pycharm里面的SGDClassifier（）没有momentum和nesterovs_momentum属性

是的，PyCharm中的SGDClassifier()确实没有momentum和nesterovs_momentum属性。这是因为SGDClassifier()使用的是随机梯度下降算法（SGD），而这些属性是用于改进SGD算法的一些变种，如带有动量的SGD和Nesterov加速梯度的SGD。如果您需要使用这些属性，可以考虑使用其他库，如TensorFlow或Keras，它们提供了更多的优化算法和选项。

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faster r-cnn pycharm代码

以下是使用PyCharm实现Faster R-CNN的Python代码示例：

# 导入必要的库
import torch
import torchvision
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
from torchvision.datasets import CocoDetection
from torchvision.transforms import ToTensor
from torch.utils.data import DataLoader

# 加载COCO数据集
train_dataset = CocoDetection(root="./data/train2017", annFile="./data/annotations/instances_train2017.json", transform=ToTensor())
test_dataset = CocoDetection(root="./data/val2017", annFile="./data/annotations/instances_val2017.json", transform=ToTensor())

# 创建数据加载器
train_loader = DataLoader(train_dataset, batch_size=2, shuffle=True, num_workers=4)
test_loader = DataLoader(test_dataset, batch_size=2, shuffle=False, num_workers=4)

# 加载预训练的Faster R-CNN模型
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)

# 替换分类器
num_classes = 91  # 用于COCO数据集的类别数
in_features = model.roi_heads.box_predictor.cls_score.in_features
model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)

# 训练和测试模型
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
model.to(device)

params = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.SGD(params, lr=0.005, momentum=0.9, weight_decay=0.0005)
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=3, gamma=0.1)

num_epochs = 10

for epoch in range(num_epochs):
    model.train()
    for images, targets in train_loader:
        images = list(image.to(device) for image in images)
        targets = [{k: v.to(device) for k, v in t.items()} for t in targets]

        loss_dict = model(images, targets)

        losses = sum(loss for loss in loss_dict.values())
        optimizer.zero_grad()
        losses.backward()
        optimizer.step()

    lr_scheduler.step()

    model.eval()
    test_loss = 0.0
    for images, targets in test_loader:
        images = list(image.to(device) for image in images)
        targets = [{k: v.to(device) for k, v in t.items()} for t in targets]

        with torch.no_grad():
            loss_dict = model(images, targets)

        test_loss += sum(loss for loss in loss_dict.values()).item()

    print(f"Epoch {epoch}: train_loss = {losses.item()}, test_loss = {test_loss / len(test_loader)}")

需要注意的是，此处的代码仅提供了一个基本框架，具体实现需要根据实际需求进行相应的修改和调整。

在pycharm中使用pytorch实现EEGNet网络

在PyCharm中使用PyTorch实现EEGNet网络，你可以按照以下步骤进行：

1. 首先，确保你已经安装了PyTorch库。可以使用以下命令在PyCharm的终端中安装PyTorch：

   pip install torch torchvision

2. 创建一个新的Python文件，并导入所需的库：

   import torch
   import torch.nn as nn
   import torch.optim as optim

3. 定义EEGNet网络的模型类。EEGNet是一种用于处理脑电图（EEG）信号的轻量级卷积神经网络。以下是一个简单的EEGNet实现示例：

   class EEGNet(nn.Module):
       def __init__(self, num_classes):
           super(EEGNet, self).__init__()
           self.firstConv = nn.Sequential(
               nn.Conv2d(1, 16, kernel_size=(1, 51), stride=(1, 1), padding=(0, 25), bias=False),
               nn.BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),
               nn.ELU(),
               nn.MaxPool2d(kernel_size=(1, 4), stride=(1, 4), padding=0),
               nn.Dropout(p=0.25)
           )
           self.depthwiseConv = nn.Sequential(
               nn.Conv2d(16, 32, kernel_size=(2, 1), stride=(1, 1), groups=16, bias=False),
               nn.BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),
               nn.ELU(),
               nn.AvgPool2d(kernel_size=(1, 4), stride=(1, 4), padding=0),
               nn.Dropout(p=0.25)
           )
           self.separableConv = nn.Sequential(
               nn.Conv2d(32, 32, kernel_size=(1, 15), stride=(1, 1), padding=(0, 7), bias=False),
               nn.BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),
               nn.ELU(),
               nn.AvgPool2d(kernel_size=(1, 8), stride=(1, 8), padding=0),
               nn.Dropout(p=0.25)
           )
           self.classifier = nn.Linear(736, num_classes)
       
       def forward(self, x):
           x = self.firstConv(x)
           x = self.depthwiseConv(x)
           x = self.separableConv(x)
           x = x.view(x.size(0), -1)
           x = self.classifier(x)
           return x

4. 创建一个实例化的EEGNet模型，并定义损失函数和优化器：

   model = EEGNet(num_classes=2)  # 替换num_classes为你的类别数目
   criterion = nn.CrossEntropyLoss()
   optimizer = optim.Adam(model.parameters(), lr=0.001)

5. 准备你的数据，并进行训练和测试循环：

   # 假设你的训练数据为train_loader，测试数据为test_loader
   for epoch in range(num_epochs):
       model.train()
       for inputs, labels in train_loader:
           optimizer.zero_grad()
           outputs = model(inputs)
           loss = criterion(outputs, labels)
           loss.backward()
           optimizer.step()
       
       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()
           
           accuracy = correct / total
           print(f"Epoch {epoch+1}/{num_epochs}, Test Accuracy: {accuracy}")

这就是在PyCharm中使用PyTorch实现EEGNet网络的基本步骤。你可以根据自己的需求进行修改和扩展。记得提前准备好你的数据集和加载器。祝你成功实现！