autoencoder.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "autoencoder.ipynb",
      "version": "0.3.2",
      "views": {},
      "default_view": {},
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "BnmksSKy__P5",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "HaLCWCLdAqjB",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "output_extras": [
            {
              "item_id": 1
            }
          ],
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ca90fe09-d6b2-45a3-d6cd-33167d664b87",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1516761174926,
          "user_tz": 360,
          "elapsed": 2177,
          "user": {
            "displayName": "José Castillo",
            "photoUrl": "//lh6.googleusercontent.com/-Rz3R3wYNNQs/AAAAAAAAAAI/AAAAAAAAjjM/p1QOaiSP3Nc/s50-c-k-no/photo.jpg",
            "userId": "105435570147088816949"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "tf.test.gpu_device_name()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/device:GPU:0'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "metadata": {
        "id": "5hydZ0_ZArjf",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "from pandas import set_option, read_csv\n",
        "import os"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "odSizFXbBUvW",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "output_extras": [
            {
              "item_id": 1
            }
          ],
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "fd818473-4529-4a26-a6c3-4a448a928f0c",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1516761342440,
          "user_tz": 360,
          "elapsed": 2782,
          "user": {
            "displayName": "José Castillo",
            "photoUrl": "//lh6.googleusercontent.com/-Rz3R3wYNNQs/AAAAAAAAAAI/AAAAAAAAjjM/p1QOaiSP3Nc/s50-c-k-no/photo.jpg",
            "userId": "105435570147088816949"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "os.getcwd()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "metadata": {
        "id": "58j1clcBBGpz",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "from google.colab import files"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "GlBaLXf5FiZT",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "output_extras": [
            {
              "item_id": 1
            }
          ],
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "cb41aafe-c546-4f73-c3a6-67f3a99730dd",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1516762478463,
          "user_tz": 360,
          "elapsed": 25713,
          "user": {
            "displayName": "José Castillo",
            "photoUrl": "//lh6.googleusercontent.com/-Rz3R3wYNNQs/AAAAAAAAAAI/AAAAAAAAjjM/p1QOaiSP3Nc/s50-c-k-no/photo.jpg",
            "userId": "105435570147088816949"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "uploaded = files.upload()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-6fde87ba-6b21-4cc0-afe5-2011457217d9\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-6fde87ba-6b21-4cc0-afe5-2011457217d9\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"</script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "gvrPPdo_FlK4",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "output_extras": [
            {
              "item_id": 1
            }
          ],
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "3ef452b6-cea0-46c9-e14a-9aecf47f7940",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1516762540085,
          "user_tz": 360,
          "elapsed": 2098,
          "user": {
            "displayName": "José Castillo",
            "photoUrl": "//lh6.googleusercontent.com/-Rz3R3wYNNQs/AAAAAAAAAAI/AAAAAAAAjjM/p1QOaiSP3Nc/s50-c-k-no/photo.jpg",
            "userId": "105435570147088816949"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "uploaded['lexicon.esna.proc.norep.tmp2']"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "b'cebolla xonaca\\najo axox\\nkilo ki\\naqu\\xc3\\xad nican\\nhaba ahuax\\ndinero tomin\\nno amo\\nchile chil\\ncucharada xumalli\\namar tlazohtla\\na\\xc3\\xb1o xihuitl\\nsal izta\\n2 ome\\nluego niman\\n8 chicuey\\n5 macuil\\nnoche yohual\\nelote elo\\njuan juan\\nvender tlanamaca\\npedernal tecpa\\n6 chicuacen\\ndiego diego\\ny huan\\n4 nahui\\nllorar choca\\ncomal comal\\nvivir nemi\\nmanteca chiyahuizotl\\nhongo nanacatl\\nflor xochi\\nnopal nohpal\\nescudo chimal\\nd\\xc3\\xada tonal\\npollo poyox\\natole atol\\nzanahoria zanahoria\\nahora axcan\\ntraficante pochteca\\nsan san\\ntrabajar tequiti\\nmorir miqui\\ncasa chan\\ncuerpo nacayo\\ncarne naca\\nmasa textli\\nzapatista zapatista\\nmar\\xc3\\xada maria\\ncarlos carlo\\narroz arroz\\naz\\xc3\\xbacar tzopel\\ndespu\\xc3\\xa9s zatepan\\nma\\xc3\\xb1ana moztla\\nropa tzotzoma\\nuno ce\\nmercado tianquiz\\nmucho miac\\nmexicano mexica\\ntamal tamalli\\nmes metz\\nguerra yao\\nenojar cualan\\nrico tlatquihua\\nsalir 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ye\\ncielo ilhui\\nnombre toca\\naparecer neci\\ncodorniz zol\\nvuelta malacacho\\nfiesta ilhui\\ncastellano caxtilan\\np\\xc3\\xa1jaro tototl\\nembriagar ihuinti\\nmilpa mil\\nderrama tzetzeloa\\nperseguir totoca\\nocelote ocelo\\nprimeramente achtopa\\ncaja petlacal\\nbroma camanal\\nvariar nepapan\\nllover ahui\\nperder polo\\nenfermedad coco\\npasta textli\\nrecipiente comitl\\npelear cualan\\nn\\xc3\\xa1huatl nahuatlahtol\\npoquito tepi\\ntren calmimilo\\nhumedad cuechahua\\nperecer polihui\\nserpiente coatl\\ncalentar totonia\\nantes achto\\npescado michi\\nviejo huehue\\nfog\\xc3\\xb3n tlecuil\\nextraordinario mahuiztic\\nrelatar pohua\\ndistraer ixpetani\\nregresar mocuep\\nescribir hcuilo\\nhora man\\ntejocotes xoco\\nagua a\\ninclinar mopechteccac\\nprisa ciuhca\\navergonzar pinauhtiz\\nle\\xc3\\xb1a cuahuitl\\ncerrar tzacua\\nplaticar nonotza\\nadmirar mahuizo\\ncarb\\xc3\\xb3n tecol\\nbarro zoqui\\nlloro choc\\ncarta ama\\ngota chipi\\ntocar tzotzona\\nembriaguez 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tlazohtla\\npregunta htlani\\nvendo tlanamaca\\nhablar tlahtohua\\nfuerza chicauh\\nfortalecer chicahuaz\\nplanta planta\\nleer pohua\\nllamarlo notza\\ncruel pitz\\nvendais tlanamaca\\npensar nicnemili\\nmismo yehua\\ndiariamente moztla\\ndejar cauh\\nt\\xc3\\xado noteahu\\njefe pochteca\\nba\\xc3\\xb1ar tealti\\npadre tata\\ncustodiar pix\\nvarios miac\\nhueso omitz\\nperdonar pohpo\\nsacerdote teopix\\nciertamente nel\\nameis tlazohtla\\ndecir hto\\ngemir choca\\nse\\xc3\\xb1or tlaca\\nextender patahuaz\\nrogar tlauhti\\ngarganta motozcatlan\\ndichoso pahpaqui\\nteporingos teporingo\\nverme itac\\njitomates toma\\nabeto acxoya\\narrastrar huilana\\nconjunto tzmolin\\nvolver cuepa\\ncuidar cuitlahui\\ncuidado cuitlahui\\njunto nahuac\\nentender nlcaci\\nchapultepec chapoltepec\\ndonde can\\nnada amo\\nmeter calaqui\\npertenecer pohui\\njard\\xc3\\xadn xochi\\nverlo taz\\nayuda palehui\\ndurazno tolachno\\ncantor cuic\\nservir serviroz\\nsemilla xinach\\nave quechol\\ncola icuitlapil\\ngorra tzotzocol\\nj\\xc3\\xadcara xical\\npersona tlaca\\nescuela tlamachtil\\nni\\xc3\\xb1o cocone\\ngrueso toma\\nhaber ic\\naceite chiyahuizotl\\nhermano icnin\\ncosta anahuaca\\nvirgen ichpoc\\nliebre naca\\nnecesario nequi\\ngolpear huitequi\\ncalma yocox\\nseguida niman\\nespa\\xc3\\xb1a caxtillan\\ncortar tequi\\nhasta ixquich\\ngran hueyi\\nart\\xc3\\xadculo tlaman\\nobscurecerse yohua\\nvivais nemi\\npajarito tototl\\nmedicina pah\\nvino ohuala\\nencontrar namiqui\\ncolgar pilca\\nenriquecer cuiltono\\nayunar zauh\\npa\\xc3\\xads tlalnan\\ntraer huica\\nl\\xc3\\xa1vese cualtiz\\ntomar cui\\nitacates ihtacatl\\nlago tezca\\nflauta pitza\\nhumo po\\ncaballero pipil\\ncautivo mal\\ntaz\\xc3\\xb3n tecoma\\nexhortaci\\xc3\\xb3n nonotza\\ncesto chiquihui\\narena xal\\nsangrar ommihzo\\npez mimic\\nadivinar xicadivinaro\\ndespedir nahuati\\ncarga mama\\ndulce tzopel\\ngrito tzatzi\\nacostar teca\\ncargar mama\\ncanoa acalli\\ningrato cemelle\\nzapato cah\\nagujero coyoc\\nespantar mauh\\nanhelo elehuia\\ntrasladar micuani\\nmueble icpal\\nescoger pepena\\npap\\xc3\\xa1 tata\\ntienda namacoyan\\ninvocar tenehui\\ndarme maquili\\ndanzar ihtoti\\nmacho oquich\\npe\\xc3\\xb1ascal texcal\\npel\\xc3\\xadcula pelicula\\nmuerto mic\\nproyecto yollamachiliz\\ndesaparecer polihui\\nchalcas chalca\\ncaliente toton\\nimagen ixiptla\\nta\\xc3\\xb1er tzotzona\\ninteresar cualita\\nretornar cuepcayo\\nmoverse olini\\nerror tlapolo\\norina cuitl\\npatria tlalnan\\nordenar nahuati\\namistad icniuh\\nentra\\xc3\\xb1a xillan\\nculhuac\\xc3\\xa1n colhuaca\\nsaludo tlahpal\\ntamboril tzotzona\\nparar quetza\\npreocuparse netequipacholo\\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "metadata": {
        "id": "nRsOCjAaF5-U",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "output_extras": [
            {
              "item_id": 1
            }
          ],
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d270aa40-e498-4933-cc9e-92d0869ddc02",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1516762666172,
          "user_tz": 360,
          "elapsed": 1743,
          "user": {
            "displayName": "José Castillo",
            "photoUrl": "//lh6.googleusercontent.com/-Rz3R3wYNNQs/AAAAAAAAAAI/AAAAAAAAjjM/p1QOaiSP3Nc/s50-c-k-no/photo.jpg",
            "userId": "105435570147088816949"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "type(uploaded)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "metadata": {
        "id": "W67590qvGIbd",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "lexicon = uploaded['lexicon.esna.proc.norep.tmp2'].decode(\"utf-8\").split(\"\\n\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "PT2UzlmVGhaG",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "output_extras": [
            {
              "item_id": 1
            }
          ],
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d68bd864-0fd6-4af6-c9a5-f42e4ec6f301",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1516762899584,
          "user_tz": 360,
          "elapsed": 942,
          "user": {
            "displayName": "José Castillo",
            "photoUrl": "//lh6.googleusercontent.com/-Rz3R3wYNNQs/AAAAAAAAAAI/AAAAAAAAjjM/p1QOaiSP3Nc/s50-c-k-no/photo.jpg",
            "userId": "105435570147088816949"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "tuple(lexicon[0].split())"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('cebolla', 'xonaca')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "metadata": {
        "id": "G1dlUJrrH9Xq",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Glc-v1KeHSBm",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "lexicon_ = list()\n",
        "for i in lexicon:\n",
        "  lexicon_.append(tuple(i.split()))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "psBHyuO5Hnot",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "lexicon_df = pd.DataFrame.from_records(lexicon_,columns=['esp','nah'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "C55PlxfxIMYn",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "lexicon_df['esp']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "qIK0QmklIXsU",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "output_extras": [
            {
              "item_id": 1
            }
          ],
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "8902159d-3a10-48b5-a899-ebfb3e6fe963",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1516764965948,
          "user_tz": 360,
          "elapsed": 104180,
          "user": {
            "displayName": "José Castillo",
            "photoUrl": "//lh6.googleusercontent.com/-Rz3R3wYNNQs/AAAAAAAAAAI/AAAAAAAAjjM/p1QOaiSP3Nc/s50-c-k-no/photo.jpg",
            "userId": "105435570147088816949"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "uploaded1 = files.upload()"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-947d4e4c-de49-4364-b11d-bf6840849672\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-947d4e4c-de49-4364-b11d-bf6840849672\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"</script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "cZIUnTBUJy_w",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "na_n2v = uploaded1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2HXrJayPJ29a",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "output_extras": [
            {
              "item_id": 1
            }
          ],
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "9f5097b6-490f-4fdd-cd69-e6f07516f193",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1516764618297,
          "user_tz": 360,
          "elapsed": 967754,
          "user": {
            "displayName": "José Castillo",
            "photoUrl": "//lh6.googleusercontent.com/-Rz3R3wYNNQs/AAAAAAAAAAI/AAAAAAAAjjM/p1QOaiSP3Nc/s50-c-k-no/photo.jpg",
            "userId": "105435570147088816949"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "uploaded = files.upload()"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-f80063b3-fcff-4869-af20-b37e917486c8\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-f80063b3-fcff-4869-af20-b37e917486c8\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"</script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "0dYJh9qRKJl9",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "es_n2v = uploaded"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pEJqaX6pPpxd",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "es_n2v = es_n2v['es.node2vec.embeddings'].decode(\"utf-8\").split(\"\\n\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "uRc4G6j-OD68",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "na_n2v = na_n2v['na.node2vec.embeddings'].decode(\"utf-8\").split(\"\\n\")\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Zi9UqHqkOf7A",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "na_tmp = list()\n",
        "\n",
        "for i in na_n2v:\n",
        "  if i == 0:\n",
        "    pass\n",
        "  else:\n",
        "    na_tmp.append(tuple(i.split()))\n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kDmj3r0LOSo5",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "output_extras": [
            {
              "item_id": 1
            }
          ],
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5da651ac-bd13-4427-8cac-da9192527f67",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1516765479941,
          "user_tz": 360,
          "elapsed": 469,
          "user": {
            "displayName": "José Castillo",
            "photoUrl": "//lh6.googleusercontent.com/-Rz3R3wYNNQs/AAAAAAAAAAI/AAAAAAAAjjM/p1QOaiSP3Nc/s50-c-k-no/photo.jpg",
            "userId": "105435570147088816949"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "na_tmp.pop(0)"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('1904', '128')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "metadata": {
        "id": "Tm-lsmY7RJTL",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "na_tmp[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xRhAB1q4RNhC",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "es_tmp = list()\n",
        "\n",
        "for i in es_n2v:\n",
        "  if i == 0:\n",
        "    pass\n",
        "  else:\n",
        "    es_tmp.append(tuple(i.split()))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "P0NKMvwxRQa1",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "output_extras": [
            {
              "item_id": 1
            }
          ],
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "74fd397c-098a-4340-c75e-a92fa97fae0e",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1516765524173,
          "user_tz": 360,
          "elapsed": 474,
          "user": {
            "displayName": "José Castillo",
            "photoUrl": "//lh6.googleusercontent.com/-Rz3R3wYNNQs/AAAAAAAAAAI/AAAAAAAAjjM/p1QOaiSP3Nc/s50-c-k-no/photo.jpg",
            "userId": "105435570147088816949"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "es_tmp.pop(0)"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('4433', '128')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "metadata": {
        "id": "6liVitRPRS6N",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "es_df = pd.DataFrame.from_records(es_tmp)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "s_Suz27cRevv",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "na_df = pd.DataFrame.from_records(na_tmp)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "YXNFF5zHRul-",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "output_extras": [
            {
              "item_id": 1
            }
          ],
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "outputId": "eca2e41e-395a-4a82-aed2-ba8945af8f14",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1516765645147,
          "user_tz": 360,
          "elapsed": 1130,
          "user": {
            "displayName": "José Castillo",
            "photoUrl": "//lh6.googleusercontent.com/-Rz3R3wYNNQs/AAAAAAAAAAI/AAAAAAAAjjM/p1QOaiSP3Nc/s50-c-k-no/photo.jpg",
            "userId": "105435570147088816949"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "es_df.head()"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>...</th>\n",
              "      <th>119</th>\n",
              "      <th>120</th>\n",
              "      <th>121</th>\n",
              "      <th>122</th>\n",
              "      <th>123</th>\n",
              "      <th>124</th>\n",
              "      <th>125</th>\n",
              "      <th>126</th>\n",
              "      <th>127</th>\n",
              "      <th>128</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>igual</td>\n",
              "      <td>-0.021394</td>\n",
              "      <td>-0.22308600000000001</td>\n",
              "      <td>0.081375000000000003</td>\n",
              "      <td>-0.44000800000000001</td>\n",
              "      <td>0.41420299999999999</td>\n",
              "      <td>-0.37752400000000003</td>\n",
              "      <td>-0.40576099999999998</td>\n",
              "      <td>-0.080980999999999997</td>\n",
              "      <td>-0.14319200000000001</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.185254</td>\n",
              "      <td>0.17538200000000001</td>\n",
              "      <td>0.326658</td>\n",
              "      <td>0.094920000000000004</td>\n",
              "      <td>-0.69681999999999999</td>\n",
              "      <td>0.0026940000000000002</td>\n",
              "      <td>-0.45110899999999998</td>\n",
              "      <td>-0.47127799999999997</td>\n",
              "      <td>0.041342999999999998</td>\n",
              "      <td>-0.38586300000000001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>hermano</td>\n",
              "      <td>-0.27513300000000002</td>\n",
              "      <td>-0.290684</td>\n",
              "      <td>-0.035325000000000002</td>\n",
              "      <td>-0.172819</td>\n",
              "      <td>-0.171268</td>\n",
              "      <td>0.19417799999999999</td>\n",
              "      <td>-0.260795</td>\n",
              "      <td>-0.131469</td>\n",
              "      <td>-0.56005700000000003</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.77275700000000003</td>\n",
              "      <td>0.26308999999999999</td>\n",
              "      <td>0.152056</td>\n",
              "      <td>-0.34808899999999998</td>\n",
              "      <td>0.31774200000000002</td>\n",
              "      <td>0.093362000000000001</td>\n",
              "      <td>-0.53920000000000001</td>\n",
              "      <td>-0.091986999999999999</td>\n",
              "      <td>0.24560499999999999</td>\n",
              "      <td>0.074918999999999999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>martajan</td>\n",
              "      <td>-0.39002700000000001</td>\n",
              "      <td>-0.18165300000000001</td>\n",
              "      <td>-0.26608799999999999</td>\n",
              "      <td>0.13489100000000001</td>\n",
              "      <td>-0.105633</td>\n",
              "      <td>-0.124488</td>\n",
              "      <td>-0.85740300000000003</td>\n",
              "      <td>-0.210449</td>\n",
              "      <td>0.10071099999999999</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.30548500000000001</td>\n",
              "      <td>-0.43815399999999999</td>\n",
              "      <td>0.63305199999999995</td>\n",
              "      <td>-0.086624000000000007</td>\n",
              "      <td>0.049135999999999999</td>\n",
              "      <td>-0.166021</td>\n",
              "      <td>-0.45888899999999999</td>\n",
              "      <td>0.21430399999999999</td>\n",
              "      <td>-0.089515999999999998</td>\n",
              "      <td>-0.083751000000000006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>fuero</td>\n",
              "      <td>0.038670999999999997</td>\n",
              "      <td>-0.12618799999999999</td>\n",
              "      <td>-0.12856699999999999</td>\n",
              "      <td>-0.10723100000000001</td>\n",
              "      <td>0.091522000000000006</td>\n",
              "      <td>-0.15167600000000001</td>\n",
              "      <td>-0.137239</td>\n",
              "      <td>-0.118836</td>\n",
              "      <td>-0.078329999999999997</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.22109699999999999</td>\n",
              "      <td>0.062376000000000001</td>\n",
              "      <td>0.249082</td>\n",
              "      <td>0.034577999999999998</td>\n",
              "      <td>-0.069553000000000004</td>\n",
              "      <td>0.11555600000000001</td>\n",
              "      <td>-0.058006000000000002</td>\n",
              "      <td>0.075348999999999999</td>\n",
              "      <td>-0.039742</td>\n",
              "      <td>0.043875999999999998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ehecatl</td>\n",
              "      <td>-0.262042</td>\n",
              "      <td>-0.34154400000000001</td>\n",
              "      <td>0.020990999999999999</td>\n",
              "      <td>-0.098941000000000001</td>\n",
              "      <td>0.0065110000000000003</td>\n",
              "      <td>-0.40626099999999998</td>\n",
              "      <td>-0.011587</td>\n",
              "      <td>-0.046593999999999997</td>\n",
              "      <td>0.20002200000000001</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.087499999999999994</td>\n",
              "      <td>-0.12940599999999999</td>\n",
              "      <td>-0.203127</td>\n",
              "      <td>-0.14116500000000001</td>\n",
              "      <td>-0.11533</td>\n",
              "      <td>-0.20407400000000001</td>\n",
              "      <td>-0.14868200000000001</td>\n",
              "      <td>-0.107582</td>\n",
              "      <td>-0.051375999999999998</td>\n",
              "      <td>0.16094600000000001</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 129 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        0                     1                     2    \\\n",
              "0     igual             -0.021394  -0.22308600000000001   \n",
              "1   hermano  -0.27513300000000002             -0.290684   \n",
              "2  martajan  -0.39002700000000001  -0.18165300000000001   \n",
              "3     fuero  0.038670999999999997  -0.12618799999999999   \n",
              "4   ehecatl             -0.262042  -0.34154400000000001   \n",
              "\n",
              "                     3                      4                      5    \\\n",
              "0   0.081375000000000003   -0.44000800000000001    0.41420299999999999   \n",
              "1  -0.035325000000000002              -0.172819              -0.171268   \n",
              "2   -0.26608799999999999    0.13489100000000001              -0.105633   \n",
              "3   -0.12856699999999999   -0.10723100000000001   0.091522000000000006   \n",
              "4   0.020990999999999999  -0.098941000000000001  0.0065110000000000003   \n",
              "\n",
              "                    6                     7                      8    \\\n",
              "0  -0.37752400000000003  -0.40576099999999998  -0.080980999999999997   \n",
              "1   0.19417799999999999             -0.260795              -0.131469   \n",
              "2             -0.124488  -0.85740300000000003              -0.210449   \n",
              "3  -0.15167600000000001             -0.137239              -0.118836   \n",
              "4  -0.40626099999999998             -0.011587  -0.046593999999999997   \n",
              "\n",
              "                     9            ...                              119  \\\n",
              "0   -0.14319200000000001          ...                        -0.185254   \n",
              "1   -0.56005700000000003          ...             -0.77275700000000003   \n",
              "2    0.10071099999999999          ...             -0.30548500000000001   \n",
              "3  -0.078329999999999997          ...             -0.22109699999999999   \n",
              "4    0.20002200000000001          ...            -0.087499999999999994   \n",
              "\n",
              "                    120                  121                    122  \\\n",
              "0   0.17538200000000001             0.326658   0.094920000000000004   \n",
              "1   0.26308999999999999             0.152056   -0.34808899999999998   \n",
              "2  -0.43815399999999999  0.63305199999999995  -0.086624000000000007   \n",
              "3  0.062376000000000001             0.249082   0.034577999999999998   \n",
              "4  -0.12940599999999999            -0.203127   -0.14116500000000001   \n",
              "\n",
              "                     123                    124                    125  \\\n",
              "0   -0.69681999999999999  0.0026940000000000002   -0.45110899999999998   \n",
              "1    0.31774200000000002   0.093362000000000001   -0.53920000000000001   \n",
              "2   0.049135999999999999              -0.166021   -0.45888899999999999   \n",
              "3  -0.069553000000000004    0.11555600000000001  -0.058006000000000002   \n",
              "4               -0.11533   -0.20407400000000001   -0.14868200000000001   \n",
              "\n",
              "                     126                    127                    128  \n",
              "0   -0.47127799999999997   0.041342999999999998   -0.38586300000000001  \n",
              "1  -0.091986999999999999    0.24560499999999999   0.074918999999999999  \n",
              "2    0.21430399999999999  -0.089515999999999998  -0.083751000000000006  \n",
              "3   0.075348999999999999              -0.039742   0.043875999999999998  \n",
              "4              -0.107582  -0.051375999999999998    0.16094600000000001  \n",
              "\n",
              "[5 rows x 129 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "metadata": {
        "id": "uin-ukXYR-z6",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "semillas_esp = list(lexicon_df[\"esp\"].values)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "X2CHnXqdSJXN",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "semillas_nah = list(lexicon_df[\"nah\"].values)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "qngUuwbrSOfs",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "index_esp = [int(es_df[es_df[0] == palabra].index.get_values())\n",
        "            for palabra in semillas_esp\n",
        "            if int(es_df[es_df[0] == palabra].index.get_values().__len__()) == 1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "bNq4enNRSraJ",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "index_nah = [int(na_df[na_df[0] == palabra].index.get_values())\n",
        "            for palabra in semillas_nah\n",
        "            if int(na_df[na_df[0] == palabra].index.get_values().__len__()) == 1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "DjAtveD6S0t2",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "def get_vectors(dataframe, index, format=np.float64):\n",
        "    \"\"\"\n",
        "    Retorna los vectores dentro del dataframe.\n",
        "    \n",
        "    Args:\n",
        "        dataframe (Pandas.dataframe): Contiene las palabras y su representación vectorial.\n",
        "        index (list): Contiene los índices que se necesitan del dataframe.\n",
        "        format (numpy format): Tipo flotante. Default float64.\n",
        "    \n",
        "    Returns:\n",
        "        Numpy array: Matriz con representaciones vectoriales.\n",
        "    \"\"\"\n",
        "\n",
        "    return np.array([(dataframe.iloc[_].loc[1::])\n",
        "                  for _ in index]).astype(format)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9i8IsBEOTAiw",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "es_vectores = get_vectors(es_df,index_esp)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kYVQ__8vTIkl",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "na_vectores = get_vectors(na_df,index_nah)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "OahSauPmTNGP",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "output_extras": [
            {
              "item_id": 1
            }
          ],
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3e1c60cb-45ea-4959-cabe-9be912357f03",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1516766078698,
          "user_tz": 360,
          "elapsed": 1277,
          "user": {
            "displayName": "José Castillo",
            "photoUrl": "//lh6.googleusercontent.com/-Rz3R3wYNNQs/AAAAAAAAAAI/AAAAAAAAjjM/p1QOaiSP3Nc/s50-c-k-no/photo.jpg",
            "userId": "105435570147088816949"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "len(es_vectores)"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "540"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "metadata": {
        "id": "Q_GuPc84TRBY",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "V3bfLdoVTeZd",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "output_extras": [
            {
              "item_id": 1
            },
            {
              "item_id": 2
            }
          ],
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "0570523d-206a-4e85-82d1-f9cdd901cf69",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1516775879236,
          "user_tz": 360,
          "elapsed": 663,
          "user": {
            "displayName": "José Castillo",
            "photoUrl": "//lh6.googleusercontent.com/-Rz3R3wYNNQs/AAAAAAAAAAI/AAAAAAAAjjM/p1QOaiSP3Nc/s50-c-k-no/photo.jpg",
            "userId": "105435570147088816949"
          }
        }
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow v1.4.1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/device:GPU:0'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 136
        }
      ]
    },
    {
      "metadata": {
        "id": "1osGl6oRTp5V",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "output_extras": [
            {
              "item_id": 48
            },
            {
              "item_id": 62
            }
          ],
          "base_uri": "https://localhost:8080/",
          "height": 1156
        },
        "outputId": "99624292-c255-4c64-bee8-086158fa6cbb"
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "__author__ = \"Olivares Castillo José Luis\"\n",
        "print(\"TensorFlow v{}\".format(tf.__version__))\n",
        "tf.set_random_seed(42)\n",
        "tf.reset_default_graph()\n",
        "print(tf.test.gpu_device_name())\n",
        "\n",
        "LEARNING_RATE = 1.0\n",
        "# Dimensión de vectores de entrada (número de neuronas en capa de entrada).\n",
        "NODES_INPUT = es_vectores[0].size\n",
        "\n",
        "# Número de neuronas en capas ocultas.\n",
        "NODES_H1 = 256  # 70 - 20 - 15\n",
        "NODES_H2 = 74  # 42 - 20\n",
        "NODES_H3 = 74  # 42 - 20\n",
        "NODES_H4 = 90  # 42 - 20\n",
        "NODES_OUPUT = na_vectores[0].size\n",
        "\n",
        "# Inicializar pesos usando xavier_init\n",
        "XAVIER_INIT = True\n",
        "\n",
        "EPOCHS = 370000\n",
        "\n",
        "\n",
        "with tf.name_scope('input'):\n",
        "    # El valor None indica que se puede modificar la dimensión de los tensores\n",
        "    # por si se usan todos los vectores o batches.\n",
        "    X = tf.placeholder(shape=[None, NODES_INPUT],dtype=tf.float64, name='input_es')\n",
        "    y = tf.placeholder(shape=[None, NODES_OUPUT],dtype=tf.float64, name='target_na')\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "def fully_connected_layer(input, size_in, size_out, name,xavier_init=True, stddev=0.1, dtype=tf.float64):\n",
        "    with tf.name_scope(name):\n",
        " \n",
        "        if xavier_init:\n",
        "            W = tf.get_variable(name=\"W\" + name, shape=[size_in, size_out], dtype=dtype,initializer=tf.contrib.layers.xavier_initializer(dtype=dtype),use_resource=True)\n",
        "        else:\n",
        "            W = tf.Variable(tf.truncated_normal([size_in, size_out], stddev=stddev,dtype=dtype), name=\"W\")\n",
        "        # Bias.\n",
        "        b = tf.Variable(tf.constant(0.1, shape=[size_out], dtype=dtype), name=\"b\")\n",
        "\n",
        "        # h(x) = (input * weights) + bias\n",
        "        output = tf.nn.xw_plus_b(input, W, b)\n",
        "        # visualizarlos en TensorBoard.\n",
        "        tf.summary.histogram(\"weights\", W)\n",
        "        tf.summary.histogram(\"pre_activations\", output)\n",
        "\n",
        "        return output\n",
        "\n",
        "\n",
        "\n",
        "def activation_function(layer, act, name, alpha=tf.constant(0.2, dtype=tf.float64)):\n",
        "    if act == \"leaky_relu\":\n",
        "        return tf.nn.leaky_relu(layer, alpha, name=name)\n",
        "\n",
        "    elif act == \"softmax\":\n",
        "        return tf.nn.softmax(layer, name=name)\n",
        "\n",
        "    elif act == \"sigmoid\":\n",
        "        return tf.nn.sigmoid(layer, name=name)\n",
        "\n",
        "    elif act == \"tanh\":\n",
        "        return tf.nn.tanh(layer, name=name)\n",
        "\n",
        "    return tf.nn.relu(layer, name=name)\n",
        "\n",
        "\n",
        "# Se definen las capas.\n",
        "\n",
        "# In[ ]:\n",
        "\n",
        "\n",
        "# Se calcula la salida de la capa.\n",
        "fc1 = fully_connected_layer(X, NODES_INPUT, NODES_H1, \"fc1\",\n",
        "                            xavier_init=XAVIER_INIT)\n",
        "\n",
        "# Activación de la capa.\n",
        "fc1 = activation_function(fc1, \"sigmoid\", \"fc1\")\n",
        "\n",
        "\n",
        "# Se añade histograma de activación de la capa para visualizar en\n",
        "# TensorBoard.\n",
        "tf.summary.histogram(\"fc1/sigmoid\", fc1)\n",
        "\n",
        "\n",
        "\n",
        "nah_predicted = fully_connected_layer(fc1, NODES_H1, NODES_OUPUT, \"output\",\n",
        "                                      xavier_init=XAVIER_INIT)\n",
        "#nah_predicted = activation_function(output, \"sigmoid\", \"output\")\n",
        "tf.summary.histogram(\"output/noact\", nah_predicted)\n",
        "\n",
        "\n",
        "loss = tf.reduce_mean(tf.squared_difference(nah_predicted, y), name=\"loss\")\n",
        "tf.summary.scalar(\"loss\", loss)\n",
        "\n",
        "\n",
        "optimiser = tf.train.MomentumOptimizer(learning_rate=LEARNING_RATE,momentum=0.5)\n",
        "\n",
        "# Compute gradients\n",
        "gradients, variables = zip(*optimiser.compute_gradients(loss))\n",
        "\n",
        "gradients, _ = tf.clip_by_global_norm(gradients, 5.0)\n",
        "\n",
        "# Apply processed gradients to optimizer.\n",
        "train_op = optimiser.apply_gradients(zip(gradients, variables))\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# Accuracy\n",
        "with tf.name_scope('accuracy'):\n",
        "    with tf.name_scope('correct_prediction'):\n",
        "        # Se compara salida de la red neuronal con el vector objetivo.\n",
        "        correct_prediction = tf.equal(\n",
        "            tf.argmax(nah_predicted, 1), tf.argmax(y, 1))\n",
        "    with tf.name_scope('accuracy'):\n",
        "        # Se calcula la precisión.\n",
        "        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float64))\n",
        "    tf.summary.scalar('accuracy', accuracy)\n",
        "\n",
        "\n",
        "# In[ ]:\n",
        "\n",
        "LOGPATH = \".logs/model2257\"\n",
        "print(\"logpath:\", LOGPATH)\n",
        "\n",
        "\n",
        "# Se crea la sesión\n",
        "sess = tf.Session()\n",
        "\n",
        "# Se ponen los histogramas y valores de las gráficas en una sola variable.\n",
        "summaryMerged = tf.summary.merge_all()\n",
        "\n",
        "# Escribir a disco el grafo generado y las gráficas para visualizar en TensorBoard.\n",
        "writer = tf.summary.FileWriter(LOGPATH, sess.graph)\n",
        "\n",
        "# Se inicializan los valores de los tensores.\n",
        "init = tf.global_variables_initializer()\n",
        "\n",
        "# Add ops to save and restore all the variables.\n",
        "saver = tf.train.Saver()\n",
        "\n",
        "# Ejecutando sesión\n",
        "sess.run(init)\n",
        "\n",
        "\n",
        "# In[ ]:\n",
        "\n",
        "\n",
        "for i in range(EPOCHS):\n",
        "    _loss, _= sess.run([loss, train_op],feed_dict={X: es_vectores,y: na_vectores})\n",
        "\n",
        "    writer.add_summary(sumOut, i)\n",
        "\n",
        "    if (i % 500) == 0:\n",
        "        train_accuracy = accuracy.eval(session=sess, feed_dict={X: es_vectores,y: na_vectores})\n",
        "        print(\"Epoch:\", i, \"/\", EPOCHS, \"\\tLoss:\",_loss, \"\\tAccuracy:\", train_accuracy)\n",
        "        \n",
        "SAVE_PATH = \"./\"\n",
        "save_model = saver.save(sess, SAVE_PATH)\n",
        "print(\"Model saved in file: %s\", SAVE_PATH)\n",
        "writer.close()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow v1.4.1\n",
            "/device:GPU:0\n",
            "trying to switch the dtype to  <dtype: 'float32'>  from  <dtype: 'float64'>\n",
            "trying to switch the dtype to  <dtype: 'float32'>  from  <dtype: 'float64'>\n",
            "logpath: .logs/model2257\n",
            "Epoch: 0 / 370000 \tLoss: 0.4779004716160095 \tAccuracy: 0.06481481481481481\n",
            "Epoch: 500 / 370000 \tLoss: 0.10367018559805877 \tAccuracy: 0.11296296296296296\n",
            "Epoch: 1000 / 370000 \tLoss: 0.09532215985166738 \tAccuracy: 0.1425925925925926\n",
            "Epoch: 1500 / 370000 \tLoss: 0.09042338182724055 \tAccuracy: 0.15185185185185185\n",
            "Epoch: 2000 / 370000 \tLoss: 0.08729785927568809 \tAccuracy: 0.1685185185185185\n",
            "Epoch: 2500 / 370000 \tLoss: 0.08519130296513798 \tAccuracy: 0.1685185185185185\n",
            "Epoch: 3000 / 370000 \tLoss: 0.08371697995371706 \tAccuracy: 0.17777777777777778\n",
            "Epoch: 3500 / 370000 \tLoss: 0.08265402156188514 \tAccuracy: 0.1814814814814815\n",
            "Epoch: 4000 / 370000 \tLoss: 0.08186695473089942 \tAccuracy: 0.18518518518518517\n",
            "Epoch: 4500 / 370000 \tLoss: 0.08126855041504559 \tAccuracy: 0.19814814814814816\n",
            "Epoch: 5000 / 370000 \tLoss: 0.0808003951576536 \tAccuracy: 0.19814814814814816\n",
            "Epoch: 5500 / 370000 \tLoss: 0.08042193069949638 \tAccuracy: 0.2\n",
            "Epoch: 6000 / 370000 \tLoss: 0.0801040136732237 \tAccuracy: 0.19444444444444445\n",
            "Epoch: 6500 / 370000 \tLoss: 0.07982506876800351 \tAccuracy: 0.1962962962962963\n",
            "Epoch: 7000 / 370000 \tLoss: 0.07956878978780235 \tAccuracy: 0.2\n",
            "Epoch: 7500 / 370000 \tLoss: 0.07932277130833264 \tAccuracy: 0.2\n",
            "Epoch: 8000 / 370000 \tLoss: 0.07907766651333623 \tAccuracy: 0.1962962962962963\n",
            "Epoch: 8500 / 370000 \tLoss: 0.07882658277834362 \tAccuracy: 0.19814814814814816\n",
            "Epoch: 9000 / 370000 \tLoss: 0.07856452854926271 \tAccuracy: 0.19814814814814816\n",
            "Epoch: 9500 / 370000 \tLoss: 0.07828784270021129 \tAccuracy: 0.2\n",
            "Epoch: 10000 / 370000 \tLoss: 0.07799364064337776 \tAccuracy: 0.2\n",
            "Epoch: 10500 / 370000 \tLoss: 0.07767935878117553 \tAccuracy: 0.20555555555555555\n",
            "Epoch: 11000 / 370000 \tLoss: 0.0773424661104189 \tAccuracy: 0.2111111111111111\n",
            "Epoch: 11500 / 370000 \tLoss: 0.07698036641092482 \tAccuracy: 0.2111111111111111\n",
            "Epoch: 12000 / 370000 \tLoss: 0.07659046610641762 \tAccuracy: 0.21666666666666667\n",
            "Epoch: 12500 / 370000 \tLoss: 0.07617034765193681 \tAccuracy: 0.21481481481481482\n",
            "Epoch: 13000 / 370000 \tLoss: 0.07571797107677944 \tAccuracy: 0.21851851851851853\n",
            "Epoch: 13500 / 370000 \tLoss: 0.07523183017608318 \tAccuracy: 0.2222222222222222\n",
            "Epoch: 14000 / 370000 \tLoss: 0.07471101789211412 \tAccuracy: 0.2222222222222222\n",
            "Epoch: 14500 / 370000 \tLoss: 0.07415519904541118 \tAccuracy: 0.2222222222222222\n",
            "Epoch: 15000 / 370000 \tLoss: 0.07356452389345393 \tAccuracy: 0.22777777777777777\n",
            "Epoch: 15500 / 370000 \tLoss: 0.07293952548391505 \tAccuracy: 0.2351851851851852\n",
            "Epoch: 16000 / 370000 \tLoss: 0.07228103276954935 \tAccuracy: 0.2351851851851852\n",
            "Epoch: 16500 / 370000 \tLoss: 0.07159011518038641 \tAccuracy: 0.2388888888888889\n",
            "Epoch: 17000 / 370000 \tLoss: 0.070868060661865 \tAccuracy: 0.24074074074074073\n",
            "Epoch: 17500 / 370000 \tLoss: 0.07011637881778376 \tAccuracy: 0.24259259259259258\n",
            "Epoch: 18000 / 370000 \tLoss: 0.06933681438275408 \tAccuracy: 0.24814814814814815\n",
            "Epoch: 18500 / 370000 \tLoss: 0.06853135535656824 \tAccuracy: 0.2518518518518518\n",
            "Epoch: 19000 / 370000 \tLoss: 0.067702224425154 \tAccuracy: 0.2574074074074074\n",
            "Epoch: 19500 / 370000 \tLoss: 0.06685184915248338 \tAccuracy: 0.2611111111111111\n",
            "Epoch: 20000 / 370000 \tLoss: 0.0659828130747778 \tAccuracy: 0.26296296296296295\n",
            "Epoch: 20500 / 370000 \tLoss: 0.06509779462176345 \tAccuracy: 0.2574074074074074\n",
            "Epoch: 21000 / 370000 \tLoss: 0.06419950297001878 \tAccuracy: 0.2611111111111111\n",
            "Epoch: 21500 / 370000 \tLoss: 0.06329061925269762 \tAccuracy: 0.2777777777777778\n",
            "Epoch: 22000 / 370000 \tLoss: 0.062373748544695455 \tAccuracy: 0.2796296296296296\n",
            "Epoch: 22500 / 370000 \tLoss: 0.06145138422642636 \tAccuracy: 0.2796296296296296\n",
            "Epoch: 23000 / 370000 \tLoss: 0.06052588342309076 \tAccuracy: 0.2777777777777778\n",
            "Epoch: 23500 / 370000 \tLoss: 0.05959945093567865 \tAccuracy: 0.28703703703703703\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch: 24000 / 370000 \tLoss: 0.05867412892756675 \tAccuracy: 0.29814814814814816\n",
            "Epoch: 24500 / 370000 \tLoss: 0.05775178989218544 \tAccuracy: 0.29814814814814816\n",
            "Epoch: 25000 / 370000 \tLoss: 0.05683413100161599 \tAccuracy: 0.30185185185185187\n",
            "Epoch: 25500 / 370000 \tLoss: 0.05592266909865989 \tAccuracy: 0.3055555555555556\n",
            "Epoch: 26000 / 370000 \tLoss: 0.05501873725663397 \tAccuracy: 0.3111111111111111\n",
            "Epoch: 26500 / 370000 \tLoss: 0.05412348520960559 \tAccuracy: 0.3111111111111111\n",
            "Epoch: 27000 / 370000 \tLoss: 0.05323788601286663 \tAccuracy: 0.31851851851851853\n",
            "Epoch: 27500 / 370000 \tLoss: 0.052362749649719355 \tAccuracy: 0.32222222222222224\n",
            "Epoch: 28000 / 370000 \tLoss: 0.051498741823649316 \tAccuracy: 0.31851851851851853\n",
            "Epoch: 28500 / 370000 \tLoss: 0.050646404528989906 \tAccuracy: 0.32037037037037036\n",
            "Epoch: 29000 / 370000 \tLoss: 0.04980617532424975 \tAccuracy: 0.32037037037037036\n",
            "Epoch: 29500 / 370000 \tLoss: 0.0489784040932831 \tAccuracy: 0.31851851851851853\n",
            "Epoch: 30000 / 370000 \tLoss: 0.048163367795504126 \tAccuracy: 0.32407407407407407\n",
            "Epoch: 30500 / 370000 \tLoss: 0.04736128402830385 \tAccuracy: 0.3296296296296296\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "3abn5XQQT7YK",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "output_extras": [
            {
              "item_id": 1
            }
          ],
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2edf8493-c506-4496-d47c-c83c8eb2a1df",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1516768969648,
          "user_tz": 360,
          "elapsed": 502,
          "user": {
            "displayName": "José Castillo",
            "photoUrl": "//lh6.googleusercontent.com/-Rz3R3wYNNQs/AAAAAAAAAAI/AAAAAAAAjjM/p1QOaiSP3Nc/s50-c-k-no/photo.jpg",
            "userId": "105435570147088816949"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "os.getcwd()"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "metadata": {
        "id": "6Ja3yuilecE4",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "model = dict()\n",
        "model['a'] = np.fromfile(\"/content/model.ckpt.data-00000-of-00001\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1ZaoNFMmfZtm",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.download(\"/content/model.ckpt.meta\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "SkZhqDgeezZB",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}