我如何在每个时期保存检查点并加载随机保存的检查点以继续训练

Question

Can you help me on my code to: save the model (architecture and weights) every epoch, and how I can continue to training my model from the 5th checkpoint like training epoch from 1 to 25 without making checkpoints(5th model I have saved).

classifier = Sequential()

classifier.add(Conv2D(6, (3, 3), input_shape = (30, 30, 3), data_format="channels_last", activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))

classifier.add(Conv2D(6, (3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))

classifier.add(Flatten())

classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dense(units = 64, activation = 'relu'))
classifier.add(Dense(units = 1, activation = 'sigmoid'))

opt = Adam(learning_rate = 0.001, beta_1 = 0.9, beta_2 = 0.999, epsilon = 1e-08, decay = 0.0)
classifier.compile(optimizer = opt, loss = 'binary_crossentropy', metrics = ['accuracy', precision, recall, fmeasure])

from keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(rescale = 1./255,
                                   horizontal_flip = True,
                                   vertical_flip = True,
                                   rotation_range = 180)

validation_datagen = ImageDataGenerator(rescale = 1./255)

training_set = train_datagen.flow_from_directory('/home/dataset/training_set',
                                                 target_size = (30, 30),
                                                 batch_size = 32,
                                                 class_mode = 'binary')

validation_set = validation_datagen.flow_from_directory('/home/dataset/validation_set',
                                                        target_size = (30, 30),
                                                        batch_size = 32,
                                                        class_mode = 'binary')

history = classifier.fit_generator(training_set,
                                   steps_per_epoch = 208170,
                                   epochs = 15,
                                   validation_data = validation_set,
                                   validation_steps = 89140)

Answer 1

I'm assuming you mean you want to save your model and weights after every epoch and then at a later stage, load the model and weights saved after the fifth epoch.

You can use the SaveModel format in TensorFlow in general like this:

classifier.save()

This will save the architecture, weights, info regarding the optimizer and the configurations you set up in compile()

Since you're using fit_generator, you can just use ModelCheckpoint() to save your models like this:

from keras.callbacks import ModelCheckpoint

checkpoint = ModelCheckpoint(path_to_save_to, save_freq = 'epoch', 
                             save_weights_only = False)

history = classifier.fit_generator(training_set,
                                   steps_per_epoch = 208170,
                                   epochs = 15,
                                   validation_data = validation_set,
                                   validation_steps = 89140,
                                    callbacks = [checkpoint])

You can format the path so that it will save the model with epoch/loss details like this path_name + '-{epoch:02d}-{val_loss:.2f}.h5'

To load the fifth checkpoint, do this:

from keras.models import load_model
classifier = load_model(path_to_fifth_checkpoint)