From cbe25bf8999ec3528b38b553d2443edbf96aa999 Mon Sep 17 00:00:00 2001
From: Alex Huddleston <shadow8t4@tamu.edu>
Date: Fri, 1 Dec 2017 20:23:13 +0000
Subject: [PATCH] Clearing all output from journal.

---
 neural_network.ipynb | 77 +++++++-------------------------------------
 1 file changed, 12 insertions(+), 65 deletions(-)

diff --git a/neural_network.ipynb b/neural_network.ipynb
index 28b5399..6d39191 100644
--- a/neural_network.ipynb
+++ b/neural_network.ipynb
@@ -2,17 +2,11 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
    "source": [
     "# If you're really new to python this code might be\n",
     "# a bit unreadable, but I tried to make it as simple\n",
@@ -35,31 +29,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "240000\n",
-      "240\n",
-      "[[ 0.88203814  0.94907443  0.32403829 ...,  0.55091702  0.10493225\n",
-      "   0.89821938]\n",
-      " [ 0.02144088  0.15692337  0.99218119 ...,  0.42989668  0.70931057\n",
-      "   0.61624769]\n",
-      " [ 0.24650157  0.68008751  0.65118644 ...,  0.44870335  0.3114548\n",
-      "   0.63668488]\n",
-      " ..., \n",
-      " [ 0.6794534   0.05525846  0.7217934  ...,  0.71333628  0.1438383\n",
-      "   0.07574106]\n",
-      " [ 0.11292803  0.19181161  0.89034259 ...,  0.60669516  0.11716027\n",
-      "   0.76495791]\n",
-      " [ 0.59152954  0.92534686  0.77183522 ...,  0.61204755  0.84179215\n",
-      "   0.50034833]]\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
    "source": [
     "# Generating dummy data so I can understand how to input.\n",
     "data = np.random.random((1000,240))\n",
@@ -73,36 +47,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/5\n",
-      "1000/1000 [==============================] - 1s 626us/step - loss: 0.0922 - acc: 0.0370\n",
-      "Epoch 2/5\n",
-      "1000/1000 [==============================] - 0s 158us/step - loss: 0.0895 - acc: 0.0360\n",
-      "Epoch 3/5\n",
-      "1000/1000 [==============================] - 0s 149us/step - loss: 0.0884 - acc: 0.0340\n",
-      "Epoch 4/5\n",
-      "1000/1000 [==============================] - 0s 163us/step - loss: 0.0879 - acc: 0.0310\n",
-      "Epoch 5/5\n",
-      "1000/1000 [==============================] - 0s 165us/step - loss: 0.0877 - acc: 0.0340\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<keras.callbacks.History at 0x7f852ccdb320>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Sets up a Sequential model, Sequential is all\n",
     "# that should need to be used for this project,\n",