Part 4.4: Tensorflow_Sentiment_Analysis.ipynb

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        },
        "outputId": "9400dfb0-997e-4507-8ae3-7bbfb5dffce9"
      },
      "source": [
        "!nvidia-smi"
      ],
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        {
          "output_type": "stream",
          "text": [
            "Thu Apr  8 09:22:33 2021       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.67       Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   45C    P8     9W /  70W |      0MiB / 15109MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
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        },
        "outputId": "28648261-543d-4b07-bc31-c86cb39a7e47"
      },
      "source": [
        "from __future__ import absolute_import, division, print_function, unicode_literals\n",
        "\n",
        "try:\n",
        "  !pip uninstall tb-nightly tensorboardX tensorboard\n",
        "  !pip install tf-nightly\n",
        "except Exception:\n",
        "  pass\n",
        "import tensorflow as tf\n",
        "\n",
        "import os\n",
        "import datetime\n",
        "import tensorflow_datasets as tfds\n",
        "\n",
        "%load_ext tensorboard"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[33mWARNING: Skipping tb-nightly as it is not installed.\u001b[0m\n",
            "\u001b[33mWARNING: Skipping tensorboardX as it is not installed.\u001b[0m\n",
            "Uninstalling tensorboard-2.4.1:\n",
            "  Would remove:\n",
            "    /usr/local/bin/tensorboard\n",
            "    /usr/local/lib/python3.7/dist-packages/tensorboard-2.4.1.dist-info/*\n",
            "    /usr/local/lib/python3.7/dist-packages/tensorboard/*\n",
            "Proceed (y/n)? y\n",
            "  Successfully uninstalled tensorboard-2.4.1\n",
            "Collecting tf-nightly\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/cc/0f/ec1987ea41ed7edc2968f5dc724ed7557ce2c0c8ec2da4b93ee17b42e3a4/tf_nightly-2.6.0.dev20210407-cp37-cp37m-manylinux2010_x86_64.whl (455.4MB)\n",
            "\u001b[K     |████████████████████████████████| 455.4MB 41kB/s \n",
            "\u001b[?25hRequirement already satisfied: absl-py~=0.10 in /usr/local/lib/python3.7/dist-packages (from tf-nightly) (0.12.0)\n",
            "Collecting grpcio~=1.34.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d5/d1/f38a91d8724706427fe973a7dfa11e938cee98aa7196b03d870a25a08bab/grpcio-1.34.1-cp37-cp37m-manylinux2014_x86_64.whl (4.0MB)\n",
            "\u001b[K     |████████████████████████████████| 4.0MB 47.6MB/s \n",
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            "Requirement already satisfied: keras-preprocessing~=1.1.2 in /usr/local/lib/python3.7/dist-packages (from tf-nightly) (1.1.2)\n",
            "Collecting gast==0.4.0\n",
            "  Downloading https://files.pythonhosted.org/packages/b6/48/583c032b79ae5b3daa02225a675aeb673e58d2cb698e78510feceb11958c/gast-0.4.0-py3-none-any.whl\n",
            "Requirement already satisfied: wheel~=0.35 in /usr/local/lib/python3.7/dist-packages (from tf-nightly) (0.36.2)\n",
            "Collecting tb-nightly~=2.5.0.a\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b0/7e/6a7d37bbf4c8276da1c1a24f2243d3616c6a506f0ab4bd1fb5377996cb41/tb_nightly-2.5.0a20210407-py3-none-any.whl (6.0MB)\n",
            "\u001b[K     |████████████████████████████████| 6.0MB 40.6MB/s \n",
            "\u001b[?25hCollecting tf-estimator-nightly~=2.5.0.dev\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/24/6c/9bf4a6004d18c8e543845d3416e50f36dd09d272161e2fb0db5678132dfd/tf_estimator_nightly-2.5.0.dev2021032601-py2.py3-none-any.whl (462kB)\n",
            "\u001b[K     |████████████████████████████████| 471kB 58.0MB/s \n",
            "\u001b[?25hRequirement already satisfied: astunparse~=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tf-nightly) (1.6.3)\n",
            "Requirement already satisfied: six~=1.15.0 in /usr/local/lib/python3.7/dist-packages (from tf-nightly) (1.15.0)\n",
            "Collecting h5py~=3.1.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9d/74/9eae2bedd8201ab464308f42c601a12d79727a1c87f0c867fdefb212c6cf/h5py-3.1.0-cp37-cp37m-manylinux1_x86_64.whl (4.0MB)\n",
            "\u001b[K     |████████████████████████████████| 4.0MB 45.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy~=1.19.2 in /usr/local/lib/python3.7/dist-packages (from tf-nightly) (1.19.5)\n",
            "Collecting keras-nightly~=2.6.0.dev\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/67/52/6bd9ee1754a64ac1a7a7a8b7d5cf3670abdbb6089a1d5e365532b2cf3f09/keras_nightly-2.6.0.dev2021040800-py2.py3-none-any.whl (1.2MB)\n",
            "\u001b[K     |████████████████████████████████| 1.2MB 55.4MB/s \n",
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            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from protobuf>=3.9.2->tf-nightly) (54.2.0)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.7/dist-packages (from tb-nightly~=2.5.0.a->tf-nightly) (2.23.0)\n",
            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tb-nightly~=2.5.0.a->tf-nightly) (1.8.0)\n",
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            "Collecting tensorboard-data-server<0.7.0,>=0.6.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/12/63/d92b4bc44261b7396558c054f78acf71468b5628bcb14cdaeb2504ea80d3/tensorboard_data_server-0.6.0-py3-none-manylinux2010_x86_64.whl (3.9MB)\n",
            "\u001b[K     |████████████████████████████████| 3.9MB 46.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tb-nightly~=2.5.0.a->tf-nightly) (0.4.3)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tb-nightly~=2.5.0.a->tf-nightly) (3.3.4)\n",
            "Collecting cached-property; python_version < \"3.8\"\n",
            "  Downloading https://files.pythonhosted.org/packages/48/19/f2090f7dad41e225c7f2326e4cfe6fff49e57dedb5b53636c9551f86b069/cached_property-1.5.2-py2.py3-none-any.whl\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tb-nightly~=2.5.0.a->tf-nightly) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tb-nightly~=2.5.0.a->tf-nightly) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tb-nightly~=2.5.0.a->tf-nightly) (2020.12.5)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tb-nightly~=2.5.0.a->tf-nightly) (3.0.4)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tb-nightly~=2.5.0.a->tf-nightly) (4.2.1)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tb-nightly~=2.5.0.a->tf-nightly) (0.2.8)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4; python_version >= \"3.6\" in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tb-nightly~=2.5.0.a->tf-nightly) (4.7.2)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tb-nightly~=2.5.0.a->tf-nightly) (1.3.0)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tb-nightly~=2.5.0.a->tf-nightly) (3.8.1)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tb-nightly~=2.5.0.a->tf-nightly) (0.4.8)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tb-nightly~=2.5.0.a->tf-nightly) (3.1.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tb-nightly~=2.5.0.a->tf-nightly) (3.4.1)\n",
            "\u001b[31mERROR: tensorflow 2.4.1 requires tensorboard~=2.4, which is not installed.\u001b[0m\n",
            "\u001b[31mERROR: tensorflow 2.4.1 has requirement gast==0.3.3, but you'll have gast 0.4.0 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: tensorflow 2.4.1 has requirement grpcio~=1.32.0, but you'll have grpcio 1.34.1 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: tensorflow 2.4.1 has requirement h5py~=2.10.0, but you'll have h5py 3.1.0 which is incompatible.\u001b[0m\n",
            "Installing collected packages: grpcio, gast, tensorboard-data-server, tb-nightly, tf-estimator-nightly, cached-property, h5py, keras-nightly, tf-nightly\n",
            "  Found existing installation: grpcio 1.32.0\n",
            "    Uninstalling grpcio-1.32.0:\n",
            "      Successfully uninstalled grpcio-1.32.0\n",
            "  Found existing installation: gast 0.3.3\n",
            "    Uninstalling gast-0.3.3:\n",
            "      Successfully uninstalled gast-0.3.3\n",
            "  Found existing installation: h5py 2.10.0\n",
            "    Uninstalling h5py-2.10.0:\n",
            "      Successfully uninstalled h5py-2.10.0\n",
            "Successfully installed cached-property-1.5.2 gast-0.4.0 grpcio-1.34.1 h5py-3.1.0 keras-nightly-2.6.0.dev2021040800 tb-nightly-2.5.0a20210407 tensorboard-data-server-0.6.0 tf-estimator-nightly-2.5.0.dev2021032601 tf-nightly-2.6.0.dev20210407\n",
            "INFO:tensorflow:Enabling eager execution\n",
            "INFO:tensorflow:Enabling v2 tensorshape\n",
            "INFO:tensorflow:Enabling resource variables\n",
            "INFO:tensorflow:Enabling tensor equality\n",
            "INFO:tensorflow:Enabling control flow v2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x2hbzsdExdry",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "384d938e-d921-42e8-d2ae-dabdb2c2ba0e"
      },
      "source": [
        "import pkg_resources\n",
        "\n",
        "for entry_point in pkg_resources.iter_entry_points('tensorboard_plugins'):\n",
        "    print(entry_point.dist)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensorboard 2.4.1\n",
            "tensorboard-plugin-wit 1.8.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nyjsHFH8yxqZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f0b51f81-7735-4c02-9041-e8dfcfb90fca"
      },
      "source": [
        "#!ls -alrt /usr/local/lib/python3.6/dist-packages/tensorboard*\n",
        "!rm -r /usr/local/lib/python3.6/dist-packages/tensorboardcolab-0.0.22.dist-info"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "rm: cannot remove '/usr/local/lib/python3.6/dist-packages/tensorboardcolab-0.0.22.dist-info': No such file or directory\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hy0fVF_yv6SD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a419cc8e-dd4a-4994-f902-e033a893b3cf"
      },
      "source": [
        "print(tf.__version__)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.6.0-dev20210407\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EB00mWXfDH7n",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2056605d-043a-40af-f017-7e24e42c5b35"
      },
      "source": [
        "tf.config.experimental.list_physical_devices()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QDH43tw-DN4y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318,
          "referenced_widgets": [
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          ]
        },
        "outputId": "c9530ed6-6908-423c-a366-ba816b1359d5"
      },
      "source": [
        "dataset, info = tfds.load('amazon_us_reviews/Mobile_Electronics_v1_00', with_info=True)\n",
        "train_dataset = dataset['train']"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1mDownloading and preparing dataset amazon_us_reviews/Mobile_Electronics_v1_00/0.1.0 (download: 21.81 MiB, generated: Unknown size, total: 21.81 MiB) to /root/tensorflow_datasets/amazon_us_reviews/Mobile_Electronics_v1_00/0.1.0...\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dbd7b2c829a84942ada75d9b3e022689",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Dl Completed...', max=1.0, style=Progre…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8ff9a685bf164e8a98e57c21bf6cb11e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Dl Size...', max=1.0, style=ProgressSty…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4e25ff8a0cdb4065a2c6ab9264266358",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Extraction completed...', max=1.0, styl…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "eda8c13222d9430899c0bf3d423007cd",
              "version_minor": 0,
              "version_major": 2
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\rShuffling and writing examples to /root/tensorflow_datasets/amazon_us_reviews/Mobile_Electronics_v1_00/0.1.0.incompleteWA0UE3/amazon_us_reviews-train.tfrecord\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\u001b[1mDataset amazon_us_reviews downloaded and prepared to /root/tensorflow_datasets/amazon_us_reviews/Mobile_Electronics_v1_00/0.1.0. Subsequent calls will reuse this data.\u001b[0m\n",
            "\r"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u1diITDtD9xD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f0ea5a24-2087-4ee4-a28c-7484f5d35540"
      },
      "source": [
        "info"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tfds.core.DatasetInfo(\n",
              "    name='amazon_us_reviews',\n",
              "    version=0.1.0,\n",
              "    description='Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.\n",
              "\n",
              "Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).\n",
              "\n",
              "Each Dataset contains the following columns : \n",
              "  marketplace       - 2 letter country code of the marketplace where the review was written.\n",
              "  customer_id       - Random identifier that can be used to aggregate reviews written by a single author.\n",
              "  review_id         - The unique ID of the review.\n",
              "  product_id        - The unique Product ID the review pertains to. In the multilingual dataset the reviews\n",
              "                      for the same product in different countries can be grouped by the same product_id.\n",
              "  product_parent    - Random identifier that can be used to aggregate reviews for the same product.\n",
              "  product_title     - Title of the product.\n",
              "  product_category  - Broad product category that can be used to group reviews \n",
              "                      (also used to group the dataset into coherent parts).\n",
              "  star_rating       - The 1-5 star rating of the review.\n",
              "  helpful_votes     - Number of helpful votes.\n",
              "  total_votes       - Number of total votes the review received.\n",
              "  vine              - Review was written as part of the Vine program.\n",
              "  verified_purchase - The review is on a verified purchase.\n",
              "  review_headline   - The title of the review.\n",
              "  review_body       - The review text.\n",
              "  review_date       - The date the review was written.',\n",
              "    homepage='https://s3.amazonaws.com/amazon-reviews-pds/readme.html',\n",
              "    features=FeaturesDict({\n",
              "        'data': FeaturesDict({\n",
              "            'customer_id': tf.string,\n",
              "            'helpful_votes': tf.int32,\n",
              "            'marketplace': tf.string,\n",
              "            'product_category': tf.string,\n",
              "            'product_id': tf.string,\n",
              "            'product_parent': tf.string,\n",
              "            'product_title': tf.string,\n",
              "            'review_body': tf.string,\n",
              "            'review_date': tf.string,\n",
              "            'review_headline': tf.string,\n",
              "            'review_id': tf.string,\n",
              "            'star_rating': tf.int32,\n",
              "            'total_votes': tf.int32,\n",
              "            'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),\n",
              "            'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),\n",
              "        }),\n",
              "    }),\n",
              "    total_num_examples=104975,\n",
              "    splits={\n",
              "        'train': 104975,\n",
              "    },\n",
              "    supervised_keys=None,\n",
              "    citation=\"\"\"\"\"\",\n",
              "    redistribution_info=,\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nZOZarx4GGX3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cb4c83b4-e5c9-4bec-ea20-2a444b2f6a91"
      },
      "source": [
        "print(train_dataset)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<PrefetchDataset shapes: {data: {customer_id: (), helpful_votes: (), marketplace: (), product_category: (), product_id: (), product_parent: (), product_title: (), review_body: (), review_date: (), review_headline: (), review_id: (), star_rating: (), total_votes: (), verified_purchase: (), vine: ()}}, types: {data: {customer_id: tf.string, helpful_votes: tf.int32, marketplace: tf.string, product_category: tf.string, product_id: tf.string, product_parent: tf.string, product_title: tf.string, review_body: tf.string, review_date: tf.string, review_headline: tf.string, review_id: tf.string, star_rating: tf.int32, total_votes: tf.int32, verified_purchase: tf.int64, vine: tf.int64}}>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZzZdLLdiG9j6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4f16b956-f497-477b-97b8-9ff7746fa3d3"
      },
      "source": [
        "len(list(train_dataset))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "104975"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0tTxEMQbDjrv"
      },
      "source": [
        "BUFFER_SIZE = 40000\n",
        "BATCH_SIZE = 196"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xIFXNHBiDlL0"
      },
      "source": [
        "train_dataset = train_dataset.shuffle(BUFFER_SIZE, reshuffle_each_iteration=False)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hr4e5_1SHNPI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7a2c3701-ff55-4466-d4f0-ba09a4cd0d26"
      },
      "source": [
        "for reviews in train_dataset.take(2):\n",
        "  print(reviews)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'data': {'customer_id': <tf.Tensor: shape=(), dtype=string, numpy=b'51683810'>, 'helpful_votes': <tf.Tensor: shape=(), dtype=int32, numpy=1>, 'marketplace': <tf.Tensor: shape=(), dtype=string, numpy=b'US'>, 'product_category': <tf.Tensor: shape=(), dtype=string, numpy=b'Mobile_Electronics'>, 'product_id': <tf.Tensor: shape=(), dtype=string, numpy=b'B007WO4ENG'>, 'product_parent': <tf.Tensor: shape=(), dtype=string, numpy=b'567489042'>, 'product_title': <tf.Tensor: shape=(), dtype=string, numpy=b'Alpatronix AX200 X-tra Strong Portable Mini Capsule Dual Stereo Speaker for iPhone / iPad / Samsung Galaxy / iPod / Smartphone / MP3 or MP4 player / Laptop or Notebook / Desktop / or Tablet PC'>, 'review_body': <tf.Tensor: shape=(), dtype=string, numpy=b\"These little speakers really sound better than expected. I would not hesitate to purchase them again. They aren't perfect but then again they sound so much better than my laptop speakers .And are very portable . Not to shabby.\">, 'review_date': <tf.Tensor: shape=(), dtype=string, numpy=b'2013-10-14'>, 'review_headline': <tf.Tensor: shape=(), dtype=string, numpy=b'better than expected'>, 'review_id': <tf.Tensor: shape=(), dtype=string, numpy=b'R35V842M7S8XKV'>, 'star_rating': <tf.Tensor: shape=(), dtype=int32, numpy=5>, 'total_votes': <tf.Tensor: shape=(), dtype=int32, numpy=1>, 'verified_purchase': <tf.Tensor: shape=(), dtype=int64, numpy=0>, 'vine': <tf.Tensor: shape=(), dtype=int64, numpy=1>}}\n",
            "{'data': {'customer_id': <tf.Tensor: shape=(), dtype=string, numpy=b'44050788'>, 'helpful_votes': <tf.Tensor: shape=(), dtype=int32, numpy=0>, 'marketplace': <tf.Tensor: shape=(), dtype=string, numpy=b'US'>, 'product_category': <tf.Tensor: shape=(), dtype=string, numpy=b'Mobile_Electronics'>, 'product_id': <tf.Tensor: shape=(), dtype=string, numpy=b'B00AB6II34'>, 'product_parent': <tf.Tensor: shape=(), dtype=string, numpy=b'247233222'>, 'product_title': <tf.Tensor: shape=(), dtype=string, numpy=b'Zeimax (TM) Adapter Converter 8 Pin to 30pin for Apple Iphone 5, Ipod 5 Nano 7th Gen USA'>, 'review_body': <tf.Tensor: shape=(), dtype=string, numpy=b\"It wasn't read by the iPad as a compatible item. Was used in 2013 Benz to upgrade the plug from the ipad 2 to the new ipad\">, 'review_date': <tf.Tensor: shape=(), dtype=string, numpy=b'2013-11-01'>, 'review_headline': <tf.Tensor: shape=(), dtype=string, numpy=b'Doesnt Work'>, 'review_id': <tf.Tensor: shape=(), dtype=string, numpy=b'R1UUNB2YC0EHLL'>, 'star_rating': <tf.Tensor: shape=(), dtype=int32, numpy=1>, 'total_votes': <tf.Tensor: shape=(), dtype=int32, numpy=0>, 'verified_purchase': <tf.Tensor: shape=(), dtype=int64, numpy=0>, 'vine': <tf.Tensor: shape=(), dtype=int64, numpy=1>}}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uyKHUS9AaJ5j",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "baddf623-6847-44ac-fc5e-2e6204185d39"
      },
      "source": [
        "for reviews in train_dataset.take(10):\n",
        "  review_text = reviews['data']\n",
        "  print(review_text.get('review_body').numpy())\n",
        "  print(review_text.get('star_rating'))\n",
        "  print(tf.where(review_text.get('star_rating')>3,1,0).numpy())"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "b\"These little speakers really sound better than expected. I would not hesitate to purchase them again. They aren't perfect but then again they sound so much better than my laptop speakers .And are very portable . Not to shabby.\"\n",
            "tf.Tensor(5, shape=(), dtype=int32)\n",
            "1\n",
            "b\"It wasn't read by the iPad as a compatible item. Was used in 2013 Benz to upgrade the plug from the ipad 2 to the new ipad\"\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "0\n",
            "b'I love the product.  The only issue I have is with the little plug in cord which is fragile and the wires began to show with just a little tug.  Other than that, it works, and I use it daily'\n",
            "tf.Tensor(4, shape=(), dtype=int32)\n",
            "1\n",
            "b\"I installed this security system on a 3rd-generation Camry.  I am not a professional installer, just a car enthusiast.  I found a wiring diagram for my car online, and followed Viper's included instructions to match up the wires.  It took me about 4 hours to complete the installation (including breaks).  Great instructions from Viper.<br /><br />The system itself was just what I was looking for: siren, shock sensor, zone triggers, remote-lock and unlock, starter disable, and a warning LED to ward off potential thieves.  The siren can be heard from about 150-200 feet away.  So far I am pleased with the purchase.\"\n",
            "tf.Tensor(5, shape=(), dtype=int32)\n",
            "1\n",
            "b'Perfect gift.'\n",
            "tf.Tensor(5, shape=(), dtype=int32)\n",
            "1\n",
            "b\"Instructions are incomprehensible.  Unit ran as hot as charcoal (figuratively).  Tried to set up on four computers, GPS USB interface was never recognized.<br /><br />I had really high hopes for this based on other reviews.  I'm hardly technologically illiterate, but I was baffled.\"\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "0\n",
            "b\"I give it 5 stars because it did a great job at protecting my ipod. However, I didn't realize how rubbery it was which allowed it to hold fuzz and slim which was really gross. I eventually ended up throwing it away out of disgust, but the product itself it good if you just want something that looks nice and really doesn't protect your ipod.\"\n",
            "tf.Tensor(5, shape=(), dtype=int32)\n",
            "1\n",
            "b'The product does its job and I have no issues with how it does the job I bought it to do.'\n",
            "tf.Tensor(5, shape=(), dtype=int32)\n",
            "1\n",
            "b\"Have had this for over a month, and so far so good.  I understand that the off-brands stop working after only a short amount of time.  Well, this one is the genuine product, cost a fraction of the retail price, and it works as expected.  I can't complain!\"\n",
            "tf.Tensor(5, shape=(), dtype=int32)\n",
            "1\n",
            "b'The case is a great color and fits the iPod 2 just as stated.  The extras were an added bonus.'\n",
            "tf.Tensor(4, shape=(), dtype=int32)\n",
            "1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i3ySnZHiaS01",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ed564756-d1f3-4479-f81e-316426970a70"
      },
      "source": [
        "tokenizer = tfds.deprecated.text.Tokenizer()\n",
        "\n",
        "vocabulary_set = set()\n",
        "for _, reviews in train_dataset.enumerate():\n",
        "  #print(reviews)\n",
        "  review_text=reviews['data']\n",
        "  reviews_tokens = tokenizer.tokenize(review_text.get('review_body').numpy())\n",
        "  vocabulary_set.update(reviews_tokens)\n",
        "\n",
        "vocab_size = len(vocabulary_set)\n",
        "vocab_size"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "73738"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gYoPn2MAly6d"
      },
      "source": [
        "encoder = tfds.deprecated.text.TokenTextEncoder(vocabulary_set, lowercase=True, strip_vocab=True)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vYqNrx4uzIHf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2432b321-ba17-4f57-c6b2-97234300aca4"
      },
      "source": [
        "print(vocabulary_set)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'blurry', 'PLAYIN', 'replicate', 'H700', 'Marshall', 'Guetta', 'MP4047', 'mecanic', 'transitional', 'Only', 'Swiveling', 'KWAV50', 'Oz', 'rookie', 'Traps', 'Windex', 'Mamouth', 'pefectly', 'Tornado', 'tolerrate', 'B003LAEJ9O', 'nests', 'rebel', 'applicator', 'sos', 'suppository', 'Dennis', 'shortness', 'loner', 'boils', 'RECHARGE', 'SANSA', 'Dio1978', 'WORKMANSHIP', 'crane', 'onlive', 'pubs', 'BALTIMORE', 'Iphone4', 'cats', 'GRACIAS', '7870', 'ShorRS', 'Fulfills', 'ONES', 'Shefko', 'wernt', 'monotonous', 'crinkling', 'STIUCKER', '9600', 'lang', 'INFAMOUS', 'senn', 'irregular', 'maxx', 'Queen', 'ROAD', 'SKar', 'disiponted', 'samlple', 'Grandmas', 'landrover', 'bassknobs', 'Orden', 'dynamically', 'Pandigitgal', 'reasonable', 'advertiser', 'orr', 'perforated', 'AT2020', 'qty', 'nada', 'Cane', '1G', 'sliced', 'protacting', 'rarley', 'Powergen', 'Wort', 'shoveling', 'Sci', 'mande', 'gates', 'purging', 'precluding', 'upgraded', 'marketers', 'faint', 'Removes', 'audis', '207', 'fantasic', 'Eggplant', 'vices', 'scepticle', 'sposed', 'T0', '181988', 'churning', 'manuver', 'CrossFit', 'GoPros', 'waterways', '1230mAh', 'unremarkable', 'explosion', 'speculating', 'cfb', 'theSilicon', 'Grylis', 'original', 'pmp', '423001', 'democracy', 'tos', '32Bit', 'MIGRATED', 'sizes', 'addresses', 'pursue', 'ITEMS', '1326127245', 'Sirf3', 'Worldwide', 'SD', 'fundamentals', 'region', 'sentences', 'predicted', 'fullest', 'queries', 'performer', '157', 'licks', 'attrmpts', 'AmazonBasic', 'Ez', 'none', 'modal', 'Department', 'OCCASSIONALLY', 'CONNECTS', 'tinnitus', 'KGC', 'underwhelmed', 'purschase', 'remodelled', 'triage', 'restart', 'papst', 'JVCeeezy', 'pausing', 'CYDLE', 'AÚN', 'thirdly', 'YEAH', 'B000NK5VY2', '2100Hz', 'cookie', 'remissness', 'iissue', 'conked', 'Foreman', 'cruiser', 'cigs', 'multinationals', 'impedance', 'cu', 'STITCHED', 'interchange', 'rewiring', 'B00A0QSLJG', 'Mmats', 'Antenna', 'TEK', 'things', 'trebles', 'x20', 'drealing', 'revews', 'tuners', 'sloopier', 'intimidating', 'extar', '12C', 'libraries', 'carabiners', 'Corps', 'duffle', 'ymca', 'hassel', 'signifying', '4149820', 'annoyingly', 'ohmage', 'Whatta', 'IBM', 'Cooltree', 'offline', 'intensities', 'deserving', 'taught', 'umbrella', 'Muggy', 'ENABLES', 'squarry', 'datector', 'walla', 'adapator', 'gogod', 'Drift', 'XNAV9525', 'Supertramp', 'X800', 'nineteen', 'Fictionwise', 'VZLA', 'spongebob', 'clumpy', 'Eight', '308', 'COLOUR', 'fpv', 'ventilation', 'propritary', 'specify', 'bace', 'Guides', 'rainbow', 'Acousta', 'terminals', '500watt', 'bing', 'flattening', 'teenagerish', 'flikker', 'misma', 'claramente', 'scolded', 'thumbscrew', 'Superadio', '22003', 'Board', 'Proved', 'Crisper', 'Basslink', 'quartered', 'patched', 'Canyon', 'archivos', 'Critical', 'hangging', 'simulate', 'puck', 'distorations', 'KennyG488', 'amplifying', 'rerouted', 'truble', 'responsibale', 'Shipment', 'producut', 'RAW', 'grandbabies', 'guesses', 'SPRAY', 'raindrop', 'paddles', 'receipt', '25in', 'COMMON', 'reconsider', 'trunck', 'secs', 'clones', 'iSight', 'LATEST', 'chill', 'blasing', '107', 'worlds', 'explicitly', 'tremendous', 'Putty', 'neat', 'Arguably', 'peak', 'school', 'rocking', 'Kanger', 'crumpling', 'Shipping', 'Towncar', 'Polyfill', 'alternitive', '19th', 'uhf', 'latching', 'conectaron', 'postions', 'Part', 'chargee', 'BOOMYOUR', 'racers', 'Opens', 'skins', 'inlcuded', 'FX', 'MIC', '900rms', 'Cyberdict', 'Balled', 'jimmies', 'Unfortinately', 'glitch', 'outsell', 'qualityl', 'Crimp', 'cunks', 'loyally', 'obsolescence', 'lab', 'TIGHTEN', 'CLK230', 'acceissble', 'standar', 'explainedwhat', 'W166', 'safisfied', 'Draughn', 'breaktime', 'C640', 'khz', 'slimmest', 'guste', 'alpine', 'Ereaders', 'manners', 'flimsey', 'apoplectic', 'chart', 'siding', 'workd', 'cordess', 'cryptically', '100pcs', 'density', 'plumped', 'flatrate', 'Btm', 'featuresfor', 'lumpy', 'shrimpin', 'waiing', 'great', 'accidentley', 'B007NXLNIA', 'Txt', 'chsrged', 'peck', 'shortcake', 'JetBook', 'branche', 'commutation', 'Seul', 'meade', 'astroid', 'iCom', 'thinnest', '493', 'fitcarsaleshelen', '260w', 'Quotient', 'RECIEVE', 'Playbook', 'Ny', 'abhorrent', 'intelligence', 'len', 'recentley', 'Grant', 'shabily', 'cranky', 'Rv', 'competition', 'SRS', 'EBookwise', 'esc', 'Keita', 'flexableand', 'packer', 'MISMATCH', 'Sach', 'Vg33e', 'enemy', 'Triangle', 'CALIBRATION', 'aol', 'careful', 'Promptly', 'variability', 'keepsakes', 'heli', 'delievers', 'TOUGHER', '56', 'Geezuz', 'enforced', 'raised', 'iHome2go', 'lately', 'outiside', 'compiles', 'endorsement', 'Pedal', 'reseaching', 'AGL', 'Cosmos', 'grout', 'resturauants', 'Sept', 'logics', 'OTT', 'solvent', 'SECOM', 'B002QINY8E', 'wanted', 'playd', '165dB', 'S401', 'Nuf', 'manhole', 'PROTECTION', 'Despised', 'pairing', 'bl', 'clocked', 'B00519RWI8', 'Dector', 'correo', 'shade', 'publishefr', 'screenprotecter', 'SJ1000', 'refrain', '9900', '855T', 'noted', 'Kudos', 'Sale', 'distinguishes', 'TAL', 'pushy', 'ashame', 'AFTERMARKET', 'unmount', 'CME', '40amps', 'pusher', 'B00LM6LOCY', 'goosh', 'KF', 'Icurve', 'L5', 'unsolder', 'clock', 'emailing', '70s', 'nurse', 'ass', 'Typical', 'DNSe', 'sprayed', 'lease', 'Bacpac', 'kilometre', 'facefirst', 'Status', 'positionable', 'theater', 'keeeps', 'youcan', 'ingition', 'ibattz', 'Business', 'switches', 'Prime', 'valuables', 'B007FHW8EW', 'BEM', 'yuk', 'writhing', '1volts', 'Hugh', 'nO', 'SIGNAL', 'Yaris', 'Trents', 'overally', 'reading', 'EXTERNO', 'elimanate', 'oddley', 'CLIPS', 'stroll', 'Necessity', '252U', 'particularl', 'unskilled', 'revisited', 'recommmend', 'BOMB', 'ld20', '9w', 'FAAAAST', 'WAYS', 'brazilan', 'reeeeally', 'E500', 'DBPower', 'vunerable', 'eveyone', 'comme', 'professionals', 'spirits', '60min', 'PROPERTY', 'Wooden', 'xx', 'Acase', 'puting', 'Jamboxes', 'schmutz', 'howard', 'Construction', 'counterpart', '5000mAH', 'Country', 'implementation', 'unbelievable', 'handier', 'tortunre', 'underworld', 'revit', 'Chains', 'Fast', 'NookHD9_KBBT_BlkBrwn', 'SEL', 'peque', 'Supposedly', 'everyhing', 'envelop', 'attatched', 'Pig', 'sone', 'vinegar', 'PAIR', 'pollutes', 'vanes', 'mind', '147c', '6990HD', 'destruct', 'streams', 'B000GUKKMW', 'vp900', 'Styluses', 'conveniant', 'ZHD', 'eads', 'Instructor', '1490t', 'abought', 'VIP', 'ossicles', 'ages', '2600mah', 'vowed', 'strickly', 'taurus', 'TUNER', 'Pulled', 'Curious', 'eelink', 'neet', 'prosthetic', 'badda', 'Amigo', 'flashbacks', 'Reliability', 'MICS', 'Llloooovvvveeeee', 'knobber', 'DT50', 'Considerof', 'NT120T', 'residents', 'decieved', 'befuddled', 'MPEG', 'Tablet', 'solar1234', 'off', 'citrus', 'openings', 'renavigate', '400G', 'trámites', 'snugglin', 'B004WA90IM', 'longre', 'tobget', 'connection', 'skepticism', 'Essentials', 'serveice', 'OH', 'equiptment', 'headjack', 'susb', 'Appstore', 'filaments', 'anchored', 'unwrapped', 'peu', 'quartet', 'ascent', 'Cootree', 'trebbly', 'EQpreset', 'UBS', 'Or', 'Ports', 'sporband', 'Rockies', 'Amega', '75RMSx4', 'discoloring', 'earphone', 'ddoing', 'swimm', 'timeout', 'C03U', 'Twice', 'Separates', 'comprable', 'vic', 'Fukushima', 'gogroove', 'detriments', 'GUMP', 'appliance', 'ThankYou', 'assesory', 'jog', 'woking', 'approximatly', 'Integration', 'Onix', 'programmer', 'lotion', 'ballhead', 'Oil', 'Amaqzing', 'Nope', 'uneconomical', 'DGTL', 'actually', 'belonging', 'shavers', 'Locked', '1600', 'awry', 'complied', 'jUST', 'porque', 'Loads', 'funcionamiento', 'Emily', 'Few', 'safty', 'thumbprints', 'diamater', 'succinct', 'unworking', 'snook', 'buttons', 'EMBALADO', 'tuxedo', 'KINETIK', 'resigned', 'Spitfires', 'Mylink', 'noxious', 'Summer', 'STi', 'cm_cr_rev_prod_titlecan', 'model', 'orienting', 'descripcion', 'subdirectories', 'unit', 'stander', 'toms', 'Tip', 'zippy', 'personalized', 'Gripping', 'Livestrong', 'geologging', 'opción', 'toddlers', 'fluid', 'defrosters', 'preceded', 'IMHO', 'overwhelming', 'fuzziness', 'mo2EAHZRYTBO6HI', '0780625277', 'bat', 'Caldwell', '00033', '4430', 'Aluratek', '25cubes', 'resive', 'SuperDuper', 'mounds', 'vessels', 'salieron', 'Mushroom', 'seting', 'warmly', 'TIA', '2360', 'niftier', 'lllongg', 'modifications', 'FlexSmart', 'protecor', 'FORTUNATELY', 'wetland', 'Seires', 'everywhere', 'iand', 'already', 'snorer', 'STG', 'Fiddly', 'basicly', 'Achieved', 'strobes', 'Rhino', 'Establishing', 'hutch', 'x395', 'stair', 'bottles', 'subtlety', 'Food', 'almot', 'attanae', 'cautions', 'Released', 'substance', 'generalized', 'advirtisemet', 'hazy', 'F', 'Fine', 'receiviing', 'Technology', 'Orderd', 'rinsing', 'DroidX2', 'Trough', 'preformance', 'SORELY', 'Am', 'B009A5204K', 'soaked', 'loves', 'STEAL', 'fists', 'grabadora', 'misreading', 'throw', 'SBTS2', 'permet', 'propagate', 'homewarming', 'unbelievably', 'squishing', 'qualms', 'Cuf3', 'yes', 'snows', 'Phantom', 'batterypack', 'Selling', 'vídeos', 'razorblade', '250WRMS', 'housing', '135º', 'goods', 'Alpina', 'pained', 'Pirate', 'comprobar', 'Server', 'baudrate', 'PHD', 'windmills', 'variants', 'gangster', 'downside', 'FreeStlye', 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'soundblaster', 'droppable', 'solely', 'Ill', 'Telephone', 'pocketing', 'klearkare', 'Linus', 'opponent', 'número', '2527074df78a55ee90e54d8138730c6', 'Bowless', 'agency', 'amplifiers', 'tailer', 'deigned', 'Jmtek', 'peacing', '4500BT', 'bnc', 'Napa', 'practicable', 'electrostatic', 'LOOKS', 'KabelDirekt', 'fill', 'OCT', 'virations', 'B007KPTYCI', 'Laissez', 'mfi', 'roadmate', 'Chef', 'UTM', 'designation', 'Matched', 'Pocketcasts', 'starred', 'professonial', '73s', 'Subwolfer', 'Hawkins', 'ox_sc_sfl_title_2', 'CSi', 'trianngle', 'cradle', 'Upon', 'C320', 'H800', 'difficult', 'squishy', 'casset', 'ANHD20KIT', 'CURRENT', 'mercado', 'inverse', 'formidable', 'Singhpeeti', 'rrived', 'nicad', 'whereby', 'Melody', 'font', 'Additionally', 'raising', 'SID', 'stiffeners', 'differentiate', 'Pelican', 'Bimmer', 'ALOT', 'Slingbox', '64MB', 'nearlyanyplace', 'microSD', 'cm_cr_ryp_prd_ttl_sol_0', 'slik', 'thumbstick', 'internat', '8000k', 'duct', 'affixing', 'snoops', 'WORE', 'prox', 'Seamed', 'RPMs', 'PPS', 'A1121', 'remanufacture', 'overX', 'staion', 'scorch', 'D8601', 'DSM5', 'bargaining', 'megaman', 'B0019RBJOE', 'Ventilator', '8900', 'ninguna', 'cousmter', 'sailor', 'toot', 'orobkem', 'advertise', '300m', 'LiBritz', 'Benz', 'Projected', 'comepletely', 'ther', 'Cts', 'Rockbox', 'Limousine', '864', 'diffuses', 'railings', 'pantyhose', 'gaimer', 'ricevuto', 'tmely', 'armed', 'voracious', 'slacker', 'Seven', 'nicley', 'unanswered', 'SEARCHING', 'wavelength', 'reccomendation', 'Wasting', 'irritant', 'windowless', 'outnumbered', 'appetite', 'Emmylou', 'iKross', 'financially', 'definetly', 'seamless', 'PASSAT', '485', 'Walmart', 'Expert', 'Tape', 'epoxied', 'belly', 'staticky', 'theyd', 'gunge', 'IMPACT', 'tamaño', 'relatives', 'manuvering', 'fat', 'ventajas', 'MUCHISIMO', 'iP', 'completly', '5x7', 'misteriously', 'Altima', 'transceiver', 'Purple', 'hodrrible', 'mango', '750D', 'former', 'skullcandy', 'slumped', 'regualar', 'insider', 'FIRESTIK', '20203R79903', 'slef', 'MIND', 'rubbery', 'XB', 'habían', 'MARE', 'DRIVEWAY', 'heartfelt', 'unconventionally', 'chtistmas', 'PDX', 'Deluxe', 'possibilities', 'Mworks', 'proven', 'DEMAND', 'MID7127', 'whenI', 'IMPORTANTE', 'Heated', 'POOF', 'Zermatt', 'frOObi', 'Coincidentally', 'lástima', 'constrasting', 'lifters', 'OxiClean', 'tranny', 'Usable', 'plexiglass', 'Rally', 'IPOD', 's1fw', 'Curiously', '8x8', 'voice', 'stolen', 'PROMISED', 'diztronic', '10000mah', 'NWD', 'SUBWOOFERS', 'studying', 'sofware', 'presentations', 'Apology', 'Accelerometer', 'welkl', 'poured', 'XBMC', 'OUTER', 'rigors', 'LEAVING', 'B5s', 'REGULAR', 'mission', 'MINUTE', 'REORDER', 'screeen', 'logorythms', 'groan', 'B000LFVFK8', 'Gunther', 'lockable', 'probes', 'nitidez', 'seduced', 'existant', 'regularmente', 'Deadmau5', 'Polks', 'GIFTS', 'DBTech', 'Kohl', 'Disco', 'RTL', 'rickety', 'touchAndroid', '3inch', 'timed', 'careers', 'Porter', 'Unobtrusive', 'HEADROOM', 'VISION', 'q3', 'PUSHED', 'ich', 'PVP', 'priced', 'Harder', 'manuevering', 'coyotes', 'Vancouver', 'waitting', 'athelte', 'superhero', 'Lance', 'eNovels', 'adainty', 'Detecting', 'dfferent', 'farm', 'asus', 'B002GYWHSQ', 'injection', 'fest', 'deperate', 'splices', 'fundida', 'SD10', 'jtag', 'hahahahahaha', 'Ups', 'conducive', 'goo', 'HPM', 'populating', 'resilience', 'NEG', '85dB', 'irritate', 'Knob', 'Continual', 'IncipioBud', '5329604', 'vezes', 'Coyotes', 'Sea', 'absorbing', 'Technocel', 'nexus', 'Korengrish', 'accessorize', 'Turnpike', 'sonar', 'volumne', 'c530', 'crude', 'comiicated', '91st', 'Excel', 'agencies', 'smartkey', 'encontrar', 'Schools', 'Angelo', 'wjen', 'c375bee', 'Ismooth', 'bubs', 'PMPs', 'åñêèé', 'age', 'defence', 'occurring', '20seconds', 'Anyhow', 'bookstore', '1AAA', 'Marking', 'Sunrnr', '17m', 'deaserves', 'persevere', 'OPENING', 'boggles', 'Jbl', 'Fr', 'CM', 'itouch4', 'OWW', '2pr', 'Olde', 'customers', 'B005C3GYE6', 'Sprinter', 'hind', 'Bundlemonster', 'Pro50se', 'fuller', 'SMOOTH', 'launches', 'B002MAPS6W', 'barrette', 'preserve', 'cowerkers', 'paperweights', 'cigarette', 'menards', 'transcriptions', 'eek', 'Honda', 'motorolla', 'Individual', 'luster', 'disables', 'Flint', 'Loq', 'TH4', 'descreet', 'funcionó', 'Wide', 'B004X2623U', 'Changed', 'crapping', 'Kinde', 'homwerx', 'hose', 'EXPERIENCES', 'kltech', 'reconfigure', 'radar', 'announcements', 'proceeded', 'deductible', 'Imp500', 'protegido', 'KoBo', 'far', 'arcane', 'distinctive', 'looked', 'MOTOROLA', 'retry', 'CTS', 'bulles', 'logging', 'protectant', 'jpolonia', 'Continuum', '2L', 'KDX', 'gunna', 'Gamers', 'ipot', 'TWEAKING', 'BargainCell', 'replies', 'UEFI', 'enthused', 'deivery', 'STOPED', 'Scirocco', 'tranmissions', 'unfinished', 'rarity', 'mow', '13BX', 'lott', 'perfected', 'suspected', 'seemm', 'ios', 'hiphop', 'frogs', 'rewiews', 'inconsistantly', 'Activation', 'caregivers', 'whichh', 'bevel', '16oz', '601dn', 'ke', 'Jeffrey', 'indent', 'Valab', '39hz', 'clips', 'ipone5', 'во', 'reaeder', 'tracked', '40mm', 'winch', 'immediatley', 'PROJECT', '5AM', '4inch', 'bitness', 'shiping', 'reaquired', 'programmers', 'SCREEN', 'jacketing', 'Othello', 'Avenger', 'flopped', 'OFFSET', 'dissapoiinted', 'MicroShell', 'agained', 'Otterman', 'pixie', 'Sergeant', 'dementions', 'rehearsals', 'vane', 'kilter', 'Whoohooo', 'appease', 'SHOGUN', '962mb', 'diminishing', 'colossal', '12W6', 'Zoneblue', 'hardwired', 'repairable', 'Nevermind', 'KNOWN', 'fighter', 'researches', 'Start', '20bucks', 'compamy', 'Beahm', 'Throat', 'faux', 'Swiss', 'unfortantly', '7yr', 'LIBRE', 'endeavors', 'inaf', 'Zamzar', 'ugliness', 'spiders', 'Extenders', 'scents', 'attatch', '09x3', 'straightening', 'exa1000', 'Carlos', 'BUBBLE', 'cancelled', 'investigating', 'opposing', 'definatelyorder', 'mm50', 'Kudosmap', 'Scrolls', 'S805', 'IMITATION', 'float', 'judgement', 'backwards', 'continues', 'Similarly', 'forgivable', 'Str', 'herein', 'Fiero', 'contols', 'Swithcing', 'ET', 'xp1200i', 'Yeah', 'kiting', 'Genuine', 'SW', 'THER', 'hopeless', 'dumbed', 'homedocking', 'repository', 'comparmets', 'P200', 'offender', 'Amy', 'glair', 'Complaints', 'hunters', 'heeded', '4x', 'Pacific', 'rodent', 'undergoes', 'marker', 'retactable', 'ee8114df92922c04ba023909463c714f', 'LOOOOONNNNNGGGG', 'rehang', 'avenues', 'Y6', 'hinting', 'stylists', 'attorney', 'NavFree', 'excactely', 'rayada', 'AMPLIAMENTE', 'prius', 'B009NLTW60', 'Formulae', 'EBOMY', 'Jeffersonville', '802', 'Wakes', 'screen', 'ACQUISITION', 'Delievery', 'logon', 'me', 'Gomatic', 'messy', 'signaling', 'NEWS', 'ded', 'recogized', 'Bogus', 'plead', 'iPega', 'outperformed', 'chipset', 'Repurposing', 'AutoPC', 'Revere', 'MOCKERY', 'weatherproofness', 'droppping', 'admirably', 'afterwords', 'created', 'philosophically', 'situps', 'profit', 'Innovate88', 'wrecks', 'Goid', 'foldout', 'yummy', 'realistically', 'Scooped', 'succession', 'conservative', 'BAM', 'appropriately', 'releasing', 'FFMPEG', 'easitly', 'quotations', 'proses', 'meaningless', 'ankle', 'Import', 'Usa', 'blitzsafe', 'awakes', 'automobilee', 'Roughly', 'COUGH', 'qnd', 'Logitec', 'comprelo', 'batter', 'doohinkies', 'waasnot', 'audia', 'Anita', 'especially', 'Mini', 'restraint', 'estaban', '34min', 'Megs', 'stroke', 'dictate', 'SALES', 'Glen', '6G', 'ongly', 'Nv', 'Unstitched', 'Px', '12W6v2D', 'uppercase', 'buyer', 'KAPSUL', 'B003VANOI6', 'municipalities', 'bestbuy', 'usesless', 'TCOM', 'shippers', 'experimenters', 'trial', 'remainder', 'loath', 'instated', 'HDs', 'deny', 'NINGUNA', 'Latch', 'tabletops', 'USES', 'COYOTES', 'acordado', '15Mbps', 'Jawbreaker', 'LOVe', 'handmedown', 'insect', 'stretch', 'pvh3424', 'Everhardt', 'submerging', 'COMPLIMENT', 'outlying', 'impersonal', 'washcloth', 'wsod', 'acdtually', 'vested', 'Fern', 'Ivation', 'fourth', '10D', 'Luckilly', 'tilted', 'overpaid', 'AFTER', 'POCKET', '99cu', 'stale', 'theftproof', 'automatically', 'rooftops', 'Notebook', 'Deulo', 'brainy', 'pdfs', 'germanium', 'amputated', 'usefulness', 'Doge', 'massively', 'readjustment', 'EYE', 'drop', 'HD2', 'pannels', 'VENTILADOR', 'uncomfortably', 'flashlogic', 'Atrix', 'centre', 'wqorth', 'tese', 'differntiation', 'Waters', 'Superstar', 'habit', 'compartimiento', 'CONSIDER', 'disorted', 'evolution', 'revomend', 'Hawkeye', 'IMAGINARY', 'trebble', 'TURNING', '800Rs', 'connector', 'MyBat', 'presuming', 'grades', 'sneaking', 'roadway', 'WP', 'HOPES', 'monoaural', 'heights', 'recuperating', 'B0009NXN7K', 'separation', '2seconds', 'WORKS', 'youtu', 'CONSTRUCTED', 'B0058AFS0O', 'Headbud', 'Tones', 'materiel', 'prático', 'smitten', 'ele', 'spams', 'beeper', '2FAssets', 'ACCESSORY', 'precession', 'beaumont', 'CaseLogic', 'Uganda', '2000mA', 'OneXL', 'laziness', 'opamp', 'kmart', 'B001LWZ4BG', 'GTM', 'Ric', 'grossness', 'gatekeeper', 'ABOVE', 'IRON', 'frogracing', 'charities', 'Emm', 'cusotmer', 'connecters', 'fd', 'boarding', 'trk', 'RAID1', 'LIGHTNING', 'snyc', 'innumberable', 'CLOSE', 'undertaking', 'AmScope', '7in1', 'F90BT', 'E10', 'scratchier', 'Loves', 'posture', 'intrigue', 'varing', 'baling', 'w112s', 'Diskless', 'Burlap', 'DJ', 'pedals', 'TANGLED', 'f450', 'follow', 'sick', 'Jazzy', 'muscles', 'angels', 'lonic', 'bestest', 'flashed', 'subscribed', 'alerta', 'agrave', 'millennial', 'absorbency', 'friendly', 'crumples', 'ducks', 'unconcerned', 'VirtualWire', 'iwould', 'gelskin', 'Roadmate', 'grille', 'ansqer', 'accompany', 'switch', 'POLICE', 'Shrug', 'CS', 'plains', 'WinX', 'gazebo', 'Uday', 'AD700', 'MUDDY', 'equiv', 'Eighth', 'wood', 'scrollingthrough', 'cept', 'passat', 'm241', 'pee', 'fuera', 'SUCKER', 'stiff', 'updates', 'GSHOCK', 'vaulted', 'Tags', 'derection', 'clank', 'mimics', '7254v', 'lighr', 'sincery', 'Ts', 'Banjo', 'efficientsy', 'boasted', 'b3b21a12a307e1afd1a027253d806b7e', '5amps', 'shedder', 'USO', 'DR400', '100k', '80Hz', 'Hummingbird', 'cob', 'Whiz', 'Goal0', 'gimmicks', 'Null', 'iBeek', 'exoect', 'FILM', 'sidekick', 'culd', 'lity', '4generation', 'crediting', 'âîçìîæíîñòü', 'gram', 'HUH', 'Handwritten', 'A7LA70', 'JAMMING', 'Broadcom', 'nonintrusive', 'tricep', 'B0024CK1EW', 'Neuros', 'TIVO', 'Res', 'speakerette', 'E11', 'JUMP', 'lumped', 'sensación', 'MobileData', 'Illumination', '15pm', 'concussion', 'knukoncepts', 'Minuses', 'Searay', 'Switcher', '1112', '48', 'Elvison', 'Armada', 'unknowm', 'used', 'Applications', 'church', 'eprance', 'bulb', '3ways', 'hittestable', 'Apply', '5655', 'units', 'bluegill', 'witht'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PdWrBf4xzdXk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bf872b6c-5af5-4d1b-f522-9d0db15af697"
      },
      "source": [
        "for reviews in train_dataset.take(5):\n",
        "  review_text = reviews['data']\n",
        "  print(review_text.get('review_body').numpy())\n",
        "  encoded_example = encoder.encode(review_text.get('review_body').numpy())\n",
        "  print(encoded_example)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "b\"These little speakers really sound better than expected. I would not hesitate to purchase them again. They aren't perfect but then again they sound so much better than my laptop speakers .And are very portable . Not to shabby.\"\n",
            "[44778, 59256, 66180, 70060, 64788, 68814, 68250, 39070, 66476, 62628, 41837, 48776, 45613, 49612, 65204, 72004, 55132, 70811, 56317, 66936, 72345, 68733, 72004, 55132, 64788, 69697, 62046, 68814, 68250, 68137, 68058, 66180, 70877, 50704, 45722, 56145, 41837, 45613, 42691]\n",
            "b\"It wasn't read by the iPad as a compatible item. Was used in 2013 Benz to upgrade the plug from the ipad 2 to the new ipad\"\n",
            "[49127, 38498, 56317, 57699, 42195, 54230, 70404, 52931, 63019, 58096, 26914, 34314, 73727, 18732, 25731, 72908, 45613, 44844, 54230, 60709, 66278, 54230, 70404, 70525, 45613, 54230, 51893, 70404]\n",
            "b'I love the product.  The only issue I have is with the little plug in cord which is fragile and the wires began to show with just a little tug.  Other than that, it works, and I use it daily'\n",
            "[66476, 73478, 54230, 48675, 54230, 16498, 18214, 66476, 47689, 60707, 62051, 54230, 59256, 60709, 18732, 28002, 48830, 60707, 58440, 70877, 54230, 57071, 48932, 45613, 57512, 62051, 66099, 63019, 59256, 35046, 69628, 68250, 50942, 49127, 73555, 70877, 66476, 35998, 49127, 71767]\n",
            "b\"I installed this security system on a 3rd-generation Camry.  I am not a professional installer, just a car enthusiast.  I found a wiring diagram for my car online, and followed Viper's included instructions to match up the wires.  It took me about 4 hours to complete the installation (including breaks).  Great instructions from Viper.<br /><br />The system itself was just what I was looking for: siren, shock sensor, zone triggers, remote-lock and unlock, starter disable, and a warning LED to ward off potential thieves.  The siren can be heard from about 150-200 feet away.  So far I am pleased with the purchase.\"\n",
            "[66476, 69748, 65848, 51365, 29891, 69857, 63019, 59412, 68244, 62259, 66476, 27418, 41837, 63019, 63400, 45721, 66099, 63019, 69581, 60720, 66476, 72723, 63019, 47512, 18714, 68835, 68137, 69581, 57012, 70877, 22245, 54149, 57611, 55227, 70486, 45613, 59321, 54637, 54230, 57071, 49127, 59445, 73381, 42841, 44780, 43175, 45613, 50367, 54230, 62433, 72108, 61795, 57597, 70486, 66278, 54149, 33957, 33957, 54230, 29891, 69613, 34314, 66099, 71279, 66476, 34314, 57116, 68835, 40068, 53022, 22180, 67625, 47859, 72274, 41933, 70877, 50802, 52241, 34752, 70877, 63019, 62862, 33974, 45613, 22472, 44168, 29075, 69192, 54230, 40068, 45538, 64228, 69279, 66278, 42841, 31058, 36817, 62023, 32678, 69697, 73195, 66476, 27418, 51592, 62051, 54230, 49612]\n",
            "b'Perfect gift.'\n",
            "[66936, 44117]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RURcWX6Q2YFv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2efd8869-6528-4af1-ebee-a13e99e914c3"
      },
      "source": [
        "for index in encoded_example:\n",
        "  print('{} ----> {}'.format(index, encoder.decode([index])))"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "66936 ----> perfect\n",
            "44117 ----> gift\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "naWRZpLLGKNk"
      },
      "source": [
        "encoder.save_to_file(\"/tmp/sa_encoder.vocab\")"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "enbxUU330JlY"
      },
      "source": [
        "def encode(text_tensor, label_tensor):\n",
        "  encoded_text = encoder.encode(text_tensor.numpy())\n",
        "  label = tf.where(label_tensor>3,1,0)\n",
        "  return encoded_text, label"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XQkgCg0X3kBn"
      },
      "source": [
        "def encode_map_fn(tensor):\n",
        "  \n",
        "  text = tensor['data'].get('review_body')\n",
        "  label = tensor['data'].get('star_rating') \n",
        "\n",
        "  encoded_text, label = tf.py_function(encode, \n",
        "                                       inp=[text, label], \n",
        "                                       Tout=(tf.int64, tf.int32))\n",
        "\n",
        "  encoded_text.set_shape([None])\n",
        "  label.set_shape([])\n",
        "\n",
        "  return encoded_text, label"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vd613Wks5KIU"
      },
      "source": [
        "ar_encoded_data = train_dataset.map(encode_map_fn)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RONAsea269dv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c815e7f8-bc48-405e-e0c6-d87c4ac92b88"
      },
      "source": [
        "for f0,f1 in ar_encoded_data.take(2):\n",
        "  print(f0)\n",
        "  print(f1)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tf.Tensor(\n",
            "[44778 59256 66180 70060 64788 68814 68250 39070 66476 62628 41837 48776\n",
            " 45613 49612 65204 72004 55132 70811 56317 66936 72345 68733 72004 55132\n",
            " 64788 69697 62046 68814 68250 68137 68058 66180 70877 50704 45722 56145\n",
            " 41837 45613 42691], shape=(39,), dtype=int64)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(\n",
            "[49127 38498 56317 57699 42195 54230 70404 52931 63019 58096 26914 34314\n",
            " 73727 18732 25731 72908 45613 44844 54230 60709 66278 54230 70404 70525\n",
            " 45613 54230 51893 70404], shape=(28,), dtype=int64)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8pZ4PzSz90RV"
      },
      "source": [
        "TAKE_SIZE=14000\n",
        "\n",
        "train_data = ar_encoded_data.skip(TAKE_SIZE).shuffle(BUFFER_SIZE)\n",
        "train_data = train_data.padded_batch(BATCH_SIZE)\n",
        "\n",
        "test_data = ar_encoded_data.take(TAKE_SIZE)\n",
        "test_data = test_data.padded_batch(BATCH_SIZE)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AXTDe5U1yVKg"
      },
      "source": [
        "\n",
        "vocab_size += 1"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MjPeEW7lEva8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "40d749a1-fb9f-4262-f2e4-bd369fb01ab3"
      },
      "source": [
        "sample_text, sample_labels = next(iter(test_data))\n",
        "\n",
        "sample_text[0], sample_labels[0]\n",
        "\n"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<tf.Tensor: shape=(1642,), dtype=int64, numpy=array([44778, 59256, 66180, ...,     0,     0,     0])>,\n",
              " <tf.Tensor: shape=(), dtype=int32, numpy=1>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "57m2OiyyN2AA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e9d1f57-5c94-46e8-e10a-cfbaa92b56e3"
      },
      "source": [
        "\n",
        "for f0,f1 in test_data.take(10):\n",
        "  print(tf.unique_with_counts(f1)[2].numpy())"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[136  60]\n",
            "[132  64]\n",
            "[142  54]\n",
            "[ 71 125]\n",
            "[120  76]\n",
            "[127  69]\n",
            "[ 62 134]\n",
            "[ 61 135]\n",
            "[141  55]\n",
            "[ 69 127]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z5rUcypcIytc"
      },
      "source": [
        "model = tf.keras.Sequential()\n",
        "model.add(tf.keras.layers.Embedding(vocab_size, 128))\n",
        "model.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(128, return_sequences=True)))\n",
        "model.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64)))\n",
        "for units in [64, 32, 16]:\n",
        "  model.add(tf.keras.layers.Dense(units, activation='relu'))\n",
        "  model.add(tf.keras.layers.Dropout(units*0.006))\n",
        "model.add(tf.keras.layers.Dense(1))\n"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tu4zxpwEUJn5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "baafe426-4d10-4d1d-9d6a-a1a80494396b"
      },
      "source": [
        "!rm -r /tmp/logs/"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "rm: cannot remove '/tmp/logs/': No such file or directory\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OpOPcVniJ6Lo"
      },
      "source": [
        "logdir = os.path.join(\"/tmp/logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
        "tensorboard_callback = tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)\n",
        "checkpointer = tf.keras.callbacks.ModelCheckpoint(filepath='/tmp/sentiment_analysis.hdf5', verbose=1, save_best_only=True)\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q9ggc2kmKDWT"
      },
      "source": [
        "history=model.fit(train_data, epochs=6, validation_data=test_data, callbacks=[tensorboard_callback,checkpointer])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "02VWrXOnCwlI"
      },
      "source": [
        "model.save('/tmp/final_sentiment_analysis.hdf5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MPSEdqQNK7eR"
      },
      "source": [
        "!ls -alrt /tmp/*.hdf5"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4zcuVcXlKfh5"
      },
      "source": [
        "eval_loss, eval_acc = model.evaluate(test_data)\n",
        "\n",
        "print('\\nEval loss: {:.3f}, Eval accuracy: {:.3f}'.format(eval_loss, eval_acc))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ATpSMfLmgO_E"
      },
      "source": [
        "for f0,f1 in test_data.take(1):\n",
        "  print(f1)\n",
        "  print(model.predict(f0))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C35jLJ1QUU2A"
      },
      "source": [
        "model.layers"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mqF5PcaKUmHH"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WKzsV6W9yRq2"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m8h7FTjc2LO1"
      },
      "source": [
        "model.get_layer('embedding').output"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k0XujLETOV_g"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SNZ49aApOnFw"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_graphs(history, metric):\n",
        "  plt.plot(history.history[metric])\n",
        "  plt.plot(history.history['val_'+metric], '')\n",
        "  plt.xlabel(\"Epochs\")\n",
        "  plt.ylabel(metric)\n",
        "  plt.legend([metric, 'val_'+metric])\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Qi68wduo3bD"
      },
      "source": [
        "plot_graphs(history, 'accuracy')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qCzGm1Bao90j"
      },
      "source": [
        "plot_graphs(history, 'loss')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "isp05R90DC4G"
      },
      "source": [
        "tf.keras.backend.clear_session()\n",
        "#sa_load = tf.keras.models.load_model('/tmp/sentiment_analysis.hdf5',compile = False)\n",
        "sa_load = tf.keras.models.load_model('/tmp/final_sentiment_analysis.hdf5',compile = False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RxII-t17evJu"
      },
      "source": [
        "def pad_to_size(vec, size):\n",
        "  zeros = [0] * (size - len(vec))\n",
        "  vec.extend(zeros)\n",
        "  return vec"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-C9PRE-yXyzX"
      },
      "source": [
        "def predict_fn(pred_text):\n",
        "  encoded_pred_text = encoder.encode(pred_text)\n",
        "  print(encoded_pred_text)\n",
        "  encoded_pred_text = pad_to_size(encoded_pred_text, 32)\n",
        "  print(encoded_pred_text)\n",
        "  encoded_pred_text = tf.cast(encoded_pred_text, tf.float32)\n",
        "  predictions = sa_load.predict(tf.expand_dims(encoded_pred_text, 0))\n",
        "\n",
        "  return (predictions)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WHNkvOmiYnKF"
      },
      "source": [
        "pred_text = ('This watch is pretty bad. Color and dial looks pathetic.')\n",
        "predictions = predict_fn(pred_text)\n",
        "print(predictions)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-YCnO_394yGh"
      },
      "source": [
        "pred_text = ('Color and dial does not look as advertised. Shipping was not any better as well')\n",
        "predictions = predict_fn(pred_text)\n",
        "print(predictions)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dg3-u8fAfFoh"
      },
      "source": [
        "pred_text = ('Amazing product. Fast Delivery. Nice packing')\n",
        "predictions = predict_fn(pred_text)\n",
        "print(predictions)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NM7UFDyJ4lpz"
      },
      "source": [
        "pred_text = ('Nice product. Packing could have been better')\n",
        "predictions = predict_fn(pred_text)\n",
        "print(predictions)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2kTYTK_Uo9zJ"
      },
      "source": [
        "print(tf.distribute.get_strategy())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EwwKrOmuurZJ"
      },
      "source": [
        "%reload_ext tensorboard"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1UWV9w0MtuKn"
      },
      "source": [
        "%tensorboard --logdir /tmp/logs"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1H4nWcJLt7gu"
      },
      "source": [
        "from tensorboard import notebook\n",
        "notebook.list()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wlvO46QYuKuH"
      },
      "source": [
        "notebook.display(port=6006, height=1000) "
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}