Your_first_neural_network.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tu primera red neuronal \n",
    "\n",
    "En este proyecto, construirás tu primera red neuronal y la usarás para predecir el número de usuarios diarios de alquiler de bicicletas. Proporcionamos parte del código, pero dejamos la implementación de la red neuronal en tus manos (en su mayor parte). Una vez que hayas entregado este proyecto, no dudes en explorar más los datos y el modelo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\comunicaciones\\Anaconda3\\envs\\radeon\\lib\\site-packages\\tqdm\\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "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\n",
    "import torch\n",
    "from torch import nn, optim\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import TensorDataset, DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "# comprobar si CUDA está disponible\n",
    "train_on_gpu = torch.cuda.is_available()\n",
    "print(train_on_gpu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cargar y preparar los datos\n",
    "\n",
    "Un paso crítico para trabajar con redes neuronales es preparar los datos correctamente. Las variables en diferentes escalas dificultan que la red aprenda de manera eficiente los pesos correctos. A continuación, hemos escrito el código para cargar y preparar los datos. ¡Te invito a buscar más información!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = 'C:/Users/comunicaciones/Documents/johan/Maestria/Aprendizaje profundo/Taller 1/hour.csv'\n",
    "\n",
    "rides = pd.read_csv(data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <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",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <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>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <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",
       "      <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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rides.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Verificando los datos\n",
    "\n",
    "Este conjunto de datos tiene el número de ciclistas para cada hora de cada día desde el 1 de enero de 2011 hasta el 31 de diciembre de 2012. El número de ciclistas se divide entre ocasionales y registrados, resumidos en la columna `cnt`. Puedes ver las primeras filas de los datos anteriores.\n",
    "\n",
    "A continuación se muestra un gráfico que muestra el número de ciclistas durante los primeros 10 días en el conjunto de datos. (Algunos días no tienen exactamente 24 entradas en el conjunto de datos, por lo que no son exactamente 10 días). Puedes ver los alquileres por hora aquí. ¡Estos datos son bastante complicados! Los fines de semana tienen un menor número total de pasajeros y hay picos cuando las personas van y vienen del trabajo en bicicleta durante la semana. Al observar los datos anteriores, también tenemos información sobre la temperatura, la humedad y la velocidad del viento, que probablemente afecten la cantidad de ciclistas. Intentarás capturar todo esto con tu modelo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='dteday'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 261,
       "width": 375
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rides[:24*10].plot(x='dteday', y='cnt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variables Dummy\n",
    "Aquí tenemos algunas variables categóricas como estación, clima, mes. Para incluirlos en nuestro modelo, necesitaremos crear variables ficticias binarias. Esto es fácil de hacer con Pandas gracias a `get_dummies()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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": 6,
     "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": [
    "### Escalando variables\n",
    "Para facilitar el entrenamiento de la red, estandarizaremos cada una de las variables continuas. Es decir, desplazaremos y escalaremos las variables de modo que tengan una media cero y una desviación estándar de 1.\n",
    "\n",
    "Los factores de escala se guardan para que podamos retroceder cuando usamos la red para las predicciones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "quant_features = ['casual', 'registered', 'cnt', 'temp', 'hum', 'windspeed']\n",
    "# Guarde las escalas en un diccionario para que podamos volver a convertirlas más tarde\n",
    "scaled_features = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Aquí se elige un tipo de normalization. Se pueden normalizar los datos enbase al valor máximo o a la desviación estandar y al promedio.\n",
    "#### En este caso se corre con la normalización recomendada por el docente mean_std:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "normalization = 'mean_std'\n",
    "\n",
    "if normalization == 'mean_std':\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\n",
    "else:\n",
    "    for each in quant_features:\n",
    "        maxim = data[each].max()\n",
    "        scaled_features[each] = maxim\n",
    "        data.loc[:, each] = data[each]/maxim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"quant_features = ['casual', 'registered', 'cnt', 'temp', 'hum', 'windspeed']\\n# Guarde las escalas en un diccionario para que podamos volver a convertirlas más tarde\\nscaled_features = {}\\nfor each in quant_features:\\n    maxim = data[each].max()\\n    scaled_features[each] = maxim\\n    data.loc[:, each] = data[each]/maxim\""
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quant_features = ['casual', 'registered', 'cnt', 'temp', 'hum', 'windspeed']\n",
    "# Guarde las escalas en un diccionario para que podamos volver a convertirlas más tarde\n",
    "scaled_features = {}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>-1.334609</td>\n",
       "      <td>0.947345</td>\n",
       "      <td>-1.553844</td>\n",
       "      <td>-0.662736</td>\n",
       "      <td>-0.930162</td>\n",
       "      <td>-0.956312</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>-1.438475</td>\n",
       "      <td>0.895513</td>\n",
       "      <td>-1.553844</td>\n",
       "      <td>-0.561326</td>\n",
       "      <td>-0.804632</td>\n",
       "      <td>-0.823998</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>-1.438475</td>\n",
       "      <td>0.895513</td>\n",
       "      <td>-1.553844</td>\n",
       "      <td>-0.622172</td>\n",
       "      <td>-0.837666</td>\n",
       "      <td>-0.868103</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>-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",
       "      <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>-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",
       "      <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  \\\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": [
    "### Dividir los datos en conjuntos de entrenamiento, prueba y validación\n",
    "\n",
    "Guardaremos los datos de los últimos 21 días aproximadamente para usarlos como conjunto de prueba después de haber entrenado la red. Usaremos este conjunto para hacer predicciones y compararlas con el número real de ciclistas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Guardar datos de aproximadamente los últimos 21 días\n",
    "test_data = data[-21*24:]\n",
    "\n",
    "# Ahora elimine los datos de prueba del conjunto de datos \n",
    "data = data[:-21*24]\n",
    "\n",
    "# Separar los datos en características y objetivos\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": [
    "Dividiremos los datos en dos conjuntos, uno para el entrenamiento y otro para la validación a medida que se entrena la red. Dado que se trata de datos de series temporales, entrenaremos con datos históricos y luego intentaremos predecir con datos futuros (el conjunto de validación)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mantenga los últimos 60 días de los datos restantes como un conjunto de validación\n",
    "train_features, train_targets = features[:-60*24], targets[:-60*24]\n",
    "val_features, val_targets = features[-60*24:], targets[-60*24:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>-1.334609</td>\n",
       "      <td>0.947345</td>\n",
       "      <td>-1.553844</td>\n",
       "      <td>-0.662736</td>\n",
       "      <td>-0.930162</td>\n",
       "      <td>-0.956312</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>-1.438475</td>\n",
       "      <td>0.895513</td>\n",
       "      <td>-1.553844</td>\n",
       "      <td>-0.561326</td>\n",
       "      <td>-0.804632</td>\n",
       "      <td>-0.823998</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>-1.438475</td>\n",
       "      <td>0.895513</td>\n",
       "      <td>-1.553844</td>\n",
       "      <td>-0.622172</td>\n",
       "      <td>-0.837666</td>\n",
       "      <td>-0.868103</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>-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",
       "      <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>-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",
       "      <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  \\\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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creación de Cargadores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "De formato Pandas debemos cambiar nuestros datos a formato numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_features_np = train_features.to_numpy()\n",
    "val_features_np = val_features.to_numpy()\n",
    "test_features_np = test_features.to_numpy()\n",
    "train_targets_np = train_targets.to_numpy()\n",
    "val_targets_np = val_targets.to_numpy()\n",
    "test_targets_np = test_targets.to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creamos tensores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_tensor = torch.tensor(train_features_np, dtype=torch.float)\n",
    "val_tensor = torch.tensor(val_features_np, dtype=torch.float)\n",
    "test_tensor = torch.tensor(test_features_np, dtype=torch.float)\n",
    "train_targets_tensor = torch.tensor(train_targets_np, dtype=torch.float)\n",
    "val_targets_tensor = torch.tensor(val_targets_np, dtype=torch.float)\n",
    "test_targets_tensor = torch.tensor(test_targets_np, dtype=torch.float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Definimos nuestro set de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define dataset\n",
    "train_data = TensorDataset(train_tensor, train_targets_tensor)\n",
    "valid_data = TensorDataset(val_tensor, val_targets_tensor)\n",
    "test_data = TensorDataset(test_tensor, test_targets_tensor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creamos dataloaders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 16\n",
    "train_loader = DataLoader(train_data, batch_size, shuffle=False)# shuffle=True)\n",
    "valid_loader = DataLoader(valid_data, batch_size, shuffle=False)# shuffle=True)\n",
    "test_loader = DataLoader(test_data, batch_size, shuffle=False)# shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hora de construir la red\n",
    "\n",
    "A continuación, construirá su red. Hemos construido la estructura. Implementará tanto el pase hacia adelante como el pase hacia atrás a través de la red. También establecerá los hiperparámetros: la tasa de aprendizaje, la cantidad de unidades ocultas y la cantidad de pases de entrenamiento.\n",
    "\n",
    "La red tiene x capas, x capas ocultas y una capa de salida. Las capas ocultas utilizarán la función relu para las activaciones. La capa de salida tiene solo un nodo y se usa para la regresión, la salida del nodo es la misma que la entrada del nodo. Es decir, la función de activación es $f(x)=x$. Una función que toma la señal de entrada y genera una señal de salida, pero tiene en cuenta el umbral, se denomina función de activación. Trabajamos a través de cada capa de nuestra red calculando las salidas para cada neurona. Todas las salidas de una capa se convierten en entradas para las neuronas de la siguiente capa. Este proceso se llama *forward propagation*.\n",
    "\n",
    "Usamos los pesos para propagar señales hacia adelante desde la entrada a las capas de salida en una red neuronal. Usamos los pesos para propagar el error hacia atrás desde la salida a la red para actualizar nuestros pesos. Esto se llama *backpropagation*.\n",
    "\n",
    "\n",
    "\n",
    "A continuación, tienes estas tareas:\n",
    "1. Definir la arquitectura de tu red.\n",
    "2. Definir la función de costo y el optimizador.\n",
    "3. Entrenar el modelo.\n",
    "4. Visualizar las curvas de costo de entrenamiento y validación para verificar si estamos \"overfitting\" o no.\n",
    "5. Pasar los datos de testeo por el modelo pre-entrenado.\n",
    "6. Descalar y Visualiza las curvas de las predicciones vs los valores reales\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([16, 58]) torch.Size([16, 1])\n"
     ]
    }
   ],
   "source": [
    "imgs, masks = next(iter(train_loader))\n",
    "print(imgs.shape, masks.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1- Definir la arquitectura de tu modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define la aquitectura\n",
    "class Classifier(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.fc1 = nn.Linear(58, 128)\n",
    "        self.fc2 = nn.Linear(128, 256) \n",
    "        self.fc3 = nn.Linear(256, 512)\n",
    "        self.fc4 = nn.Linear(512, 1024)\n",
    "        self.fc5 = nn.Linear(1024, 512)  \n",
    "        self.fc6 = nn.Linear(512, 256)\n",
    "        self.fc7 = nn.Linear(256, 128)  \n",
    "        self.fc8  = nn.Linear(128,58)\n",
    "        self.fc9  = nn.Linear(58,1)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.fc1(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.fc2(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.fc3(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.fc4(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.fc5(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.fc6(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.fc7(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.fc8(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.fc9(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier(\n",
      "  (fc1): Linear(in_features=58, out_features=128, bias=True)\n",
      "  (fc2): Linear(in_features=128, out_features=256, bias=True)\n",
      "  (fc3): Linear(in_features=256, out_features=512, bias=True)\n",
      "  (fc4): Linear(in_features=512, out_features=1024, bias=True)\n",
      "  (fc5): Linear(in_features=1024, out_features=512, bias=True)\n",
      "  (fc6): Linear(in_features=512, out_features=256, bias=True)\n",
      "  (fc7): Linear(in_features=256, out_features=128, bias=True)\n",
      "  (fc8): Linear(in_features=128, out_features=58, bias=True)\n",
      "  (fc9): Linear(in_features=58, out_features=1, bias=True)\n",
      ")\n",
      "cpu\n"
     ]
    }
   ],
   "source": [
    "#Instancia tu modelo\n",
    "model = Classifier()\n",
    "print(model)\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "print(device)\n",
    "if train_on_gpu:\n",
    "    model.cuda()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Entrenando la red\n",
    "\n",
    "Aquí establecerás los hiperparámetros para la red. La estrategia aquí es encontrar hiperparámetros de modo que el error en el conjunto de entrenamiento sea bajo, pero no se esté sobreajustando a los datos. Si entrenas la red demasiado tiempo o tiene demasiados nodos ocultos, puede volverse demasiado específica para el conjunto de entrenamiento y no podrá generalizarse al conjunto de validación. Es decir, la pérdida en el conjunto de validación comenzará a aumentar a medida que disminuya la pérdida del conjunto de entrenamiento.\n",
    "\n",
    "También utilizarás un método conocido como Stochastic Gradient Descent (SGD) para entrenar la red. La idea es que para cada pase de entrenamiento, tome una muestra aleatoria de los datos en lugar de usar todo el conjunto de datos. Utiliza muchas más pasadas de entrenamiento que con el normal gradient descent, pero cada pasada es mucho más rápida. Esto termina entrenando la red de manera más eficiente. \n",
    "\n",
    "### Elija el número de iteraciones (epócas)\n",
    "Este es el número de lotes de muestras de los datos de entrenamiento que usaremos para entrenar la red. Cuantas más iteraciones utilices, mejor se ajustará el modelo a los datos. Sin embargo, este proceso puede tener rendimientos decrecientes y puede desperdiciar recursos computacionales si utiliza demasiadas iteraciones. Aquí deseas encontrar un número en el que la red tenga una pérdida de entrenamiento baja y la pérdida de validación sea mínima. El número ideal de iteraciones sería un nivel que se detenga poco después de que la pérdida de validación deje de disminuir.\n",
    "\n",
    "### Elija la tasa de aprendizaje\n",
    "Esto escala el tamaño de las actualizaciones de peso. Si es demasiado grande, los pesos tienden a explotar y la red no logra ajustar los datos. Normalmente, una buena opción para empezar es 0.1. Si la red tiene problemas para ajustar los datos, intenta reducir la tasa de aprendizaje. Ten en cuenta que cuanto menor sea la tasa de aprendizaje, menores serán los pasos en las actualizaciones de peso y más tiempo tardará la red neuronal en converger.\n",
    "\n",
    "### Elige el número de nodos ocultos\n",
    "En un modelo donde todos los pesos están optimizados, cuantos más nodos ocultos tenga, más precisas serán las predicciones del modelo. Sin embargo, cuantos más nodos ocultos tenga, más difícil será optimizar los pesos del modelo y más probable será que los pesos subóptimos conduzcan a sobreajuste. Con el sobreajuste, el modelo memorizará los datos de entrenamiento en lugar de aprender el patrón verdadero y no generalizará bien a los datos no vistos.\n",
    "\n",
    "Prueba algunos números diferentes y mira cómo afecta el rendimiento. Si el número de unidades ocultas es demasiado bajo, el modelo no tendrá suficiente espacio para aprender y si es demasiado alto, habrá demasiadas opciones para la dirección que puede tomar el aprendizaje. El truco aquí es encontrar el equilibrio correcto en la cantidad de unidades ocultas que elijas. En general, encontrará que la mejor cantidad de nodos ocultos para usar termina estando entre la cantidad de nodos de entrada y salida."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2- Define la función de costo y el optimizador\n",
    "Recuerda que es un problema de regresión"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define la función de costo\n",
    "criterion = nn.MSELoss()\n",
    "#Define el optimizador\n",
    "optimizer = optim.Adam(model.parameters(), lr = 1e-3)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3- Entrena el modelo\n",
    "Recuerda que aquí puedes usar la técnica \"stop learning\" (revisa los cuadernos de las clases)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 \tTraining Loss: 0.037901 \tValidation Loss: 0.052329\n",
      "Validation loss decreased (inf --> 0.052329).  Saving model ...\n",
      "Epoch: 2 \tTraining Loss: 0.014202 \tValidation Loss: 0.012041\n",
      "Validation loss decreased (0.052329 --> 0.012041).  Saving model ...\n",
      "Epoch: 3 \tTraining Loss: 0.012942 \tValidation Loss: 0.002608\n",
      "Validation loss decreased (0.012041 --> 0.002608).  Saving model ...\n",
      "Epoch: 4 \tTraining Loss: 0.011769 \tValidation Loss: 0.277009\n",
      "Epoch: 5 \tTraining Loss: 0.007217 \tValidation Loss: 0.002336\n",
      "Validation loss decreased (0.002608 --> 0.002336).  Saving model ...\n",
      "Epoch: 6 \tTraining Loss: 0.007915 \tValidation Loss: 0.121958\n",
      "Epoch: 7 \tTraining Loss: 0.008227 \tValidation Loss: 0.003078\n",
      "Epoch: 8 \tTraining Loss: 0.007662 \tValidation Loss: 0.003428\n",
      "Epoch: 9 \tTraining Loss: 0.005431 \tValidation Loss: 0.019589\n",
      "Epoch: 10 \tTraining Loss: 0.003810 \tValidation Loss: 0.006003\n",
      "Epoch: 11 \tTraining Loss: 0.005054 \tValidation Loss: 0.027671\n",
      "Epoch: 12 \tTraining Loss: 0.005795 \tValidation Loss: 0.025031\n",
      "Epoch: 13 \tTraining Loss: 0.004148 \tValidation Loss: 0.001981\n",
      "Validation loss decreased (0.002336 --> 0.001981).  Saving model ...\n",
      "Epoch: 14 \tTraining Loss: 0.004694 \tValidation Loss: 0.005217\n",
      "Epoch: 15 \tTraining Loss: 0.003236 \tValidation Loss: 0.004129\n",
      "Epoch: 16 \tTraining Loss: 0.003809 \tValidation Loss: 0.004432\n",
      "Epoch: 17 \tTraining Loss: 0.003674 \tValidation Loss: 0.010213\n",
      "Epoch: 18 \tTraining Loss: 0.002479 \tValidation Loss: 0.002985\n",
      "Epoch: 19 \tTraining Loss: 0.003643 \tValidation Loss: 0.003396\n",
      "Epoch: 20 \tTraining Loss: 0.002953 \tValidation Loss: 0.010513\n",
      "Epoch: 21 \tTraining Loss: 0.002304 \tValidation Loss: 0.002103\n",
      "Epoch: 22 \tTraining Loss: 0.003638 \tValidation Loss: 0.011270\n",
      "Epoch: 23 \tTraining Loss: 0.002065 \tValidation Loss: 0.004484\n",
      "Epoch: 24 \tTraining Loss: 0.003737 \tValidation Loss: 0.008844\n",
      "Epoch: 25 \tTraining Loss: 0.003546 \tValidation Loss: 0.007258\n"
     ]
    }
   ],
   "source": [
    "# Número de épocas del modelo\n",
    "n_epochs = 25\n",
    "\n",
    "valid_loss_min = np.Inf # Valor inicial de la función de pérdida\n",
    "train_graph=[]\n",
    "valid_graph=[]\n",
    "\n",
    "for epoch in range(1, n_epochs+1):\n",
    "\n",
    "    train_loss = 0.0\n",
    "    valid_loss = 0.0\n",
    "    \n",
    "    ############################\n",
    "    # Entrenamiento del modelo #\n",
    "    ############################\n",
    "    model.train()\n",
    "    for batch_idx, (data, target) in enumerate(train_loader):\n",
    "        # Se pasan os tensores a GPU si están disponibles\n",
    "        if train_on_gpu:\n",
    "            data, target = data.cuda(), target.cuda()\n",
    "        # Se reinician los gradientes\n",
    "        optimizer.zero_grad()\n",
    "        # Predicción\n",
    "        output = model(data)\n",
    "        # Cálculo de la función de pérdida\n",
    "        loss = criterion(output, target)\n",
    "        # Computariza los gradientes\n",
    "        loss.backward()\n",
    "        # Actualiza los pesos\n",
    "        optimizer.step()\n",
    "        # Acumula la función de pérdida\n",
    "        train_loss += loss.item()*data.size(0)\n",
    "        \n",
    "    #########################    \n",
    "    # Validación del modelo #\n",
    "    #########################\n",
    "    model.eval()\n",
    "    for batch_idx, (data, target) in enumerate(valid_loader):\n",
    "        if train_on_gpu:\n",
    "            data, target = data.cuda(), target.cuda()\n",
    "        output = model(data)\n",
    "        loss = criterion(output, target)\n",
    "        valid_loss += loss.item()*data.size(0)\n",
    "    \n",
    "    # Se cálcula la media de la función de pérdida\n",
    "    train_loss = train_loss/len(train_loader.sampler)\n",
    "    valid_loss = valid_loss/len(valid_loader.sampler)\n",
    "    train_graph.append(train_loss)\n",
    "    valid_graph.append(valid_loss)  \n",
    "    # Muestra de datos\n",
    "    print('Epoch: {} \\tTraining Loss: {:.6f} \\tValidation Loss: {:.6f}'.format(\n",
    "        epoch, train_loss, valid_loss))\n",
    "    \n",
    "    #Se guarda el mejor modelo\n",
    "    if valid_loss <= valid_loss_min:\n",
    "        print('Validation loss decreased ({:.6f} --> {:.6f}).  Saving model ...'.format(\n",
    "        valid_loss_min,\n",
    "        valid_loss))\n",
    "        torch.save(model.state_dict(), 'model_augmented.pt')\n",
    "        valid_loss_min = valid_loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4- Visualiza las cuevas de error de entrenamiento y validación"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2cf61243240>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 248,
       "width": 382
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualiza los errores de entrenamiento y validación\n",
    "plt.plot(train_graph, label='Training loss')\n",
    "plt.plot(valid_graph, label='Validation loss')\n",
    "plt.legend(frameon=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5- Consulta tus pronósticos\n",
    "\n",
    "Aquí, usa los datos de testeo para ver qué tan bien tu red está modelando los datos. Si algo está completamente mal aquí, asegúrate de que cada paso en tu red se implemente correctamente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odict_keys(['fc1.weight', 'fc1.bias', 'fc2.weight', 'fc2.bias', 'fc3.weight', 'fc3.bias', 'fc4.weight', 'fc4.bias', 'fc5.weight', 'fc5.bias', 'fc6.weight', 'fc6.bias', 'fc7.weight', 'fc7.bias', 'fc8.weight', 'fc8.bias', 'fc9.weight', 'fc9.bias'])\n",
      "Classifier(\n",
      "  (fc1): Linear(in_features=58, out_features=128, bias=True)\n",
      "  (fc2): Linear(in_features=128, out_features=256, bias=True)\n",
      "  (fc3): Linear(in_features=256, out_features=512, bias=True)\n",
      "  (fc4): Linear(in_features=512, out_features=1024, bias=True)\n",
      "  (fc5): Linear(in_features=1024, out_features=512, bias=True)\n",
      "  (fc6): Linear(in_features=512, out_features=256, bias=True)\n",
      "  (fc7): Linear(in_features=256, out_features=128, bias=True)\n",
      "  (fc8): Linear(in_features=128, out_features=58, bias=True)\n",
      "  (fc9): Linear(in_features=58, out_features=1, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "#Pasa los datos de testeo por tu modelo pre-entrenado\n",
    "state_dict = torch.load('model_augmented.pt')\n",
    "print(state_dict.keys())\n",
    "model.load_state_dict(state_dict)\n",
    "print(model)\n",
    "predictions=[]\n",
    "test_loss=0.0\n",
    "model.eval()\n",
    "for batch_idx, (data, target) in enumerate(test_loader):\n",
    "        if train_on_gpu:\n",
    "            data, target = data.cuda(), target.cuda()\n",
    "        output = model(data)\n",
    "        if train_on_gpu:\n",
    "            predictions=predictions+output.cpu().tolist()\n",
    "        else:\n",
    "            predictions=predictions+output.tolist()\n",
    "        loss = criterion(output, target)\n",
    "        test_loss += loss.item()*data.size(0)\n",
    "    \n",
    "test_loss = test_loss/len(test_loader.sampler)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6- Descalar y Visualiza las curvas de las predicciones vs los valores reales"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Según los resultados obtenidos, se concluye que el modelo de prediccón es adecuado para obtener los valores requeridos, ya que los valores predichos se acercan mucho a los valores reales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2cf6cde65f8>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 248,
       "width": 375
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if normalization == 'mean_std':\n",
    "    scaled_predictions=np.asarray(predictions)*scaled_features['cnt'][1] + scaled_features['cnt'][0] \n",
    "    plt.plot(test_targets_np*scaled_features['cnt'][1] + scaled_features['cnt'][0], label='Reales')\n",
    "    plt.plot(scaled_predictions, label='predictions', alpha = 0.8)\n",
    "    plt.legend(frameon=False)\n",
    "else:\n",
    "    scaled_predictions=np.squeeze(np.asarray(predictions))*scaled_features['cnt']\n",
    "    plt.plot(scaled_predictions, label='predictions')\n",
    "    plt.plot(test_targets_np*scaled_features['cnt'], label='Reales')\n",
    "    plt.legend(frameon=False)\n"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.13"
  },
  "vscode": {
   "interpreter": {
    "hash": "3b34821c144b8c76800a90811dcbd41f4108176e6c128396250549ca1c895441"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}