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diff --git a/options/train_options.py b/options/train_options.py
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+++ b/options/train_options.py
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+from .base_options import BaseOptions
+
+class TrainOptions(BaseOptions):
+    def initialize(self):
+        BaseOptions.initialize(self)
+        self.parser.add_argument('--display_freq', type=int, default=100, help='frequency of showing training results on screen')
+        self.parser.add_argument('--print_freq', type=int, default=100, help='frequency of showing training results on console')
+        self.parser.add_argument('--save_latest_freq', type=int, default=5000, help='frequency of saving the latest results')
+        self.parser.add_argument('--save_epoch_freq', type=int, default=5, help='frequency of saving checkpoints at the end of epochs')
+        self.parser.add_argument('--save_display_freq', type=int, default=2500, help='save the current display of results every save_display_freq_iterations')
+        self.parser.add_argument('--continue_train', action='store_true', help='if continue training, load the latest model')
+        self.parser.add_argument('--phase', type=str, default='train', help='train, val, test, etc')
+        self.parser.add_argument('--which_epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model')
+        self.parser.add_argument('--niter', type=int, default=100, help='# of iter at starting learning rate')
+        self.parser.add_argument('--niter_decay', type=int, default=100, help='# of iter to linearly decay learning rate to zero')
+        self.parser.add_argument('--beta1', type=float, default=0.5, help='momentum term of adam')
+        self.parser.add_argument('--ntrain', type=int, default=float("inf"), help='# of examples per epoch.')
+        self.parser.add_argument('--lr', type=float, default=0.0002, help='initial learning rate for adam')
+        self.parser.add_argument('--no_lsgan', action='store_true', help='if true, do *not* use least square GAN, if false, use vanilla GAN')
+        self.parser.add_argument('--lambda_A', type=float, default=10.0, help='weight for cycle loss (A -> B -> A)')
+        self.parser.add_argument('--lambda_B', type=float, default=10.0, help='weight for cycle loss (B -> A -> B)')
+        self.parser.add_argument('--pool_size', type=int, default=0, help='the size of image buffer that stores previously generated images')
+        self.parser.add_argument('--preprocessing', type=str, default='resize_and_crop', help='resizing/cropping strategy')
+        self.isTrain = True