我的图没有显示微调和传递学习的正确结果，它是在每幅图中显示单行，而不是两行。

Question

acc += history_fine.history['accuracy']

val_acc += history_fine.history['val_accuracy']

loss += history_fine.history['loss']

val_loss += history_fine.history['val_loss']

plt.figure(figsize=(8, 8))
plt.subplot(2, 1, 1)
plt.plot(acc, label='Training Accuracy')
plt.plot(val_acc, label='Validation Accuracy')
plt.ylim([0.8, 1])
plt.plot([initial_epochs-1,initial_epochs-1],
          plt.ylim(), label='Start Fine Tuning')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')

plt.subplot(2, 1, 2)
plt.plot(loss, label='Training Loss')

plt.plot(val_loss, label='Validation Loss')
plt.ylim([0, 1.0])
plt.plot([initial_epochs-1,initial_epochs-1],
         plt.ylim(), label='Start Fine Tuning')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.xlabel('epoch')
plt.show()

Answer 1

根据这些数据，我从你的评论中提取：

initial_epochs = 2.0
val_acc = [0.6486, 0.6486, 0.6486]
acc = [0.7000, 0.7000, 0.7000]
loss = [0.6015, 0.5935, 0.4653]
val_loss = [0.6964, 0.5359, 0.6738]

我可以用：

plt.figure(figsize=(8, 8))
plt.subplot(2, 1, 1)
plt.plot(acc, label='Training Accuracy')
plt.plot(val_acc, label='Validation Accuracy')

plt.ylim([0.4, 1]) # You had plt.ylim([0.8, 1])
plt.plot([initial_epochs-1, initial_epochs-1], plt.ylim(), label='Start Fine Tuning')
plt.legend(loc='lower right')
plt.grid() # I added a grid for both plots
plt.title('Training and Validation Accuracy')

plt.subplot(2, 1, 2)
plt.plot(loss, label='Training Loss')
plt.plot(val_loss, label='Validation Loss')

plt.ylim([0, 1.0])
plt.plot([initial_epochs-1, initial_epochs-1], plt.ylim(), label='Start Fine Tuning')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.xlabel('epoch')
plt.grid() # I added a grid for both plots
plt.show()

我假设(根据我所拥有的数据)问题是由plt.ylim([0.8, 1])引起的，因为val_acc和acc都是< 0.8，这意味着轴限值集中在没有数据的地块上。

请参见使用plt.ylim([0.4, 1])的下列情节：