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  "cells": [
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      "metadata": {
        "id": "L6038DmWxg8o"
      },
      "source": [
        "#### Mục tiêu: Xây dựng mô hình có độ chính xác trên tập validation khoảng 87%"
      ]
    },
    {
      "cell_type": "code",
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        "from tensorflow.keras.models import Sequential\n",
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        "import tensorflow as tf\n",
        "\n",
        "import tensorflow_datasets as tfds\n",
        "imdb, info = tfds.load(\"imdb_reviews/subwords8k\", with_info=True, as_supervised=True)"
      ],
      "execution_count": null,
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            "WARNING:absl:TFDS datasets with text encoding are deprecated and will be removed in a future version. Instead, you should use the plain text version and tokenize the text using `tensorflow_text` (See: https://www.tensorflow.org/tutorials/tensorflow_text/intro#tfdata_example)\n"
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            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-c958bfbe93d8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtensorflow_datasets\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtfds\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mimdb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtfds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"imdb_reviews/subwords8k\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwith_info\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mas_supervised\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_datasets/core/load.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(name, split, data_dir, batch_size, shuffle_files, download, as_supervised, decoders, read_config, with_info, builder_kwargs, download_and_prepare_kwargs, as_dataset_kwargs, try_gcs)\u001b[0m\n\u001b[1;32m    342\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mdownload\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    343\u001b[0m     \u001b[0mdownload_and_prepare_kwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdownload_and_prepare_kwargs\u001b[0m \u001b[0;32mor\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[0;32m--> 344\u001b[0;31m     \u001b[0mdbuilder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdownload_and_prepare\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mdownload_and_prepare_kwargs\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    345\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    346\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mas_dataset_kwargs\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_datasets/core/dataset_builder.py\u001b[0m in \u001b[0;36mdownload_and_prepare\u001b[0;34m(self, download_dir, download_config)\u001b[0m\n\u001b[1;32m    385\u001b[0m           self._download_and_prepare(\n\u001b[1;32m    386\u001b[0m               \u001b[0mdl_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdl_manager\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 387\u001b[0;31m               download_config=download_config)\n\u001b[0m\u001b[1;32m    388\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    389\u001b[0m           \u001b[0;31m# NOTE: If modifying the lines below to put additional information in\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_datasets/core/dataset_builder.py\u001b[0m in \u001b[0;36m_download_and_prepare\u001b[0;34m(self, dl_manager, download_config)\u001b[0m\n\u001b[1;32m   1022\u001b[0m     super(GeneratorBasedBuilder, self)._download_and_prepare(\n\u001b[1;32m   1023\u001b[0m         \u001b[0mdl_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdl_manager\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1024\u001b[0;31m         \u001b[0mmax_examples_per_split\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdownload_config\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_examples_per_split\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   1025\u001b[0m     )\n\u001b[1;32m   1026\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_datasets/core/dataset_builder.py\u001b[0m in \u001b[0;36m_download_and_prepare\u001b[0;34m(self, dl_manager, **prepare_split_kwargs)\u001b[0m\n\u001b[1;32m    972\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    973\u001b[0m       \u001b[0;31m# Prepare split will record examples associated to the split\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 974\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prepare_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msplit_generator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mprepare_split_kwargs\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    975\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    976\u001b[0m     \u001b[0;31m# Update the info object with the splits.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_datasets/core/dataset_builder.py\u001b[0m in \u001b[0;36m_prepare_split\u001b[0;34m(self, split_generator, max_examples_per_split)\u001b[0m\n\u001b[1;32m   1037\u001b[0m                                      hash_salt=split_generator.name)\n\u001b[1;32m   1038\u001b[0m     for key, record in utils.tqdm(generator, unit=\" examples\",\n\u001b[0;32m-> 1039\u001b[0;31m                                   total=split_info.num_examples, leave=False):\n\u001b[0m\u001b[1;32m   1040\u001b[0m       \u001b[0mexample\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode_example\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecord\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1041\u001b[0m       \u001b[0mwriter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexample\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tqdm/notebook.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    213\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__iter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    214\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 215\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtqdm_notebook\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__iter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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[0m\u001b[1;32m    216\u001b[0m                 \u001b[0;31m# return super(tqdm...) will not catch exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    217\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tqdm/std.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1102\u001b[0m                 fp_write=getattr(self.fp, 'write', sys.stderr.write))\n\u001b[1;32m   1103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1104\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\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   1105\u001b[0m             \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1106\u001b[0m             \u001b[0;31m# Update and possibly print the progressbar.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_datasets/text/imdb.py\u001b[0m in \u001b[0;36m_generate_examples\u001b[0;34m(self, archive, directory, labeled)\u001b[0m\n\u001b[1;32m    149\u001b[0m     reg = re.compile(\n\u001b[1;32m    150\u001b[0m         os.path.join(\"^%s\" % directory, reg_path, \"\").replace(\"\\\\\", \"\\\\\\\\\"))\n\u001b[0;32m--> 151\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimdb_f\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marchive\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    152\u001b[0m       \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_datasets/core/download/extractor.py\u001b[0m in \u001b[0;36miter_tar\u001b[0;34m(arch_f, stream)\u001b[0m\n\u001b[1;32m    157\u001b[0m   \u001b[0;32mwith\u001b[0m \u001b[0m_open_or_pass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0march_f\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfobj\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    158\u001b[0m     \u001b[0mtar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtarfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mread_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfileobj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 159\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mmember\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtar\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    160\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mstream\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmember\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mislnk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mmember\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0missym\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\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    161\u001b[0m         \u001b[0;31m# Links cannot be dereferenced in stream mode.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/tarfile.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   2413\u001b[0m                 \u001b[0mtarinfo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmembers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2414\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_loaded\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2415\u001b[0;31m                 \u001b[0mtarinfo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnext\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[0m\u001b[1;32m   2416\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtarinfo\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2417\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_loaded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/tarfile.py\u001b[0m in \u001b[0;36mnext\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   2290\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moffset\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtell\u001b[0m\u001b[0;34m(\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   2291\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseek\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moffset\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2292\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\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[0m\u001b[1;32m   2293\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mReadError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"unexpected end of data\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2294\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/gzip.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, size)\u001b[0m\n\u001b[1;32m    269\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mlength\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    270\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 271\u001b[0;31m     \u001b[0;32mdef\u001b[0m \u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\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[0m\u001b[1;32m    272\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_not_closed\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    273\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mREAD\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2JI6wmV1sOkA"
      },
      "source": [
        "train_data, test_data = imdb['train'], imdb['test']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gVTScvbNsV1e"
      },
      "source": [
        "tokenizer = info.features['text'].encoder"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zY640-tO3HIm"
      },
      "source": [
        "train_data = train_data.padded_batch(64, tf.compat.v1.data.get_output_shapes(train_data))\n",
        "test_data = test_data.padded_batch(64, tf.compat.v1.data.get_output_shapes(test_data))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XX-ttChq3I9d"
      },
      "source": [
        "for item in train_data:\n",
        "  print(item)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yUIsazhosWi-"
      },
      "source": [
        "# Xây dựng model\n",
        "\n",
        "# FIXME: Tạo model Sequential\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "# FIXME: Thêm một lớp Embedding\n",
        "\n",
        "model.add(Embedding(tokenizer.vocab_size, 64))\n",
        "\n",
        "model.add(GlobalAveragePooling1D()) # Ta hoàn toàn có thể sử dụng một lớp Convolution 1D sau embedding để tìm mối quan hệ giữa các từ\n",
        "\n",
        "# FIXME: Thêm một lớp Dense, chú ý có activation\n",
        "\n",
        "model.add(Dense(6, activation='relu'))\n",
        "\n",
        "# FIXME: Thêm một lớp Dense cuối, chú ý có activation\n",
        "\n",
        "model.add(Dense(1, activation='sigmoid'))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ut1m3lFlubiM"
      },
      "source": [
        "num_epochs = 10\n",
        "\n",
        "# FIXME: Compile Optimizer và Loss function\n",
        "\n",
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['acc'])\n",
        "\n",
        "# FIXME: Tiến hành training. Mục tiêu: 87 %\n",
        "\n",
        "history = model.fit(train_data, epochs=num_epochs, validation_data=test_data)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}