SGD with Momentum如何写在pycharm中

SGD (Stochastic Gradient Descent) 加上动量(Momentum)是一种常用的优化算法，用于更新神经网络模型的权重。在PyCharm中实现这一过程通常涉及到深度学习库如TensorFlow或PyTorch。以下是使用PyTorch的一个简短示例：

import torch
import torch.nn as nn

# 定义一个有动量的SGD优化器
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9)

# 假设`loss.backward()`已经完成了反向传播
for group in optimizer.param_groups:
    for param in group['params']:
        if param.grad is not None:
            param.grad.data.add_(group['momentum'] * param.grad.data)
            # 更新参数
            param.data -= group['lr'] * param.grad.data

在这个例子中，`model.parameters()`获取模型的所有参数，`learning_rate`是学习率，`momentum=0.9`设置了动量值。每次迭代，先计算梯度的加权平均（动量项），然后根据这个平均值和学习率进行参数更新。

相关问题

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。

在PyCharm中不使用torchvision完成cifar10的卷积神经网络代码

好的，让我来回答你的问题。

首先，我们需要导入必要的库，包括PyTorch、NumPy等：

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np

接着，我们需要定义一个卷积神经网络。在这个例子中，我们定义一个包含两个卷积层和两个全连接层的神经网络。代码如下：

class Net(nn.Module):
    def __init__(self):
        super().__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(torch.relu(self.conv1(x)))
        x = self.pool(torch.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x

接下来，我们需要定义一些超参数，包括学习率、损失函数、优化器等：

lr = 0.001
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=lr, momentum=0.9)

然后，我们需要加载CIFAR-10数据集并进行预处理：

from torchvision import datasets, transforms

transform = transforms.Compose([transforms.ToTensor(),
                                transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

trainset = datasets.CIFAR10(root='./data', train=True,
                            download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
                                          shuffle=True, num_workers=2)

testset = datasets.CIFAR10(root='./data', train=False,
                           download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
                                         shuffle=False, num_workers=2)

但是由于你不想使用torchvision，我们需要手动加载数据集并进行预处理：

import pickle

def load_batch(filename):
    with open(filename, 'rb') as f:
        data = pickle.load(f, encoding='bytes')
        images = data[b'data']
        labels = data[b'labels']
        images = images.reshape((len(images), 3, 32, 32))
        images = images.transpose((0, 2, 3, 1))
        labels = np.array(labels)
        return images, labels

def load_data():
    train_images = []
    train_labels = []
    for i in range(1, 6):
        filename = f'./data_batch_{i}'
        images, labels = load_batch(filename)
        train_images.append(images)
        train_labels.append(labels)
    train_images = np.concatenate(train_images, axis=0)
    train_labels = np.concatenate(train_labels, axis=0)

    test_images, test_labels = load_batch('./test_batch')

    return train_images, train_labels, test_images, test_labels

train_images, train_labels, test_images, test_labels = load_data()

train_images = train_images / 255.0
test_images = test_images / 255.0

train_images = torch.from_numpy(train_images).float()
train_labels = torch.from_numpy(train_labels).long()
test_images = torch.from_numpy(test_images).float()
test_labels = torch.from_numpy(test_labels).long()

trainset = torch.utils.data.TensorDataset(train_images, train_labels)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
                                          shuffle=True, num_workers=2)

testset = torch.utils.data.TensorDataset(test_images, test_labels)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
                                         shuffle=False, num_workers=2)

最后，我们可以开始训练模型了：

for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = net(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

print('Finished Training')

在训练完成后，我们可以对模型进行测试并输出准确率：

correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = net(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))

这就是一个不使用torchvision完成cifar10的卷积神经网络的代码。希望能对你有所帮助！