fixing the plots

Author: Qwlouse

Datasets/MNIST+Shape.ipynb

@@ -140,7 +140,7 @@
      "data": {
       "image/png": 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62LoeelOMa6w/UJ/WUwAAAAJBEQAAgEBQBAAAIOh6RvHarE3r72/y3YljyTjwvKwxczzA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\nP4IAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f113a29cb00>"
+       "<matplotlib.figure.Figure at 0x7ff1c4f13f60>"
       ]
      },
      "metadata": {},

Datasets/Simple Superposition.ipynb

@@ -214,7 +214,7 @@
      "data": {
       "image/png": 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8YesGquqMJHe7qwsZY3w8s0OEGWN8tqp+P7N6ftdd3QYrM5k5SvLpJJ8eY3wlyf+pqncm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\npUdd2KKVLQAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f4358a419e8>"
+       "<matplotlib.figure.Figure at 0x7f0b9e9cac50>"
       ]
      },
      "metadata": {},
@@ -294,7 +294,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {
     "collapsed": true
    },
@@ -309,7 +309,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {
     "collapsed": true
    },

Get Search Results.ipynb

@@ -43,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 39,
    "metadata": {
     "collapsed": true
    },
@@ -52,7 +52,8 @@
     "project = {'config.dataset.salt_n_pepper',\n",
     "           'config.training.learning_rate',\n",
     "           'config.network_spec',\n",
-    "           'info.best_val_loss'}"
+    "           'info.best_val_loss',\n",
+    "           'seed'}"
    ]
   },
   {
@@ -66,7 +67,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 40,
    "metadata": {
     "collapsed": false
    },
@@ -197,7 +198,7 @@
        "9                       0.032720           22.748329  0.383396  "
       ]
      },
-     "execution_count": 32,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }