vgg5thtraining.py

from keras.optimizers import RMSprop
from keras.callbacks import ModelCheckpoint, EarlyStopping
                   
checkpoint = ModelCheckpoint("facemodel.h5",
                             monitor="val_loss",
                             mode="min",
                             save_best_only = True,
                             verbose=1)

earlystop = EarlyStopping(monitor = 'val_loss', 
                          min_delta = 0, 
                          patience = 3,
                          verbose = 1,
                          restore_best_weights = True)

# we put our call backs into a callback list
callbacks = [earlystop, checkpoint]

# Note we use a very small learning rate 
modelnew.compile(loss = 'categorical_crossentropy',
              optimizer = RMSprop(lr = 0.001),
              metrics = ['accuracy'])

nb_train_samples = 544
nb_validation_samples = 200
epochs = 3
batch_size = 64

history = modelnew.fit_generator(
    train_generator,
    steps_per_epoch = nb_train_samples // batch_size,
    epochs = epochs,
    callbacks = callbacks,
    validation_data = validation_generator,
    validation_steps = nb_validation_samples // batch_size)

modelnew.save("facemodel.h5")