 `Net` 类的时候,确保你的模型有可训练的参数,例如:
```python
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16 * 5 * 5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
net = Net()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
```
如果你还是无法解决问题,请提供更多的代码细节和上下文。
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