Correcting indentation and line wrap

Author: mit0110

tensorflow_tutorial_2.ipynb

@@ -16,7 +16,9 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -39,6 +41,29 @@
     "To keep things simple we will create a simple multilayer perceptron with only one hidden layer with size 5000 (half the size of the input) and see how it goes."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Load the dataset\n",
+    "newsgroups = np.load('./resources/newsgroup.npz')\n",
+    "\n",
+    "# Define the model\n",
+    "model = MLPClassifier(\n",
+    "    activation='relu',  # Rectifier Linear Unit activation\n",
+    "    hidden_layer_sizes=(5000,),  # 1 hidden layer of size 5000\n",
+    "    max_iter=5,  # Each epochs takes a lot of time so we keep it to 5\n",
+    "    batch_size=100,  # The batch size is set to 100 elements\n",
+    "    solver='adam')  # We use the adam solver\n",
+    "\n",
+    "model.fit(newsgroups['train_data'],\n",
+    "          newsgroups['train_target'])"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -78,37 +103,23 @@
     }
    ],
    "source": [
-    "# Load the dataset\n",
-    "newsgroups = np.load('./resources/newsgroup.npz')\n",
-    "\n",
-    "# Define the model\n",
-    "model = MLPClassifier(\n",
-    "    activation='relu',  # Rectifier Linear Unit activation\n",
-    "    hidden_layer_sizes=(5000,),  # 1 hidden layer of size 5000\n",
-    "    max_iter=5,  # Each epochs takes a lot of time so we keep it to 5\n",
-    "    batch_size=100,  # The batch size is set to 100 elements\n",
-    "    solver='adam')  # We use the adam solver\n",
-    "\n",
-    "model.fit(newsgroups['train_data'],\n",
-    "          newsgroups['train_target'])\n",
-    "\n",
     "accuracy = accuracy_score(\n",
-    "            newsgroups['test_target'],\n",
-    "            model.predict(newsgroups['test_data']))\n",
+    "    newsgroups['test_target'],\n",
+    "    model.predict(newsgroups['test_data']))\n",
     "\n",
     "print(\"Accuracy: %.2f\" % accuracy)\n",
     "\n",
     "print(classification_report(\n",
-    "        newsgroups['test_target'],\n",
-    "        model.predict(newsgroups['test_data'])))"
+    "    newsgroups['test_target'],\n",
+    "    model.predict(newsgroups['test_data'])))"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:tensorflow-tutorial]",
+   "display_name": "Python [conda env:env_edm]",
    "language": "python",
-   "name": "conda-env-tensorflow-tutorial-py"
+   "name": "conda-env-env_edm-py"
   },
   "language_info": {
    "codemirror_mode": {