diff --git a/datasets/mnist.py b/datasets/mnist.py
index 1bbc192..b872e2c 100644
--- a/datasets/mnist.py
+++ b/datasets/mnist.py
@@ -8,7 +8,7 @@ import os
 def get_mnist(dataset_root, batch_size, train):
     """Get MNIST datasets loader."""
     # image pre-processing
-    pre_process = transforms.Compose([transforms.Resize(28), # different img size settings for mnist(28) and svhn(32).
+    pre_process = transforms.Compose([transforms.Resize(32), # different img size settings for mnist(28) and svhn(32).
                                       transforms.ToTensor(),
                                       transforms.Normalize(
                                           mean=(0.5),
diff --git a/experiments/svhn_mnist.py b/experiments/svhn_mnist.py
index c053ad9..7d02fd2 100644
--- a/experiments/svhn_mnist.py
+++ b/experiments/svhn_mnist.py
@@ -1,23 +1,20 @@
 import os
 import sys
 import datetime
-from tensorboardX import SummaryWriter
 
 import torch
-sys.path.append('../')
+sys.path.append(os.path.abspath('.'))
 from models.model import SVHNmodel
 from core.train import train_dann
 from utils.utils import get_data_loader, init_model, init_random_seed
+from utils.altutils import setLogger
 
 
 class Config(object):
     # params for path
-    model_name = "svhn-mnist"
-    model_base = '/home/wogong/models/pytorch-dann'
-    model_root = os.path.expanduser(os.path.join('~', 'Models', 'pytorch-DANN', model_name))
-    note = 'paper-structure'
-    model_root = os.path.join(model_base, model_name, note + '_' + datetime.datetime.now().strftime('%m%d_%H%M%S'))
-    os.makedirs(model_root)
+    currentDir = os.path.dirname(os.path.realpath(__file__))
+    dataset_root = os.environ["DATASETDIR"]
+    model_root = os.path.join(currentDir, 'checkpoints')
     config = os.path.join(model_root, 'config.txt')
     finetune_flag = False
     lr_adjust_flag = 'simple'
@@ -28,13 +25,11 @@ class Config(object):
 
     # params for source dataset
     src_dataset = "svhn"
-    src_image_root = os.path.join('/home/wogong/datasets', 'svhn')
     src_model_trained = True
     src_classifier_restore = os.path.join(model_root, src_dataset + '-source-classifier-final.pt')
 
     # params for target dataset
     tgt_dataset = "mnist"
-    tgt_image_root = os.path.join('/home/wogong/datasets', 'mnist')
     tgt_model_trained = True
     dann_restore = os.path.join(model_root, src_dataset + '-' + tgt_dataset + '-dann-final.pt')
 
@@ -61,24 +56,22 @@ class Config(object):
     momentum = 0.9
     weight_decay = 1e-6
 
-    def __init__(self):
-        public_props = (name for name in dir(self) if not name.startswith('_'))
-        with open(self.config, 'w') as f:
-            for name in public_props:
-                f.write(name + ': ' + str(getattr(self, name)) + '\n')
-
 params = Config()
-logger = SummaryWriter(params.model_root)
+
+currentDir = os.path.dirname(os.path.realpath(__file__))
+logFile = os.path.join(currentDir+'/../', 'dann-{}-{}.log'.format(params.src_dataset, params.tgt_dataset))
+loggi = setLogger(logFile)
+
 device = torch.device("cuda:" + params.gpu_id if torch.cuda.is_available() else "cpu")
 
 # init random seed
 init_random_seed(params.manual_seed)
 
 # load dataset
-src_data_loader = get_data_loader(params.src_dataset, params.src_image_root, params.batch_size, train=True)
-src_data_loader_eval = get_data_loader(params.src_dataset, params.src_image_root, params.batch_size, train=False)
-tgt_data_loader = get_data_loader(params.tgt_dataset, params.tgt_image_root, params.batch_size, train=True)
-tgt_data_loader_eval = get_data_loader(params.tgt_dataset, params.tgt_image_root, params.batch_size, train=False)
+src_data_loader = get_data_loader(params.src_dataset, params.dataset_root, params.batch_size, train=True)
+src_data_loader_eval = get_data_loader(params.src_dataset, params.dataset_root, params.batch_size, train=False)
+tgt_data_loader = get_data_loader(params.tgt_dataset, params.dataset_root, params.batch_size, train=True)
+tgt_data_loader_eval = get_data_loader(params.tgt_dataset, params.dataset_root, params.batch_size, train=False)
 
 # load dann model
 dann = init_model(net=SVHNmodel(), restore=None)
@@ -86,4 +79,4 @@ dann = init_model(net=SVHNmodel(), restore=None)
 # train dann model
 print("Training dann model")
 if not (dann.restored and params.dann_restore):
-    dann = train_dann(dann, params, src_data_loader, tgt_data_loader, tgt_data_loader_eval, device, logger)
+    dann = train_dann(dann, params, src_data_loader, tgt_data_loader, tgt_data_loader_eval, device, loggi)