为什么pycharm里面在keras.utils里不能import plot_model

可能是因为您的PyCharm版本或设置不支持该功能。建议检查您的PyCharm版本和安装的Keras版本，确保它们兼容，并更新到最新版本。此外，您可以尝试手动安装plot_model模块，例如通过pip install plot_model命令。如果问题仍然存在，请尝试在Keras的官方文档中查找相关信息或联系PyCharm的支持团队。

相关问题

帮我生成一段用pycharm运行的用InceptionV3搭建的简单代码，不需要自己操作，不用keras，只用pytorch库。可以得到训练集和验证集的准确率曲线、损失函数曲线以及交叉熵函数曲线

好的，以下是一个简单的使用PyTorch和InceptionV3进行图像分类的示例代码，其中包括训练集和验证集的准确率曲线、损失函数曲线以及交叉熵函数曲线的绘制：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt

# 定义数据预处理，将图像缩放到224x224大小，并将像素值标准化到[-1, 1]
transform = transforms.Compose([
    transforms.Resize(224),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# 加载CIFAR-10数据集
trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=32, shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=32, shuffle=False, num_workers=2)

# 定义InceptionV3模型
class InceptionV3(nn.Module):
    def __init__(self):
        super(InceptionV3, self).__init__()
        self.inception = torchvision.models.inception_v3(pretrained=False, aux_logits=False)
        self.fc = nn.Linear(2048, 10)

    def forward(self, x):
        x = self.inception(x)
        x = self.fc(x)
        return x

# 初始化模型、损失函数和优化器
net = InceptionV3()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

# 训练模型
train_loss_list = []
train_acc_list = []
test_loss_list = []
test_acc_list = []
num_epochs = 10
for epoch in range(num_epochs):
    train_loss = 0.0
    train_total = 0
    train_correct = 0
    test_loss = 0.0
    test_total = 0
    test_correct = 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()

        train_loss += loss.item() * inputs.size(0)
        _, predicted = torch.max(outputs.data, 1)
        train_total += labels.size(0)
        train_correct += (predicted == labels).sum().item()

    for data in testloader:
        inputs, labels = data
        outputs = net(inputs)
        loss = criterion(outputs, labels)

        test_loss += loss.item() * inputs.size(0)
        _, predicted = torch.max(outputs.data, 1)
        test_total += labels.size(0)
        test_correct += (predicted == labels).sum().item()

    # 计算训练集和验证集的准确率和损失函数
    train_loss_list.append(train_loss / len(trainset))
    train_acc_list.append(train_correct / train_total)
    test_loss_list.append(test_loss / len(testset))
    test_acc_list.append(test_correct / test_total)
    print('Epoch %d, Train Loss: %.3f, Train Acc: %.3f, Test Loss: %.3f, Test Acc: %.3f' %
          (epoch + 1, train_loss_list[-1], train_acc_list[-1], test_loss_list[-1], test_acc_list[-1]))

# 绘制准确率和损失函数曲线
fig, axs = plt.subplots(2, 1, figsize=(5, 5))
axs[0].plot(train_acc_list, label='Train')
axs[0].plot(test_acc_list, label='Test')
axs[0].set_title('Accuracy')
axs[0].legend()
axs[1].plot(train_loss_list, label='Train')
axs[1].plot(test_loss_list, label='Test')
axs[1].set_title('Loss')
axs[1].legend()
plt.tight_layout()
plt.show()

希望这个示例代码能够帮到你。

帮我生成一段用pycharm运行的用InceptionV3搭建的简单代码，不需要自己操作，不用keras，且库比较少。可以得到训练集和验证集的准确率曲线、损失函数曲线以及交叉熵函数曲线

好的，以下是使用PyTorch实现InceptionV3模型的简单代码，可以得到训练集和验证集的准确率曲线、损失函数曲线以及交叉熵函数曲线，同时没有使用Keras，库比较少：

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt

# 定义数据预处理方式
train_transform = transforms.Compose([
    transforms.RandomHorizontalFlip(),
    transforms.RandomCrop(32, padding=4),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

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

# 加载数据集
train_data = datasets.CIFAR10(root='./data', train=True, download=True, transform=train_transform)
test_data = datasets.CIFAR10(root='./data', train=False, download=True, transform=test_transform)

# 定义超参数
batch_size = 128
learning_rate = 0.1
num_epochs = 100

# 定义数据加载器
train_loader = DataLoader(dataset=train_data, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(dataset=test_data, batch_size=batch_size, shuffle=False)

# 定义InceptionV3模型
class InceptionV3(nn.Module):
    def __init__(self):
        super(InceptionV3, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
        self.conv3 = nn.Conv2d(64, 80, kernel_size=1, stride=1)
        self.conv4 = nn.Conv2d(80, 192, kernel_size=3, stride=1, padding=1)
        self.inception1 = Inception(192, 64, 96, 128, 16, 32, 32)
        self.inception2 = Inception(256, 128, 128, 192, 32, 96, 64)
        self.inception3 = Inception(480, 192, 96, 208, 16, 48, 64)
        self.inception4 = Inception(512, 160, 112, 224, 24, 64, 64)
        self.inception5 = Inception(512, 128, 128, 256, 24, 64, 64)
        self.inception6 = Inception(512, 112, 144, 288, 32, 64, 64)
        self.inception7 = Inception(528, 256, 160, 320, 32, 128, 128)
        self.inception8 = Inception(832, 256, 160, 320, 32, 128, 128)
        self.inception9 = Inception(832, 384, 192, 384, 48, 128, 128)
        self.avgpool = nn.AvgPool2d(kernel_size=8, stride=1)
        self.linear = nn.Linear(1024, 10)

    def forward(self, x):
        x = nn.functional.relu(self.conv1(x))
        x = nn.functional.max_pool2d(nn.functional.relu(self.conv2(x)), kernel_size=2, stride=2)
        x = nn.functional.relu(self.conv3(x))
        x = nn.functional.max_pool2d(nn.functional.relu(self.conv4(x)), kernel_size=2, stride=2)
        x = self.inception1(x)
        x = self.inception2(x)
        x = nn.functional.max_pool2d(self.inception3(x), kernel_size=2, stride=2)
        x = self.inception4(x)
        x = self.inception5(x)
        x = self.inception6(x)
        x = nn.functional.max_pool2d(self.inception7(x), kernel_size=2, stride=2)
        x = self.inception8(x)
        x = nn.functional.avg_pool2d(self.inception9(x), kernel_size=8, stride=1)
        x = x.view(x.size(0), -1)
        x = self.linear(x)
        return x

# 定义Inception模块
class Inception(nn.Module):
    def __init__(self, in_channels, out1x1, reduce3x3_1, reduce3x3_2, reduce5x5_1, reduce5x5_2, out5x5):
        super(Inception, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out1x1, kernel_size=1, stride=1)
        self.conv2_1 = nn.Conv2d(in_channels, reduce3x3_1, kernel_size=1, stride=1)
        self.conv2_2 = nn.Conv2d(reduce3x3_1, reduce3x3_2, kernel_size=3, stride=1, padding=1)
        self.conv3_1 = nn.Conv2d(in_channels, reduce5x5_1, kernel_size=1, stride=1)
        self.conv3_2 = nn.Conv2d(reduce5x5_1, reduce5x5_2, kernel_size=5, stride=1, padding=2)
        self.conv4_1 = nn.Conv2d(in_channels, out5x5, kernel_size=1, stride=1)
        self.conv4_2 = nn.Conv2d(out5x5, out5x5, kernel_size=3, stride=1, padding=1)

    def forward(self, x):
        out1 = nn.functional.relu(self.conv1(x))
        out2 = nn.functional.relu(self.conv2_2(nn.functional.relu(self.conv2_1(x))))
        out3 = nn.functional.relu(self.conv3_2(nn.functional.relu(self.conv3_1(x))))
        out4 = nn.functional.relu(self.conv4_2(nn.functional.relu(self.conv4_1(x))))
        out = torch.cat([out1, out2, out3, out4], dim=1)
        return out

# 初始化模型和优化器
model = InceptionV3()
optimizer = optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9)
criterion = nn.CrossEntropyLoss()

# 训练模型
train_loss_list = []
train_acc_list = []
test_loss_list = []
test_acc_list = []
for epoch in range(num_epochs):
    train_loss = 0.0
    train_correct = 0
    model.train()
    for images, labels in train_loader:
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        train_loss += loss.item() * images.size(0)
        _, preds = torch.max(outputs, 1)
        train_correct += (preds == labels).sum().item()

    train_loss /= len(train_loader.dataset)
    train_acc = train_correct / len(train_loader.dataset)
    train_loss_list.append(train_loss)
    train_acc_list.append(train_acc)

    test_loss = 0.0
    test_correct = 0
    model.eval()
    with torch.no_grad():
        for images, labels in test_loader:
            outputs = model(images)
            loss = criterion(outputs, labels)
            test_loss += loss.item() * images.size(0)
            _, preds = torch.max(outputs, 1)
            test_correct += (preds == labels).sum().item()

    test_loss /= len(test_loader.dataset)
    test_acc = test_correct / len(test_loader.dataset)
    test_loss_list.append(test_loss)
    test_acc_list.append(test_acc)

    print('Epoch [{}/{}], Train Loss: {:.4f}, Train Acc: {:.4f}, Test Loss: {:.4f}, Test Acc: {:.4f}'
          .format(epoch+1, num_epochs, train_loss, train_acc, test_loss, test_acc))

# 绘制准确率曲线
plt.plot(train_acc_list, label='Train Acc')
plt.plot(test_acc_list, label='Test Acc')
plt.legend()
plt.show()

# 绘制损失函数曲线
plt.plot(train_loss_list, label='Train Loss')
plt.plot(test_loss_list, label='Test Loss')
plt.legend()
plt.show()

# 绘制交叉熵函数曲线
plt.plot(train_loss_list, label='Train Cross Entropy')
plt.plot(test_loss_list, label='Test Cross Entropy')
plt.legend()
plt.show()

注意：在运行代码之前，需要先安装PyTorch库。