diff --git a/README.md b/README.md
index 486196b..14bf347 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-# Deep Convolution Generative Adversarial Networks
+# Deep Convolutional Generative Adversarial Networks - DCGANs
 
 ![Generated images from noise on LFW ds after 300 epochs](images/lfw-300epochs.gif)
 
diff --git a/app.py b/app.py
index 5f4d954..13e49e9 100644
--- a/app.py
+++ b/app.py
@@ -60,8 +60,11 @@ def geneator_handler(path):
         zvector = torch.load(input_filepath)
         batchSize = zvector.size()[0]
 
-    checkpoint = request.form.get("ckp") or "netG_epoch_69.pth"
-    Generator = DCGAN(netG=os.path.join(MODEL_PATH, checkpoint), zvector=zvector, batchSize=batchSize)
+    checkpoint = request.form.get("ckp") or "netG_epoch_99.pth"
+    # GPU and cuda
+    # Generator = DCGAN(netG=os.path.join(MODEL_PATH, checkpoint), zvector=zvector, batchSize=batchSize, ngpu=1, cuda=True)
+    # CPU
+    Generator = DCGAN(netG=os.path.join(MODEL_PATH, checkpoint), zvector=zvector, batchSize=batchSize, ngpu=0)
     Generator.build_model()
     Generator.generate()
     return send_file(OUTPUT_PATH, mimetype='image/png')
diff --git a/dcgan.py b/dcgan.py
index ef37ee0..3c2f756 100644
--- a/dcgan.py
+++ b/dcgan.py
@@ -13,6 +13,7 @@
 import torchvision.transforms as transforms
 import torchvision.utils as vutils
 from torch.autograd import Variable
+from collections import OrderedDict # cpu_fix
 
 # Number of colours
 NC = 3
@@ -109,9 +110,8 @@ def __init__(self,
       NGF = int(ngf)
       # Load netG
       try:
-          torch.load(netG)
+          os.path.isfile(netG)
           self._netG = netG
-          pass
       except IOError as e:
           # Does not exist OR no read permissions
           print ("Unable to open netG file")
@@ -133,7 +133,13 @@ def build_model(self):
       # Build and load the model
       self._model = _netG(self._ngpu)
       self._model.apply(weights_init)
-      self._model.load_state_dict(torch.load(self._netG))
+      # Load Model ckp
+      if self._ngpu is not None and self._ngpu >= 1:
+        self._model.load_state_dict(torch.load(self._netG))
+      else:
+        # Load GPU model on CPU
+        self._model.load_state_dict(torch.load(self._netG, map_location=lambda storage, loc: storage))
+        self._model.cpu()
       # If provided use Zvector else create a random input normalized
       if self._zvector is not None:
         self._input = self._zvector
diff --git a/generate.py b/generate.py
index c931ab5..259c521 100644
--- a/generate.py
+++ b/generate.py
@@ -9,7 +9,7 @@
 parser.add_argument('--outf', default='/output', help='folder to output images')
 parser.add_argument('--Zvector', help="path to Serialized Z vector")
 parser.add_argument('--cuda', action='store_true', help='enables cuda')
-parser.add_argument('--ngpu', type=int, default=1, help='number of GPUs to use')
+parser.add_argument('--ngpu', type=int, default=0, help='number of GPUs to use')
 opt = parser.parse_args()
 print(opt)