装载一个训练有素的克拉斯模型并继续训练

我想知道是否有可能保存一个部分训练的克拉斯模型，并继续训练后再次装载模型。

原因是，我将有更多的训练数据在未来，我不想再次训练整个模型。

我使用的功能是:

#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: 添加完整的工作示例

对于10个纪元后的第一个数据集，最后一个纪元的损失为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丢失率和准确率分别为0.1711和0.9504。

这是由新的训练数据引起的，还是由一个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: tensorflow. keras 备注

对于 tensorflow. keras，在模型拟合中将参数 nb _ epochs 更改为 epochs:

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