print('Epoch: ', epoch, '| train loss: %.4f' % loss.data.cpu().numpy(), '| test accuracy: %.2f' % accuracy)

这段代码是用来在训练过程中打印每个epoch的训练损失和测试准确率的。其中，epoch表示当前训练的轮数，loss.data.cpu().numpy()表示当前epoch的训练损失，accuracy表示当前epoch的测试准确率。通过'%.4f' % loss.data.cpu().numpy()和'%.2f' % accuracy将损失和准确率的值格式化为浮点数，并进行四舍五入保留4位和2位小数。最终输出的结果为：Epoch: X | train loss: Y | test accuracy: Z，其中X为当前训练的轮数，Y为当前epoch的训练损失，Z为当前epoch的测试准确率。

相关问题

我想让MTCNN代码运行后输出准确率折线图该怎样修改代码for epoch in range(epoch_num): for batch_id, (img, label, bbox, landmark) in enumerate(train_loader): img = img.to(device) label = label.to(device).long() bbox = bbox.to(device) landmark = landmark.to(device) class_out, bbox_out, landmark_out = model(img) cls_loss = class_loss(class_out, label) box_loss = bbox_loss(bbox_out, bbox, label) landmarks_loss = landmark_loss(landmark_out, landmark, label) total_loss = radio_cls_loss * cls_loss + radio_bbox_loss * box_loss + radio_landmark_loss * landmarks_loss optimizer.zero_grad() total_loss.backward() optimizer.step() if batch_id % 100 == 0: acc = accuracy(class_out, label) print('[%s] Train epoch %d, batch %d, total_loss: %f, cls_loss: %f, box_loss: %f, landmarks_loss: %f, ' 'accuracy：%f' % (datetime.now(), epoch, batch_id, total_loss, cls_loss, box_loss, landmarks_loss, acc)) scheduler.step()

要在 MTCNN 代码运行后输出准确率折线图，可以使用 Matplotlib 库绘制折线图，并在训练过程中保存每个 epoch 的准确率和损失值，最后将其绘制成折线图。

具体来说，可以按照以下步骤进行修改：

1. 在代码开头引入 Matplotlib 库，并定义一个空列表来保存每个 epoch 的准确率和损失值，例如：

import matplotlib.pyplot as plt

epoch_acc = []
epoch_loss = []

2. 在训练过程中，在每个 epoch 结束后计算当前 epoch 的准确率和损失值，并将其添加到列表中，例如：

if batch_id % 100 == 0:
    acc = accuracy(class_out, label)
    epoch_acc.append(acc)
    epoch_loss.append(total_loss)

    print('[%s] Train epoch %d, batch %d, total_loss: %f, cls_loss: %f, box_loss: %f, landmarks_loss: %f, '
          'accuracy：%f' % (datetime.now(), epoch, batch_id, total_loss, cls_loss, box_loss, landmarks_loss, acc))

3. 在训练结束后，使用 Matplotlib 库绘制准确率和损失值的折线图，例如：

plt.plot(range(1, epoch_num + 1), epoch_acc, label='Accuracy')
plt.plot(range(1, epoch_num + 1), epoch_loss, label='Loss')
plt.xlabel('Epoch')
plt.legend()
plt.show()

这样，就可以在训练过程中保存准确率和损失值，并最终将其绘制成折线图。需要注意的是，Matplotlib 库需要单独安装，并且在绘图前需要先将数据转化为 NumPy 数组。

epoch in range(epoch_num): for batch_id, (img, label, bbox, landmark) in enumerate(train_loader): img = img.to(device) label = label.to(device).long() bbox = bbox.to(device) landmark = landmark.to(device) class_out, bbox_out, landmark_out = model(img) cls_loss = class_loss(class_out, label) box_loss = bbox_loss(bbox_out, bbox, label) landmarks_loss = landmark_loss(landmark_out, landmark, label) total_loss = radio_cls_loss * cls_loss + radio_bbox_loss * box_loss + radio_landmark_loss * landmarks_loss optimizer.zero_grad() total_loss.backward() optimizer.step() if batch_id % 100 == 0: acc = accuracy(class_out, label) print('[%s] Train epoch %d, batch %d, total_loss: %f, cls_loss: %f, box_loss: %f, landmarks_loss: %f, ' 'accuracy：%f' % (datetime.now(), epoch, batch_id, total_loss, cls_loss, box_loss, landmarks_loss, acc)) scheduler.step()我想绘制cls_loss, box_loss, landmarks_loss, acc随自变量变化的图像，那自变量选什么比较合适，帮我修改成完整的代码

自变量应该选取 epoch，因为这是训练的迭代次数。下面是修改后的完整代码：

import matplotlib.pyplot as plt
import numpy as np
from datetime import datetime

# define your train_loader, model, optimizer, scheduler, etc.

# define the lists to store the training statistics
train_losses = []
train_cls_losses = []
train_box_losses = []
train_landmarks_losses = []
train_accs = []

epoch_num = 10
radio_cls_loss = 1.0
radio_bbox_loss = 1.0
radio_landmark_loss = 1.0

device = 'cuda' if torch.cuda.is_available() else 'cpu'

for epoch in range(epoch_num):
    for batch_id, (img, label, bbox, landmark) in enumerate(train_loader):
        img = img.to(device)
        label = label.to(device).long()
        bbox = bbox.to(device)
        landmark = landmark.to(device)
        class_out, bbox_out, landmark_out = model(img)
        cls_loss = class_loss(class_out, label)
        box_loss = bbox_loss(bbox_out, bbox, label)
        landmarks_loss = landmark_loss(landmark_out, landmark, label)
        total_loss = radio_cls_loss * cls_loss + radio_bbox_loss * box_loss + radio_landmark_loss * landmarks_loss
        optimizer.zero_grad()
        total_loss.backward()
        optimizer.step()
        if batch_id % 100 == 0:
            acc = accuracy(class_out, label)
            print('[%s] Train epoch %d, batch %d, total_loss: %f, cls_loss: %f, box_loss: %f, landmarks_loss: %f, '
                  'accuracy：%f' % (datetime.now(), epoch, batch_id, total_loss, cls_loss, box_loss, landmarks_loss, acc))
            # append the training statistics to the lists
            train_losses.append(total_loss.item())
            train_cls_losses.append(cls_loss.item())
            train_box_losses.append(box_loss.item())
            train_landmarks_losses.append(landmarks_loss.item())
            train_accs.append(acc.item())
    scheduler.step()

# plot the training statistics
x = np.arange(epoch_num)
plt.plot(x, train_losses, label='total_loss')
plt.plot(x, train_cls_losses, label='cls_loss')
plt.plot(x, train_box_losses, label='box_loss')
plt.plot(x, train_landmarks_losses, label='landmarks_loss')
plt.plot(x, train_accs, label='accuracy')
plt.legend()
plt.show()