Loading a trained Keras model and continue training
I was wondering if it was possible to save a partly trained Keras model and continue the training after loading the model again.
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.
The functions which I am using are:
#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)
Edit 1: added fully working example
With the first dataset after 10 epochs the loss of the last epoch will be 0.0748 and the accuracy 0.9863.
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.
Is this caused by the new training data or by a completely re-trained model?
""" 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 * X_train.shape X_train = X_train.reshape(X_train.shape, num_pixels).astype('float32') X_test = X_test.reshape(X_test.shape, 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 # 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*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*100))
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.
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