Future Prediction Training

run_models.py

@@ -10,7 +10,7 @@ from distutils.util import strtobool
 from networks.auto_encoder import AutoEncoder, AutoEncoder_LO
 from networks.variational_auto_encoder import VariationalAE, VAE_LO
 from networks.adverserial_auto_encoder import AdversarialAE_LO, AdversarialAE
-from networks.seperating_adversarial_auto_encoder import SeperatingAAE, SeparatingAAE_LO, SuperSeperatingAAE
+from networks.seperating_adversarial_auto_encoder import SeperatingAAE, SeparatingAAE_LO
 from networks.modules import LightningModule
 
 from pytorch_lightning import Trainer
@@ -22,7 +22,7 @@ args.add_argument('--step', default=5)
 args.add_argument('--features', default=6)
 args.add_argument('--size', default=9)
 args.add_argument('--latent_dim', default=2)
-args.add_argument('--model', default='VAE_Model')
+args.add_argument('--model', default='AE_Model')
 args.add_argument('--refresh', type=strtobool, default=False)
 
 
@@ -78,20 +78,6 @@ class SAAE_Model(SeparatingAAE_LO, LightningModule):
         pass
 
 
-class SSAAE_Model(SeparatingAAE_LO, LightningModule):
-
-    def __init__(self, parameters: Namespace):
-        assert all([x in parameters for x in ['step', 'size', 'latent_dim', 'features']])
-        self.size = parameters.size
-        self.latent_dim = parameters.latent_dim
-        self.features = parameters.features
-        self.step = parameters.step
-        super(SSAAE_Model, self).__init__()
-        self.normal = Normal(0, 1)
-        self.network = SuperSeperatingAAE(self.latent_dim, self.features)
-        pass
-
-
 if __name__ == '__main__':
     arguments = args.parse_args()