def test():
datamanager = CT('CT_seq', train_ratio=0.8, expand_dim=3, seed=0)
autoencoder = AutoEncoder(2, 128, 'AE')
trainer = Trainer(64, datamanager, autoencoder, version='cnn', save_path='save/cnn_ae/cnn/', gpu='2')
trainer.train(301) #
# trainer.load_model()
trainer.cal_loss()
def gru_test():
datamanager = CT('CT_seq', train_ratio=0.8, expand_dim=None, seed=0)
autoencoder = AutoEncoder(2, 64, 'gru', [128], [128], 'AE')
trainer = Trainer(64, datamanager, autoencoder, version='test', save_path='save/rnn_ae/gru/', gpu='1')
# trainer.train(301)
trainer.load_model()
trainer.cal_loss()
def lstm_test():
datamanager = CT('CT_seq', train_ratio=0.8, expand_dim=3, seed=0)
autoencoder = AutoEncoder(2, 64, 'lstm', [128], [128], 'AE')
trainer = Trainer(64,
datamanager,
autoencoder,
version='vae',
save_path='save/crnn_vae/lstm/',
gpu='1')
trainer.train(301)
def rnn_test():
datamanager = CT('CT_seq', train_ratio=0.8, expand_dim=None, seed=0)
autoencoder = AutoEncoder(2, 64, 'rnn', [128], [128], 'AE')
trainer = Trainer(64,
datamanager,
autoencoder,
version='vae',
save_path='save/rnn_vae/rnn/',
gpu='0')
trainer.train(301)
def lstm_test():
datamanager = CT('CT_seq', train_ratio=0.8, expand_dim=3, seed=0)
autoencoder = AutoEncoder(2, 64, 'lstm', [128], [128], 'AE')
trainer = Trainer(64,
datamanager,
autoencoder,
version='lstm',
save_path='save/resrnn_ae/lstm/',
gpu='2')
trainer.train(301) #
# trainer.load_model()
trainer.cal_loss()