这是我下面给出的代码，我想绘制准确性和损失图

Question

def plot_training(hist):
    '''
    This function take training model and plot history of accuracy and losses with the best epoch in both of them.
    '''

    # Define needed variables
    tr_acc = hist.history['accuracy']
    tr_loss = hist.history['loss']

    val_acc = hist.history['val_accuracy']
    val_loss = hist.history['val_loss']

    index_acc = np.argmax(val_acc)
    acc_highest = val_acc[index_acc]
    index_loss = np.argmin(val_loss)
    val_lowest = val_loss[index_loss]

    Epochs = [i+1 for i in range(len(tr_acc))]

    acc_label = f'best epoch= {str(index_acc + 1)}'
    loss_label = f'best epoch= {str(index_loss + 1)}'

    # Plot training history
    plt.figure(figsize= (20, 20))
    plt.style.use('fivethirtyeight')

    # Training Accuracy
    plt.subplot(2, 2, 1)
    plt.plot(Epochs, tr_acc, 'r', label= 'Training Accuracy')
    plt.plot(Epochs, val_acc, 'g', label= 'Validation Accuracy')
    plt.scatter(index_acc + 1 , acc_highest, s= 150, c= 'blue', label= acc_label)
    plt.title('Training and Validation Accuracy')
    plt.xlabel('Epochs')
    plt.ylabel('Accuracy')
    plt.legend()

    # Training Loss
    plt.subplot(2, 2, 2)
    plt.plot(Epochs, tr_loss, 'r', label= 'Training loss')
    plt.plot(Epochs, val_loss, 'g', label= 'Validation loss')
    plt.scatter(index_loss + 1, val_lowest, s= 150, c= 'blue', label= loss_label)
    plt.title('Training and Validation Loss')
    plt.xlabel('Epochs')
    plt.ylabel('Loss')
    plt.legend()

    plt.tight_layout
    plt.show()

我尝试了不同的代码，我只得到了一张空白图，没有训练验证曲线

Answer 1

固定代码：

import numpy as np
import matplotlib.pyplot as plt

# Mock data to simulate model training
hist = {
    'accuracy': np.random.uniform(0.7, 0.9, 10),
    'loss': np.random.uniform(0.2, 0.4, 10),
    'val_accuracy': np.random.uniform(0.6, 0.8, 10),
    'val_loss': np.random.uniform(0.3, 0.5, 10)
}

# Define needed variables
tr_acc = hist['accuracy']
tr_loss = hist['loss']
val_acc = hist['val_accuracy']
val_loss = hist['val_loss']

index_acc = np.argmax(val_acc)
acc_highest = val_acc[index_acc]
index_loss = np.argmin(val_loss)
val_lowest = val_loss[index_loss]

Epochs = [i+1 for i in range(len(tr_acc))]

acc_label = f'best epoch= {str(index_acc + 1)}'
loss_label = f'best epoch= {str(index_loss + 1)}'

# Plot training history
plt.figure(figsize= (12, 10))
plt.style.use('fivethirtyeight')

# Training Accuracy
plt.subplot(2, 1, 1)
plt.plot(Epochs, tr_acc, 'r', label= 'Training Accuracy')
plt.plot(Epochs, val_acc, 'g', label= 'Validation Accuracy')
plt.scatter(index_acc + 1 , acc_highest, s= 150, c= 'blue', label= acc_label)
plt.title('Training and Validation Accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend()

# Training Loss
plt.subplot(2, 1, 2)
plt.plot(Epochs, tr_loss, 'r', label= 'Training loss')
plt.plot(Epochs, val_loss, 'g', label= 'Validation loss')
plt.scatter(index_loss + 1, val_lowest, s= 150, c= 'blue', label= loss_label)
plt.title('Training and Validation Loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()

plt.tight_layout()
plt.show()