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diff --git a/Codes/train.py b/Codes/train.py
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+import tensorflow as tf
+import os
+
+from models import generator, discriminator, flownet, initialize_flownet
+from loss_functions import intensity_loss, gradient_loss
+from utils import DataLoader, load, save, psnr_error
+from constant import const
+
+
+os.environ['CUDA_DEVICES_ORDER'] = "PCI_BUS_ID"
+os.environ['CUDA_VISIBLE_DEVICES'] = const.GPU
+
+dataset_name = const.DATASET
+train_folder = const.TRAIN_FOLDER
+test_folder = const.TEST_FOLDER
+
+batch_size = const.BATCH_SIZE
+iterations = const.ITERATIONS
+num_his = const.NUM_HIS
+height, width = 256, 256
+flow_height, flow_width = const.FLOW_HEIGHT, const.FLOW_WIDTH
+
+l_num = const.L_NUM
+alpha_num = const.ALPHA_NUM
+lam_lp = const.LAM_LP
+lam_gdl = const.LAM_GDL
+lam_adv = const.LAM_ADV
+lam_flow = const.LAM_FLOW
+adversarial = (lam_adv != 0)
+
+summary_dir = const.SUMMARY_DIR
+snapshot_dir = const.SNAPSHOT_DIR
+
+
+print(const)
+
+# define dataset
+with tf.name_scope('dataset'):
+    train_loader = DataLoader(train_folder, resize_height=height, resize_width=width)
+    train_dataset = train_loader(batch_size=batch_size, time_steps=num_his, num_pred=1)
+
+    train_it = train_dataset.make_one_shot_iterator()
+    train_videos_clips_tensor = train_it.get_next()
+    train_videos_clips_tensor.set_shape([batch_size, height, width, 3*(num_his + 1)])
+
+    train_inputs = train_videos_clips_tensor[..., 0:num_his*3]
+    train_gt = train_videos_clips_tensor[..., -3:]
+
+    print('train inputs = {}'.format(train_inputs))
+    print('train prediction gt = {}'.format(train_gt))
+
+    test_loader = DataLoader(test_folder, resize_height=height, resize_width=width)
+    test_dataset = test_loader(batch_size=batch_size, time_steps=num_his, num_pred=1)
+    test_it = test_dataset.make_one_shot_iterator()
+    test_videos_clips_tensor = test_it.get_next()
+    test_videos_clips_tensor.set_shape([batch_size, height, width, 3*(num_his + 1)])
+
+    test_inputs = test_videos_clips_tensor[..., 0:num_his*3]
+    test_gt = test_videos_clips_tensor[..., -3:]
+
+    print('test inputs = {}'.format(test_inputs))
+    print('test prediction gt = {}'.format(test_gt))
+
+# define training generator function
+with tf.variable_scope('generator', reuse=None):
+    print('training = {}'.format(tf.get_variable_scope().name))
+    train_outputs = generator(train_inputs, layers=4, output_channel=3)
+    train_psnr_error = psnr_error(gen_frames=train_outputs, gt_frames=train_gt)
+
+# define testing generator function
+with tf.variable_scope('generator', reuse=True):
+    print('testing = {}'.format(tf.get_variable_scope().name))
+    test_outputs = generator(test_inputs, layers=4, output_channel=3)
+    test_psnr_error = psnr_error(gen_frames=test_outputs, gt_frames=test_gt)
+
+
+# define intensity loss
+if lam_lp != 0:
+    lp_loss = intensity_loss(gen_frames=train_outputs, gt_frames=train_gt, l_num=l_num)
+else:
+    lp_loss = tf.constant(0.0, dtype=tf.float32)
+
+
+# define gdl loss
+if lam_gdl != 0:
+    gdl_loss = gradient_loss(gen_frames=train_outputs, gt_frames=train_gt, alpha=alpha_num)
+else:
+    gdl_loss = tf.constant(0.0, dtype=tf.float32)
+
+
+# define flow loss
+if lam_flow != 0:
+    train_gt_flow = flownet(input_a=train_inputs[..., -3:], input_b=train_gt,
+                            height=flow_height, width=flow_width, reuse=None)
+    train_pred_flow = flownet(input_a=train_inputs[..., -3:], input_b=train_outputs,
+                              height=flow_height, width=flow_width, reuse=True)
+    flow_loss = tf.reduce_mean(tf.abs(train_gt_flow - train_pred_flow))
+else:
+    flow_loss = tf.constant(0.0, dtype=tf.float32)
+
+
+# define adversarial loss
+if adversarial:
+    with tf.variable_scope('discriminator', reuse=None):
+        real_logits, real_outputs = discriminator(inputs=train_gt)
+    with tf.variable_scope('discriminator', reuse=True):
+        fake_logits, fake_outputs = discriminator(inputs=train_outputs)
+
+    print('real_outputs = {}'.format(real_outputs))
+    print('fake_outputs = {}'.format(fake_outputs))
+
+    adv_loss = tf.reduce_mean(tf.square(fake_outputs - 1) / 2)
+    dis_loss = tf.reduce_mean(tf.square(real_outputs - 1) / 2) + tf.reduce_mean(tf.square(fake_outputs) / 2)
+else:
+    adv_loss = tf.constant(0.0, dtype=tf.float32)
+    dis_loss = tf.constant(0.0, dtype=tf.float32)
+
+
+with tf.name_scope('training'):
+    g_loss = tf.add_n([lp_loss * lam_lp, gdl_loss * lam_gdl, adv_loss * lam_adv, flow_loss * lam_flow], name='g_loss')
+
+    g_step = tf.Variable(0, dtype=tf.int32, trainable=False, name='g_step')
+    g_lrate = tf.train.piecewise_constant(g_step, boundaries=const.LRATE_G_BOUNDARIES, values=const.LRATE_G)
+    g_optimizer = tf.train.AdamOptimizer(learning_rate=g_lrate, name='g_optimizer')
+    g_vars = tf.get_collection(key=tf.GraphKeys.TRAINABLE_VARIABLES, scope='generator')
+
+    g_train_op = g_optimizer.minimize(g_loss, global_step=g_step, var_list=g_vars, name='g_train_op')
+
+    if adversarial:
+        # training discriminator
+        d_step = tf.Variable(0, dtype=tf.int32, trainable=False, name='d_step')
+        d_lrate = tf.train.piecewise_constant(d_step, boundaries=const.LRATE_D_BOUNDARIES, values=const.LRATE_D)
+        d_optimizer = tf.train.AdamOptimizer(learning_rate=d_lrate, name='g_optimizer')
+        d_vars = tf.get_collection(key=tf.GraphKeys.TRAINABLE_VARIABLES, scope='discriminator')
+
+        d_train_op = d_optimizer.minimize(dis_loss, global_step=d_step, var_list=d_vars, name='d_optimizer')
+    else:
+        d_step = None
+        d_lrate = None
+        d_train_op = None
+
+# add all to summaries
+tf.summary.scalar(tensor=train_psnr_error, name='train_psnr_error')
+tf.summary.scalar(tensor=test_psnr_error, name='test_psnr_error')
+tf.summary.scalar(tensor=g_loss, name='g_loss')
+tf.summary.scalar(tensor=adv_loss, name='adv_loss')
+tf.summary.scalar(tensor=dis_loss, name='dis_loss')
+tf.summary.image(tensor=train_outputs, name='train_outputs')
+tf.summary.image(tensor=train_gt, name='train_gt')
+tf.summary.image(tensor=test_outputs, name='test_outputs')
+tf.summary.image(tensor=test_gt, name='test_gt')
+summary_op = tf.summary.merge_all()
+
+config = tf.ConfigProto()
+config.gpu_options.allow_growth = True
+with tf.Session(config=config) as sess:
+    # summaries
+    summary_writer = tf.summary.FileWriter(summary_dir, graph=sess.graph)
+
+    # initialize weights
+    sess.run(tf.global_variables_initializer())
+    print('Init successfully!')
+
+    if lam_flow != 0:
+        # initialize flownet
+        initialize_flownet(sess, const.FLOWNET_CHECKPOINT)
+
+    # tf saver
+    saver = tf.train.Saver(var_list=tf.global_variables(), max_to_keep=None)
+    restore_var = [v for v in tf.global_variables()]
+    loader = tf.train.Saver(var_list=restore_var)
+    if os.path.isdir(snapshot_dir):
+        ckpt = tf.train.get_checkpoint_state(snapshot_dir)
+        if ckpt and ckpt.model_checkpoint_path:
+            load(loader, sess, ckpt.model_checkpoint_path)
+        else:
+            print('No checkpoint file found.')
+    else:
+        load(loader, sess, snapshot_dir)
+
+    _step, _loss, _summaries = 0, None, None
+    while _step < iterations:
+        try:
+            if adversarial:
+                print('Training discriminator...')
+                _, _d_lr, _d_step, _dis_loss = sess.run([d_train_op, d_lrate, d_step, dis_loss])
+            else:
+                _d_step = 0
+                _d_lr = 0
+                _dis_loss = 0
+
+            print('Training generator...')
+            _, _g_lr, _step, _lp_loss, _gdl_loss, _adv_loss, _flow_loss, _g_loss, _train_psnr, _summaries = sess.run(
+                [g_train_op, g_lrate, g_step, lp_loss, gdl_loss, adv_loss, flow_loss, g_loss, train_psnr_error, summary_op])
+
+            if _step % 10 == 0:
+                print('DiscriminatorModel: Step {} | Global Loss: {:.6f}, lr = {:.6f}'.format(_d_step, _dis_loss, _d_lr))
+                print('GeneratorModel : Step {}, lr = {:.6f}'.format(_step, _g_lr))
+                print('                 Global      Loss : ', _g_loss)
+                print('                 intensity   Loss : ({:.4f} * {:.4f} = {:.4f})'.format(_lp_loss, lam_lp, _lp_loss * lam_lp))
+                print('                 gradient    Loss : ({:.4f} * {:.4f} = {:.4f})'.format( _gdl_loss, lam_gdl, _gdl_loss * lam_gdl))
+                print('                 adversarial Loss : ({:.4f} * {:.4f} = {:.4f})'.format(_adv_loss, lam_adv, _adv_loss * lam_adv))
+                print('                 flownet     Loss : ({:.4f} * {:.4f} = {:.4f})'.format(_flow_loss, lam_flow, _flow_loss * lam_flow))
+                print('                 PSNR  Error      : ', _train_psnr)
+            if _step % 100 == 0:
+                summary_writer.add_summary(_summaries, global_step=_step)
+                print('Save summaries...')
+
+            if _step % 1000 == 0:
+                save(saver, sess, snapshot_dir, _step)
+
+        except tf.errors.OutOfRangeError:
+            print('Finish successfully!')
+            save(saver, sess, snapshot_dir, _step)
+            break