Merge pull request #311 from ahmedag91/master

Fix issue #310 #201

Author: abhiojha8

cycle-gan/CycleGAN_Solution.ipynb

@@ -40,11 +40,18 @@
     "\n",
     "## Load and Visualize the Data\n",
     "\n",
-    "We'll first load in and visualize the training data, importing the necessary libraries to do so.\n",
-    "\n",
-    "> If you are working locally, you'll need to download the data as a zip file by [clicking here](https://s3.amazonaws.com/video.udacity-data.com/topher/2018/November/5be66e78_summer2winter-yosemite/summer2winter-yosemite.zip).\n",
-    "\n",
-    "It may be named `summer2winter-yosemite` with a dash or an underscore, so take note and make sure the below `image_dir` accordingly. Then you can proceed with the following loading code."
+    "We'll first load in and visualize the training data, importing the necessary libraries to do so."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# !unzip summer2winter_yosemite.zip # can comment out after executing once"
    ]
   },
   {
@@ -263,7 +270,7 @@
     "def scale(x, feature_range=(-1, 1)):\n",
     "    ''' Scale takes in an image x and returns that image, scaled\n",
     "       with a feature_range of pixel values from -1 to 1. \n",
-    "       This function assumes that the input x is already scaled from 0-1.'''\n",
+    "       This function assumes that the input x is already scaled from 0-255.'''\n",
     "    \n",
     "    # scale from 0-1 to feature_range\n",
     "    min, max = feature_range\n",
@@ -382,7 +389,8 @@
     "        self.conv4 = conv(conv_dim*4, conv_dim*8, 4) # (8, 8, 512)\n",
     "        \n",
     "        # Classification layer\n",
-    "        self.conv5 = conv(conv_dim*8, 1, 4, stride=1, batch_norm=False)\n",
+    "        self.conv5 = conv(conv_dim*8, 1, 8, stride=1, padding=0, batch_norm=False)\n",
+    "        \n",
     "\n",
     "    def forward(self, x):\n",
     "        # relu applied to all conv layers but last\n",
@@ -1003,7 +1011,7 @@
     "    return torch.mean((D_out-1)**2)\n",
     "\n",
     "def fake_mse_loss(D_out):\n",
-    "    # how close is the produced output from being \"fake\"?\n",
+    "    # how close is the produced output from being \"false\"?\n",
     "    return torch.mean(D_out**2)\n",
     "\n",
     "def cycle_consistency_loss(real_im, reconstructed_im, lambda_weight):\n",
@@ -1944,7 +1952,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [default]",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -1958,7 +1966,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.6.3"
   }
  },
  "nbformat": 4,