with open(os.path.join(args.output, 'model.json'), 'w') as f:
f.write(model.to_json())
try:
if args.weights:
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:02d}-{val_loss:.3f}.hdf5'))
early_stop = EarlyStopping(monitor='val_loss', patience=100, 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(df_train, args),
nb_epoch=args.epoch,
samples_per_epoch=args.batch*50,
validation_data=generate_thunderhill_batches(df_val, args),
nb_val_samples=args.batch*10,
callbacks=[
checkpointer,
ModelCheckpoint(os.path.join(args.output, 'model.h5'), monitor='val_loss', save_best_only=True),
early_stop,
logger,
board
]
)
# for X, y in generate_thunderhill_batches(df_train, args):
# print(X.shape, y.shape)
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]
)