180304 keras中图像化查看模型训练过程中的acc+loss+val_acc+val_loss

• 第一步

# define the function
def training_vis(hist):
    loss = hist.history['loss']
    val_loss = hist.history['val_loss']
    acc = hist.history['acc']  # new version => hist.history['accuracy'] 
    val_acc = hist.history['val_acc'] #=> hist.history['val_accuracy'] 
    
    # make a figure
    fig = plt.figure(figsize=(8,4))
    # subplot loss
    ax1 = fig.add_subplot(121)
    ax1.plot(loss,label='train_loss')
    ax1.plot(val_loss,label='val_loss')
    ax1.set_xlabel('Epochs')
    ax1.set_ylabel('Loss')
    ax1.set_title('Loss on Training and Validation Data')
    ax1.legend()
    # subplot acc
    ax2 = fig.add_subplot(122)
    ax2.plot(acc,label='train_acc')
    ax2.plot(val_acc,label='val_acc')
    ax2.set_xlabel('Epochs')
    ax2.set_ylabel('Accuracy')
    ax2.set_title('Accuracy  on Training and Validation Data')
    ax2.legend()
    plt.tight_layout()

• 第二步

# train the model
hist = model.fit(...)

• 第三步

# call the function
training_vis(hist)