Loading val_acc and val_loss from a saved .h5 cnn saved model

Question

I have built a CNN classifier using Keras, plot the history of the validation accuracy and validation loss over 3 epochs and then saved the model using classifier.save("name.h5:).

I have loaded the classifier with .load() command successfully later on. However, I cannot reload the validation accuraccy and validation loss. Is there any way to do it?

I tried evaluate() function but no use.

from keras.models import Sequential
from keras.layers import Conv2D,Activation,MaxPooling2D,Dense,Flatten,Dropout
import numpy as np
from keras.preprocessing.image import ImageDataGenerator
from IPython.display import display
import matplotlib.pyplot as plt
from PIL import Image
from keras.models import load_model
from sklearn.metrics import classification_report, confusion_matrix

classifier = Sequential()
classifier.add(Conv2D(32,(3,3),input_shape=(64,64,3)))
classifier.add(Activation('relu'))
classifier.add(MaxPooling2D(pool_size =(2,2)))
classifier.add(Conv2D(32,(3,3)))
classifier.add(Activation('relu'))
classifier.add(MaxPooling2D(pool_size =(2,2)))
classifier.add(Conv2D(64,(3,3)))
classifier.add(Activation('relu'))
classifier.add(MaxPooling2D(pool_size =(2,2)))
classifier.add(Flatten())
classifier.add(Dense(64))
classifier.add(Activation('relu'))
classifier.add(Dropout(0.5))
classifier.add(Dense(2))
classifier.add(Activation('softmax'))
classifier.summary()
classifier.compile(optimizer ='rmsprop',
                   loss ='categorical_crossentropy',
                   metrics =['accuracy'])
train_datagen = ImageDataGenerator(rescale =1./255,
                                   shear_range =0.2,
                                   zoom_range = 0.2,
                                   horizontal_flip =True)
test_datagen = ImageDataGenerator(rescale = 1./255)

batchsize=60
training_set = train_datagen.flow_from_directory('/home/osboxes/Downloads/Downloads/dogs-vs-cats/train/',
                                                target_size=(64,64),
                                                batch_size= batchsize,
                                                class_mode='categorical')

test_set = test_datagen.flow_from_directory('/home/osboxes/Downloads/Downloads/dogs-vs-cats/test/',
                                           target_size = (64,64),
                                           batch_size = batchsize,
                       shuffle=False,
                                           class_mode ='categorical')
history=classifier.fit_generator(training_set,
                        steps_per_epoch =9000 // batchsize,
                        epochs = 3,
                        validation_data =test_set,
                        validation_steps = 4500 // batchsize)

classifier.save('my_model3.h5')
Y_pred = classifier.predict_generator(test_set, steps=4500 // batchsize)
y_pred = np.argmax(Y_pred, axis=1)
print('Confusion Matrix')
print(confusion_matrix(test_set.classes, y_pred))
print('Classification Report')
target_names = test_set.classes
class_labels = list(test_set.class_indices.keys()) 
target_names = ['cats', 'dogs'] 
report = classification_report(test_set.classes, y_pred, target_names=class_labels)
print(report) 

# summarize history for accuracy
#plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['test'], loc='upper left')
plt.show()
# summarize history for loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()

Answer 1

I am afraid not, History is an object that gets return as a product of the fit() function. The model itself doesn't keep this information and thus is not saved.

The only way you could have gotten the History back is if you would have saved it specially.

Otherwise, you might also get the same result(History) if when you first trained your model you set a random seed. Then you can repeat the process with the same seed and get the same result.