从保存的.h5 cnn保存模型加载val_acc和val_损失

Question

我使用Keras构建了一个CNN分类器，绘制了3个时期的验证准确性和验证损失历史，然后使用classifier.save（“name.h5:）保存模型

稍后，我已使用.load（）命令成功加载分类器。但是，我无法重新加载验证精度和验证丢失。有什么方法可以这样做吗

我尝试了evaluate（）函数，但没有用

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

恐怕不行，历史是一个对象，它作为fit（）函数的产物返回。 模型本身不保留此信息，因此不会保存

你能找回历史的唯一方法是，如果你能特别保存它

否则，如果在第一次训练模型时设置了随机种子，也可能会得到相同的结果（历史）。然后你可以用同样的种子重复这个过程，得到同样的结果