--- a
+++ b/params/train_params.py
@@ -0,0 +1,50 @@
+from .basic_params import BasicParams
+
+
+class TrainParams(BasicParams):
+    """
+    This class is a son class of BasicParams.
+    This class includes parameters for training and parameters inherited from the father class.
+    """
+    def initialize(self, parser):
+        parser = BasicParams.initialize(self, parser)
+
+        # Training parameters
+        parser.add_argument('--epoch_num_p1', type=int, default=50,
+                            help='epoch number for phase 1')
+        parser.add_argument('--epoch_num_p2', type=int, default=50,
+                            help='epoch number for phase 2')
+        parser.add_argument('--epoch_num_p3', type=int, default=100,
+                            help='epoch number for phase 3')
+        parser.add_argument('--lr', type=float, default=1e-4,
+                            help='initial learning rate')
+        parser.add_argument('--beta1', type=float, default=0.5,
+                            help='momentum term of adam')
+        parser.add_argument('--lr_policy', type=str, default='linear',
+                            help='The learning rate policy for the scheduler. [linear | step | plateau | cosine]')
+        parser.add_argument('--epoch_count', type=int, default=1,
+                            help='the starting epoch count, default start from 1')
+        parser.add_argument('--epoch_num_decay', type=int, default=50,
+                            help='Number of epoch to linearly decay learning rate to zero (lr_policy == linear)')
+        parser.add_argument('--decay_step_size', type=int, default=50,
+                            help='The original learning rate multiply by a gamma every decay_step_size epoch (lr_policy == step)')
+        parser.add_argument('--weight_decay', type=float, default=1e-4,
+                            help='weight decay (L2 penalty)')
+
+        # Network saving and loading parameters
+        parser.add_argument('--continue_train', action='store_true',
+                            help='load the latest model and continue training')
+        parser.add_argument('--save_model', action='store_true',
+                            help='save the model during training')
+        parser.add_argument('--save_epoch_freq', type=int, default=-1,
+                            help='frequency of saving checkpoints at the end of epochs, -1 means only save the last epoch')
+
+        # Logging and visualization
+        parser.add_argument('--print_freq', type=int, default=1,
+                            help='frequency of showing results on console')
+        parser.add_argument('--save_latent_space', action='store_true',
+                            help='save the latent space of input data to disc')
+
+        self.isTrain = True
+        self.isTest = False
+        return parser