Fix via nbfmt

PiperOrigin-RevId: 330639253

Author: yashk2810

courses/udacity_deep_learning/1_notmnist.ipynb

@@ -1,22 +1,9 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "version": "0.3.2",
-      "views": {},
-      "default_view": {},
-      "name": "3_regularization.ipynb",
-      "provenance": [],
-      "toc_visible": true
-    }
-  },
   "cells": [
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "kR-4eNdK6lYS",
-        "colab_type": "text"
+        "id": "kR-4eNdK6lYS"
       },
       "source": [
         "Deep Learning\n",
@@ -32,73 +19,48 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
-        "id": "JLpLa8Jt7Vu4",
-        "colab_type": "code",
-        "colab": {
-          "autoexec": {
-            "startup": false,
-            "wait_interval": 0
-          }
-        },
-        "cellView": "both"
+        "cellView": "both",
+        "id": "JLpLa8Jt7Vu4"
       },
+      "outputs": [],
       "source": [
         "# These are all the modules we'll be using later. Make sure you can import them\n",
         "# before proceeding further.\n",
         "from __future__ import print_function\n",
         "import numpy as np\n",
         "import tensorflow as tf\n",
         "from six.moves import cPickle as pickle"
-      ],
-      "outputs": [],
-      "execution_count": 0
+      ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "1HrCK6e17WzV",
-        "colab_type": "text"
+        "id": "1HrCK6e17WzV"
       },
       "source": [
         "First reload the data we generated in `1_notmnist.ipynb`."
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
-        "id": "y3-cj1bpmuxc",
-        "colab_type": "code",
-        "colab": {
-          "autoexec": {
-            "startup": false,
-            "wait_interval": 0
-          },
-          "output_extras": [
-            {
-              "item_id": 1
-            }
-          ]
-        },
         "cellView": "both",
-        "executionInfo": {
-          "elapsed": 11777,
-          "status": "ok",
-          "timestamp": 1449849322348,
-          "user": {
-            "color": "",
-            "displayName": "",
-            "isAnonymous": false,
-            "isMe": true,
-            "permissionId": "",
-            "photoUrl": "",
-            "sessionId": "0",
-            "userId": ""
-          },
-          "user_tz": 480
-        },
-        "outputId": "e03576f1-ebbe-4838-c388-f1777bcc9873"
+        "id": "y3-cj1bpmuxc"
       },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Training set (200000, 28, 28) (200000,)\n",
+            "Validation set (10000, 28, 28) (10000,)\n",
+            "Test set (18724, 28, 28) (18724,)\n"
+          ]
+        }
+      ],
       "source": [
         "pickle_file = 'notMNIST.pickle'\n",
         "\n",
@@ -114,25 +76,12 @@
         "  print('Training set', train_dataset.shape, train_labels.shape)\n",
         "  print('Validation set', valid_dataset.shape, valid_labels.shape)\n",
         "  print('Test set', test_dataset.shape, test_labels.shape)"
-      ],
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Training set (200000, 28, 28) (200000,)\n",
-            "Validation set (10000, 28, 28) (10000,)\n",
-            "Test set (18724, 28, 28) (18724,)\n"
-          ],
-          "name": "stdout"
-        }
-      ],
-      "execution_count": 0
+      ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "L7aHrm6nGDMB",
-        "colab_type": "text"
+        "id": "L7aHrm6nGDMB"
       },
       "source": [
         "Reformat into a shape that's more adapted to the models we're going to train:\n",
@@ -142,39 +91,22 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
-        "id": "IRSyYiIIGIzS",
-        "colab_type": "code",
-        "colab": {
-          "autoexec": {
-            "startup": false,
-            "wait_interval": 0
-          },
-          "output_extras": [
-            {
-              "item_id": 1
-            }
-          ]
-        },
         "cellView": "both",
-        "executionInfo": {
-          "elapsed": 11728,
-          "status": "ok",
-          "timestamp": 1449849322356,
-          "user": {
-            "color": "",
-            "displayName": "",
-            "isAnonymous": false,
-            "isMe": true,
-            "permissionId": "",
-            "photoUrl": "",
-            "sessionId": "0",
-            "userId": ""
-          },
-          "user_tz": 480
-        },
-        "outputId": "3f8996ee-3574-4f44-c953-5c8a04636582"
+        "id": "IRSyYiIIGIzS"
       },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Training set (200000, 784) (200000, 10)\n",
+            "Validation set (10000, 784) (10000, 10)\n",
+            "Test set (18724, 784) (18724, 10)\n"
+          ]
+        }
+      ],
       "source": [
         "image_size = 28\n",
         "num_labels = 10\n",
@@ -190,46 +122,26 @@
         "print('Training set', train_dataset.shape, train_labels.shape)\n",
         "print('Validation set', valid_dataset.shape, valid_labels.shape)\n",
         "print('Test set', test_dataset.shape, test_labels.shape)"
-      ],
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Training set (200000, 784) (200000, 10)\n",
-            "Validation set (10000, 784) (10000, 10)\n",
-            "Test set (18724, 784) (18724, 10)\n"
-          ],
-          "name": "stdout"
-        }
-      ],
-      "execution_count": 0
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
-        "id": "RajPLaL_ZW6w",
-        "colab_type": "code",
-        "colab": {
-          "autoexec": {
-            "startup": false,
-            "wait_interval": 0
-          }
-        },
-        "cellView": "both"
+        "cellView": "both",
+        "id": "RajPLaL_ZW6w"
       },
+      "outputs": [],
       "source": [
         "def accuracy(predictions, labels):\n",
         "  return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))\n",
         "          / predictions.shape[0])"
-      ],
-      "outputs": [],
-      "execution_count": 0
+      ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "sgLbUAQ1CW-1",
-        "colab_type": "text"
+        "id": "sgLbUAQ1CW-1"
       },
       "source": [
         "---\n",
@@ -244,8 +156,7 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "na8xX2yHZzNF",
-        "colab_type": "text"
+        "id": "na8xX2yHZzNF"
       },
       "source": [
         "---\n",
@@ -259,8 +170,7 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "ww3SCBUdlkRc",
-        "colab_type": "text"
+        "id": "ww3SCBUdlkRc"
       },
       "source": [
         "---\n",
@@ -276,8 +186,7 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "-b1hTz3VWZjw",
-        "colab_type": "text"
+        "id": "-b1hTz3VWZjw"
       },
       "source": [
         "---\n",
@@ -297,5 +206,17 @@
         " ---\n"
       ]
     }
-  ]
+  ],
+  "metadata": {
+    "colab": {
+      "name": "3_regularization.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
 }