print('Loading weights from file ...') model.load_weights(args.weights) except IOError: print("No model found") checkpointer = ModelCheckpoint(os.path.join( args.output, 'weights.{epoch:04d}-{val_loss:.3f}.hdf5'), monitor='val_loss', verbose=0, save_best_only=True, save_weights_only=False, mode='auto', period=1) # early_stop = EarlyStopping(monitor='val_loss', patience=50, verbose=0, mode='auto') logger = CSVLogger(filename=os.path.join(args.output, 'history.csv')) board = TensorBoard(log_dir=args.output, histogram_freq=0, write_graph=True, write_images=True) history = model.fit_generator( generate_thunderhill_batches(genAll(args.dataset), args.batch), nb_epoch=args.epoch, samples_per_epoch=50 * args.batch, validation_data=generate_thunderhill_batches(genSim001(args.dataset), args.batch), nb_val_samples=5 * args.batch, callbacks=[checkpointer, logger, board] #, early_stop] )
print('Loading weights from file ...') model.load_weights(args.weights) except IOError: print("No model found") checkpointer = ModelCheckpoint(os.path.join( args.output, 'weights.{epoch:04d}-{val_loss:.3f}.hdf5'), monitor='val_loss', verbose=0, save_best_only=True, save_weights_only=False, mode='auto', period=1) # early_stop = EarlyStopping(monitor='val_loss', patience=50, verbose=0, mode='auto') logger = CSVLogger(filename=os.path.join(args.output, 'history.csv')) board = TensorBoard(log_dir=args.output, histogram_freq=0, write_graph=True, write_images=True) history = model.fit_generator( generate_thunderhill_batches_time(genSim003(args.dataset), args.batch), nb_epoch=args.epoch, samples_per_epoch=args.batch * 50, validation_data=generate_thunderhill_batches_time( genSim001(args.dataset), args.batch), nb_val_samples=args.batch * 5, callbacks=[checkpointer, logger, board] #, early_stop] )