我在想是否可以保存部分训练好的Keras模型，然后在加载该模型后继续训练。
原因是我将来会有更多的训练数据，而我不想重新训练整个模型。
我正在使用的功能是：
#Partly train model
model.fit(first_training, first_classes, batch_size=32, nb_epoch=20)
#Save partly trained model
model.save('partly_trained.h5')
#Load partly trained model
from keras.models import load_model
model = load_model('partly_trained.h5')
#Continue training
model.fit(second_training, second_classes, batch_size=32, nb_epoch=20)
编辑1：添加了完全可工作的示例
在第一个数据集上进行10个时期后，最后一个时期的损失将为0.0748，准确度为0.9863。
保存、删除和重新加载模型后，在第二个数据集上训练的模型的损失和准确度分别为0.1711和0.9504。
这是由新的训练数据引起的还是由完全重新训练的模型引起的？
"""
Model by: http://machinelearningmastery.com/
"""
# load (downloaded if needed) the MNIST dataset
import numpy
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense
from keras.utils import np_utils
from keras.models import load_model
numpy.random.seed(7)
def baseline_model():
model = Sequential()
model.add(Dense(num_pixels, input_dim=num_pixels, init='normal', activation='relu'))
model.add(Dense(num_classes, init='normal', activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
if __name__ == '__main__':
# load data
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# flatten 28*28 images to a 784 vector for each image
num_pixels = X_train.shape[1] * X_train.shape[2]
X_train = X_train.reshape(X_train.shape[0], num_pixels).astype('float32')
X_test = X_test.reshape(X_test.shape[0], num_pixels).astype('float32')
# normalize inputs from 0-255 to 0-1
X_train = X_train / 255
X_test = X_test / 255
# one hot encode outputs
y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)
num_classes = y_test.shape[1]
# build the model
model = baseline_model()
#Partly train model
dataset1_x = X_train[:3000]
dataset1_y = y_train[:3000]
model.fit(dataset1_x, dataset1_y, nb_epoch=10, batch_size=200, verbose=2)
# Final evaluation of the model
scores = model.evaluate(X_test, y_test, verbose=0)
print("Baseline Error: %.2f%%" % (100-scores[1]*100))
#Save partly trained model
model.save('partly_trained.h5')
del model
#Reload model
model = load_model('partly_trained.h5')
#Continue training
dataset2_x = X_train[3000:]
dataset2_y = y_train[3000:]
model.fit(dataset2_x, dataset2_y, nb_epoch=10, batch_size=200, verbose=2)
scores = model.evaluate(X_test, y_test, verbose=0)
print("Baseline Error: %.2f%%" % (100-scores[1]*100))
编辑2：关于tensorflow.keras的说明
对于tensorflow.keras，请在模型拟合中将参数nb_epochs更改为epochs。导入和basemodel函数如下：
import numpy
from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.models import load_model
numpy.random.seed(7)
def baseline_model():
model = Sequential()
model.add(Dense(num_pixels, input_dim=num_pixels, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model