log_tensorboard=True参数
时间: 2023-08-17 08:06:44 浏览: 52
log_tensorboard=True参数是指在训练模型时是否将日志信息写入Tensorboard。如果将log_tensorboard参数设置为True,那么在模型的编译和训练过程中会生成相应的日志文件,这些日志文件可以用于可视化模型的性能和训练过程。具体来说,可以使用tensorboard命令打开Tensorboard,并指定日志文件的路径,如"tensorboard --logdir logs/fit"。在Tensorboard中,可以查看模型的损失函数、准确率等指标的变化情况,以及可视化训练数据的图像等信息。\[1\]
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相关问题
tbCallBack = TensorBoard(log_dir='./logs/logs_{}'.format(cnt), # log ?????? histogram_freq=0, # ?????¡ì??????¨¦????????epoch????????????????????????0???????????? # batch_size=32, # ??¡§?¡è??¡è¡ì¨¦???????¡ã?????????????????? write_graph=True, # ???????????¡§??????????????? write_grads=True, # ??????????¡ì??????????????????? write_images=True,# ??????????¡ì?????????? embeddings_freq=0, embeddings_layer_names=None, embeddings_metadata=None)
这是一段使用TensorBoard的代码,用于可视化模型的训练过程和结果。log_dir参数指定了日志文件的存放路径,histogram_freq参数指定了记录直方图的频率,batch_size参数指定了输入数据的批大小,write_graph参数指定是否将模型的图结构写入日志文件中,write_grads参数指定是否记录梯度信息,write_images参数指定是否记录模型的图像信息,embeddings_freq参数指定记录嵌入向量的频率,embeddings_layer_names参数指定哪些层的嵌入向量需要记录,embeddings_metadata参数指定嵌入向量的元数据。
# 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。