From cadc3ef2f02f280b2e5261d2ce149ce33c36349a Mon Sep 17 00:00:00 2001
From: Will Breaden Madden <zicodian@gmail.com>
Date: Thu, 20 Aug 2015 23:37:07 +0200
Subject: [PATCH] Make whitespace consistent, change string formatting from
 modulo to format

---
 dream.ipynb | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/dream.ipynb b/dream.ipynb
index 39b5037..e38c7e2 100644
--- a/dream.ipynb
+++ b/dream.ipynb
@@ -93,7 +93,7 @@
       "\n",
       "net = caffe.Classifier('tmp.prototxt', param_fn,\n",
       "                       mean = np.float32([104.0, 116.0, 122.0]), # ImageNet mean, training set dependent\n",
-      "                       channel_swap = (2,1,0)) # the reference model has channels in BGR order instead of RGB\n",
+      "                       channel_swap = (2, 1, 0)) # the reference model has channels in BGR order instead of RGB\n",
       "\n",
       "# a couple of utility functions for converting to and from Caffe's input image layout\n",
       "def preprocess(net, img):\n",
@@ -194,7 +194,7 @@
       "    # prepare base images for all octaves\n",
       "    octaves = [preprocess(net, base_img)]\n",
       "    for i in xrange(octave_n-1):\n",
-      "        octaves.append(nd.zoom(octaves[-1], (1, 1.0/octave_scale,1.0/octave_scale), order=1))\n",
+      "        octaves.append(nd.zoom(octaves[-1], (1, 1.0/octave_scale, 1.0/octave_scale), order=1))\n",
       "    \n",
       "    src = net.blobs['data']\n",
       "    detail = np.zeros_like(octaves[-1]) # allocate image for network-produced details\n",
@@ -203,7 +203,7 @@
       "        if octave > 0:\n",
       "            # upscale details from the previous octave\n",
       "            h1, w1 = detail.shape[-2:]\n",
-      "            detail = nd.zoom(detail, (1, 1.0*h/h1,1.0*w/w1), order=1)\n",
+      "            detail = nd.zoom(detail, (1, 1.0*h/h1, 1.0*w/w1), order=1)\n",
       "\n",
       "        src.reshape(1,3,h,w) # resize the network's input image size\n",
       "        src.data[0] = octave_base+detail\n",
@@ -416,7 +416,7 @@
       "s = 0.05 # scale coefficient\n",
       "for i in xrange(100):\n",
       "    frame = deepdream(net, frame)\n",
-      "    PIL.Image.fromarray(np.uint8(frame)).save(\"frames/%04d.jpg\"%frame_i)\n",
+      "    PIL.Image.fromarray(np.uint8(frame)).save(\"frames/{index}.jpg\".format(index = frame_i))\n",
       "    frame = nd.affine_transform(frame, [1-s,1-s,1], [h*s/2,w*s/2,0], order=1)\n",
       "    frame_i += 1"
      ],
@@ -511,7 +511,7 @@
       "end = 'inception_3b/output'\n",
       "h, w = guide.shape[:2]\n",
       "src, dst = net.blobs['data'], net.blobs[end]\n",
-      "src.reshape(1,3,h,w)\n",
+      "src.reshape(1, 3, h, w)\n",
       "src.data[0] = preprocess(net, guide)\n",
       "net.forward(end=end)\n",
       "guide_features = dst.data[0].copy()"
@@ -536,10 +536,10 @@
       "    x = dst.data[0].copy()\n",
       "    y = guide_features\n",
       "    ch = x.shape[0]\n",
-      "    x = x.reshape(ch,-1)\n",
-      "    y = y.reshape(ch,-1)\n",
+      "    x = x.reshape(ch, -1)\n",
+      "    y = y.reshape(ch, -1)\n",
       "    A = x.T.dot(y) # compute the matrix of dot-products with guide features\n",
-      "    dst.diff[0].reshape(ch,-1)[:] = y[:,A.argmax(1)] # select ones that match best\n",
+      "    dst.diff[0].reshape(ch, -1)[:] = y[:, A.argmax(1)] # select ones that match best\n",
       "\n",
       "_=deepdream(net, img, end=end, objective=objective_guide)"
      ],
@@ -575,4 +575,4 @@
    "metadata": {}
   }
  ]
-}
+}
\ No newline at end of file