如何使用matplotlib简单代码在pytorch上显示损失和准确性图

Question

我是Pytorch的新手，我想知道如何显示损失和准确图表以及我应该如何存储这些值，因为知道我正在使用Resnet34和101

这是我的代码

def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
since = time.time()

best_model_wts = copy.deepcopy(model.state_dict())
best_acc = 0.0

for epoch in range(num_epochs):
    print(f'Epoch {epoch}/{num_epochs - 1}')
    print('-' * 10)

    # Each epoch has a training and validation phase
    for phase in ['train', 'val']:
        if phase == 'train':
            model.train()  # Set model to training mode
        else:
            model.eval()   # Set model to evaluate mode

        running_loss = 0.0
        running_corrects = 0

        # Iterate over data.
        for inputs, labels in dataloaders[phase]:
            inputs = inputs.to(device)
            labels = labels.to(device)

            # zero the parameter gradients
            optimizer.zero_grad()

            # forward
            # track history if only in train
            with torch.set_grad_enabled(phase == 'train'):
                outputs = model(inputs)
                _, preds = torch.max(outputs, 1)
                loss = criterion(outputs, labels)

                # backward + optimize only if in training phase
                if phase == 'train':
                    loss.backward()
                    optimizer.step()

            # statistics
            running_loss += loss.item() * inputs.size(0)
            running_corrects += torch.sum(preds == labels.data)
        if phase == 'train':
            scheduler.step()

        epoch_loss = running_loss / dataset_sizes[phase]
        epoch_acc = running_corrects.double() / dataset_sizes[phase]

        print(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')

        # deep copy the model
        if phase == 'val' and epoch_acc > best_acc:
            best_acc = epoch_acc
            best_model_wts = copy.deepcopy(model.state_dict())

    print()

time_elapsed = time.time() - since
print(f'Training complete in {time_elapsed // 60:.0f}m {time_elapsed % 60:.0f}s')
print(f'Best val Acc: {best_acc:4f}')

# load best model weights
model.load_state_dict(best_model_wts)
return model

下一个代码

def visualize_model(model, num_images=6):
was_training = model.training
model.eval()
images_so_far = 0
fig = plt.figure()

with torch.no_grad():
    for i, (inputs, labels) in enumerate(dataloaders['val']):
        inputs = inputs.to(device)
        labels = labels.to(device)

        outputs = model(inputs)
        _, preds = torch.max(outputs, 1)

        for j in range(inputs.size()[0]):
            images_so_far += 1
            ax = plt.subplot(num_images//2, 2, images_so_far)
            ax.axis('off')
            ax.set_title(f'predicted: {class_names[preds[j]]}')
            imshow(inputs.cpu().data[j])

            if images_so_far == num_images:
                model.train(mode=was_training)
                return
    model.train(mode=was_training)

** plese为我提供了使用matplotlib **的损失和准确性的代码。

Answer 1

您应该创建两个列表，以节省所有时期的损失和准确性，以稍后绘制它们。

    def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
    since = time.time()

    best_model_wts = copy.deepcopy(model.state_dict())
    best_acc = 0.0
    epoch_losses= [] 
    epoch_accuracies =[]

    epoch_loss = running_loss / dataset_sizes[phase]
    epoch_losses.append(epoch_loss)

    epoch_acc = running_corrects.double() / dataset_sizes[phase]
    epoch_accuracies.append(epoch_acc)

    import matplotlib.pyplot as plt
    plt.plot(np.arange(len(epoch_losses)), epoch_losses, 'r')
    plt.xlabel('Epoch')
    plt.ylabel('Loss')
    plt.show()

    plt.plot(np.arange(len(epoch_accuracies )), epoch_accuracies , 'b')
    plt.xlabel('Epoch')
    plt.ylabel('Accuracy')
    plt.show()