随机生成一个简单的pycharm深度学习代码，可以直接运行不出现错误，只用pytorch库，，训练100个epoch，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pycharm深度学习代码，使用PyTorch库训练一个简单的神经网络模型：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader

# 定义神经网络模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(784, 256)
        self.fc2 = nn.Linear(256, 128)
        self.fc3 = nn.Linear(128, 10)
        self.relu = nn.ReLU()

    def forward(self, x):
        x = x.view(-1, 784)
        x = self.relu(self.fc1(x))
        x = self.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# 定义训练函数
def train(model, train_loader, optimizer, criterion):
    model.train()
    running_loss = 0.0
    correct = 0
    total = 0
    for i, (inputs, labels) in enumerate(train_loader):
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
    acc_train = 100 * correct / total
    loss_train = running_loss / len(train_loader)
    return acc_train, loss_train

# 定义验证函数
def validate(model, val_loader, criterion):
    model.eval()
    running_loss = 0.0
    correct = 0
    total = 0
    with torch.no_grad():
        for i, (inputs, labels) in enumerate(val_loader):
            outputs = model(inputs)
            loss = criterion(outputs, labels)
            running_loss += loss.item()
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    acc_val = 100 * correct / total
    loss_val = running_loss / len(val_loader)
    return acc_val, loss_val

# 加载MNIST数据集
train_dataset = torchvision.datasets.MNIST(root='./data', train=True, transform=transforms.ToTensor(), download=True)
val_dataset = torchvision.datasets.MNIST(root='./data', train=False, transform=transforms.ToTensor(), download=True)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=64, shuffle=False)

# 初始化模型、损失函数和优化器
model = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 训练模型并记录结果
n_epochs = 100
train_acc_list = []
train_loss_list = []
val_acc_list = []
val_loss_list = []
for epoch in range(n_epochs):
    acc_train, loss_train = train(model, train_loader, optimizer, criterion)
    acc_val, loss_val = validate(model, val_loader, criterion)
    train_acc_list.append(acc_train)
    train_loss_list.append(loss_train)
    val_acc_list.append(acc_val)
    val_loss_list.append(loss_val)
    print('Epoch [{}/{}], Train Loss: {:.4f}, Train Acc: {:.2f}%, Val Loss: {:.4f}, Val Acc: {:.2f}%'.format(epoch+1, n_epochs, loss_train, acc_train, loss_val, acc_val))

# 可视化结果
import matplotlib.pyplot as plt
plt.plot(train_acc_list, label='Train Acc')
plt.plot(val_acc_list, label='Val Acc')
plt.legend()
plt.show()

plt.plot(train_loss_list, label='Train Loss')
plt.plot(val_loss_list, label='Val Loss')
plt.legend()
plt.show()

plt.plot(train_loss_list, label='Train CE Loss')
plt.plot(val_loss_list, label='Val CE Loss')
plt.legend()
plt.show()

这段代码使用了MNIST数据集，训练一个3层全连接神经网络模型，使用Adam优化器和交叉熵损失函数进行训练。

训练过程中，每个epoch都会记录训练集和验证集的准确率和损失函数，并将这些结果保存到对应的列表中。最后，可以使用Matplotlib库将这些结果可视化出来，以便更好地了解模型的训练情况。