nlp_lab02_Word2Vec - Embeddings_rocco.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"nlp_lab02_Word2Vec - Embeddings (LEO).ipynb","provenance":[{"file_id":"1hNkoN0yPs2B-uk4lEw06vylpFdP0dFke","timestamp":1643599421240},{"file_id":"https://github.com/deeplearning-itba/NLP-Embeddings/blob/master/02-Word2Vec_Embeddings.ipynb","timestamp":1642963890055}],"machine_shape":"hm","collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"r-mpXuyJT9UO"},"source":["from keras.datasets import imdb\n","from keras.preprocessing.sequence import pad_sequences # input sentences are of various lengths. To handle a large amount of data, it is more efficient to keep the length of the data constant. https://mmsankosho.com/en/nlp-simple-introduction-to-pad_sequences-and-timeseriesgenerator/\n","import numpy as np\n","import gensim # NLP no supervisado. https://www.tutorialspoint.com/gensim/gensim_quick_guide.htm\n","import pandas as pd"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"qILN_kfqIp0x"},"source":["# Dataset: IMDB Movie reviews sentiment classification"]},{"cell_type":"code","metadata":{"id":"8FgeQzCkIjMM","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1643569243049,"user_tz":180,"elapsed":7528,"user":{"displayName":"Leonardo gonzalez","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"14511382415623592606"}},"outputId":"2e653d1d-d1d9-4dda-aee4-81df620b10c1"},"source":["# ARMAMOS DATASET\n","num_words=30000\n","INDEX_FROM=3  # idx 0 => PAD, idx 1 => START, idx 2 => OOV (out of vocab.) # se deja espacio libre para usar codigos\n","(training_data, training_targets), (testing_data, testing_targets) = imdb.load_data(num_words=num_words+2,)\n","data = np.concatenate((training_data, testing_data), axis=0)\n","targets = np.concatenate((training_targets, testing_targets), axis=0)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz\n","17465344/17464789 [==============================] - 0s 0us/step\n","17473536/17464789 [==============================] - 0s 0us/step\n"]}]},{"cell_type":"code","metadata":{"id":"jEiPTqOBT_V7","outputId":"54bd7730-881e-436c-8490-b72b1d910530","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1643569276467,"user_tz":180,"elapsed":1930,"user":{"displayName":"Leonardo gonzalez","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"14511382415623592606"}}},"source":["# EXPLORAMOS NUM CATEGORIAS Y PALABRAS \n","num_words=len(np.unique(np.hstack(data))) #hstack: Stack arrays in sequence horizontally (column wise).\n","print(\"Categories:\", np.unique(targets)) \n","print(\"Number of unique words:\", num_words)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Categories: [0 1]\n","Number of unique words: 30000\n"]}]},{"cell_type":"code","source":["# EXPLORAMOS\n","#pd_data = pd.DataFrame(data)\n","#pd_data.head()\n","pd_training = pd.DataFrame(training_data)\n","pd_target = pd.DataFrame(training_targets)\n","print('**target sample:**');\n","pd_target.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":225},"id":"Cr9SDpjui8Oy","executionInfo":{"status":"ok","timestamp":1643569315428,"user_tz":180,"elapsed":297,"user":{"displayName":"Leonardo gonzalez","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"14511382415623592606"}},"outputId":"a0988e88-2b1e-4b98-e362-3b6b6056c511"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["**target sample:**\n"]},{"output_type":"execute_result","data":{"text/html":["\n","  <div id=\"df-bbff4db0-fd03-4198-bc38-59a6a47f996f\">\n","    <div class=\"colab-df-container\">\n","      <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","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bbff4db0-fd03-4198-bc38-59a6a47f996f')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-bbff4db0-fd03-4198-bc38-59a6a47f996f button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-bbff4db0-fd03-4198-bc38-59a6a47f996f');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "],"text/plain":["   0\n","0  1\n","1  0\n","2  0\n","3  1\n","4  0"]},"metadata":{},"execution_count":4}]},{"cell_type":"code","source":["# EXPLORAMOS TRAINING\n","print('sample de training');\n","pd_training.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":225},"id":"OXXbCgAQku1b","executionInfo":{"status":"ok","timestamp":1643569327631,"user_tz":180,"elapsed":10,"user":{"displayName":"Leonardo gonzalez","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"14511382415623592606"}},"outputId":"f80ced9b-9153-4ffb-9f59-0db0622761d1"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["sample de training\n"]},{"output_type":"execute_result","data":{"text/html":["\n","  <div id=\"df-383a6fc9-f159-474f-b830-493cba41bec6\">\n","    <div class=\"colab-df-container\">\n","      <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","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>[1, 14, 22, 16, 43, 530, 973, 1622, 1385, 65, ...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>[1, 194, 1153, 194, 8255, 78, 228, 5, 6, 1463,...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>[1, 14, 47, 8, 30, 31, 7, 4, 249, 108, 7, 4, 5...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>[1, 4, 18609, 16085, 33, 2804, 4, 2040, 432, 1...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>[1, 249, 1323, 7, 61, 113, 10, 10, 13, 1637, 1...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-383a6fc9-f159-474f-b830-493cba41bec6')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-383a6fc9-f159-474f-b830-493cba41bec6 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-383a6fc9-f159-474f-b830-493cba41bec6');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "],"text/plain":["                                                   0\n","0  [1, 14, 22, 16, 43, 530, 973, 1622, 1385, 65, ...\n","1  [1, 194, 1153, 194, 8255, 78, 228, 5, 6, 1463,...\n","2  [1, 14, 47, 8, 30, 31, 7, 4, 249, 108, 7, 4, 5...\n","3  [1, 4, 18609, 16085, 33, 2804, 4, 2040, 432, 1...\n","4  [1, 249, 1323, 7, 61, 113, 10, 10, 13, 1637, 1..."]},"metadata":{},"execution_count":5}]},{"cell_type":"code","source":["len(pd_training[0][3]) #traigo un sample x"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lJnK5I5il2Yz","executionInfo":{"status":"ok","timestamp":1643569330552,"user_tz":180,"elapsed":15,"user":{"displayName":"Leonardo gonzalez","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"14511382415623592606"}},"outputId":"1879cd89-9909-414e-c83d-5d35f0fb4338"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["550"]},"metadata":{},"execution_count":6}]},{"cell_type":"markdown","metadata":{"id":"nef02I90wigz"},"source":["Agregar el siguiente archivo al Google Drive\n","\n","https://drive.google.com/open?id=1K5r423yMxBb1Yz2uDT7lto60lu1jqEjl"]},{"cell_type":"code","metadata":{"id":"Y9mmn9fpUIY6","outputId":"d53695ff-c104-4b3e-c26d-5d2bf643c698","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1643569362250,"user_tz":180,"elapsed":20301,"user":{"displayName":"Leonardo gonzalez","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"14511382415623592606"}}},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","metadata":{"id":"9tOvUY-MUJGH","outputId":"0f8ebc4d-1014-436a-afbb-096051f221ae","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1643569390299,"user_tz":180,"elapsed":373,"user":{"displayName":"Leonardo gonzalez","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"14511382415623592606"}}},"source":["# EXPLORE: VEMOS CANT PALABRAS POR INSTANCIAS\n","length = [len(i) for i in data]\n","print(\"Average Review length:\", np.mean(length))\n","print(\"Standard Deviation:\", round(np.std(length)))"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Average Review length: 234.75892\n","Standard Deviation: 173\n"]}]},{"cell_type":"code","metadata":{"id":"jHY8EhzvUdzt","outputId":"ae73ee37-fdce-4b5b-ebcf-e82ef710fd81","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1643569447284,"user_tz":180,"elapsed":14,"user":{"displayName":"Leonardo gonzalez","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"14511382415623592606"}}},"source":["# EXPLORE: veamos el target y las palabras ejemplos para una instancia particular\n","print(\"Label:\", targets[0])\n","print(data[0])"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Label: 1\n","[1, 14, 22, 16, 43, 530, 973, 1622, 1385, 65, 458, 4468, 66, 3941, 4, 173, 36, 256, 5, 25, 100, 43, 838, 112, 50, 670, 22665, 9, 35, 480, 284, 5, 150, 4, 172, 112, 167, 21631, 336, 385, 39, 4, 172, 4536, 1111, 17, 546, 38, 13, 447, 4, 192, 50, 16, 6, 147, 2025, 19, 14, 22, 4, 1920, 4613, 469, 4, 22, 71, 87, 12, 16, 43, 530, 38, 76, 15, 13, 1247, 4, 22, 17, 515, 17, 12, 16, 626, 18, 19193, 5, 62, 386, 12, 8, 316, 8, 106, 5, 4, 2223, 5244, 16, 480, 66, 3785, 33, 4, 130, 12, 16, 38, 619, 5, 25, 124, 51, 36, 135, 48, 25, 1415, 33, 6, 22, 12, 215, 28, 77, 52, 5, 14, 407, 16, 82, 10311, 8, 4, 107, 117, 5952, 15, 256, 4, 2, 7, 3766, 5, 723, 36, 71, 43, 530, 476, 26, 400, 317, 46, 7, 4, 12118, 1029, 13, 104, 88, 4, 381, 15, 297, 98, 32, 2071, 56, 26, 141, 6, 194, 7486, 18, 4, 226, 22, 21, 134, 476, 26, 480, 5, 144, 30, 5535, 18, 51, 36, 28, 224, 92, 25, 104, 4, 226, 65, 16, 38, 1334, 88, 12, 16, 283, 5, 16, 4472, 113, 103, 32, 15, 16, 5345, 19, 178, 32]\n"]}]},{"cell_type":"markdown","metadata":{"id":"mSnvI2LuM5So"},"source":["# Traemos el vocabulario y armamos indice reverso"]},{"cell_type":"code","metadata":{"id":"4lY7mGCLUf0M","outputId":"ee19e60a-7ed2-4b19-938f-6bb3e950e219","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1643570578381,"user_tz":180,"elapsed":477,"user":{"displayName":"Leonardo gonzalez","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"14511382415623592606"}}},"source":["# le asigna a cada palabra un index\n","index = imdb.get_word_index()\n","reverse_index = dict([(value, key) for (key, value) in index.items()]) \n","decoded = \" \".join( [reverse_index.get(i - INDEX_FROM, \"#\") for i in data[1]] )\n","print(decoded)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb_word_index.json\n","1646592/1641221 [==============================] - 0s 0us/step\n","1654784/1641221 [==============================] - 0s 0us/step\n","# big hair big boobs bad music and a giant safety pin these are the words to best describe this terrible movie i love cheesy horror movies and i've seen hundreds but this had got to be on of the worst ever made the plot is paper thin and ridiculous the acting is an abomination the script is completely laughable the best is the end showdown with the cop and how he worked out who the killer is it's just so damn terribly written the clothes are sickening and funny in equal measures the hair is big lots of boobs bounce men wear those cut tee shirts that show off their stomachs sickening that men actually wore them and the music is just # trash that plays over and over again in almost every scene there is trashy music boobs and paramedics taking away bodies and the gym still doesn't close for # all joking aside this is a truly bad film whose only charm is to look back on the disaster that was the 80's and have a good old laugh at how bad everything was back then\n"]}]},{"cell_type":"code","source":[""],"metadata":{"id":"xm215tEgvFu6"},"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"6R5A_xU9cGM2"},"source":["# TRAEMOS EMBEDDINGS\n","w2v = gensim.models.KeyedVectors.load_word2vec_format(\"/content/drive/My Drive/GoogleNews-vectors-negative300.bin\", binary=True)  "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"SDEVfp7RY489","outputId":"5d2fef3e-4b3c-4818-9025-95aac4be8cc7","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1643570690978,"user_tz":180,"elapsed":24,"user":{"displayName":"Leonardo gonzalez","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"14511382415623592606"}}},"source":["# EJ DE EMBEDDING PARA \"CAR\"\n","w2v.wv[\"car\"] # https://radimrehurek.com/gensim/models/word2vec.html"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: DeprecationWarning: Call to deprecated `wv` (Attribute will be removed in 4.0.0, use self instead).\n","  \n"]},{"output_type":"execute_result","data":{"text/plain":["array([ 0.13085938,  0.00842285,  0.03344727, -0.05883789,  0.04003906,\n","       -0.14257812,  0.04931641, -0.16894531,  0.20898438,  0.11962891,\n","        0.18066406, -0.25      , -0.10400391, -0.10742188, -0.01879883,\n","        0.05200195, -0.00216675,  0.06445312,  0.14453125, -0.04541016,\n","        0.16113281, -0.01611328, -0.03088379,  0.08447266,  0.16210938,\n","        0.04467773, -0.15527344,  0.25390625,  0.33984375,  0.00756836,\n","       -0.25585938, -0.01733398, -0.03295898,  0.16308594, -0.12597656,\n","       -0.09912109,  0.16503906,  0.06884766, -0.18945312,  0.02832031,\n","       -0.0534668 , -0.03063965,  0.11083984,  0.24121094, -0.234375  ,\n","        0.12353516, -0.00294495,  0.1484375 ,  0.33203125,  0.05249023,\n","       -0.20019531,  0.37695312,  0.12255859,  0.11425781, -0.17675781,\n","        0.10009766,  0.0030365 ,  0.26757812,  0.20117188,  0.03710938,\n","        0.11083984, -0.09814453, -0.3125    ,  0.03515625,  0.02832031,\n","        0.26171875, -0.08642578, -0.02258301, -0.05834961, -0.00787354,\n","        0.11767578, -0.04296875, -0.17285156,  0.04394531, -0.23046875,\n","        0.1640625 , -0.11474609, -0.06030273,  0.01196289, -0.24707031,\n","        0.32617188, -0.04492188, -0.11425781,  0.22851562, -0.01647949,\n","       -0.15039062, -0.13183594,  0.12597656, -0.17480469,  0.02209473,\n","       -0.1015625 ,  0.00817871,  0.10791016, -0.24609375, -0.109375  ,\n","       -0.09375   , -0.01623535, -0.20214844,  0.23144531, -0.05444336,\n","       -0.05541992, -0.20898438,  0.26757812,  0.27929688,  0.17089844,\n","       -0.17578125, -0.02770996, -0.20410156,  0.02392578,  0.03125   ,\n","       -0.25390625, -0.125     , -0.05493164, -0.17382812,  0.28515625,\n","       -0.23242188,  0.0234375 , -0.20117188, -0.13476562,  0.26367188,\n","        0.00769043,  0.20507812, -0.01708984, -0.12988281,  0.04711914,\n","        0.22070312,  0.02099609, -0.29101562, -0.02893066,  0.17285156,\n","        0.04272461, -0.19824219, -0.04003906, -0.16992188,  0.10058594,\n","       -0.09326172,  0.15820312, -0.16503906, -0.06054688,  0.19433594,\n","       -0.07080078, -0.06884766, -0.09619141, -0.07226562,  0.04882812,\n","        0.07324219,  0.11035156,  0.04858398, -0.17675781, -0.33789062,\n","        0.22558594,  0.16308594,  0.05102539, -0.08251953,  0.07958984,\n","        0.08740234, -0.16894531, -0.02160645, -0.19238281,  0.03857422,\n","       -0.05102539,  0.21972656,  0.08007812, -0.21191406, -0.07519531,\n","       -0.15039062,  0.3046875 , -0.17089844,  0.12353516, -0.234375  ,\n","       -0.10742188, -0.06787109,  0.01904297, -0.14160156, -0.22753906,\n","       -0.16308594,  0.14453125, -0.15136719, -0.296875  ,  0.22363281,\n","       -0.10205078, -0.0456543 , -0.21679688, -0.09033203,  0.09375   ,\n","       -0.15332031, -0.01550293,  0.3046875 , -0.23730469,  0.08935547,\n","        0.03710938,  0.02941895, -0.28515625,  0.15820312, -0.00306702,\n","        0.06054688,  0.00497437, -0.15234375, -0.00836182,  0.02197266,\n","       -0.12109375, -0.13867188, -0.2734375 , -0.06835938,  0.08251953,\n","       -0.26367188, -0.16992188,  0.14746094,  0.08496094,  0.02075195,\n","        0.13671875, -0.04931641, -0.0100708 , -0.00369263, -0.10839844,\n","        0.14746094, -0.15527344,  0.16113281,  0.05615234, -0.05004883,\n","       -0.1640625 , -0.26953125,  0.4140625 ,  0.06079102, -0.046875  ,\n","       -0.02514648,  0.10595703,  0.1328125 , -0.16699219, -0.04907227,\n","        0.04663086,  0.05151367, -0.07958984, -0.16503906, -0.29882812,\n","        0.06054688, -0.15332031, -0.00598145,  0.06640625, -0.04516602,\n","        0.24316406, -0.07080078, -0.36914062, -0.23144531, -0.11914062,\n","       -0.08300781,  0.14746094, -0.05761719,  0.23535156, -0.12304688,\n","        0.14648438,  0.13671875,  0.15429688,  0.02111816, -0.09570312,\n","        0.05859375,  0.03979492, -0.08105469,  0.0559082 , -0.16601562,\n","        0.27148438, -0.20117188, -0.00915527,  0.07324219,  0.10449219,\n","        0.34570312, -0.26367188,  0.02099609, -0.40039062, -0.03417969,\n","       -0.15917969, -0.08789062,  0.08203125,  0.23339844,  0.0213623 ,\n","       -0.11328125,  0.05249023, -0.10449219, -0.02380371, -0.08349609,\n","       -0.04003906,  0.01916504, -0.01226807, -0.18261719, -0.06787109,\n","       -0.08496094, -0.03039551, -0.05395508,  0.04248047,  0.12792969,\n","       -0.27539062,  0.28515625, -0.04736328,  0.06494141, -0.11230469,\n","       -0.02575684, -0.04125977,  0.22851562, -0.14941406, -0.15039062],\n","      dtype=float32)"]},"metadata":{},"execution_count":12}]},{"cell_type":"code","source":["# VER CUANTAS VARIABLES TIENE EL EMBEDDING\n","np.shape(w2v.wv[\"car\"]) # un embedding q tiene 300 vars"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KaUsF1Hbzds8","executionInfo":{"status":"ok","timestamp":1643570796667,"user_tz":180,"elapsed":14,"user":{"displayName":"Leonardo gonzalez","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"14511382415623592606"}},"outputId":"d495eb80-5fe8-45ae-ab7f-6387e6ee3e73"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: DeprecationWarning: Call to deprecated `wv` (Attribute will be removed in 4.0.0, use self instead).\n","  \n"]},{"output_type":"execute_result","data":{"text/plain":["(300,)"]},"metadata":{},"execution_count":13}]},{"cell_type":"markdown","metadata":{"id":"lyJyLhkhNJNX"},"source":["# Armamos la matriz de embeddings"]},{"cell_type":"code","metadata":{"id":"DlY6kxXlUv_b","outputId":"6ba9e747-7814-438c-ee83-da95b214448d","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1643570851521,"user_tz":180,"elapsed":441,"user":{"displayName":"Leonardo gonzalez","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"14511382415623592606"}}},"source":["# COMPLETAMOS CON ZEROS Y ARMAMOS LOS VALORES DE EMBEDDINGS PARA CADA UNA DE LAS 30000 PALABRAS\n","embed_dim=300\n","embedding_matrix=np.zeros([num_words+4,embed_dim])\n","for word, idx in index.items():\n","  if idx <= num_words and word in w2v.wv:\n","    embedding_matrix[idx+INDEX_FROM,:]=w2v.wv[word]\n","    \n","embedding_matrix.shape"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: DeprecationWarning: Call to deprecated `wv` (Attribute will be removed in 4.0.0, use self instead).\n","  \"\"\"\n","/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: DeprecationWarning: Call to deprecated `wv` (Attribute will be removed in 4.0.0, use self instead).\n","  \n"]},{"output_type":"execute_result","data":{"text/plain":["(30004, 300)"]},"metadata":{},"execution_count":14}]},{"cell_type":"code","source":["embedding_matrix[100][:].shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0qYXiXIHNbaK","executionInfo":{"status":"ok","timestamp":1643570853470,"user_tz":180,"elapsed":3,"user":{"displayName":"Leonardo gonzalez","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"14511382415623592606"}},"outputId":"88296c91-595d-4b8d-a102-a9745cf1ddb1"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(300,)"]},"metadata":{},"execution_count":15}]},{"cell_type":"markdown","metadata":{"id":"4PJzpmFbRL7j"},"source":["# Hacemos que todos los reviews tengan el mismo largo"]},{"cell_type":"code","metadata":{"id":"_HdJ8KrrZszC"},"source":["maxlen=1000"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"6r9jiL4QarNw"},"source":["#pad_sequences is used to ensure that all sequences in a list have the same length. By default this is done by padding 0 in the beginning of each sequence until each sequence has the same length as the longest sequence.\n","data = pad_sequences(data, maxlen=maxlen, value=0.0) # data era training data + test data"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"OiL4twmwas3F","outputId":"316cba99-4af5-4906-91ad-39522985d03b","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1643570866333,"user_tz":180,"elapsed":15,"user":{"displayName":"Leonardo gonzalez","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"14511382415623592606"}}},"source":["len(data[0])"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1000"]},"metadata":{},"execution_count":18}]},{"cell_type":"code","metadata":{"id":"XoPdGTyaaudk","outputId":"114a6c73-339f-4c1a-b8d9-c5326dbbc873","colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["len(data[1])"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1000"]},"metadata":{"tags":[]},"execution_count":15}]},{"cell_type":"code","source":["# vemos un ejemplo de como queda con pad sequences\n","data[1]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"f8cJ6McFOF_u","executionInfo":{"status":"ok","timestamp":1642996464019,"user_tz":180,"elapsed":278,"user":{"displayName":"Leonardo gonzalez","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"14511382415623592606"}},"outputId":"1947cf32-63ed-483a-e17e-673ddf82041a"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([    0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     1,   194,  1153,   194,  8255,    78,   228,     5,\n","           6,  1463,  4369,  5012,   134,    26,     4,   715,     8,\n","         118,  1634,    14,   394,    20,    13,   119,   954,   189,\n","         102,     5,   207,   110,  3103,    21,    14,    69,   188,\n","           8,    30,    23,     7,     4,   249,   126,    93,     4,\n","         114,     9,  2300,  1523,     5,   647,     4,   116,     9,\n","          35,  8163,     4,   229,     9,   340,  1322,     4,   118,\n","           9,     4,   130,  4901,    19,     4,  1002,     5,    89,\n","          29,   952,    46,    37,     4,   455,     9,    45,    43,\n","          38,  1543,  1905,   398,     4,  1649,    26,  6853,     5,\n","         163,    11,  3215, 10156,     4,  1153,     9,   194,   775,\n","           7,  8255, 11596,   349,  2637,   148,   605, 15358,  8003,\n","          15,   123,   125,    68, 23141,  6853,    15,   349,   165,\n","        4362,    98,     5,     4,   228,     9,    43,     2,  1157,\n","          15,   299,   120,     5,   120,   174,    11,   220,   175,\n","         136,    50,     9,  4373,   228,  8255,     5, 25249,   656,\n","         245,  2350,     5,     4,  9837,   131,   152,   491,    18,\n","           2,    32,  7464,  1212,    14,     9,     6,   371,    78,\n","          22,   625,    64,  1382,     9,     8,   168,   145,    23,\n","           4,  1690,    15,    16,     4,  1355,     5,    28,     6,\n","          52,   154,   462,    33,    89,    78,   285,    16,   145,\n","          95], dtype=int32)"]},"metadata":{},"execution_count":32}]},{"cell_type":"code","metadata":{"id":"H9HdnsE2awBy"},"source":["data=np.array(data)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ri0xt9ueaxlK","outputId":"e443a206-8d6b-41a8-ab19-646dfd9fbad3","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1642996563475,"user_tz":180,"elapsed":9,"user":{"displayName":"Leonardo gonzalez","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"14511382415623592606"}}},"source":["data.shape"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(50000, 1000)"]},"metadata":{},"execution_count":34}]},{"cell_type":"markdown","metadata":{"id":"5Nd_FmZGRVLJ"},"source":["# Armamos el modelo con una Conv1D"]},{"cell_type":"code","metadata":{"id":"CjoQz25NazxB"},"source":["from keras.layers import Embedding, Conv1D, MaxPooling1D, GlobalMaxPooling1D, Dropout, Dense\n","from keras.models import Sequential\n","from keras import optimizers\n","from tensorflow.keras.optimizers import Adam\n","\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"4GUS1aYWReop"},"source":["# explicacion teorica de CNN para embeddings, word2vec embeddings y como funciona: \n","# https://cezannec.github.io/CNN_Text_Classification/\n","\n","# para seguir:\n","# https://towardsdatascience.com/nlp-with-cnns-a6aa743bdc1e\n","# https://ml2021.medium.com/multi-class-text-classification-using-cnn-and-word2vec-b17daff45260\n","\n","# cuando haces 1 factor:"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["![imagen.png](data:image/png;base64,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)\n","![imagen.png](data:image/png;base64,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)"],"metadata":{"id":"2tFUrrjIiWRi"}},{"cell_type":"code","source":["# DEFINIMOS DONDE IMPORTAMOS EL CALLBACK DE TENSORBOARD\n","from tensorflow.keras import callbacks\n","tensorboard_callback = callbacks.TensorBoard(log_dir=\"logs/\")"],"metadata":{"id":"pFwEF2mN2PIC"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# DEFINE THE MMODEL\n","# Hacemos modelo en keras, incorporando el embedding, haciendo CNN 1D, max pooling, etc:\n","nb_words=num_words+4\n","num_filters=64\n","model = Sequential()\n","model.add(Embedding(nb_words, embed_dim, weights=[embedding_matrix], input_length=maxlen, trainable=False))\n","model.add(Conv1D(num_filters, 7, activation='relu', padding='same'))\n","model.add(MaxPooling1D(2))\n","model.add(Conv1D(num_filters*2, 7, activation='relu', padding='same'))\n","model.add(GlobalMaxPooling1D())\n","model.add(Dropout(0.5))\n","model.add(Dense(32, activation='relu'))\n","model.add(Dense(1, activation='sigmoid'))  #multi-label (k-hot encoding)\n","\n","# COMPILE THE MODEL\n","adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)\n","model.compile(loss='binary_crossentropy', optimizer=adam, metrics=['accuracy'])\n","model.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"z8YA817yWHBs","executionInfo":{"status":"ok","timestamp":1643579312905,"user_tz":180,"elapsed":15,"user":{"displayName":"Leonardo gonzalez","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"14511382415623592606"}},"outputId":"e1b9b657-b6a8-491c-fd11-7ce3b7f60f2a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential_3\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," embedding_3 (Embedding)     (None, 1000, 300)         9001200   \n","                                                                 \n"," conv1d_6 (Conv1D)           (None, 1000, 64)          134464    \n","                                                                 \n"," max_pooling1d_3 (MaxPooling  (None, 500, 64)          0         \n"," 1D)                                                             \n","                                                                 \n"," conv1d_7 (Conv1D)           (None, 500, 128)          57472     \n","                                                                 \n"," global_max_pooling1d_3 (Glo  (None, 128)              0         \n"," balMaxPooling1D)                                                \n","                                                                 \n"," dropout_3 (Dropout)         (None, 128)               0         \n","                                                                 \n"," dense_6 (Dense)             (None, 32)                4128      \n","                                                                 \n"," dense_7 (Dense)             (None, 1)                 33        \n","                                                                 \n","=================================================================\n","Total params: 9,197,297\n","Trainable params: 196,097\n","Non-trainable params: 9,001,200\n","_________________________________________________________________\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n","  super(Adam, self).__init__(name, **kwargs)\n"]}]},{"cell_type":"code","source":["# FIT THE MODEL\n","# // RE QUE ESTA OVERFITEANDO:\n","#  loss: 0.0240 - accuracy: 0.9907 - val_loss: 0.6539 - val_accuracy: 0.8938\n","model.fit(data,targets,batch_size=32,epochs=20,validation_split=0.2, callbacks=[tensorboard_callback])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uLQU6Vm8Y827","executionInfo":{"status":"ok","timestamp":1643579719968,"user_tz":180,"elapsed":403835,"user":{"displayName":"Leonardo gonzalez","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"14511382415623592606"}},"outputId":"33cc23ac-9346-44e3-bb26-3dd4e7a0d79c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/20\n","1250/1250 [==============================] - 17s 13ms/step - loss: 0.3825 - accuracy: 0.8223 - val_loss: 0.3058 - val_accuracy: 0.8670\n","Epoch 2/20\n","1250/1250 [==============================] - 17s 14ms/step - loss: 0.2770 - accuracy: 0.8860 - val_loss: 0.2519 - val_accuracy: 0.8968\n","Epoch 3/20\n","1250/1250 [==============================] - 17s 14ms/step - loss: 0.2304 - accuracy: 0.9079 - val_loss: 0.2577 - val_accuracy: 0.8980\n","Epoch 4/20\n","1250/1250 [==============================] - 17s 14ms/step - loss: 0.1870 - accuracy: 0.9261 - val_loss: 0.2887 - val_accuracy: 0.8793\n","Epoch 5/20\n","1250/1250 [==============================] - 16s 13ms/step - loss: 0.1510 - accuracy: 0.9424 - val_loss: 0.2539 - val_accuracy: 0.8968\n","Epoch 6/20\n","1250/1250 [==============================] - 16s 13ms/step - loss: 0.1166 - accuracy: 0.9559 - val_loss: 0.3056 - val_accuracy: 0.8948\n","Epoch 7/20\n","1250/1250 [==============================] - 17s 14ms/step - loss: 0.0942 - accuracy: 0.9653 - val_loss: 0.3143 - val_accuracy: 0.8953\n","Epoch 8/20\n","1250/1250 [==============================] - 16s 13ms/step - loss: 0.0769 - accuracy: 0.9717 - val_loss: 0.3540 - val_accuracy: 0.8957\n","Epoch 9/20\n","1250/1250 [==============================] - 16s 13ms/step - loss: 0.0668 - accuracy: 0.9756 - val_loss: 0.3833 - val_accuracy: 0.8984\n","Epoch 10/20\n","1250/1250 [==============================] - 17s 14ms/step - loss: 0.0575 - accuracy: 0.9786 - val_loss: 0.4492 - val_accuracy: 0.8873\n","Epoch 11/20\n","1250/1250 [==============================] - 17s 14ms/step - loss: 0.0512 - accuracy: 0.9810 - val_loss: 0.3887 - val_accuracy: 0.8974\n","Epoch 12/20\n","1250/1250 [==============================] - 17s 14ms/step - loss: 0.0465 - accuracy: 0.9821 - val_loss: 0.4291 - val_accuracy: 0.8961\n","Epoch 13/20\n","1250/1250 [==============================] - 16s 13ms/step - loss: 0.0413 - accuracy: 0.9849 - val_loss: 0.4560 - val_accuracy: 0.8942\n","Epoch 14/20\n","1250/1250 [==============================] - 16s 13ms/step - loss: 0.0381 - accuracy: 0.9858 - val_loss: 0.5103 - val_accuracy: 0.8932\n","Epoch 15/20\n","1250/1250 [==============================] - 16s 13ms/step - loss: 0.0328 - accuracy: 0.9883 - val_loss: 0.5923 - val_accuracy: 0.8907\n","Epoch 16/20\n","1250/1250 [==============================] - 16s 13ms/step - loss: 0.0361 - accuracy: 0.9863 - val_loss: 0.6241 - val_accuracy: 0.8835\n","Epoch 17/20\n","1250/1250 [==============================] - 17s 14ms/step - loss: 0.0296 - accuracy: 0.9887 - val_loss: 0.5453 - val_accuracy: 0.8945\n","Epoch 18/20\n","1250/1250 [==============================] - 17s 14ms/step - loss: 0.0310 - accuracy: 0.9889 - val_loss: 0.5734 - val_accuracy: 0.8978\n","Epoch 19/20\n","1250/1250 [==============================] - 17s 14ms/step - loss: 0.0286 - accuracy: 0.9896 - val_loss: 0.6194 - val_accuracy: 0.8952\n","Epoch 20/20\n","1250/1250 [==============================] - 17s 14ms/step - loss: 0.0250 - accuracy: 0.9905 - val_loss: 0.6664 - val_accuracy: 0.8946\n"]},{"output_type":"execute_result","data":{"text/plain":["<keras.callbacks.History at 0x7f9d4bd536d0>"]},"metadata":{},"execution_count":63}]},{"cell_type":"code","source":["# proximos pasos:\n","# 1. predecir algo\n","# evaluate the model\n","model.predict()"],"metadata":{"id":"rjETNkn_bauv","colab":{"base_uri":"https://localhost:8080/","height":249},"executionInfo":{"status":"error","timestamp":1643575417529,"user_tz":180,"elapsed":26,"user":{"displayName":"Leonardo gonzalez","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"14511382415623592606"}},"outputId":"90d6a8d6-8e29-4f67-dbec-a135310afc4d"},"execution_count":null,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-24-a916043dc944>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# 1. predecir algo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# evaluate the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"%s: %.2f%%\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics_names\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscores\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'X' is not defined"]}]},{"cell_type":"markdown","source":["# SAVE / LOAD MODEL\n","\n","Keras separates the concerns of saving your model architecture and saving your model weights.\n","\n","Model weights are saved to HDF5 format. This is a grid format that is ideal for storing multi-dimensional arrays of numbers.\n","\n","The model structure can be described and saved using two different formats: JSON and YAML.\n","\n","--------------------\n","**Keras also supports a simpler interface to save both the model weights and model architecture together into a single H5 file.**\n","\n","Saving the model in this way includes everything we need to know about the model, including:\n","\n","    Model weights.\n","    Model architecture.\n","    Model compilation details (loss and metrics).\n","    Model optimizer state.\n","\n","This means that we can load and use the model directly, without having to re-compile it as we did in the examples above.\n"],"metadata":{"id":"frwMFWK1tr9G"}},{"cell_type":"code","source":["# ---------------------------- SAVE MODEL --------------------\n","# save model and architecture to single file\n","model.save(\"word2vec_model.h5\")\n","print(\"Saved model to disk\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nlgelEhknz9U","executionInfo":{"status":"ok","timestamp":1643576239557,"user_tz":180,"elapsed":294,"user":{"displayName":"Leonardo gonzalez","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"14511382415623592606"}},"outputId":"177c4b77-71c9-45ce-cc8a-2a61f4f7659b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Saved model to disk\n"]}]},{"cell_type":"code","source":["# lo copiamos al drive\n","%cp  /content/word2vec_model.h5 /content/drive/MyDrive/word2vec_model.h5"],"metadata":{"id":"RKXvht6Hvjrb"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# ----------------------------- LOAD MODEL ------------------------\n","from tensorflow.keras import models \n","# SI QUEREMOS CARGAR EL MODELO\n","%cp  /content/drive/MyDrive/word2vec_model.h5 /content/word2vec_model.h5\n","my_h5_saved_model = models.load_model('/content/word2vec_model.h5')"],"metadata":{"id":"BvXWQDyVwU7b"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# TENSORBOARD"],"metadata":{"id":"K0Qm1s-M8_3h"}},{"cell_type":"code","source":["#### CARGAR TENSORBOARD"],"metadata":{"id":"a2E2KAsU2tlA"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Load the TensorBoard notebook extension\n","%load_ext tensorboard"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sAhV001U1rZ6","executionInfo":{"status":"ok","timestamp":1643579201160,"user_tz":180,"elapsed":314,"user":{"displayName":"Leonardo gonzalez","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"14511382415623592606"}},"outputId":"47f32091-cc01-4cca-a9ea-f15230900a3f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["The tensorboard extension is already loaded. To reload it, use:\n","  %reload_ext tensorboard\n"]}]},{"cell_type":"code","source":["# Clear any logs from previous runs\n","!rm -rf ./logs/ "],"metadata":{"id":"Y136TFpt8sSq"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["%tensorboard --logdir logs/"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":821},"id":"me17JK8v1Uoc","executionInfo":{"status":"ok","timestamp":1643579719973,"user_tz":180,"elapsed":53,"user":{"displayName":"Leonardo gonzalez","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"14511382415623592606"}},"outputId":"9abc9763-60f7-43c4-b038-7a3543710453"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"application/javascript":["\n","        (async () => {\n","            const url = new URL(await google.colab.kernel.proxyPort(6009, {'cache': true}));\n","            url.searchParams.set('tensorboardColab', 'true');\n","            const iframe = document.createElement('iframe');\n","            iframe.src = url;\n","            iframe.setAttribute('width', '100%');\n","            iframe.setAttribute('height', '800');\n","            iframe.setAttribute('frameborder', 0);\n","            document.body.appendChild(iframe);\n","        })();\n","    "],"text/plain":["<IPython.core.display.Javascript object>"]},"metadata":{}}]},{"cell_type":"markdown","source":["# EXPLORE AREA\n"],"metadata":{"id":"p6Y62Gob-97R"}},{"cell_type":"code","source":["data[1].shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uAqbuDPc-9d9","executionInfo":{"status":"ok","timestamp":1643579734899,"user_tz":180,"elapsed":24,"user":{"displayName":"Leonardo gonzalez","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"14511382415623592606"}},"outputId":"29eafa3a-ca22-402d-b410-c0ac4db79c1d"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(1000,)"]},"metadata":{},"execution_count":65}]},{"cell_type":"code","source":["data.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ORtCDfya8KKz","executionInfo":{"status":"ok","timestamp":1643579747079,"user_tz":180,"elapsed":3159,"user":{"displayName":"Leonardo gonzalez","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"14511382415623592606"}},"outputId":"1b944207-8853-49d5-f459-e9913d11bd91"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(50000, 1000)"]},"metadata":{},"execution_count":66}]},{"cell_type":"code","source":["p=model.predict(data)"],"metadata":{"id":"6F31Xpoz_I83"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["model.predict(data[51:500]).round(2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"meu_WB-r_OzX","executionInfo":{"status":"ok","timestamp":1643579895158,"user_tz":180,"elapsed":12,"user":{"displayName":"Leonardo gonzalez","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"14511382415623592606"}},"outputId":"33223e95-960e-4aea-c5fc-a74e54002ef8"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.97],\n","       [1.  ],\n","       [1.  ],\n","       [0.89],\n","       [1.  ],\n","       [1.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.99],\n","       [1.  ],\n","       [0.01],\n","       [1.  ],\n","       [0.  ],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.98],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.88],\n","       [0.  ],\n","       [1.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [1.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.95],\n","       [0.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.02],\n","       [0.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.99],\n","       [0.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.98],\n","       [0.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.98],\n","       [1.  ],\n","       [1.  ],\n","       [1.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.91],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [1.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.76],\n","       [1.  ],\n","       [1.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.8 ],\n","       [0.96],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.01],\n","       [1.  ],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.98],\n","       [0.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.31],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.97],\n","       [1.  ],\n","       [0.99],\n","       [1.  ],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.97],\n","       [0.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [1.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.97],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.95],\n","       [1.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.87],\n","       [1.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.89],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.94],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.99],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.96],\n","       [1.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.01],\n","       [0.99],\n","       [1.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.85],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.89],\n","       [0.98],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.99],\n","       [0.44],\n","       [1.  ],\n","       [0.99],\n","       [1.  ],\n","       [1.  ],\n","       [1.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.  ],\n","       [1.  ],\n","       [1.  ],\n","       [0.  ],\n","       [1.  ],\n","       [0.  ],\n","       [0.  ]], dtype=float32)"]},"metadata":{},"execution_count":70}]},{"cell_type":"code","source":["from keras.preprocessing.text import Tokenizer\n","\n","test_text =[\"She should not have been allowed to rule on this with the conflict of interest.\\\n","She should have removed herself. Should be a slam dunk appeal on that grounds alone.\"]\n","test_seq = Tokenizer.texts_to_sequences(test_text)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":230},"id":"LDKCDXTc_iB-","executionInfo":{"status":"error","timestamp":1643580076950,"user_tz":180,"elapsed":533,"user":{"displayName":"Leonardo gonzalez","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"14511382415623592606"}},"outputId":"177adf4e-6b2f-491d-cc3b-bada41ba9431"},"execution_count":null,"outputs":[{"output_type":"error","ename":"TypeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-73-e564e8751da1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mtest_text\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"She should not have been allowed to rule on this with the conflict of interest.She should have removed herself. Should be a slam dunk appeal on that grounds alone.\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mtest_seq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtexts_to_sequences\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_text\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mTypeError\u001b[0m: texts_to_sequences() missing 1 required positional argument: 'texts'"]}]},{"cell_type":"code","source":[""],"metadata":{"id":"7ctgbRUe_65l"},"execution_count":null,"outputs":[]}]}