Restricted-Boltzmann-Machine.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Restricted Boltzmann Machines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Boltzmann Machines\n",
    "# !pip install torch\n",
    "# Importing the libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.parallel\n",
    "import torch.optim as optim\n",
    "import torch.utils.data\n",
    "from torch.autograd import Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the dataset\n",
    "movies = pd.read_csv('ml-1m/movies.dat', sep = '::', header = None, engine = 'python', encoding = 'latin-1')\n",
    "users = pd.read_csv('ml-1m/users.dat', sep = '::', header = None, engine = 'python', encoding = 'latin-1')\n",
    "ratings = pd.read_csv('ml-1m/ratings.dat', sep = '::', header = None, engine = 'python', encoding = 'latin-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children's|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Heat (1995)</td>\n",
       "      <td>Action|Crime|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Sabrina (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Tom and Huck (1995)</td>\n",
       "      <td>Adventure|Children's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Sudden Death (1995)</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "      <td>Action|Adventure|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>American President, The (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>Dracula: Dead and Loving It (1995)</td>\n",
       "      <td>Comedy|Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>Balto (1995)</td>\n",
       "      <td>Animation|Children's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>Nixon (1995)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>Cutthroat Island (1995)</td>\n",
       "      <td>Action|Adventure|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>Casino (1995)</td>\n",
       "      <td>Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>Sense and Sensibility (1995)</td>\n",
       "      <td>Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>Four Rooms (1995)</td>\n",
       "      <td>Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>Ace Ventura: When Nature Calls (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>Money Train (1995)</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0                                      1                             2\n",
       "0    1                       Toy Story (1995)   Animation|Children's|Comedy\n",
       "1    2                         Jumanji (1995)  Adventure|Children's|Fantasy\n",
       "2    3                Grumpier Old Men (1995)                Comedy|Romance\n",
       "3    4               Waiting to Exhale (1995)                  Comedy|Drama\n",
       "4    5     Father of the Bride Part II (1995)                        Comedy\n",
       "5    6                            Heat (1995)         Action|Crime|Thriller\n",
       "6    7                         Sabrina (1995)                Comedy|Romance\n",
       "7    8                    Tom and Huck (1995)          Adventure|Children's\n",
       "8    9                    Sudden Death (1995)                        Action\n",
       "9   10                       GoldenEye (1995)     Action|Adventure|Thriller\n",
       "10  11         American President, The (1995)          Comedy|Drama|Romance\n",
       "11  12     Dracula: Dead and Loving It (1995)                 Comedy|Horror\n",
       "12  13                           Balto (1995)          Animation|Children's\n",
       "13  14                           Nixon (1995)                         Drama\n",
       "14  15                Cutthroat Island (1995)      Action|Adventure|Romance\n",
       "15  16                          Casino (1995)                Drama|Thriller\n",
       "16  17           Sense and Sensibility (1995)                 Drama|Romance\n",
       "17  18                      Four Rooms (1995)                      Thriller\n",
       "18  19  Ace Ventura: When Nature Calls (1995)                        Comedy\n",
       "19  20                     Money Train (1995)                        Action"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 4,
     "metadata": {},
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   ],
   "source": [
    "users.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preparing the training set and the test set\n",
    "training_set = pd.read_csv('ml-100k/u1.base', delimiter = '\\t')\n",
    "training_set = np.array(training_set, dtype = 'int')\n",
    "test_set = pd.read_csv('ml-100k/u1.test', delimiter = '\\t')\n",
    "test_set = np.array(test_set, dtype = 'int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[        1,         2,         3, 876893171],\n",
       "       [        1,         3,         4, 878542960],\n",
       "       [        1,         4,         3, 876893119],\n",
       "       ...,\n",
       "       [      943,      1188,         3, 888640250],\n",
       "       [      943,      1228,         3, 888640275],\n",
       "       [      943,      1330,         3, 888692465]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[        1,        10,         3, 875693118],\n",
       "       [        1,        12,         5, 878542960],\n",
       "       [        1,        14,         5, 874965706],\n",
       "       ...,\n",
       "       [      459,       934,         3, 879563639],\n",
       "       [      460,        10,         3, 882912371],\n",
       "       [      462,       682,         5, 886365231]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the number of users and movies\n",
    "nb_users = int(max(max(training_set[:,0]), max(test_set[:,0])))\n",
    "nb_movies = int(max(max(training_set[:,1]), max(test_set[:,1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "943"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb_users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1682"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb_movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting the data into an array with users in lines and movies in columns\n",
    "def convert(data):\n",
    "    new_data = []\n",
    "    for id_users in range(1, nb_users + 1):\n",
    "        id_movies = data[:,1][data[:,0] == id_users]\n",
    "        id_ratings = data[:,2][data[:,0] == id_users]\n",
    "        ratings = np.zeros(nb_movies)\n",
    "        ratings[id_movies - 1] = id_ratings\n",
    "        new_data.append(list(ratings))\n",
    "    return new_data\n",
    "training_set = convert(training_set)\n",
    "test_set = convert(test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting the data into Torch tensors\n",
    "training_set = torch.FloatTensor(training_set)\n",
    "test_set = torch.FloatTensor(test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting the ratings into binary ratings 1 (Liked) or 0 (Not Liked)\n",
    "training_set[training_set == 0] = -1\n",
    "training_set[training_set == 1] = 0\n",
    "training_set[training_set == 2] = 0\n",
    "training_set[training_set >= 3] = 1\n",
    "test_set[test_set == 0] = -1\n",
    "test_set[test_set == 1] = 0\n",
    "test_set[test_set == 2] = 0\n",
    "test_set[test_set >= 3] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the architecture of the Neural Network\n",
    "class RBM():\n",
    "    def __init__(self, nv, nh):\n",
    "        self.W = torch.randn(nh, nv)\n",
    "        self.a = torch.randn(1, nh)\n",
    "        self.b = torch.randn(1, nv)\n",
    "        \n",
    "    def sample_h(self, x):\n",
    "        wx = torch.mm(x, self.W.t())\n",
    "        activation = wx + self.a.expand_as(wx)\n",
    "        p_h_given_v = torch.sigmoid(activation)\n",
    "        return p_h_given_v, torch.bernoulli(p_h_given_v)\n",
    "    \n",
    "    def sample_v(self, y):\n",
    "        wy = torch.mm(y, self.W)\n",
    "        activation = wy + self.b.expand_as(wy)\n",
    "        p_v_given_h = torch.sigmoid(activation)\n",
    "        return p_v_given_h, torch.bernoulli(p_v_given_h)\n",
    "    \n",
    "    def train(self, v0, vk, ph0, phk):\n",
    "        self.W += (torch.mm(v0.t(), ph0) - torch.mm(vk.t(), phk)).t()\n",
    "        self.b += torch.sum((v0 - vk), 0)\n",
    "        self.a += torch.sum((ph0 - phk), 0)\n",
    "\n",
    "nv = len(training_set[0])\n",
    "nh = 100\n",
    "rbm = RBM(nv, nh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1 loss: tensor(0.3476)\n",
      "epoch: 2 loss: tensor(0.2485)\n",
      "epoch: 3 loss: tensor(0.2496)\n",
      "epoch: 4 loss: tensor(0.2503)\n",
      "epoch: 5 loss: tensor(0.2464)\n",
      "epoch: 6 loss: tensor(0.2447)\n",
      "epoch: 7 loss: tensor(0.2497)\n",
      "epoch: 8 loss: tensor(0.2501)\n",
      "epoch: 9 loss: tensor(0.2468)\n",
      "epoch: 10 loss: tensor(0.2491)\n",
      "epoch: 11 loss: tensor(0.2483)\n",
      "epoch: 12 loss: tensor(0.2486)\n",
      "epoch: 13 loss: tensor(0.2452)\n",
      "epoch: 14 loss: tensor(0.2511)\n",
      "epoch: 15 loss: tensor(0.2484)\n",
      "epoch: 16 loss: tensor(0.2474)\n",
      "epoch: 17 loss: tensor(0.2483)\n",
      "epoch: 18 loss: tensor(0.2453)\n",
      "epoch: 19 loss: tensor(0.2498)\n",
      "epoch: 20 loss: tensor(0.2479)\n",
      "epoch: 21 loss: tensor(0.2470)\n",
      "epoch: 22 loss: tensor(0.2458)\n",
      "epoch: 23 loss: tensor(0.2491)\n",
      "epoch: 24 loss: tensor(0.2458)\n",
      "epoch: 25 loss: tensor(0.2479)\n",
      "epoch: 26 loss: tensor(0.2493)\n",
      "epoch: 27 loss: tensor(0.2460)\n",
      "epoch: 28 loss: tensor(0.2474)\n",
      "epoch: 29 loss: tensor(0.2485)\n",
      "epoch: 30 loss: tensor(0.2464)\n",
      "epoch: 31 loss: tensor(0.2487)\n",
      "epoch: 32 loss: tensor(0.2469)\n",
      "epoch: 33 loss: tensor(0.2488)\n",
      "epoch: 34 loss: tensor(0.2488)\n",
      "epoch: 35 loss: tensor(0.2449)\n",
      "epoch: 36 loss: tensor(0.2485)\n",
      "epoch: 37 loss: tensor(0.2473)\n",
      "epoch: 38 loss: tensor(0.2482)\n",
      "epoch: 39 loss: tensor(0.2463)\n",
      "epoch: 40 loss: tensor(0.2452)\n",
      "epoch: 41 loss: tensor(0.2453)\n",
      "epoch: 42 loss: tensor(0.2481)\n",
      "epoch: 43 loss: tensor(0.2450)\n",
      "epoch: 44 loss: tensor(0.2503)\n",
      "epoch: 45 loss: tensor(0.2477)\n",
      "epoch: 46 loss: tensor(0.2486)\n",
      "epoch: 47 loss: tensor(0.2496)\n",
      "epoch: 48 loss: tensor(0.2467)\n",
      "epoch: 49 loss: tensor(0.2484)\n",
      "epoch: 50 loss: tensor(0.2456)\n"
     ]
    }
   ],
   "source": [
    "# Training the RBM\n",
    "nb_epoch = 50\n",
    "batch_size = 100\n",
    "\n",
    "for epoch in range(1, nb_epoch + 1):\n",
    "    train_loss = 0\n",
    "    s = 0.\n",
    "    for id_user in range(0, nb_users - batch_size, batch_size):\n",
    "        vk = training_set[id_user : id_user+batch_size]\n",
    "        v0 = training_set[id_user : id_user+batch_size]\n",
    "        ph0,_ = rbm.sample_h(v0)\n",
    "        for k in range(10):\n",
    "            _,hk = rbm.sample_h(vk)\n",
    "            _,vk = rbm.sample_v(hk)\n",
    "            vk[v0<0] = v0[v0<0]\n",
    "        phk,_ = rbm.sample_h(vk)\n",
    "        rbm.train(v0, vk, ph0, phk)\n",
    "        train_loss += torch.mean(torch.abs(v0[v0>=0] - vk[v0>=0]))\n",
    "        s += 1.\n",
    "    print('epoch: '+str(epoch)+' loss: '+str(train_loss/s))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test loss: tensor(0.2333)\n"
     ]
    }
   ],
   "source": [
    "# Testing the RBM\n",
    "test_loss = 0\n",
    "s = 0.\n",
    "for id_user in range(nb_users):\n",
    "    v = training_set[id_user:id_user+1]\n",
    "    vt = test_set[id_user:id_user+1]\n",
    "    if len(vt[vt>=0]) > 0:\n",
    "        _,h = rbm.sample_h(v)\n",
    "        _,v = rbm.sample_v(h)\n",
    "        test_loss += torch.mean(torch.abs(vt[vt>=0] - v[vt>=0]))\n",
    "        s += 1.\n",
    "print('test loss: '+str(test_loss/s))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "         0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "         0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-1.,  1., -1.,  ..., -1., -1., -1.]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(107.0935)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "459.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "id_user = 800\n",
    "v = training_set[id_user:id_user+1]\n",
    "vt = test_set[id_user:id_user+1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(-1.)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v[0][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children's|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3878</th>\n",
       "      <td>3948</td>\n",
       "      <td>Meet the Parents (2000)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3879</th>\n",
       "      <td>3949</td>\n",
       "      <td>Requiem for a Dream (2000)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3880</th>\n",
       "      <td>3950</td>\n",
       "      <td>Tigerland (2000)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3881</th>\n",
       "      <td>3951</td>\n",
       "      <td>Two Family House (2000)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3882</th>\n",
       "      <td>3952</td>\n",
       "      <td>Contender, The (2000)</td>\n",
       "      <td>Drama|Thriller</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3883 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0                                   1                             2\n",
       "0        1                    Toy Story (1995)   Animation|Children's|Comedy\n",
       "1        2                      Jumanji (1995)  Adventure|Children's|Fantasy\n",
       "2        3             Grumpier Old Men (1995)                Comedy|Romance\n",
       "3        4            Waiting to Exhale (1995)                  Comedy|Drama\n",
       "4        5  Father of the Bride Part II (1995)                        Comedy\n",
       "...    ...                                 ...                           ...\n",
       "3878  3948             Meet the Parents (2000)                        Comedy\n",
       "3879  3949          Requiem for a Dream (2000)                         Drama\n",
       "3880  3950                    Tigerland (2000)                         Drama\n",
       "3881  3951             Two Family House (2000)                         Drama\n",
       "3882  3952               Contender, The (2000)                Drama|Thriller\n",
       "\n",
       "[3883 rows x 3 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "phu,h = rbm.sample_h(v)\n",
    "pvu,v = rbm.sample_v(h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phu[0][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.7203)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pvu[0][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "         0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "         0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(1.)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v[0][1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FIM"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}