加载经过训练的 Keras 模型并继续训练

本文介绍了加载经过训练的 Keras 模型并继续训练的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述

我想知道是否可以保存部分训练的 Keras 模型并在再次加载模型后继续训练.

I was wondering if it was possible to save a partly trained Keras model and continue the training after loading the model again.

这样做的原因是我以后会有更多的训练数据，我不想再次重新训练整个模型.

The reason for this is that I will have more training data in the future and I do not want to retrain the whole model again.

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:添加了完整的工作示例

对于 10 个 epoch 后的第一个数据集，最后一个 epoch 的损失为 0.0748，准确度为 0.9863.

With the first dataset after 10 epochs the loss of the last epoch will be 0.0748 and the accuracy 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率将分别为 0.1711 和 0.9504.

After saving, deleting and reloading the model the loss and accuracy of the model trained on the second dataset will be 0.1711 and 0.9504 respectively.

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?

Is this caused by the new training data or by a completely re-trained model?

""" Model by: machinelearningmastery/ """ # 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')) modelpile(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 评论

对于 tensorflow.keras，在模型拟合中将参数 nb_epochs 更改为 epochs.导入和基础模型函数是:

For tensorflow.keras change the parameter nb_epochs to epochs in the model fit. The imports and basemodel function are:

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')) modelpile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) return model

推荐答案

实际上 - model.save 保存了在您的案例中重新开始训练所需的所有信息.重新加载模型可能会破坏的唯一事情是您的优化器状态.要检查 - 尝试 save 并重新加载模型并在训练数据上对其进行训练.

Actually - model.save saves all information need for restarting training in your case. The only thing which could be spoiled by reloading model is your optimizer state. To check that - try to save and reload model and train it on training data.