verbose=0,
mode='auto')
ts = calendar.timegm(time.gmtime())
checkpointer = ModelCheckpoint(filepath=cnn_first_phase_folder +
str(ts) + 'best_weights.hdf5',
verbose=0,
save_best_only=True) # save best model
model.fit_generator(train_img_class_gen,
steps_per_epoch=steps_per_small_epoch,
epochs=small_epochs,
verbose=2,
validation_data=val_img_class_gen,
validation_steps=val_steps_per_small_epoch,
workers=4,
callbacks=[monitor, checkpointer])
if i % saves_per_epoch == 0:
print('{} epoch completed'.format(int(i / saves_per_epoch)))
best_model.load_weights(cnn_first_phase_folder + str(ts) +
'best_weights.hdf5')
best_model.save(cnn_first_phase_folder + str(ts) + 'best_models.h5')
best_model.save(cnn_folder + 'cnn_first_phase.h5')
if __name__ == '__main__':
train_img_class_gen, val_img_class_gen = make_generators(isPlain=True)
cnn(trained=False)
workers=4,
callbacks=[tensorboard])
# history = new_inception_model.fit_generator(train_img_class_gen,
# steps_per_epoch=steps_per_small_epoch,
# epochs=small_epochs, verbose=2,
# validation_data=val_img_class_gen, validation_steps=val_steps_per_small_epoch,
# workers=4, callbacks=[LosswiseKerasCallback(tag='keras inception model')])
print("iteration", i)
if i % saves_per_epoch == 0:
print('{} epoch completed'.format(int(i / saves_per_epoch)))
if i >= 5:
ts = calendar.timegm(time.gmtime())
new_inception_model.save(third_phase_folder + str(ts) +
'_inception_model.h5')
save_obj(history.history,
str(ts) + '_inception_history.h5',
folder=third_phase_folder)
new_inception_model.save(data_folder + '3rd_phase_inception_model.h5')
if __name__ == '__main__':
train_img_class_gen, val_img_class_gen = make_generators(isSimple=True)
# iinception(trained=True)
# first_phase(trained=False, printGap=False, first_phase_train_reps=10)
# second_phase()
third_phase(trained=False, third_phase_train_reps=10)
# second_second_phase(trained=True)
# continue_second(trained=True)