tensorboard可视化

• Tensorboard导入与可视化图片
• 模型网络结构的可视化
• 标量数据的可视化

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以手写数字分类mnist数据集为例：
下载mnist数据集，构造dataset：

train_ds = datasets.MNIST(
                          'data/',
                          train=True,
                          transform=transformation,
                          download=True  
)
test_ds = datasets.MNIST(
                          'data/',
                          train=False,
                          transform=transformation,
                          download=True  
)

查看mnist数据集包含的图片：

def imshow(img):
    npimg = img.numpy()
    npimg = np.squeeze(npimg)
    plt.imshow(npimg)
plt.figure(figsize=(10, 1))
for i, img in enumerate(imgs[:10]):
    plt.subplot(1, 10, i+1)
    imshow(img)

导入tensorboard：
可视化两步骤：
1.在代码中将需要可视化的数据写入磁盘
2.在命令行中打开tensorboard，并指定写入的文件位置，进行可视化

from torch.utils.tensorboard import SummaryWriter

writer = SummaryWriter('my_log/mnist')  # 指定写入位置

显示图片：

images, labels = next(iter(train_dl))

# create grid of images
img_grid = torchvision.utils.make_grid(images[-8:]) # 将多张图片合并在一起成一张图片
npimg = img_grid.permute(1, 2, 0).numpy()
plt.imshow(npimg)

写入图片到tensorboard：

writer.add_image('eight_mnist_images', img_grid)

在命令行窗口输入以下指令：

tensorboard --logdir=D:\PycharmProjects\PythonScript\Pytorch_Course_Study\my_log

打开给出的网址：http://localhost:6006/
查看到动态显示的图片：

模型网络结构的可视化

创建模型：

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 6, 5)   
        self.pool = nn.MaxPool2d((2, 2))
        self.conv2 = nn.Conv2d(6, 16, 5) 
        self.liner_1 = nn.Linear(16*4*4, 256)
        self.liner_2 = nn.Linear(256, 10)
    def forward(self, input):
        x = F.relu(self.conv1(input))
        x = self.pool(x)
        x = F.relu(self.conv2(x))
        x = self.pool(x)
#        print(x.size())    # torch.Size([64, 16, 4, 4])
        x = x.view(-1, 16*4*4)
        x = F.relu(self.liner_1(x))
        x = self.liner_2(x)
        return x

model = Model()

显示模型：

writer.add_graph(model, images) # images为输入模型的数据

在tensorboard中查看模型：

双击model可查看模型内部结构：

标量数据的可视化

动态显示训练过程中的loss 和 acc的变化：

model.to(device)
loss_fn = torch.nn.CrossEntropyLoss()  # 损失函数

使用write.add_scalar()方法：

def fit(epoch, model, trainloader, testloader):
    correct = 0
    total = 0
    running_loss = 0
    for x, y in trainloader:
        x, y = x.to(device), y.to(device)
        y_pred = model(x)
        loss = loss_fn(y_pred, y)
        optim.zero_grad()
        loss.backward()
        optim.step()
        with torch.no_grad():
            y_pred = torch.argmax(y_pred, dim=1)
            correct += (y_pred == y).sum().item()
            total += y.size(0)
            running_loss += loss.item()
        
    epoch_loss = running_loss / len(trainloader.dataset)
    epoch_acc = correct / total
    
    writer.add_scalar('training loss',
                        epoch_loss,
                        epoch)
        
        
    test_correct = 0
    test_total = 0
    test_running_loss = 0 
    
    with torch.no_grad():
        for x, y in testloader:
            x, y = x.to(device), y.to(device)
            y_pred = model(x)
            loss = loss_fn(y_pred, y)
            y_pred = torch.argmax(y_pred, dim=1)
            test_correct += (y_pred == y).sum().item()
            test_total += y.size(0)
            test_running_loss += loss.item()
    
    epoch_test_loss = test_running_loss / len(testloader.dataset)
    epoch_test_acc = test_correct / test_total
    
    writer.add_scalar('test loss',
                        epoch_test_loss,
                        epoch)
    
        
    print('epoch: ', epoch, 
          'loss： ', round(epoch_loss, 3),
          'accuracy:', round(epoch_acc, 3),
          'test_loss： ', round(epoch_test_loss, 3),
          'test_accuracy:', round(epoch_test_acc, 3)
             )
        
    return epoch_loss, epoch_acc, epoch_test_loss, epoch_test_acc

optim = torch.optim.Adam(model.parameters(), lr=0.001)
epochs = 20
train_loss = []
train_acc = []
test_loss = []
test_acc = []

for epoch in range(epochs):
    epoch_loss, epoch_acc, epoch_test_loss, epoch_test_acc = fit(epoch,
                                                                 model,
                                                                 train_dl,
                                                                 test_dl)
    train_loss.append(epoch_loss)
    train_acc.append(epoch_acc)
    test_loss.append(epoch_test_loss)
    test_acc.append(epoch_test_acc)

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