dir(mymodule1)是什么意思
时间: 2024-05-18 11:12:54 浏览: 14
`dir(module_name)`是Python中的一个内置函数,用来列出一个模块中定义的所有名称(变量、函数、类等)。
例如,当我们执行`dir(mymodule1)`时,Python会在当前环境中搜索名为`mymodule1`的模块,并返回该模块中定义的所有名称的列表。这些名称包括模块中定义的函数、变量、类、模块级别的常量等。返回的列表是一个字符串列表,其中包含模块中定义的所有名称。
需要注意的是,`dir()`函数可以用在任何对象上,而不仅仅是模块。对于其他对象,`dir()`函数会返回该对象所定义的所有属性和方法的列表。
相关问题
import torch from torch import nn from torch.utils.tensorboard import SummaryWriter class MyModule(nn.Module): def __init__(self): super(MyModule, self).__init__() self.model1 = nn.Sequential( nn.Flatten(), nn.Linear(3072, 100), nn.ReLU(), nn.Linear(100, 1), nn.Sigmoid() ) def forward(self, x): x = self.model1(x) return x import torch import torchvision from PIL.Image import Image from torch.utils.tensorboard import SummaryWriter from torch import nn, optim from torch.utils.data import dataloader from torchvision.transforms import transforms from module import MyModule train = torchvision.datasets.CIFAR10(root="../data",train=True, download=True, transform= transforms.ToTensor()) vgg_model = torchvision.models.vgg16(pretrained=True) vgg_model.classifier.add_module('add_linear', nn.Linear(1000,2)) #ToImage = transforms.ToPILImage() #Image.show(ToImage(train[0][0])) train_data = dataloader.DataLoader(train, batch_size = 128, shuffle=True) model = MyModule() #criterion = nn.BCELoss() epochs = 5 learningRate = 1e-3 optimizer = optim.SGD(model.parameters(),lr = learningRate) loss = nn.CrossEntropyLoss() Writer = SummaryWriter(log_dir="Training") step = 0 for epoch in range(epochs): total_loss = 0 for data,labels in train_data: y = vgg_model(data) los = loss(y,labels) optimizer.zero_grad() los.backward() optimizer.step() Writer.add_scalar("Training",los,step) step = step + 1 if step%100 == 0: print("Training for {0} times".format(step)) total_loss += los print("total_loss is {0}".format(los)) Writer.close() torch.save(vgg_model,"model_vgg.pth")修改变成VGG16-两分类模型
好的,您的问题是如何将这段代码修改成一个基于VGG16的两分类模型?我可以给您一些指导。
首先,我们需要将原先的MyModule替换成VGG16模型。可以使用torchvision中已经实现好的VGG16模型,但需要修改最后一层的输出为两个类别。具体实现方法如下:
```python
vgg_model = torchvision.models.vgg16(pretrained=True)
vgg_model.classifier.add_module('add_linear', nn.Linear(1000,2))
```
这段代码会下载预训练好的VGG16模型,并在其分类器中添加一个新的线性层,输出为2个类别。
其次,我们需要修改损失函数。因为我们现在是进行两分类,所以可以使用交叉熵损失函数(nn.CrossEntropyLoss())。
最后,修改训练过程中的模型、优化器、损失函数等参数即可。具体代码实现如下:
```python
import torch
import torchvision
from torch.utils.tensorboard import SummaryWriter
from torch import nn, optim
from torch.utils.data import dataloader
from torchvision.transforms import transforms
train = torchvision.datasets.CIFAR10(root="../data",train=True, download=True, transform= transforms.ToTensor())
vgg_model = torchvision.models.vgg16(pretrained=True)
vgg_model.classifier.add_module('add_linear', nn.Linear(1000,2))
train_data = dataloader.DataLoader(train, batch_size = 128, shuffle=True)
criterion = nn.CrossEntropyLoss()
epochs = 5
learningRate = 1e-3
optimizer = optim.SGD(vgg_model.parameters(),lr = learningRate)
Writer = SummaryWriter(log_dir="Training")
step = 0
for epoch in range(epochs):
total_loss = 0
for data,labels in train_data:
optimizer.zero_grad()
y = vgg_model(data)
los = criterion(y,labels)
los.backward()
optimizer.step()
Writer.add_scalar("Training",los,step)
step = step + 1
if step%100 == 0:
print("Training for {0} times".format(step))
total_loss += los
print("total_loss is {0}".format(total_loss))
Writer.close()
torch.save(vgg_model,"model_vgg.pth")
```
希望这个回答能够帮到您!
# This is a sample Python script. # Press Shift+F10 to execute it or replace it with your code. # Press Double Shift to search everywhere for classes, files, tool windows, actions, and settings. import torch import torchvision from PIL.Image import Image from torch.utils.tensorboard import SummaryWriter from torch import nn, optim from torch.utils.data import dataloader from torchvision.transforms import transforms from module import MyModule train = torchvision.datasets.CIFAR10(root="../data",train=True, download=True, transform= transforms.ToTensor()) vgg_model = torchvision.models.vgg16(pretrained=True) vgg_model.classifier.add_module('add_linear', nn.Linear(1000,2)) #ToImage = transforms.ToPILImage() #Image.show(ToImage(train[0][0])) train_data = dataloader.DataLoader(train, batch_size = 128, shuffle=True) model = MyModule() epochs = 5 learningRate = 1e-3 optimizer = optim.SGD(model.parameters(),lr = learningRate) loss = nn.CrossEntropyLoss() Writer = SummaryWriter(log_dir="Training") step = 0 for epoch in range(epochs): total_loss = 0 for data,labels in train_data: y = vgg_model(data) los = loss(y,labels) optimizer.zero_grad() los.backward() optimizer.step() Writer.add_scalar("Training",los,step) step = step + 1 if step%100 == 0: print("Training for {0} times".format(step)) total_loss += los print("total_loss is {0}".format(los)) Writer.close() torch.save(vgg_model,"model_vgg.pth")将以上代码修改为二分类
好的,以下是将代码修改为二分类的版本:
```
import torch
import torchvision
from torch.utils.tensorboard import SummaryWriter
from torch import nn, optim
from torch.utils.data import DataLoader
from torchvision.transforms import transforms
from module import MyModule
# 加载数据集
train_transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
train_dataset = torchvision.datasets.CIFAR10(root="../data", train=True, download=True, transform=train_transform)
train_loader = DataLoader(train_dataset, batch_size=128, shuffle=True)
# 构建VGG网络并修改输出层
vgg_model = torchvision.models.vgg16(pretrained=True)
vgg_model.classifier = nn.Sequential(
nn.Linear(25088, 4096),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(4096, 4096),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(4096, 2),
nn.LogSoftmax(dim=1)
)
model = MyModule()
# 设置超参数
epochs = 5
learning_rate = 0.001
optimizer = optim.SGD(vgg_model.parameters(), lr=learning_rate)
loss_fn = nn.NLLLoss()
writer = SummaryWriter(log_dir="Training")
step = 0
# 训练模型
for epoch in range(epochs):
total_loss = 0
for data, labels in train_loader:
optimizer.zero_grad()
output = vgg_model(data)
loss = loss_fn(output, labels)
loss.backward()
optimizer.step()
writer.add_scalar("Training Loss", loss, step)
step += 1
if step % 100 == 0:
print("Training for {0} times".format(step))
total_loss += loss.item()
print("Epoch {0}, total loss: {1}".format(epoch + 1, total_loss))
writer.close()
# 保存模型
torch.save(vgg_model, "model_vgg.pth")
```
在这个版本中,我们使用了`nn.LogSoftmax`作为最后一层的激活函数,并使用`nn.NLLLoss()`作为损失函数。同时,我们将VGG网络的输出层修改为一个具有两个输出节点的线性层。另外,我们还对输入图片进行了大小调整和归一化处理。
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