Kerasでmodel学習のhistory結果をグラブ表示する方法

参考にさせてもらいました↓(書籍「PythonとKerasによるディープラーニング」より)
https://github.com/fchollet/deep-learning-with-python-notebooks/blob/master/5.3-using-a-pretrained-convnet.ipynb

表示用のメソッド

compare_TV.py

    def compare_TV(history):
        import matplotlib.pyplot as plt
        
        # Setting Parameters
        acc = history.history['acc']
        val_acc = history.history['val_acc']
        loss = history.history['loss']
        val_loss = history.history['val_loss']

        epochs = range(len(acc))
        
        # 1) Accracy Plt
        plt.plot(epochs, acc, 'bo' ,label = 'training acc')
        plt.plot(epochs, val_acc, 'b' , label= 'validation acc')
        plt.title('Training and Validation acc')
        plt.legend()

        plt.figure()
        
        # 2) Loss Plt
        plt.plot(epochs, loss, 'bo' ,label = 'training loss')
        plt.plot(epochs, val_loss, 'b' , label= 'validation loss')
        plt.title('Training and Validation loss')
        plt.legend()

        plt.show()

実行するとき。

from keras import models 
from keras import layers
from keras import optimizers

model = models.Sequential()
##################### 
# modelの詳細は省略  #
#####################
# declear History
history = model.fit(train_features, train_labels,
                    epochs=30,
                    batch_size=20,
                    validation_data=(validation_features, validation_labels)
                   )
#show Accuracy and Loss History
compare_TV(history)

結果はこのようになります。