history.history.keys() plt.plot(history.epoch, history.history.get('accuracy'), label='accuracy') plt.plot(history.epoch, history.history.get('val_accuracy'), label='val_accuracy') plt.legend()

`history.history.keys()`返回一个包含历史记录中所有指标的列表。这些指标可能包括训练损失、训练准确率、验证损失和验证准确率等。

`plt.plot(history.epoch, history.history.get('accuracy'), label='accuracy')`使用matplotlib库绘制了训练准确率（accuracy）随时间的变化曲线。`history.epoch`表示训练的迭代次数，`history.history.get('accuracy')`获取了训练过程中记录的准确率的数值。`label='accuracy'`指定了曲线的标签为'accuracy'。

同样地，`plt.plot(history.epoch, history.history.get('val_accuracy'), label='val_accuracy')`绘制了验证准确率（val_accuracy）随时间的变化曲线。

最后，通过调用`plt.legend()`方法添加图例，以便区分训练准确率和验证准确率的曲线。

这段代码用于可视化训练过程中准确率的变化情况，可以帮助我们分析模型的训练效果和过拟合情况。

相关问题

plt.plot(history.history['accuracy'])

As an AI language model, I don't have access to the full context of your code. However, based on the function name plt.plot(), it is likely that you are trying to plot the accuracy of a model during training using the history object.

The history object is typically returned by the fit() method in Keras and contains information about the training process such as the loss and accuracy at each epoch.

The code snippet you provided is plotting the accuracy values over each epoch of the training process. This can be useful for visualizing how the accuracy of the model changes over time and identifying any overfitting or underfitting issues.

plt.plot(history.history['val_accuracy'], label = 'val_accuracy')什么意思

？这是一个使用Python中matplotlib库绘制折线图的代码。其中，history.history['val_accuracy']代表模型训练过程中每个epoch的验证集准确率，plt.plot()用于将这些准确率绘制出来，并给图例命名为'val_accuracy'。