Your_first_neural_network.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Your first neural network\n",
    "\n",
    "In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementation of the neural network up to you (for the most part). After you've submitted this project, feel free to explore the data and the model more.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and prepare the data\n",
    "\n",
    "A critical step in working with neural networks is preparing the data correctly. Variables on different scales make it difficult for the network to efficiently learn the correct weights. Below, we've written the code to load and prepare the data. You'll learn more about this soon!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_path = 'Bike-Sharing-Dataset/hour.csv'\n",
    "\n",
    "rides = pd.read_csv(data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
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       "      <td>0.24</td>\n",
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       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-01</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.2727</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
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       "      <td>6</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  hr  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1   0        0        6           0   \n",
       "1        2  2011-01-01       1   0     1   1        0        6           0   \n",
       "2        3  2011-01-01       1   0     1   2        0        6           0   \n",
       "3        4  2011-01-01       1   0     1   3        0        6           0   \n",
       "4        5  2011-01-01       1   0     1   4        0        6           0   \n",
       "\n",
       "   weathersit  temp   atemp   hum  windspeed  casual  registered  cnt  \n",
       "0           1  0.24  0.2879  0.81        0.0       3          13   16  \n",
       "1           1  0.22  0.2727  0.80        0.0       8          32   40  \n",
       "2           1  0.22  0.2727  0.80        0.0       5          27   32  \n",
       "3           1  0.24  0.2879  0.75        0.0       3          10   13  \n",
       "4           1  0.24  0.2879  0.75        0.0       0           1    1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rides.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking out the data\n",
    "\n",
    "This dataset has the number of riders for each hour of each day from January 1 2011 to December 31 2012. The number of riders is split between casual and registered, summed up in the `cnt` column. You can see the first few rows of the data above.\n",
    "\n",
    "Below is a plot showing the number of bike riders over the first 10 days or so in the data set. (Some days don't have exactly 24 entries in the data set, so it's not exactly 10 days.) You can see the hourly rentals here. This data is pretty complicated! The weekends have lower over all ridership and there are spikes when people are biking to and from work during the week. Looking at the data above, we also have information about temperature, humidity, and windspeed, all of these likely affecting the number of riders. You'll be trying to capture all this with your model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f47abe26b38>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f47ac71e940>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 380
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rides[:24*10].plot(x='dteday', y='cnt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dummy variables\n",
    "Here we have some categorical variables like season, weather, month. To include these in our model, we'll need to make binary dummy variables. This is simple to do with Pandas thanks to `get_dummies()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>temp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "      <th>season_1</th>\n",
       "      <th>season_2</th>\n",
       "      <th>...</th>\n",
       "      <th>hr_21</th>\n",
       "      <th>hr_22</th>\n",
       "      <th>hr_23</th>\n",
       "      <th>weekday_0</th>\n",
       "      <th>weekday_1</th>\n",
       "      <th>weekday_2</th>\n",
       "      <th>weekday_3</th>\n",
       "      <th>weekday_4</th>\n",
       "      <th>weekday_5</th>\n",
       "      <th>weekday_6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   yr  holiday  temp   hum  windspeed  casual  registered  cnt  season_1  \\\n",
       "0   0        0  0.24  0.81        0.0       3          13   16         1   \n",
       "1   0        0  0.22  0.80        0.0       8          32   40         1   \n",
       "2   0        0  0.22  0.80        0.0       5          27   32         1   \n",
       "3   0        0  0.24  0.75        0.0       3          10   13         1   \n",
       "4   0        0  0.24  0.75        0.0       0           1    1         1   \n",
       "\n",
       "   season_2    ...      hr_21  hr_22  hr_23  weekday_0  weekday_1  weekday_2  \\\n",
       "0         0    ...          0      0      0          0          0          0   \n",
       "1         0    ...          0      0      0          0          0          0   \n",
       "2         0    ...          0      0      0          0          0          0   \n",
       "3         0    ...          0      0      0          0          0          0   \n",
       "4         0    ...          0      0      0          0          0          0   \n",
       "\n",
       "   weekday_3  weekday_4  weekday_5  weekday_6  \n",
       "0          0          0          0          1  \n",
       "1          0          0          0          1  \n",
       "2          0          0          0          1  \n",
       "3          0          0          0          1  \n",
       "4          0          0          0          1  \n",
       "\n",
       "[5 rows x 59 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_fields = ['season', 'weathersit', 'mnth', 'hr', 'weekday']\n",
    "for each in dummy_fields:\n",
    "    dummies = pd.get_dummies(rides[each], prefix=each, drop_first=False)\n",
    "    rides = pd.concat([rides, dummies], axis=1)\n",
    "\n",
    "fields_to_drop = ['instant', 'dteday', 'season', 'weathersit', \n",
    "                  'weekday', 'atemp', 'mnth', 'workingday', 'hr']\n",
    "data = rides.drop(fields_to_drop, axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling target variables\n",
    "To make training the network easier, we'll standardize each of the continuous variables. That is, we'll shift and scale the variables such that they have zero mean and a standard deviation of 1.\n",
    "\n",
    "The scaling factors are saved so we can go backwards when we use the network for predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "quant_features = ['casual', 'registered', 'cnt', 'temp', 'hum', 'windspeed']\n",
    "# Store scalings in a dictionary so we can convert back later\n",
    "scaled_features = {}\n",
    "for each in quant_features:\n",
    "    mean, std = data[each].mean(), data[each].std()\n",
    "    scaled_features[each] = [mean, std]\n",
    "    data.loc[:, each] = (data[each] - mean)/std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>temp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "      <th>season_1</th>\n",
       "      <th>season_2</th>\n",
       "      <th>...</th>\n",
       "      <th>hr_21</th>\n",
       "      <th>hr_22</th>\n",
       "      <th>hr_23</th>\n",
       "      <th>weekday_0</th>\n",
       "      <th>weekday_1</th>\n",
       "      <th>weekday_2</th>\n",
       "      <th>weekday_3</th>\n",
       "      <th>weekday_4</th>\n",
       "      <th>weekday_5</th>\n",
       "      <th>weekday_6</th>\n",
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       "  </thead>\n",
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       "      <td>-0.561326</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.334609</td>\n",
       "      <td>0.636351</td>\n",
       "      <td>-1.553844</td>\n",
       "      <td>-0.662736</td>\n",
       "      <td>-0.949983</td>\n",
       "      <td>-0.972851</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.334609</td>\n",
       "      <td>0.636351</td>\n",
       "      <td>-1.553844</td>\n",
       "      <td>-0.723582</td>\n",
       "      <td>-1.009445</td>\n",
       "      <td>-1.039008</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   yr  holiday      temp       hum  windspeed    casual  registered       cnt  \\\n",
       "0   0        0 -1.334609  0.947345  -1.553844 -0.662736   -0.930162 -0.956312   \n",
       "1   0        0 -1.438475  0.895513  -1.553844 -0.561326   -0.804632 -0.823998   \n",
       "2   0        0 -1.438475  0.895513  -1.553844 -0.622172   -0.837666 -0.868103   \n",
       "3   0        0 -1.334609  0.636351  -1.553844 -0.662736   -0.949983 -0.972851   \n",
       "4   0        0 -1.334609  0.636351  -1.553844 -0.723582   -1.009445 -1.039008   \n",
       "\n",
       "   season_1  season_2    ...      hr_21  hr_22  hr_23  weekday_0  weekday_1  \\\n",
       "0         1         0    ...          0      0      0          0          0   \n",
       "1         1         0    ...          0      0      0          0          0   \n",
       "2         1         0    ...          0      0      0          0          0   \n",
       "3         1         0    ...          0      0      0          0          0   \n",
       "4         1         0    ...          0      0      0          0          0   \n",
       "\n",
       "   weekday_2  weekday_3  weekday_4  weekday_5  weekday_6  \n",
       "0          0          0          0          0          1  \n",
       "1          0          0          0          0          1  \n",
       "2          0          0          0          0          1  \n",
       "3          0          0          0          0          1  \n",
       "4          0          0          0          0          1  \n",
       "\n",
       "[5 rows x 59 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the data into training, testing, and validation sets\n",
    "\n",
    "We'll save the data for the last approximately 21 days to use as a test set after we've trained the network. We'll use this set to make predictions and compare them with the actual number of riders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Save data for approximately the last 21 days \n",
    "test_data = data[-21*24:]\n",
    "\n",
    "# Now remove the test data from the data set \n",
    "data = data[:-21*24]\n",
    "\n",
    "# Separate the data into features and targets\n",
    "target_fields = ['cnt', 'casual', 'registered']\n",
    "features, targets = data.drop(target_fields, axis=1), data[target_fields]\n",
    "test_features, test_targets = test_data.drop(target_fields, axis=1), test_data[target_fields]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll split the data into two sets, one for training and one for validating as the network is being trained. Since this is time series data, we'll train on historical data, then try to predict on future data (the validation set)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Hold out the last 60 days or so of the remaining data as a validation set\n",
    "train_features, train_targets = features[:-60*24], targets[:-60*24]\n",
    "val_features, val_targets = features[-60*24:], targets[-60*24:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time to build the network\n",
    "\n",
    "Below you'll build your network. We've built out the structure. You'll implement both the forward pass and backwards pass through the network. You'll also set the hyperparameters: the learning rate, the number of hidden units, and the number of training passes.\n",
    "\n",
    "<img src=\"assets/neural_network.png\" width=300px>\n",
    "\n",
    "The network has two layers, a hidden layer and an output layer. The hidden layer will use the sigmoid function for activations. The output layer has only one node and is used for the regression, the output of the node is the same as the input of the node. That is, the activation function is $f(x)=x$. A function that takes the input signal and generates an output signal, but takes into account the threshold, is called an activation function. We work through each layer of our network calculating the outputs for each neuron. All of the outputs from one layer become inputs to the neurons on the next layer. This process is called *forward propagation*.\n",
    "\n",
    "We use the weights to propagate signals forward from the input to the output layers in a neural network. We use the weights to also propagate error backwards from the output back into the network to update our weights. This is called *backpropagation*.\n",
    "\n",
    "> **Hint:** You'll need the derivative of the output activation function ($f(x) = x$) for the backpropagation implementation. If you aren't familiar with calculus, this function is equivalent to the equation $y = x$. What is the slope of that equation? That is the derivative of $f(x)$.\n",
    "\n",
    "Below, you have these tasks:\n",
    "1. Implement the sigmoid function to use as the activation function. Set `self.activation_function` in `__init__` to your sigmoid function.\n",
    "2. Implement the forward pass in the `train` method.\n",
    "3. Implement the backpropagation algorithm in the `train` method, including calculating the output error.\n",
    "4. Implement the forward pass in the `run` method.\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#############\n",
    "# In the my_answers.py file, fill out the TODO sections as specified\n",
    "#############\n",
    "\n",
    "from my_answers import NeuralNetwork"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def MSE(y, Y):\n",
    "    return np.mean((y-Y)**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unit tests\n",
    "\n",
    "Run these unit tests to check the correctness of your network implementation. This will help you be sure your network was implemented correctly befor you starting trying to train it. These tests must all be successful to pass the project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "....."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.37275328]\n",
      " [-0.03172939]]\n",
      "[[ 0.10562014 -0.20185996]\n",
      " [ 0.39775194  0.50074398]\n",
      " [-0.29887597  0.19962801]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "----------------------------------------------------------------------\n",
      "Ran 5 tests in 0.015s\n",
      "\n",
      "OK\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<unittest.runner.TextTestResult run=5 errors=0 failures=0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import unittest\n",
    "\n",
    "inputs = np.array([[0.5, -0.2, 0.1]])\n",
    "targets = np.array([[0.4]])\n",
    "test_w_i_h = np.array([[0.1, -0.2],\n",
    "                       [0.4, 0.5],\n",
    "                       [-0.3, 0.2]])\n",
    "test_w_h_o = np.array([[0.3],\n",
    "                       [-0.1]])\n",
    "\n",
    "class TestMethods(unittest.TestCase):\n",
    "    \n",
    "    ##########\n",
    "    # Unit tests for data loading\n",
    "    ##########\n",
    "    \n",
    "    def test_data_path(self):\n",
    "        # Test that file path to dataset has been unaltered\n",
    "        self.assertTrue(data_path.lower() == 'bike-sharing-dataset/hour.csv')\n",
    "        \n",
    "    def test_data_loaded(self):\n",
    "        # Test that data frame loaded\n",
    "        self.assertTrue(isinstance(rides, pd.DataFrame))\n",
    "    \n",
    "    ##########\n",
    "    # Unit tests for network functionality\n",
    "    ##########\n",
    "\n",
    "    def test_activation(self):\n",
    "        network = NeuralNetwork(3, 2, 1, 0.5)\n",
    "        # Test that the activation function is a sigmoid\n",
    "        self.assertTrue(np.all(network.activation_function(0.5) == 1/(1+np.exp(-0.5))))\n",
    "        \n",
    "     \n",
    "\n",
    "    def test_train(self):\n",
    "        # Test that weights are updated correctly on training\n",
    "        network = NeuralNetwork(3, 2, 1, 0.5)\n",
    "        network.weights_input_to_hidden = test_w_i_h.copy()\n",
    "        network.weights_hidden_to_output = test_w_h_o.copy()\n",
    "        \n",
    "        network.train(inputs, targets)\n",
    "       # print(\"test\")\n",
    "        #print(network.weights_hidden_to_output)\n",
    "        #print(network.weights_input_to_hidden)\n",
    "        self.assertTrue(np.allclose(network.weights_hidden_to_output, \n",
    "                                    np.array([[ 0.37275328], \n",
    "                                              [-0.03172939]])))\n",
    "        self.assertTrue(np.allclose(network.weights_input_to_hidden,\n",
    "                                    np.array([[ 0.10562014, -0.20185996], \n",
    "                                              [0.39775194, 0.50074398], \n",
    "                                              [-0.29887597, 0.19962801]])))        \n",
    "    \n",
    "    def test_run(self):\n",
    "        # Test correctness of run method\n",
    "        network = NeuralNetwork(3, 2, 1, 0.5)\n",
    "        network.weights_input_to_hidden = test_w_i_h.copy()\n",
    "        network.weights_hidden_to_output = test_w_h_o.copy()\n",
    "        \n",
    "       # print(\"Run output\")\n",
    "        \n",
    "        self.assertTrue(np.allclose(network.run(inputs), 0.09998924)) \n",
    "        #self.assertTrue(np.allclose(network.run(inputs), 0.5249765)) \n",
    "        \n",
    "\n",
    "suite = unittest.TestLoader().loadTestsFromModule(TestMethods())\n",
    "unittest.TextTestRunner().run(suite)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the network\n",
    "\n",
    "Here you'll set the hyperparameters for the network. The strategy here is to find hyperparameters such that the error on the training set is low, but you're not overfitting to the data. If you train the network too long or have too many hidden nodes, it can become overly specific to the training set and will fail to generalize to the validation set. That is, the loss on the validation set will start increasing as the training set loss drops.\n",
    "\n",
    "You'll also be using a method know as Stochastic Gradient Descent (SGD) to train the network. The idea is that for each training pass, you grab a random sample of the data instead of using the whole data set. You use many more training passes than with normal gradient descent, but each pass is much faster. This ends up training the network more efficiently. You'll learn more about SGD later.\n",
    "\n",
    "### Choose the number of iterations\n",
    "This is the number of batches of samples from the training data we'll use to train the network. The more iterations you use, the better the model will fit the data. However, this process can have sharply diminishing returns and can waste computational resources if you use too many iterations.  You want to find a number here where the network has a low training loss, and the validation loss is at a minimum. The ideal number of iterations would be a level that stops shortly after the validation loss is no longer decreasing.\n",
    "\n",
    "### Choose the learning rate\n",
    "This scales the size of weight updates. If this is too big, the weights tend to explode and the network fails to fit the data. Normally a good choice to start at is 0.1; however, if you effectively divide the learning rate by n_records, try starting out with a learning rate of 1. In either case, if the network has problems fitting the data, try reducing the learning rate. Note that the lower the learning rate, the smaller the steps are in the weight updates and the longer it takes for the neural network to converge.\n",
    "\n",
    "### Choose the number of hidden nodes\n",
    "In a model where all the weights are optimized, the more hidden nodes you have, the more accurate the predictions of the model will be.  (A fully optimized model could have weights of zero, after all.) However, the more hidden nodes you have, the harder it will be to optimize the weights of the model, and the more likely it will be that suboptimal weights will lead to overfitting. With overfitting, the model will memorize the training data instead of learning the true pattern, and won't generalize well to unseen data.  \n",
    "\n",
    "Try a few different numbers and see how it affects the performance. You can look at the losses dictionary for a metric of the network performance. If the number of hidden units is too low, then the model won't have enough space to learn and if it is too high there are too many options for the direction that the learning can take. The trick here is to find the right balance in number of hidden units you choose.  You'll generally find that the best number of hidden nodes to use ends up being between the number of input and output nodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress: 0.7% ... Training loss: 1.034 ... Validation loss: 5.148"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/workspace/first-neural-network/my_answers.py:25: RuntimeWarning: overflow encountered in exp\n",
      "  self.activation_function = lambda x : 1/(1+np.exp(-x))  # Replace 0 with your sigmoid calculation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress: 7.6% ... Training loss: nan ... Validation loss: nan"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:2: RuntimeWarning: overflow encountered in square\n",
      "  \n",
      "/home/workspace/first-neural-network/my_answers.py:123: RuntimeWarning: invalid value encountered in multiply\n",
      "  hidden_error_term = hidden_error*hidden_outputs*(1-hidden_outputs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress: 28.6% ... Training loss: nan ... Validation loss: nan"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "####################\n",
    "### Set the hyperparameters in you myanswers.py file ###\n",
    "####################\n",
    "\n",
    "from my_answers import iterations, learning_rate, hidden_nodes, output_nodes\n",
    "\n",
    "\n",
    "N_i = train_features.shape[1]\n",
    "network = NeuralNetwork(N_i, hidden_nodes, output_nodes, learning_rate)\n",
    "\n",
    "losses = {'train':[], 'validation':[]}\n",
    "for ii in range(iterations):\n",
    "    # Go through a random batch of 128 records from the training data set\n",
    "    batch = np.random.choice(train_features.index, size=128)\n",
    "    X, y = train_features.ix[batch].values, train_targets.ix[batch]['cnt']\n",
    "                             \n",
    "    network.train(X, y)\n",
    "    \n",
    "    # Printing out the training progress\n",
    "    train_loss = MSE(network.run(train_features).T, train_targets['cnt'].values)\n",
    "    val_loss = MSE(network.run(val_features).T, val_targets['cnt'].values)\n",
    "    sys.stdout.write(\"\\rProgress: {:2.1f}\".format(100 * ii/float(iterations)) \\\n",
    "                     + \"% ... Training loss: \" + str(train_loss)[:5] \\\n",
    "                     + \" ... Validation loss: \" + str(val_loss)[:5])\n",
    "    sys.stdout.flush()\n",
    "    \n",
    "    losses['train'].append(train_loss)\n",
    "    losses['validation'].append(val_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f47a3f87cc0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 258,
       "width": 374
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(losses['train'], label='Training loss')\n",
    "plt.plot(losses['validation'], label='Validation loss')\n",
    "plt.legend()\n",
    "_ = plt.ylim()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check out your predictions\n",
    "\n",
    "Here, use the test data to view how well your network is modeling the data. If something is completely wrong here, make sure each step in your network is implemented correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:10: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f47a3fc7208>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 272,
       "width": 502
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,4))\n",
    "\n",
    "mean, std = scaled_features['cnt']\n",
    "predictions = network.run(test_features).T*std + mean\n",
    "ax.plot(predictions[0], label='Prediction')\n",
    "ax.plot((test_targets['cnt']*std + mean).values, label='Data')\n",
    "ax.set_xlim(right=len(predictions))\n",
    "ax.legend()\n",
    "\n",
    "dates = pd.to_datetime(rides.ix[test_data.index]['dteday'])\n",
    "dates = dates.apply(lambda d: d.strftime('%b %d'))\n",
    "ax.set_xticks(np.arange(len(dates))[12::24])\n",
    "_ = ax.set_xticklabels(dates[12::24], rotation=45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OPTIONAL: Thinking about your results(this question will not be evaluated in the rubric).\n",
    " \n",
    "Answer these questions about your results. How well does the model predict the data? Where does it fail? Why does it fail where it does?\n",
    "\n",
    "> **Note:** You can edit the text in this cell by double clicking on it. When you want to render the text, press control + enter\n",
    "\n",
    "#### Your answer below"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Submitting:\n",
    "Open up the 'jwt' file in the first-neural-network directory (which also contains this notebook) for submission instructions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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