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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/srivatsan88/YouTubeLI/blob/master/Tensorflow_Sentiment_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6NnkIHfhC82L",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "outputId": "f65fe8ff-b912-4c9a-8462-bcb774136838"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mon Feb 24 21:22:27 2020       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 440.48.02    Driver Version: 418.67       CUDA Version: 10.1     |\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",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   35C    P0    27W / 250W |      0MiB / 16280MiB |      0%      Default |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                       GPU Memory |\n",
            "|  GPU       PID   Type   Process name                             Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fUbFvR7LDB3U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 872
        },
        "outputId": "8a2c9caf-1266-4569-aa24-3fcc5e983058"
      },
      "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": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Uninstalling tb-nightly-2.2.0a20200224:\n",
            "  Would remove:\n",
            "    /usr/local/bin/tensorboard\n",
            "    /usr/local/lib/python3.6/dist-packages/tb_nightly-2.2.0a20200224.dist-info/*\n",
            "    /usr/local/lib/python3.6/dist-packages/tensorboard/*\n",
            "Proceed (y/n)? y\n",
            "  Successfully uninstalled tb-nightly-2.2.0a20200224\n",
            "\u001b[33mWARNING: Skipping tensorboardX as it is not installed.\u001b[0m\n",
            "\u001b[33mWARNING: Skipping tensorboard as it is not installed.\u001b[0m\n",
            "Requirement already satisfied: tf-nightly in /usr/local/lib/python3.6/dist-packages (2.2.0.dev20200218)\n",
            "Requirement already satisfied: numpy<2.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.17.5)\n",
            "Requirement already satisfied: protobuf>=3.8.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (3.10.0)\n",
            "Requirement already satisfied: gast==0.3.3 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (0.3.3)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.27.1)\n",
            "Requirement already satisfied: astunparse==1.6.3 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.6.3)\n",
            "Requirement already satisfied: keras-preprocessing>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.1.0)\n",
            "Requirement already satisfied: h5py<2.11.0,>=2.10.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (2.10.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.8 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (0.1.8)\n",
            "Collecting tb-nightly<2.3.0a0,>=2.2.0a0\n",
            "  Using cached https://files.pythonhosted.org/packages/a2/70/a4867bf086f6c1826da3998c9f0c6242eb04845aba384a2dd9cfaaabac4a/tb_nightly-2.2.0a20200224-py3-none-any.whl\n",
            "Requirement already satisfied: tf-estimator-nightly in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (2.1.0.dev2020022418)\n",
            "Requirement already satisfied: wheel>=0.26; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (0.34.2)\n",
            "Requirement already satisfied: scipy==1.4.1; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.4.1)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.12.0)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (0.9.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.1.0)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (3.1.0)\n",
            "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.11.2)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.8.0->tf-nightly) (45.1.0)\n",
            "Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<2.3.0a0,>=2.2.0a0->tf-nightly) (1.7.2)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<2.3.0a0,>=2.2.0a0->tf-nightly) (1.0.0)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<2.3.0a0,>=2.2.0a0->tf-nightly) (2.21.0)\n",
            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<2.3.0a0,>=2.2.0a0->tf-nightly) (1.6.0.post2)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<2.3.0a0,>=2.2.0a0->tf-nightly) (0.4.1)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<2.3.0a0,>=2.2.0a0->tf-nightly) (3.2.1)\n",
            "Requirement already satisfied: cachetools<3.2,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tb-nightly<2.3.0a0,>=2.2.0a0->tf-nightly) (3.1.1)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tb-nightly<2.3.0a0,>=2.2.0a0->tf-nightly) (0.2.8)\n",
            "Requirement already satisfied: rsa<4.1,>=3.1.4 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tb-nightly<2.3.0a0,>=2.2.0a0->tf-nightly) (4.0)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tb-nightly<2.3.0a0,>=2.2.0a0->tf-nightly) (3.0.4)\n",
            "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tb-nightly<2.3.0a0,>=2.2.0a0->tf-nightly) (2.8)\n",
            "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tb-nightly<2.3.0a0,>=2.2.0a0->tf-nightly) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tb-nightly<2.3.0a0,>=2.2.0a0->tf-nightly) (2019.11.28)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tb-nightly<2.3.0a0,>=2.2.0a0->tf-nightly) (1.3.0)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.6/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tb-nightly<2.3.0a0,>=2.2.0a0->tf-nightly) (0.4.8)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tb-nightly<2.3.0a0,>=2.2.0a0->tf-nightly) (3.1.0)\n",
            "Installing collected packages: tb-nightly\n",
            "Successfully installed tb-nightly-2.2.0a20200224\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x2hbzsdExdry",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "4b85e178-8299-45e4-a571-da9d207286bc"
      },
      "source": [
        "import pkg_resources\n",
        "\n",
        "for entry_point in pkg_resources.iter_entry_points('tensorboard_plugins'):\n",
        "    print(entry_point.dist)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensorboard-plugin-wit 1.6.0.post2\n",
            "tb-nightly 2.2.0a20200224\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nyjsHFH8yxqZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6a91b110-96fa-48d0-938d-46124e8eba4a"
      },
      "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": null,
      "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_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5fb31581-c706-4333-d325-0d6eb27402c5"
      },
      "source": [
        "print(tf.__version__)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.2.0-dev20200218\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EB00mWXfDH7n",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "7ef06029-085c-4ad3-8688-0e9df24b670c"
      },
      "source": [
        "tf.config.experimental.list_physical_devices()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'),\n",
              " PhysicalDevice(name='/physical_device:XLA_CPU:0', device_type='XLA_CPU'),\n",
              " PhysicalDevice(name='/physical_device:XLA_GPU:0', device_type='XLA_GPU'),\n",
              " PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QDH43tw-DN4y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356,
          "referenced_widgets": [
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            "1730958ed5ed492f808328ff8a42d562",
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            "600b4d8f8b58415a904a87251f3c7fab",
            "eb74fc0f03694f3f925edfb17a248c8b",
            "c4beadc4fa5345e19eff007b3ce2295b",
            "1fa669298b9b48afa9637c53b8d8b3b8",
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            "1c1582c475bd41a1952b551b28d9e8d6",
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            "cfbb2021e82c4de8b270924019606615",
            "b6899f0f05634c87b16bcad8571f8d59",
            "c8b107002b62494eb5c73ffae0f22cd2",
            "3b7aad2b8e414b7c9d07641757458447",
            "581636ea6d754cf1ab172a2e9677ef9f",
            "4b40ac074e464c6c81cafad04566a809",
            "27bac98803d0437bbe5869c1d9829467",
            "b076fae3356e4ef9b10bd588882b2081",
            "9d3dca875cb940fab06d40467b3e2c8e",
            "1d360ffe43e443c89a700c70804ff744",
            "d04d229f3631453e8e09b319b71f8ecb"
          ]
        },
        "outputId": "d334096f-5a95-4a17-ec47-a52716703017"
      },
      "source": [
        "dataset, info = tfds.load('amazon_us_reviews/Mobile_Electronics_v1_00', with_info=True)\n",
        "train_dataset = dataset['train']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1mDownloading and preparing dataset amazon_us_reviews (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": "0d08ba74785e4f258aa3c8129be5e688",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=1, bar_style='info', description='Dl Completed...', max=1, style=ProgressStyl…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "39999c4e7cae4895b861ac7975a594ff",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=1, bar_style='info', description='Dl Size...', max=1, style=ProgressStyle(des…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "26f63df70dee4a23876a9eff15b00b91",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=1, bar_style='info', description='Extraction completed...', max=1, style=Prog…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "920549f67c7547869631198306b57efa",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), 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.incompleteR44MXI/amazon_us_reviews-train.tfrecord\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e051e2a55304467c9f5cc49ad35da8b0",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=104975), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "55d7d4f475354c69a7b6e1ea2b2c99a7",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, description='Computing statistics...', max=1, style=ProgressStyle(descript…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3b7aad2b8e414b7c9d07641757458447",
              "version_minor": 0,
              "version_major": 2
            },
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              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "ERROR:absl:Statistics generation doesn't work for nested structures yet\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u1diITDtD9xD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 996
        },
        "outputId": "438d0a5f-2c82-4d01-cbab-00af60a0ef1b"
      },
      "source": [
        "info"
      ],
      "execution_count": null,
      "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",
              "',\n",
              "    homepage='https://s3.amazonaws.com/amazon-reviews-pds/readme.html',\n",
              "    features=FeaturesDict({\n",
              "        'data': FeaturesDict({\n",
              "            'customer_id': Tensor(shape=(), dtype=tf.string),\n",
              "            'helpful_votes': Tensor(shape=(), dtype=tf.int32),\n",
              "            'marketplace': Tensor(shape=(), dtype=tf.string),\n",
              "            'product_category': Tensor(shape=(), dtype=tf.string),\n",
              "            'product_id': Tensor(shape=(), dtype=tf.string),\n",
              "            'product_parent': Tensor(shape=(), dtype=tf.string),\n",
              "            'product_title': Tensor(shape=(), dtype=tf.string),\n",
              "            'review_body': Tensor(shape=(), dtype=tf.string),\n",
              "            'review_date': Tensor(shape=(), dtype=tf.string),\n",
              "            'review_headline': Tensor(shape=(), dtype=tf.string),\n",
              "            'review_id': Tensor(shape=(), dtype=tf.string),\n",
              "            'star_rating': Tensor(shape=(), dtype=tf.int32),\n",
              "            'total_votes': Tensor(shape=(), dtype=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_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "58fd5ef7-f009-4fc5-d0d8-10a95bae10da"
      },
      "source": [
        "print(train_dataset)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<DatasetV1Adapter 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_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b9dbeec0-391e-490a-f0f5-7aa17bb3f8b4"
      },
      "source": [
        "len(list(train_dataset))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "104975"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0tTxEMQbDjrv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "BUFFER_SIZE = 40000\n",
        "BATCH_SIZE = 196"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xIFXNHBiDlL0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_dataset = train_dataset.shuffle(BUFFER_SIZE, reshuffle_each_iteration=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hr4e5_1SHNPI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "4a72c0de-3f98-4956-adbd-c9f28dd83bff"
      },
      "source": [
        "for reviews in train_dataset.take(2):\n",
        "  print(reviews)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'data': {'customer_id': <tf.Tensor: shape=(), dtype=string, numpy=b'1725988'>, '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'B004BJT75U'>, 'product_parent': <tf.Tensor: shape=(), dtype=string, numpy=b'662015355'>, 'product_title': <tf.Tensor: shape=(), dtype=string, numpy=b'Van Gogh - Blossoming Almond Tree Design iPod classic 80GB/ 120GB Protector Skin Decal Sticker'>, 'review_body': <tf.Tensor: shape=(), dtype=string, numpy=b\"This. Is. Amazing!! So it arrived on time, actually earlier than expected, and it makes my iPod classic look so beautiful!! I can't stop staring at it! Applying it wasn't the easiest since U was trying to make it PERFECT, but it's not hard either! But once you apply it it's all worth it. 10/10.\">, 'review_date': <tf.Tensor: shape=(), dtype=string, numpy=b'2014-12-20'>, 'review_headline': <tf.Tensor: shape=(), dtype=string, numpy=b'Amazing.'>, 'review_id': <tf.Tensor: shape=(), dtype=string, numpy=b'R2RUZ1YVU4KTC5'>, 'star_rating': <tf.Tensor: shape=(), dtype=int32, numpy=5>, '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",
            "{'data': {'customer_id': <tf.Tensor: shape=(), dtype=string, numpy=b'35649107'>, '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'B00AJD0H88'>, 'product_parent': <tf.Tensor: shape=(), dtype=string, numpy=b'626857861'>, 'product_title': <tf.Tensor: shape=(), dtype=string, numpy=b'X-1 (Powered by H2O Audio) Surge Mini Waterproof In-Ear Headphones'>, 'review_body': <tf.Tensor: shape=(), dtype=string, numpy=b'I bought these to use for swimming and running and I really love them.  Sound is great and they stay in p[lace perfectly.  They block out so much outside sound that I often run with just one so I can talk to my running buddies.  Great product.'>, 'review_date': <tf.Tensor: shape=(), dtype=string, numpy=b'2014-04-03'>, 'review_headline': <tf.Tensor: shape=(), dtype=string, numpy=b'Love these ear buds'>, 'review_id': <tf.Tensor: shape=(), dtype=string, numpy=b'R3H2Z4GVAXAL6M'>, '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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uyKHUS9AaJ5j",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 570
        },
        "outputId": "4764bd3e-9cc1-4048-a999-9717a5dc38ce"
      },
      "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": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "b\"This. Is. Amazing!! So it arrived on time, actually earlier than expected, and it makes my iPod classic look so beautiful!! I can't stop staring at it! Applying it wasn't the easiest since U was trying to make it PERFECT, but it's not hard either! But once you apply it it's all worth it. 10/10.\"\n",
            "tf.Tensor(5, shape=(), dtype=int32)\n",
            "1\n",
            "b'I bought these to use for swimming and running and I really love them.  Sound is great and they stay in p[lace perfectly.  They block out so much outside sound that I often run with just one so I can talk to my running buddies.  Great product.'\n",
            "tf.Tensor(5, shape=(), dtype=int32)\n",
            "1\n",
            "b'The case for my Nook was exactly what I wanted...the quality is top-notch, the leather is a beautiful color and the price was less that half of what I was asked to pay at a store....even when tax and shipping are included. I am very pleased with this purchase.........CeCe'\n",
            "tf.Tensor(5, shape=(), dtype=int32)\n",
            "1\n",
            "b'Loved it'\n",
            "tf.Tensor(5, shape=(), dtype=int32)\n",
            "1\n",
            "b'Worked great!  I would absolutely recommend this product and plan to buy it again myself when needed in the future.'\n",
            "tf.Tensor(5, shape=(), dtype=int32)\n",
            "1\n",
            "b'Just what she wanted for her Nook.  It added the keyboard and versatility she wanted to supplement her touchscreen.  She is very happy with it.'\n",
            "tf.Tensor(5, shape=(), dtype=int32)\n",
            "1\n",
            "b'Best dash camera for the money. Great picture in 1080P'\n",
            "tf.Tensor(5, shape=(), dtype=int32)\n",
            "1\n",
            "b\"Waited almost a month to receive it, once it came in I paid an installer to put it in for me and turns out the harness it comes with doesn't support the stock harness and it's impossible to get sound from it unless you re wire the whole car in addition you loose the subwoofer, have to pay $300 in labor (cheapest place within 100 miles of my house) don't waste your time with this it's more hassle than it's worth !!!!\"\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "0\n",
            "b'Broke after about a month of use. DO NOT BUY.'\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "0\n",
            "b'Great product-easy to use.  Charged Iphone 5 with no problems.  Usually took about 1-2 hours to get full charge back on phone when less than 15%.  Easy to carry in purse.  Does require you to purchase an Apple Lightening Chord--recommend shorter one for less bulkiness.  Highly recommend for people on the go who need external charger.'\n",
            "tf.Tensor(5, shape=(), dtype=int32)\n",
            "1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i3ySnZHiaS01",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "244d4e55-e83a-4f39-fe76-ef2b23475f77"
      },
      "source": [
        "tokenizer = tfds.features.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": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "73738"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gYoPn2MAly6d",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "encoder = tfds.features.text.TokenTextEncoder(vocabulary_set, lowercase=True, strip_vocab=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vYqNrx4uzIHf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "a335a627-0bc8-4461-ae6e-e516707d1821"
      },
      "source": [
        "print(vocabulary_set)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'voiceovers', 'Q1200', 'Passport', 'Runescape', 'vid', 'happenes', '201', 'bye', 'lighr', 'ojo', 'flips', 'bluethooth', 'c15', '20x25', 'bday', 'ba', 'kidloves', 'TAGG', 'UWaterG2', 'Vegas', 'ala', 'MVCI', 'philps', 'iLink', 'period', 'AUTHOR', 'midstep', 'B003UOMCOU', 'GPSlim', 'mustered', 'KIV', 'pockeet', 'MEGALLAN', 'slovenly', 'Eliminator', 'Estoy', 'excellently', 'FMOD', 'MapCreate', 'wifey', 'turning', 'friedly', 'accepts', 'jokes', 'brooklyn', 'sensive', 'solitary', 'kleptos', 'Amberella', 'roped', 'Dacalrus', 'boombox', 'each', 'Fidelio', 'consice', 'Arc', 'makezine', 'meg', 'stewardesses', 'toutch', 'Woche', 'outskirts', 'Yellowed', 'YuanDao', 'fay', 'Infinitis', 'reflective', 'G70', 'CALIBRATION', 'slipcovers', 'Linked', 'd53d4c100607c54dc05c3c31d790835c', 'pcmcia', 'lod', 'BRAD', '20gb', 'bead', 'fanfare', 'grouping', 'tid', 'suiting', 'cabel', 'unofficial', 'costo', 'EXISTENT', 'megaphone', '108MHz', '60w', 'doller', 'faintest', 'conveyed', 'lhasa', 'transformed', 'dimmed', 'Speaker', 'whitish', 'Considering', 'Watt', 'September', 'Deena', 'Dual', 'lololololz', 'Cnt', 'visibilidad', 'Hayward', 'POSSIBILITY', 'armgear', 'unliked', 'opotion', 'authorizes', 'Dissappointed', 'meses', 'Insert', 'greatjob', 'andwithall', 'spaniel', '4bps', 'HBS', 'CERWIN', 'butchering', 'autotuner', 'rewinds', 'Mireo', '3597LMT', 'loud', 'outback', 'helpful', 'mingle', 'THICK', 'Animals', 'color', 'rein', 'tale', 'houston', 't1500', 'opps', 'Fold', 'information', 'japan', 'M7', 'Contained', 'spied', 'keyword', 'crevices', 'ratcheting', 'avalanche', 'inacessible', 'pony', 'Present', 'WJM', 'Understandably', 'Astriun', 'muffler', 'embroider', 'gazillion', '950w', 'Mopar', 'capping', 'luckily', 'gusset', 'founds', 'cougar', '10KHz', 'Located', 'inconveniently', 'radtech', '700ma', 'saturates', 'Idle', '0C', 'rma', '9856', 'allllll', 'slumped', 'FORWARD', 'Verbatim', 'bushwhack', 'inconvienence', 'housings', 'BUZZED', 'cree', 'iPad4', 'stratched', 'slightly', 'produzca', '5volts', 'lectern', 'consumer', 'summary', 'passcode', 'Thunder', 'TWO', 'crevice', 'identify', 'prodcut', 'development', 'Breaks', 'elite', 'Ideally', 'transitional', 'revolutionize', 'Consultants', 'plased', 'nits', 'fatigued', 'crewed', '500meg', 'ey', 'visiongaurd', 'iSync', 'Diskstations', 'giuve', 'Threaded', 'birdy', 'hc2000', 'RK', 'Cycled', 'Streetlight', 'challenged', 'NukAlert', '540S', 'mo3TE39X7EOXCS3', 'LEARNED', 'Couln', 'B0030T0UGW', 'epics', 'Stang', 'Midrange', 'intsructions', 'Aurvana', 'tanta', 'rainbow', 'Scared', 'bluecostco', '850', 'holster', 'facotry', 'chasis', 'vented', 'activaion', 'B004FEEZHQ', 'Jefferson', 'relocate', 'Pixels', 'SlotMusic', 'hexagon', 'sknit', 'tha', 'peeps', 'commuter', 'Lobe', 'indulgence', 'entered', 'CUTY', 'whating', 'flapis', 'APL2', 'half', 'Hop', 'Inear', '44khz', 'klutzes', 'lousy', 'Araldite', 'SABER', 'reinitializing', 'watchin', 'flexable', 'pringles', 'GTO429', '320kB', 'altercations', 'obsessive', 'unblocked', 'yamaha', 'sabrán', 'Sanoxy', '905', 'msi', 'distressed', 'checklist', 'consist', 'apology', 'soaked', 'Bankers', 'pastime', 'ITEMS', 'Neon', 'Landfill', 'Shield', 'Bassy', 'grained', 'inlets', 'THOUSANDS', 'WNCI', 'Capabilities', 'ONTIME', 'frogz', 'ratings', 'Scrooll', 'loco', 'MARKING', 'cuzn', 'beenie', 'knowed', 'headreasts', '350mW', 'TRAVELING', '4v3', 'clogs', 'bran', 'Pc', 'attempting', 'usd', '9406', 'li', 'tapeplayer', 'Plye', 'incoveniences', 'strobes', 'remianing', 'hangouts', 'Euro', 'reworked', 'purchesed', 'MrniceguyVOAS', 'deceive', 'constricted', 'rastor', 'execution', 'Acoustic', 'almos', 'organigram', 'swayed', 'O', 'statistically', 'crasher', 'aveos', 'dressing', 'func', 'preseit', 'istalled', 'hoy', '255', 'ATENCIÓN', 'DVRs', '328i', '11yo', 'WTF', 'interviews', 'infrequent', 'unsung', 'expen', 'talkabout', 'jace', 'critiques', 'sratch', '60V', 'irreversibly', 'POIS', 'iphone3gs', 'BB2', 'gif5', 'disappears', 'victory', 'byways', 'Decrescent', 'Worldwide', '80ma', 'Loaded', 'Hmmmm', 'inaf', 'miscellaneous', 'videoediting', 'Haleluah', 'wage', 'B005EUTA3E', 'SIGNALS', 'opengs', 'scrolled', 'disturbed', 'illegally', 'H435D', 'Xtech', 'burr', 'LOOOOONNNNNGGGG', 'transmisison', 'beleived', 'recorder', 'pricy', 'GHz', 'BlackView', 'prdouct', 'negligible', 'exports', 'Intense', 'layers', 'contained', 'staces', 'purcahsed', 'BUYING', 'Mdse', 'Comfy', 'unclutter', 'Palo', 'Discontinued', 'exlon', 'unzips', 'clasp', 'G1', 'straightened', 'frowny', 'acts', 'hindge', 'unreplaceable', 'android_secure', '2015', 'riscontrato', 'stonger', 'lucks', 'DISTURBING', 'grades', 'Videos', 'sharpied', 'SPECIFIC', 'bacuse', 'playerg', 'groupies', 'critize', 'fuse', 'Marketed', 'CyberDict', 'R60', 'sister', 'inflict', 'referrals', 'switcher', 'velocities', 'consistantly', 'estupendo', 'Background', 'VERTICAL', 'tpms', 'Spain', 'Covers', 'ardor', 'Accesory', 'datalogging', 'frontmost', 'Napa', '1ST', 'explode', 'coast', 'fubar', 'drivers', 'B000EE9WHY', 'biodegrade', 'Fraudulent', 'PLC', 'atrix', 'constructions', 'ultrabooks', 'iTorch', 'hor', 'INSTRUCTED', 'comparison', 'alluring', 'walkman', 'ADAPTER', 'hindered', 'Libratone', 'Pumpkin', 'whala', 'FUNCTIONS', '433', 'necessário', 'SON', 'lawsuit', 'monstrous', 'attractable', 'zForce', 'Confederate', 'srereo', 'filling', 'RECIEVING', 'spasms', 'radiofrequency', 'Sellbuz', 'silocone', 'ALO', 'kts', 'DESIGNED', 'wilkins', 'modal', 'recycle', 'DB', 'Adam', 'PWRIC3000', 'eepad', 'MCE', 'M200', 'eBigValue', 'Upgrader', 'Atrac', 'exporting', 'calendar', 'spliced', 'discontent', 'bulkly', 'inno', 'Laputa', 'TIMESET', 'lapicito', 'Strap', 'coily', 'Muzzy', 'hobyist', 'Salmon', 'longevity', 'DDX', 'sleezy', 'outerbox', 'vehichle', 'subdivide', 'junkie', 'Gigantic', 'Befor', 'vise', 'squeegie', 'Grabbed', 'sony12', 'antishock', 'BEC', 'milliseconds', '950rms', 'resedue', 'MOVIE', 'portable', 'ledge', 'deliberating', 'festure', 'powder', 'metering', 'redirected', 'stylelist', 'expressly', 'SUMMARY', 'loovers', 'Vci', 'xbone', 'avoids', 'XNAV9525', 'interlink', 'shotgun', 'respectable', 'printer', 'hastily', 'rapid', 'Funkadelic', 'unreliability', 'cd', 'beatuiful', 'btu', 'attendent', 'kknew', 'N14644', 'televisor', 'hex', 'holeshots', 'yahoo', 'defense', 'B000VXQH6W', 'receieve', '6c11c095b941f3576ef72501d99e880', 'Fantasik', 'materiales', 'kendel', 'ISGM', 'Moment', 'Zumbo', 'worthes', 'Subsonic', 'longlast', 'Nubby', 'csr', 'libraray', 'soles', 'MUSTANG', 'XKR', 'BMB', 'satisafy', 'iSmoothe', 'BestBuy', 'QUAILITY', 'ANL', 'permeates', 'bristles', 'CHARGERS', 'samething', 'MOVING', 'undamaged', 'sli', 'horribly', 'E450', 'bind', 'prohibitively', 'lateral', 'streamer', 'FOLLOW', 'extensively', 'macro', 'Joseph', 'programmers', 'ermine', 'ilimited', 'Laugh', 'WRITTER', 'Sog', 'biggers', 'persone', 'ober', 'Mfg', 'depends', 'unscathed', 'Deulo', 'Client', 'Spliced', 'MP235', 'GENERIC', '20w', 'ROLLER', 'dignified', 'oSD', 'ozone', 'Sunpak', 'makayama', 'Led', 'audiopipes', 'cleanliness', 'disopointing', 'knotted', '13w3v3', 'bubles', 'pf_rd_m', 'SECURITY', 'Chose', 'advancement', 'ebestrade', '120pounds', 'stipulated', 'AlwaysOnline', 'Forth', 'SIGUE', 'woun', 'controles', 'disagreement', 'OnlineGoodShop', '100606B', 'hierarchies', 'Hitting', 'FJ041', '200bpm', 'Tekcharge', 'MP3', 'Seller', 'PIE', 'jiggle', 'destination', 'rippoff', 'PRS900', '533i', 'predominent', 'Compliance', 'sr_1_2', 'conectado', 'unconfigured', '1296', 'BEER', 'durabiity', 'employing', 'supersedes', 'paragon', 'pouney', 'envelops', 'beagles', '180', 'therapist', 'desastre', 'AR400', '2PAC', 'preschooler', 'overly', '300c', 'B002JPITY8', '68', 'vices', 'worreid', 'unsnapped', 'LandAirSea', 'Scrapbook', 'bastards', 'audios', 'g1wh', 'reatach', 'squares', 'internalize', '440MHz', 'PowerBolt', 'REGULATION', 'likeness', 'moms', 'Opinions', 'Amplifying', 'malls', 'Promise', 'armbad', 'payed', 'Cert', 'Maxxonics', 'PATH', 'outdid', 'Denon', 'Bough', 'basshead', 'afflicts', 'revised', 'granny', 'land', 'crowd', 'satnav', '140Watts', 'Stream', 'diminished', 'ouy', '36', 'USb', 'B000F39C9W', 'darkish', 'overtray', 'Wand', 'fuxzy', 'inhibited', 'fibbed', 'Uh', 'TRAIN', 'SHAZAM', 'lastd', 'unto', 'Salt', 'ause', 'SHAKE', 'BUS', 'thrust', 'concentrated', 'Anal', 'goooooood', 'EPOC', 'anotherone', 'Transportation', 'kinks', 'x2¼', 'nailed', 'Mexican', 'welcoming', 'Lion', 'doesnto', 'waaay', 'B003VANOI6', 'Taught', 'exextremely', 'mving', '650X', 'Scorza', 'JamZZZ', 'boiling', 'FrozR', 'aversion', 'soooooooooooo', 'harmful', 'someones', 'UNSATISFIED', 'logictech', 'porro', 'Eee', 'geotag', 'drink', 'pixilation', 'Alternator', 'corroborarlo', 'texas', 'allot', 'gorgouse', 'powera', 'iTour', 'nap', 'Pronto', 'bamboozled', 'Wednesdays', 'vogue', 'Seaclear', 'SOLUCION', 'Tada', 'othewise', 'Schoche', 'microscope', 'specimens', 'Rubicons', 'Navigation', 'they', 'kar', 'and', 'diff', 'Public', 'Nexis', 'vest', 'PRESTO', 'pantech', 'Revitalized', 'Booooooooooo', 'Pages', 'CAPACITOR', 'amspoiled', '1S', 'Terracan', 'Genres', '250Mb', 'young', 'fitbit', 'SIMPLER', 'rectangle', 'inconveniente', '30K', 'THOSE', 'crossbodies', 'youwant', 'PMC', 'scrapped', 'B0046RE3G0', 'aught', '128k', 'proactively', 'FD1330B', 'declaration', 'fufilled', 'scuffs', 'BATTERY', 'qawret', 'does', 'DriveLink', 'status', 'browser', 'Spoiled', '100km', 'tempramental', 'Measurements', 'V600', 'iLevel', 'peeves', 'Supermoto', 'took', 'AMFM', 'threelevel', 'Dad', 'mispresenting', '000', 'sumamente', '7873', 'bitter', 'exclude', 'Fretlight', 'B001CIBCUG', 'imp120d', 'camcleats', 'Auxilliary', '30mW', 'CCW', 'Billy', 'EWER', 'ABRO', 'Convt', 'HAHAHAHA', 'PAL', 'ipads', 'willed', 'indicators', 'believer', 'APPLICATION', 'hwhe', 'KINGTOP', 'Pixie', 'fet4', 'chirped', 'rompiendo', 'runkeeper', 'consiguen', 'displays', 'nHeld', 'highpass', 'bested', 'effected', 'predetermined', 'estetica', 'c350', 'WP7', '120GB', 'flavors', '100db', 'iOS7', 'cames', 'Retrofone', 'TOT', 'reviewone', 'B00455IS1O', 'AD700', 'odors', 'sooooooooooooo', 'Barner', 'captain', 'irresponsible', 'i1010', 'Faucet', 'algunas', 'Festiva', 'Pono', 'Entune', 'extreamy', 'codex', 'Piggy', 'IMP70D', 'Mp4', '60hz', 'scums', 'classy', 'mangetic', '4channels', 'CANNOT', 'Gaucho', 'KEWL', 'loveeeee', 'errant', 'mistreatment', 'Steals', 'vibrating', 'Ability', 'Candle', 'althoughof', 'generates', 'sizing', 'approaching', 'COMPRE', 'Thia', 'Digikey', 'Audioforge', 'mission', 'rv6', 'rf', 'ai', 'series', 'tricked', 'Daryl', 'SHOOTING', 'perchased', 'pickle', 'LTR', 'throug', 'rackcase', '1500', 'delineate', 'FCC', '10th', 'clove', 'AM', 'expeted', 'Apparent', 'civic', 'WIRESTER', 'PRS600', 'rties', 'connectionwith', 'mhz', 'flims', 'andrefrigerators', 'WHOM', 'HGH', 'stingy', 'Gator', '3minutes', 'sticy', 'GPs', 'amazingly', '704', 'kickstand', 'GLX', 'mitsubishi', 'etronics', 'sutble', 'counsel', 'Soooooo', 'forfeiting', 'WAN', 'HUNGER', 'NITRO', 'recover', 'YTD', 'años', 'handsom', 'NOOOOOOOOOO', 'unwrapping', 'bridal', 'Handling', 'parametric', 'flasheswhen', '10k', 'Kbps', 'chicopee', '150kb', '20Khz', 'strip', 'demonstation', '7600', 'geosynch', 'probobly', 'realisticlly', 'squaretrade', 'B10', 'es', 'STRESS', 'resonator', 'pouncing', '________________________________________________', 'Windshied', 'konow', 'thecamera', 'tendencies', 'keep', 'jazzes', 'offerd', 'despiye', 'swelled', 'TYPING', 'B004BD6VSW', 'Renewed', 'nit', 'TICKS', 'methond', 'Hidizs', 'offhand', 'Mood', 'xplodes', 'Offal', 'road', 'strickly', 'Mono', 'primarilly', 'clumsey', 'minutues', 'electricity', 'California', 'DVCs', 'ZITRADES', 'longgg', 'Someguy', '02153', 'slickest', 'tranferring', '2WAY', 'Locally', 'ARCHOS', 'associate', 'cited', 'rugged', 'PowerMax', 'eprance', 'luxury', 'choral', 'scrating', 'farrite', 'Thr', 'Pocono', 'lifeguard', 'continually', 'penquin', 'shutdown', 'brave', 'SMOOSHED', 'CENTER', 'Ai', 'locking', 'LOOOOVES', 'rearranged', 'excelente', 'bd', 'spearhead', 'lady', 'voicecoils', 'Hindsight', 'flukie', 'PROVIDED', 'lst', 'mediocrity', 'discontinue', 'Jams', 'You', 'plu', 'MARAVILLOSA', 'houseboat', 'insuladed', 'wallets', 'jimmies', 'Chistmas', 'automagically', 'eny', 'What', 'piece', 'DIDNT', 'Augmented', 'KEEP', 'Cudos', 'p1000', 'Sheridan', 'innacurate', 'Swimp3', 'imbalanced', 'B0017PG8KS', 'SLOT', 'kobobooks', '1260762778', 'Pictures', 'cherished', 'auditorium', 'wery', 'Inability', 'Piolts', 'Obenaufs', 'bundles', 'Controlling', 'Leisure', 'GW', 'Link1', 'DX425', 'FOURTH', 'allergies', 'physically', 'Zagg', 'Digitizer', 'FILL', 'EJ001', '76CSx', 'B003XUU1WG', 'Closeout', 'dxi65', 'inital', '2620', 'provato', 'flushed', 'soused', 'unmounts', 'QUEDO', 'everyother', 'managers', '70bucks', 'WAY', 'viability', 'brilliantly', 'tablebeside', 'Travel', '5550', 'Cube', 'sweetlife999', 'ot', 'bether', 'Teryx', 'Titles', 'ValleyBuy', 'poppin', 'Location', 'DNX', 'triangulate', 'there', 'A2B', 'drives', 'subway', 'bien', 'Waas', 'inconveniet', 'proportionally', 'Vocal', 'âñå', 'consecuence', 'COPPER', 'WEEKS', 'Tomtom', 'derived', 'XC', 'dackground', 'Vine', 'B001DA92FK', 'mgr', 'foudn', 'Massively', 'orchestrations', '1280x1024', 'fahrenheit', 'Niether', 'Suede', 'TSI', 'grind', 'HiFonics', 'Zunes', 'Skunked', 'M500', 'abyssmal', 'Surefire', 'kepp', 'EJ1000', 'Guillermo', 'Rider', 'breadboarding', 'standardize', 'Paine', 'diffective', '219', 'popup', 'unquestionably', 'iOS4', '9hrs', 'sincs', 'tissue', 'grain', 'ties', 'technique', 'RAV', 'WKAR', 'OUTRAGEOUS', 'tensioning', 'Bring', 'insects', 'preemptively', 'CorelDraw', '05V', 'MAGNIFIC', 'wires', 'irregular', 'Bosch', 'unfourtunitly', 'Smartwraps', 'COBRA', 'spec', 'ridiculousness', 'orange', 'snaking', 'paraphrasing', 'peck', 'M97xE', 'holland', 'soooooooooo', 'understandably', 'FLH', 'WRAPSOL', 'Gulp', 'fogged', 'swich', 'zu', 'Offical', 'sandwiched', '10volt', 'Facil', 'Ant', 'Contemp', 'bedmates', 'enhancements', 'Discharging', '59', 'existent', 'ESTO', 'ebsite', 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'hostess', 'Brainwavz', 'manual', 'PREPAID', 'Roger', 'Incident', 'trailed', 'itineraries', 'conquer', 'Whistles', 'investigate', 'Janus', 'moto', 'mounds', 'Superman', 'impossiable', 'Uglier', 'fixin', 'checklists', 'Nowdays', 'vfr', 'sounding', 'Terms', 'quickerthan', 'hsd', 'hold', '15765', 'db', 'superbright', 'insubordinate', 'Cateye', 'INDOORS', 'App', 'voucher', 'Stitches', 'Explain', '2nedeli', 'ease', 'CHOICE', 'overstretching', 'p4300dvd', 'Dawg', 'VIII', 'Hifiman', 'cording', 'hdc3', 'Ditched', 'arm', 'fetured', 'discontinuing', 'REASONABLY', 'Id', 'retardant', 'hopefuly', 'unite', 'obliterate', 'ip87', 'BOUGHT', 'Juicer', 'Reach', 'tic', 'producut', 'Acuras', '15000', 's1fw', 'cheapers', 'final', 'M1000', 'Overhead', 'theory', 'verging', 'Swirl', '6300bt', 'Powerpoint', 'E355', 'XD1222', 'HANDICAP', '10231202', 'alots', 'Referencing', 'augment', 'carbon', 'boating', 'maxium', 'HEADROOM', 'bravo', 'helluva', 'stock', 'sincere', 'creaked', 'bango', 'leash', 'Concerning', 'pealed', 'amazin', 'neoprine', 'gassed', '806', '2705b79f739f5b6f613cf9467ea722f6', 'sand', 'HDEQ', 'distress', 'nota', 'tremendous', 'spks', 'outwieght', 'INCONSISTENT', 'offriading', '6m', 'cuality', 'soley', 'oppinium', 'KDX11', 'GET', 'wowuld', 'Fidelity', 'fish', 'versus', 'interstation', 'iHome2go', 'condenses', 'Rondo', 'Avital', '300w', 'Kellards', 'shaving', 'testament', 'Xacti', 'onboard', 'EPIRB', 'Expansion', 'normaly', 'Altech', 'raymarine', 'ballpoint', 'Magnavox', 'sothat', 'Aerb', 'sr_1_17', 'Cheer', 'Pounds', 'story', 'eyecups', 'convex', 'deliverly', 'GelaSkin', 'UBS', 'spoiling', 'compatiblity', '338', 'Wipes', 'unison', 'GS3', 'ConceptR', 'SAUNA', 'Frost', 'Actal', 'ESCUCHA', 'sleeves', 'KLM', 'involve', 'v3', 'bandwith', 'desktops', 'sposed', 'excursions', 'happend', 'drawn', 'handycap', 'throuth', 'DashCams', 'hisses', '13S', 'muttled', 'alerts', 'retriever', 'cowerkers', 'pointing', 'Alot', 'sII', 'ml', 'occupying', '602', 'downright', '32C', 'enouch', 'Techstream', 'turnover', 'cambiar', 'psychologically', 'cropped', 'handers', 'windscreen', '8L16', 'increíble', 'ceases', 'B001H4B0AC', '780', 'DOAMS', 'admiring', 'unles', 'luminous', 'been', 'trk', 'ESTE', 'Heat', 'SUPPLIES', 'serif', 'counterpart', 'Edward', 'delicates', 'believable', 'configurated', 'sans', 'Moving', 'johnnybravo234', 'Differernce', 'traversal', 'creative', 'Difficult', 'fiancée', 'Utilize', 'Vandersteens', 'marketing', 'combinations', 'warm', 'dine', 'relock', 'NAVFREE', 'xr4s', 'footswitch', 'lindo', 'november', '32pin', 'gilft', 'times', 'reduced', 'Suzuki', 'Mid', 'greatness', 'deivery', 'corria', 'uPgrade', 'Friends', 'Trabajan', 'llena', 'revving', 'waze', 'CAR', 'KEN', 'HX370', 'waistline', 'Victoria', 'lenghty', 'indention', 'wherewithall', 'speradicaly', 'WITH', 'thinness', 'RFD7090', 'iI', 'Loctite', 'thoose', 'ame', 's320', 'remarks', 'aplique', 'manly', 'VisionDrive', 'strang', 'offensively', 'pretension', 'TOWARD', 'bonfire', 'tottaly', 'bluetooh', 'SCAM', 'BlueSoblell', '90C', 'Olivia', 'RED', 'Addition', 'P409S', 'uncareful', 'coffee', 'AWARE', 'arrested', 'PAIR', 'yard', 'Give', 'Saddlebred', 'Superseal', 'ohhhh', 'RECEIVER', 'Atuo', 'umprotected', 'horrific', 'inspector', 'callback', 'dale', '282087', 'Modified', 'GAVE', 'DS214', 'Trackmaster', 'garages', 'glommed', 'felicitaciones', '2amp', 'G3', 'magellan', 'XtraFlex2', 'Corollain', 'seriesed', 'HAND', 'budging', 'fait', '3900', 'Awaiting', 'xdhc', 'lappy', 'pinion', 'GPSMap60c', '48k', 'capacitie', 'eat', 'shy', 'trip', 'FUZE', 'reccomed', 'metre', 'words20', 'Beyerdynamic', 'inherit', 'Improvement', 'B00548QAPC', 'subtlety', 'Fl', 'logbook', 'Hawn', 'Forerunners', 'Dan', 'Dyno', 'buuuuut', 'alongside', 'childs', 'Generic', 'GTSUN', 'currect', 'HAve', 'bk7', 'repack', 'BEFORE', 'backpedals', 'Toggle', 'brances', 'vehicile', 'Graphic', 'Professonal', 'sats', 'anteen', 'Diys', 'tredmill', 'X993', 'S305', 'Measures', 'reasonably', 'starters', 'RECIVE', 'tachet', 'iHoe', 'Garavito', 'Grampa', 'hopin', 'Speakerphones', 'انا', 'SmartSet', 'Aquvie', 'mo2ERB3BSAJGMNB', 'shopko', 'recenlty', 'shrapnel', 'iwatch', 'excepts', 'ergononmics', 'DEPTH', 'woouldn', 'germane', 'Clean', 'Fort', 'WHICH', '9690', 'ssd', 'Programmer', '10Mbps', 'yeah', 'powerful', 'CORRECT', 'volums', 'logorythms', 'Kodak', 'facilities', 'exa1000', 'hual', 'Shotty', 'sirrus', 'mo1KL55M03TMIU0', 'mm6501', 'Transmitters', 'Switcher', 'Including', '3or', 'Renegade', 'rechargible', 'overhears', 'Bootloader', 'assembled', 'Gonna', 'm4a', 'Hearter', 'Tam', 'Corvettes', 'imp1000', 'CHYRVD', 'esperado', 'Torx', 'PUSHING', 'brushes', 'AC2100V', 'promp', 'Nov', 'Attachable', '771', 'Technia', 'CompVR', 'naked', 'WEEK', 'oz', 'peaceful', 'FF2', 'IM207', 'durations', 'zeeroed', 'dollars', 'okey', 'EAS', 'prove', 'asin', 'Ok', 'Meyers', 'deluxe', 'cracking', 'Pushcart', 'e12', 'blurb', 'FED', 'XR600', 'BestPriceShop', 'submersed', '5g', 'memoryfoam', 'straigtforward', 'Boyo', 'AUX', '2oms', 'river', 'LANDMARKS', 'Halsea', 'caraudiocloseout', 'UNIDAD', 'globes', 'thirteen', 'gtp', 'craftsmanship', 'County', '8405', 'Civilization', 'barely', 'dimly', 'Kind', 'Ileft', 'KR', 'allowed', 'DDR', 'eagles', 'flight', 'courts', '3band', 'availaible', 'IASCA', '5in', 'Patazon', 'debe', 'Rainex', 'NOV', 'primal', 'FOLDED', 'deslership', 'dansautodetailing', 'Halo', 'frenzied', 'handmedown', 'xantrex', 'annouce', 'cfg', 'wneh', 'PDX', 'pieza', 'motorboating', 'Gerald', 'walkthroughs', 'purchases', '51v', 'junker', 'attenuating', 'preffer', 'CED', 'Shaking', 'correctness', 'two', 'Target', 'runnung', 'Efffing', 'explaing', 'institutions', 'turbulent', 'deforming', 'rejecting', 'Nissan', 'gpx', 'ECM', 'NAS', 'MATT', 'Vendors', 'Videomic', 'hilariously', 'KB', 'Blckberry', 'pittasoft', 'nite', 'B002M03KVC', 'Def', 'CaseStar', 'siple', 'live', '3800MAH', 'ARA', 'Possesed', 'Wouxon', '0Mhz', 'realistic', 'nightly', '20D', 'sennheiser', 'Restored', 'innie', '8x24', 'derection', 'bionic', 'Telecoil', 'demand', 'MEDIA', 'reflected', 'R320', 'morrow', 'precises', 'Values', 'modelo', 'Nikko', 'sub', 'woofer', 'promt', 'verbose', 'earthquakes', 'soundqubed', 'son', 'Audiostation', '5mi', 'lathing', '5cf', 'Ended', 'HDS', 'UVS', 'JEW', 'Subpar', 'vaca', 'LEFTY', 'bruse', 'rabid', 'Elec', 'lookes', 'reps', 'movies', 'Dutch', 'xrt', 'sputtering', '601dn', 'tobacco', 'Marshall', 'convoy', 'IMP71T', 'Karaoke', 'reptile', 'recommonded', 'tiltable', 'wouold', 'Exciter', 'mustardy', 'i931', 'Th', 'wounds', 'ject', 'unboxed', '540T', 'Conductor', 'desiving', 'sruggle', 'uncalled', '590SG', 'rinden', 'cobra', 'Spektor', '1977', 'disppointed', 'uncomfortalbe', 'Blemishes', 'ulseless', 'RAVO', 'burger', 'Uzbekistan', 'fix', '68mm', 'BlueSea', 'Madrid', 'comped', 'intutively', 'reproducing', 'Dealkings', 'advising', 'brill', 'monogrammed', 'iuguyg', 'KILLS', 'lids', 'Accoustics', 'experimentally', 'ctrl', 'notebooks', 'FUBAR', 'doownloaded', 'CABLES', 'datable', '16g', 'success', 'etc', 'VOLVO', 'Pauls', '309', 'CDW', 'halogen', 'SpotLifter', 'champs', 'Ebigvalue', 'descriptioN', 'ier', 'Beaker', 'Ummmm', 'Oversampling', 'Buddy', 'sited', 'equate', 'gravity', 'cursor', 'towncars', 'CCD', 'spakers', 'B002GX8ZSI', 'simulate', '2200T', 'exercising', 'WJCT', '105', 'allmost', 'rivets', '3minute', 'RSS100', 'recommdend', 'elf', 'Seminole', 'bickering', 'HWSBTA2W', 'Scrubs', 'passthrough', 'provie', 'roots', 'recommending', 'tarjetas', 'squawk', 'aficionado', 'famously', 'CHA', 'thump', 'modulation', 'LXCC', 'desgin', 'C2R', 'arliberator', '250A', 'FRAPS', 'gimmicky', 'bying', 'V60', 'loves', 'BTX500', 'Starry', 'MPE', 'seeped', 'rising', 'Lexan', 'TRD', 'embarrassing', 'shortages', 'fo', 'Bumpy', 'reviewers', 'maild', 'incompatibilities', 'gyroscope', 'ventured', 'proceede', 'ANALYZER', 'HNN9008A', 'STi', 'NT90C', 'scratces', 'liquor', '5F', '2220T', '420', 'corrispondeva', 'mico', 'p1010', 'certification', '3000watt', 'thereof', 'iceberg', 'W4JRO', 'smirked', 'midtones', '24K', 'htx', 'lands', 'multifunctional', 'IMEI', 'goals', 'chuckling', 'iRoom', 'NOR', 'morning', 'waving', 'actally', 'DTCs', 'dealerships', 'Expected', 'Stonehenge', 'FOREVER', 'Mocreo', 'ultrathin', 'luxry', 'crammed', 'woorks', 'Instruments', 'depicting', 'Morale', 'declaired', 'NT96650', 'G8', 'Enya', 'tactic', 'Shiba', 'Looks', 'minuets', 'PAPERWORK', 'updatable', 'lic', 'sich', 'bp', 'esthetically', 'PL112D', 'OHP', 'Enlighten', 'Otterbox', 'Headphone', 'giftbox', 'ELECTRONICS', 'Dispersing', 'installa', 'Dazed', '40db', 'tm480', 'PICOGLF30W12V120VR', 'E95', 'ForeRunner', 'BTY', 'morons', 'AUTONOMÍA', 'Summation', 'observant', 'Casemate', 'reacived', 'presentations', 'nage', 'offshored', 'Macallys', 'donnot', '560SL', 'stringed', '20x20', 'mov', 'saprq', 'deceivingly', 'AudioQuest', 'fitin', 'Uploaded', 'skeptics', 'flown', 'chunks', 'Orwell', 'x395', 'ridged', 'citys', 'Hardness', 'alcance', 'Sparkly', 'Forest', 'EEEK', 'imaged', 'C530', 'Sticker', 'Adata', 'weatherstripping', 'creates', 'yeh', 'Begins', 'wwaaayyyyyzzz', 'estara', 'tAPE', 'mi', 'Defenders', 'Tc', 'WEP', 'duable', 'creen', 'extremely', 'Saratoga', 'BALLAST', 'behavior', 'Ni', 'opertaion', 'scrapper', 'Gyroscope', 'Sportsman', 'wallah', 'produces', 'beltway', 'Diode', 'photographed', 'vob', 'evaporator', 'equit', 'Mencoder', 'nab', 'swipping', 'np60', 'Thunderform', 'KA1101', 'elevate', 'Linear', 'WROK', 'pebbles', 'Laws', 'Done', 'eastern', 'represenative', 'pinches', 'vibrance', 'THANK', 'supervisor', 'hugging', 'Proceed', 'Essentials', '10SD', 'underwater', 'definition', 'flipsreen', '06ZN1ZCTYMF0BNZGDPME', 'termial', 'circuits', 'imho', '5Wx2', 'fabulas', 'microwaving', 'reliabilty', 'contrasted', 'cancelled', 'folios', 'Unfortunitly', 'used', 'made', '125A', 'tarnished', 'Moultrie', 'm4p', 'SD', 'Talkabouts', 'scared', '100th', 'advertiser', 'Molds', 'eTraverse', 'doesnt', 'Woks', 'holdster', 'ZHIQIANG', '27th', 'probley', 'tentative', 'DUE', 'm97xe', 'SPECIFICALLY', 'despachan', 'slouching', 'notorious', 'Swing', 'possiblely', '22003', 'PROMISING', 'Vigo', 'OFFERED', 'Binging', 'subwoffers', 'duracells', 'F56', 'Nood', 'Typhoon', 'resizing', 'flinching', 'batwing', 'diffence', 'Delorean', 'evan', 'dado', 'iis', 'depresses', 'screenprotecter', 'VOLTS', 'LANDSCAPE', 'BOOEY', 'Yea', 'rats', 'kamikaze', '8W', '272', 'TWEAKlNG', 'multipack', 'Rockbirds', 'rotating', 'wen', 'fuerte', 'recommned', 'Abbey', 'ATTACHED', '710', 'ELSEWHERE', '9pm', '10w', '204', 'forro', '9020T', 'openn', '544445F2', 'employ', 'analagous', 'TOSlink', 'IMP880', 'ftdi', 'lock', 'CAUGHT', 'CLICKER', 'excruciating', 'Whenever', 'dismantled', 'absolulely', 'or', 'Explore', 'malibu', 'BEZEL', 'identification', 'writeup', 'ducts', 'miscelleneous', 'Carries', 'Rooster', 'diner', 'manyplaces', 'pho', 'Handie', 'articulation', 'ignition', '10849144', 'max', '10mph', 'Player', 'LYRA', 'calibre', 'electronics', 'COLORFUL', 'previeous', 'mojo', 'simultaneously', 'arguements', 'VERY', 'reseal', 'gensets', 'peacock', 'KByte', 'syllable', 'Sustained', 'Phono', 'sed', 'MOLLE', 'However', 'irreplaceable', '01118', 'FORGET', 'Tri', 'UPSIDE', 'bulkiness', '115200', 'immersions', 'CoD', 'thingamajig', 'sum', '7b', 'moreflaws', 'divx', 'RMAs', 'Lover', 'bustling', 'Halifax', 'JAPAN', 'legal', '19Mbps', 'everythig', 'owns', 'appradio4', 'therefore', 'Faux', 'nameless', 'modle', 'Valentines', 'AVRCP', 'HR', 'annoys', 'seasonally', 'onlooker', 'Cas', 'embraces', 'levers', 'inventing', '7MB', 'Chronicles', 'Beefy', 'Slates', 'B005ZKK8PM', 'higer', 'anymore', 'cigarette', 'Sansui', 'plasit', 'Barring', 'activeate', 'hurricane', 'VXI', 'nitified', 'shaddered', 'bonitas', 'fb26c0fa6a5188165230806480fe3ea1', 'fews', 'BC', 'im', 'fd', '9020', '16pin', 'safisfied', 'snap', 'storeage', 'Satetchi', 'MIGHT', 'ipad', 'Union', 'WHEEL', 'tend', 'Newegg', 'spy', 'PHA', 'supressors', '65mph', 'chimney', 'SLOW', 'SmartCord', 'especifico', 'papire', 'longtime', 'deployment', 'convinient', 'Bus', 'motorhome', 'cupids', 'Velineon', '8hrs', 'B003ZTT7YS', 'recahrged', 'VENTILADOR', 'Equalizers', 'RESIST', 'SDSDQM', 'vegetable', '17ft', '6k', '720px', 'somehwere', 'strechiness', 'vendor', 'kayscase', 'iphone5s', '0V', 'mowing', 'News', 'skeptically', 'awd', 'alarms', 'ít', 'StrapWrap', 'chipmunks', 'OxiClean', 'Barbie', 'Solo', 'presionarlo', 'RF900', 'CSR', 'Measuring', 'stylusmprotects', 'shove', 'Pass', 'CROWN', 'APIs', 'intakes', 'boner', 'objected', 'bridgeable', 'Sarcasm', 'WIRED', 'complementary', 'inquiring', '707', 'RHINOLINED', 'RNDs', 'movded', 'reccharging', 'JBL', 'WhiteLabel', 'CASH', 'corseted', 'antiglare', 'Grills', 'book', 'Took', 'rearrange', 'moHTWCNJV7F5GJ', 'devil', 'ZeroForce', 'squeak', 'glorious', 'Shockproof', 'Example', 'thar', 'interconnect', 'ISS', 'antiskip', 'Turned', 'moron', 'nini', 'XMax', 'musice', 'specific', 'welders', 'gizmos', 'tolerate', 'Dodge', 'Leslie', 'CHECKS', 'Slipknot', 'paperish', 'rehearsal', 'Z150BH', 'beggar', 'Jae', 'puting', 'Wiggled', 'handling', 'Latency', 'breaked', 'touchstone', 'waypoints', 'Range', 'DaMink', 'Spot', 'Bean', 'aisles', 'lovr', 'oversensitive', 'charitably', 'homemade', 'parse', 'presentar', 'laced', 'lumens', 'giraffe', 'éste', 'boxed', 'mother', 'Ifly', 'Rewired', 'Garin', 'jersey', 'Ifyou', 'stair', 'Expensive', 'FELL', 'middleman', 'estimator', 'text', 'Screw', 'av500', 'RENEWED', 'una', 'stipulations', 'ZN7II', 'Ruckus', 'rathered', 'B004Z4MDGG', 'couldbe', 'Securely', 'doen', 'quelch', 'SOLUTIONS', 'Mainland', 'quar', '1500mAh', 'eyeliner', 'reopen', 'usa', '6A', '10db', 'Navitech', 'Overly', 'goofed', 'thank', 'Seirra', 'expactations', '5436', 'preciso', 'NOSEY', 'megabit', 'runner', 'mo1IQO9JRQVLOVX', 'K40', 'inferred', '640w', '24pin', 'Simnet', 'GOPRO', 'QUALITY', 'recomendaría', 'Grill', 'sculpted', 'distributers', 'supposd', 'businesscards', 'commenced', 'bridge', 'abstructed', 'grant', 'showroom', 'GLOWlight', 'quakes', 'buble', 'Bulk', 'turnkey', 'monthes', '4X10s', 'gesture', 'Acting', '100hrs', 'strengthened', 'Pods', 'EZSkin', 'predictably', 'helicopter', 'eve', 'drinks', 'orphaned', 'directive', 'B00400TGEC', 'Somethings', 'repair', 'address', 'HDS315', 'hasen', 'corrupting', 'outgassing', 'masked', 'etcetera', 'Coltrane', 'picasso', 'regaining', 'cememnting', 'Rz', 'stayoff', '2x40W', 'entertaining', 'Flasher', 'keywords', 'worrier', 'bleeding', 'tampa', 'attendance', 'Da', 'Textured', 'implications', 'startles', 'shirts', 'Audiobahn', 'FAVORITE', 'deverses', 'joined', 'justs', 'snorer', 'linen', 'requests', 'adamantium', 'V2', '214', 'sensibility', 'bot', 'superconductor'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PdWrBf4xzdXk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214
        },
        "outputId": "f0f9f004-82ab-4756-8d7e-c0cda924aaed"
      },
      "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": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "b\"This. Is. Amazing!! So it arrived on time, actually earlier than expected, and it makes my iPod classic look so beautiful!! I can't stop staring at it! Applying it wasn't the easiest since U was trying to make it PERFECT, but it's not hard either! But once you apply it it's all worth it. 10/10.\"\n",
            "[62013, 38917, 50991, 63915, 72278, 41151, 71419, 49207, 41495, 66777, 71586, 73145, 66915, 72278, 47503, 66300, 67032, 59188, 46916, 63915, 57757, 71435, 62514, 45643, 39508, 62564, 21485, 72278, 55559, 72278, 59340, 45643, 59919, 26131, 55462, 62361, 66796, 60308, 38681, 65632, 72278, 44831, 70253, 72278, 55404, 66386, 44951, 64289, 70253, 67954, 62262, 71226, 72278, 72278, 55404, 57783, 37567, 72278, 34434, 34434]\n",
            "b'I bought these to use for swimming and running and I really love them.  Sound is great and they stay in p[lace perfectly.  They block out so much outside sound that I often run with just one so I can talk to my running buddies.  Great product.'\n",
            "[71435, 72439, 70103, 38681, 54673, 69997, 36179, 66915, 66218, 66915, 71435, 70298, 71151, 11092, 35753, 38917, 70302, 66915, 65483, 52371, 19013, 68620, 45893, 53237, 65483, 59147, 55179, 63915, 30607, 60084, 35753, 70102, 71435, 62623, 26901, 72639, 35223, 62833, 63915, 71435, 62514, 50040, 38681, 66300, 66218, 67245, 70302, 68174]\n",
            "b'The case for my Nook was exactly what I wanted...the quality is top-notch, the leather is a beautiful color and the price was less that half of what I was asked to pay at a store....even when tax and shipping are included. I am very pleased with this purchase.........CeCe'\n",
            "[59919, 53457, 69997, 66300, 57415, 66796, 66161, 66087, 71435, 49038, 59919, 73665, 38917, 54616, 51172, 59919, 52340, 38917, 58387, 57757, 28828, 66915, 59919, 66438, 66796, 67594, 70102, 47860, 66803, 66087, 71435, 66796, 57515, 38681, 62600, 21485, 58387, 46687, 56684, 65434, 40818, 66915, 53575, 38207, 49871, 71435, 64378, 73403, 58907, 72639, 62013, 64364, 37722]\n",
            "b'Loved it'\n",
            "[67024, 72278]\n",
            "b'Worked great!  I would absolutely recommend this product and plan to buy it again myself when needed in the future.'\n",
            "[13351, 70302, 71435, 49133, 33906, 64801, 62013, 68174, 66915, 67600, 38681, 70485, 72278, 62159, 56396, 65434, 50416, 19013, 59919, 45586]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RURcWX6Q2YFv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "outputId": "abe8eb73-e146-4dd2-de88-0e3445a5461b"
      },
      "source": [
        "for index in encoded_example:\n",
        "  print('{} ----> {}'.format(index, encoder.decode([index])))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "13351 ----> worked\n",
            "70302 ----> great\n",
            "71435 ----> i\n",
            "49133 ----> would\n",
            "33906 ----> absolutely\n",
            "64801 ----> recommend\n",
            "62013 ----> this\n",
            "68174 ----> product\n",
            "66915 ----> and\n",
            "67600 ----> plan\n",
            "38681 ----> to\n",
            "70485 ----> buy\n",
            "72278 ----> it\n",
            "62159 ----> again\n",
            "56396 ----> myself\n",
            "65434 ----> when\n",
            "50416 ----> needed\n",
            "19013 ----> in\n",
            "59919 ----> the\n",
            "45586 ----> future\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "naWRZpLLGKNk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "encoder.save_to_file(\"/tmp/sa_encoder.vocab\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "enbxUU330JlY",
        "colab_type": "code",
        "colab": {}
      },
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XQkgCg0X3kBn",
        "colab_type": "code",
        "colab": {}
      },
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vd613Wks5KIU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ar_encoded_data = train_dataset.map(encode_map_fn)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RONAsea269dv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 247
        },
        "outputId": "46dfc4d9-a506-4d4a-ca30-fd3b262a78d1"
      },
      "source": [
        "for f0,f1 in ar_encoded_data.take(2):\n",
        "  print(f0)\n",
        "  print(f1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tf.Tensor(\n",
            "[62013 38917 50991 63915 72278 41151 71419 49207 41495 66777 71586 73145\n",
            " 66915 72278 47503 66300 67032 59188 46916 63915 57757 71435 62514 45643\n",
            " 39508 62564 21485 72278 55559 72278 59340 45643 59919 26131 55462 62361\n",
            " 66796 60308 38681 65632 72278 44831 70253 72278 55404 66386 44951 64289\n",
            " 70253 67954 62262 71226 72278 72278 55404 57783 37567 72278 34434 34434], shape=(60,), dtype=int64)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(\n",
            "[71435 72439 70103 38681 54673 69997 36179 66915 66218 66915 71435 70298\n",
            " 71151 11092 35753 38917 70302 66915 65483 52371 19013 68620 45893 53237\n",
            " 65483 59147 55179 63915 30607 60084 35753 70102 71435 62623 26901 72639\n",
            " 35223 62833 63915 71435 62514 50040 38681 66300 66218 67245 70302 68174], shape=(48,), dtype=int64)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8pZ4PzSz90RV",
        "colab_type": "code",
        "colab": {}
      },
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AXTDe5U1yVKg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "vocab_size += 1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MjPeEW7lEva8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "08285f46-011f-49e8-ff3b-1d4094defecb"
      },
      "source": [
        "sample_text, sample_labels = next(iter(test_data))\n",
        "\n",
        "sample_text[0], sample_labels[0]\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<tf.Tensor: shape=(847,), dtype=int64, numpy=\n",
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              "        46916, 63915, 57757, 71435, 62514, 45643, 39508, 62564, 21485,\n",
              "        72278, 55559, 72278, 59340, 45643, 59919, 26131, 55462, 62361,\n",
              "        66796, 60308, 38681, 65632, 72278, 44831, 70253, 72278, 55404,\n",
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              "            0])>, <tf.Tensor: shape=(), dtype=int32, numpy=1>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "57m2OiyyN2AA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "outputId": "8bb44007-9daf-4396-fb4e-5efb333889b5"
      },
      "source": [
        "for f0,f1 in test_data.take(10):\n",
        "  print(tf.unique_with_counts(f1)[2].numpy())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[134  62]\n",
            "[ 75 121]\n",
            "[119  77]\n",
            "[135  61]\n",
            "[132  64]\n",
            "[144  52]\n",
            "[ 75 121]\n",
            "[127  69]\n",
            "[127  69]\n",
            "[130  66]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z5rUcypcIytc",
        "colab_type": "code",
        "colab": {}
      },
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tu4zxpwEUJn5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!rm -r /tmp/logs/"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OpOPcVniJ6Lo",
        "colab_type": "code",
        "colab": {}
      },
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q9ggc2kmKDWT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "outputId": "d9eb5c17-5aca-4342-910a-cf1bb2fd20b8"
      },
      "source": [
        "history=model.fit(train_data, epochs=6, validation_data=test_data, callbacks=[tensorboard_callback,checkpointer])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/6\n",
            "    465/Unknown - 239s 514ms/step - loss: 0.3650 - accuracy: 0.8263\n",
            "Epoch 00001: val_loss improved from inf to 0.32376, saving model to /tmp/sentiment_analysis.hdf5\n",
            "465/465 [==============================] - 261s 561ms/step - loss: 0.3650 - accuracy: 0.8263 - val_loss: 0.3238 - val_accuracy: 0.8639\n",
            "Epoch 2/6\n",
            "464/465 [============================>.] - ETA: 0s - loss: 0.2542 - accuracy: 0.8969\n",
            "Epoch 00002: val_loss improved from 0.32376 to 0.28821, saving model to /tmp/sentiment_analysis.hdf5\n",
            "465/465 [==============================] - 256s 551ms/step - loss: 0.2542 - accuracy: 0.8969 - val_loss: 0.2882 - val_accuracy: 0.8796\n",
            "Epoch 3/6\n",
            "464/465 [============================>.] - ETA: 0s - loss: 0.2187 - accuracy: 0.9146\n",
            "Epoch 00003: val_loss improved from 0.28821 to 0.28588, saving model to /tmp/sentiment_analysis.hdf5\n",
            "465/465 [==============================] - 253s 545ms/step - loss: 0.2186 - accuracy: 0.9146 - val_loss: 0.2859 - val_accuracy: 0.8858\n",
            "Epoch 4/6\n",
            "464/465 [============================>.] - ETA: 0s - loss: 0.1788 - accuracy: 0.9328\n",
            "Epoch 00004: val_loss did not improve from 0.28588\n",
            "465/465 [==============================] - 256s 550ms/step - loss: 0.1787 - accuracy: 0.9329 - val_loss: 0.2903 - val_accuracy: 0.8796\n",
            "Epoch 5/6\n",
            "464/465 [============================>.] - ETA: 0s - loss: 0.1504 - accuracy: 0.9433\n",
            "Epoch 00005: val_loss did not improve from 0.28588\n",
            "465/465 [==============================] - 255s 549ms/step - loss: 0.1504 - accuracy: 0.9433 - val_loss: 0.3286 - val_accuracy: 0.8887\n",
            "Epoch 6/6\n",
            "464/465 [============================>.] - ETA: 0s - loss: 0.1276 - accuracy: 0.9537\n",
            "Epoch 00006: val_loss did not improve from 0.28588\n",
            "465/465 [==============================] - 253s 544ms/step - loss: 0.1274 - accuracy: 0.9537 - val_loss: 0.3292 - val_accuracy: 0.8807\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "02VWrXOnCwlI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.save('/tmp/final_sentiment_analysis.hdf5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MPSEdqQNK7eR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "283a1256-f93b-42fd-b8b6-cb126f26f8a9"
      },
      "source": [
        "!ls -alrt /tmp/*.hdf5"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-rw-r--r-- 1 root root 118625184 Feb 24 22:37 /tmp/sentiment_analysis.hdf5\n",
            "-rw-r--r-- 1 root root 118625184 Feb 24 22:51 /tmp/final_sentiment_analysis.hdf5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4zcuVcXlKfh5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "bd846433-258b-493e-8511-538a7a10eb4b"
      },
      "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": [
        {
          "output_type": "stream",
          "text": [
            "     72/Unknown - 20s 284ms/step - loss: 0.3292 - accuracy: 0.8807\n",
            "Eval loss: 0.329, Eval accuracy: 0.881\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ATpSMfLmgO_E",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "6a5a4aa1-fdc3-4bb7-e3d2-efb116c95aac"
      },
      "source": [
        "for f0,f1 in test_data.take(1):\n",
        "  print(f1)\n",
        "  print(model.predict(f0))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tf.Tensor(\n",
            "[1 1 1 1 1 1 1 0 0 1 1 0 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1 1 0 1 0 0 1 1 1 1 0\n",
            " 0 1 1 1 1 1 0 0 1 1 1 1 0 0 1 0 1 0 1 1 0 1 0 1 0 1 1 1 1 0 1 1 1 0 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 0 0 0 1 1 0 0 0 1 1 1 1 1 0 0 1 1 1 1 1 0 1 1 1 1 1 1 0\n",
            " 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 0 1 1 1 0 0 1 1 0 0 0 1 1 0 1 0 1 1 0 0 0\n",
            " 0 1 0 0 1 1 1 1 1 0 0 1 1 1 1 0 1 0 1 1 1 1 1 1 0 1 1 1 0 1 1 0 1 1 1 1 0\n",
            " 1 0 1 0 0 0 1 1 0 1 1], shape=(196,), dtype=int32)\n",
            "[[ 7.495976  ]\n",
            " [ 7.029666  ]\n",
            " [ 7.165345  ]\n",
            " [ 3.041151  ]\n",
            " [ 7.3181715 ]\n",
            " [ 7.882741  ]\n",
            " [ 7.4492493 ]\n",
            " [-6.6606913 ]\n",
            " [-7.8830442 ]\n",
            " [ 7.39373   ]\n",
            " [ 3.0437136 ]\n",
            " [-4.6626296 ]\n",
            " [ 5.3866115 ]\n",
            " [-4.9347215 ]\n",
            " [ 7.2289596 ]\n",
            " [-6.1002197 ]\n",
            " [ 7.70189   ]\n",
            " [-6.053842  ]\n",
            " [ 7.2251835 ]\n",
            " [ 4.559285  ]\n",
            " [-0.03752804]\n",
            " [-5.4026113 ]\n",
            " [ 2.4705436 ]\n",
            " [ 4.1706076 ]\n",
            " [ 5.380473  ]\n",
            " [ 7.327431  ]\n",
            " [ 1.6872168 ]\n",
            " [ 3.7911913 ]\n",
            " [-8.867253  ]\n",
            " [ 3.6289697 ]\n",
            " [-4.7485714 ]\n",
            " [ 5.937135  ]\n",
            " [ 7.5449977 ]\n",
            " [ 6.58335   ]\n",
            " [-1.1908131 ]\n",
            " [ 4.802243  ]\n",
            " [-8.876009  ]\n",
            " [-6.6632457 ]\n",
            " [ 6.4592214 ]\n",
            " [ 5.877935  ]\n",
            " [ 0.6011947 ]\n",
            " [ 0.4575925 ]\n",
            " [ 6.1077137 ]\n",
            " [ 6.0237846 ]\n",
            " [-5.9849076 ]\n",
            " [ 2.5648806 ]\n",
            " [ 2.624496  ]\n",
            " [ 7.1037803 ]\n",
            " [ 3.1986117 ]\n",
            " [-6.4826717 ]\n",
            " [-5.1072392 ]\n",
            " [ 7.223669  ]\n",
            " [-1.9995201 ]\n",
            " [ 7.1064625 ]\n",
            " [-8.063197  ]\n",
            " [ 7.1625824 ]\n",
            " [ 6.7912326 ]\n",
            " [-0.74844474]\n",
            " [ 2.9396043 ]\n",
            " [-3.858695  ]\n",
            " [ 5.473102  ]\n",
            " [-4.3891044 ]\n",
            " [-0.67900074]\n",
            " [ 5.4496307 ]\n",
            " [ 5.861375  ]\n",
            " [ 5.649327  ]\n",
            " [ 1.7582366 ]\n",
            " [ 6.6236935 ]\n",
            " [ 5.38461   ]\n",
            " [ 0.59275746]\n",
            " [-5.7907906 ]\n",
            " [ 5.8104906 ]\n",
            " [ 5.7703333 ]\n",
            " [ 6.9396906 ]\n",
            " [ 4.948041  ]\n",
            " [ 2.8670144 ]\n",
            " [ 0.82089895]\n",
            " [ 2.103472  ]\n",
            " [ 6.118626  ]\n",
            " [ 6.5201197 ]\n",
            " [-4.6291075 ]\n",
            " [ 5.9033895 ]\n",
            " [ 7.7884192 ]\n",
            " [-8.860111  ]\n",
            " [-8.148621  ]\n",
            " [-3.942738  ]\n",
            " [ 5.6546    ]\n",
            " [ 8.2785015 ]\n",
            " [-3.0266457 ]\n",
            " [-1.2165005 ]\n",
            " [ 5.3236256 ]\n",
            " [ 2.0502264 ]\n",
            " [ 1.397965  ]\n",
            " [ 6.930476  ]\n",
            " [ 6.9117365 ]\n",
            " [ 6.3165298 ]\n",
            " [ 0.570281  ]\n",
            " [-3.2157927 ]\n",
            " [ 7.063463  ]\n",
            " [ 6.6129093 ]\n",
            " [ 5.3361835 ]\n",
            " [ 4.5391064 ]\n",
            " [ 7.4732294 ]\n",
            " [ 2.3933182 ]\n",
            " [ 7.251116  ]\n",
            " [ 7.062133  ]\n",
            " [ 4.531335  ]\n",
            " [ 6.7202053 ]\n",
            " [ 1.3275208 ]\n",
            " [ 5.5354347 ]\n",
            " [-2.3176022 ]\n",
            " [ 7.392502  ]\n",
            " [ 3.5275903 ]\n",
            " [ 7.6275597 ]\n",
            " [-2.5396216 ]\n",
            " [ 6.1889763 ]\n",
            " [ 7.373785  ]\n",
            " [ 5.3600883 ]\n",
            " [-5.068271  ]\n",
            " [ 5.119535  ]\n",
            " [ 0.30224243]\n",
            " [-3.869893  ]\n",
            " [ 5.331089  ]\n",
            " [ 5.220827  ]\n",
            " [ 6.329244  ]\n",
            " [ 5.5008726 ]\n",
            " [ 5.8411016 ]\n",
            " [-0.9635821 ]\n",
            " [ 7.3815393 ]\n",
            " [ 6.1196766 ]\n",
            " [ 4.696781  ]\n",
            " [-6.3371515 ]\n",
            " [-1.8586938 ]\n",
            " [ 0.6257059 ]\n",
            " [ 1.3875978 ]\n",
            " [-3.4414701 ]\n",
            " [-5.3921914 ]\n",
            " [-2.263193  ]\n",
            " [ 7.086251  ]\n",
            " [ 2.675359  ]\n",
            " [ 1.0029185 ]\n",
            " [ 5.5335555 ]\n",
            " [-3.7031481 ]\n",
            " [ 6.128113  ]\n",
            " [ 7.175952  ]\n",
            " [ 0.0927209 ]\n",
            " [-2.3018894 ]\n",
            " [-4.8901596 ]\n",
            " [-6.478455  ]\n",
            " [ 5.6538134 ]\n",
            " [-5.127794  ]\n",
            " [-6.734212  ]\n",
            " [ 7.851818  ]\n",
            " [ 6.892066  ]\n",
            " [ 5.7433553 ]\n",
            " [-0.54952675]\n",
            " [ 6.5515833 ]\n",
            " [-4.431055  ]\n",
            " [-7.144766  ]\n",
            " [-1.624866  ]\n",
            " [ 6.806513  ]\n",
            " [ 6.3769293 ]\n",
            " [ 1.1533594 ]\n",
            " [-1.0400944 ]\n",
            " [ 6.9177933 ]\n",
            " [ 4.6529756 ]\n",
            " [ 5.3701425 ]\n",
            " [ 6.81416   ]\n",
            " [ 6.4857855 ]\n",
            " [-3.7767472 ]\n",
            " [ 2.0768375 ]\n",
            " [ 5.5370245 ]\n",
            " [-8.0805025 ]\n",
            " [ 7.3753977 ]\n",
            " [-0.88763195]\n",
            " [ 6.40944   ]\n",
            " [-5.4650145 ]\n",
            " [ 5.722841  ]\n",
            " [-0.78157574]\n",
            " [ 2.8342044 ]\n",
            " [ 6.3593755 ]\n",
            " [ 7.4628563 ]\n",
            " [ 7.130736  ]\n",
            " [ 7.574717  ]\n",
            " [-1.9753656 ]\n",
            " [ 7.280985  ]\n",
            " [-3.3945246 ]\n",
            " [ 6.3779535 ]\n",
            " [-4.1781497 ]\n",
            " [-4.542902  ]\n",
            " [-5.913582  ]\n",
            " [ 7.4093456 ]\n",
            " [-3.557212  ]\n",
            " [-1.3721347 ]\n",
            " [ 7.1030293 ]\n",
            " [ 7.800881  ]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C35jLJ1QUU2A",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "outputId": "672ae5b0-e7a4-4707-8f06-6a9e828aa19f"
      },
      "source": [
        "model.layers"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<tensorflow.python.keras.layers.embeddings.Embedding at 0x7fb1f7af14a8>,\n",
              " <tensorflow.python.keras.layers.wrappers.Bidirectional at 0x7fb1f7ae9208>,\n",
              " <tensorflow.python.keras.layers.wrappers.Bidirectional at 0x7fb1f7972898>,\n",
              " <tensorflow.python.keras.layers.core.Dense at 0x7fb1f7979860>,\n",
              " <tensorflow.python.keras.layers.core.Dropout at 0x7fb2285f8ba8>,\n",
              " <tensorflow.python.keras.layers.core.Dense at 0x7fb1f54a90f0>,\n",
              " <tensorflow.python.keras.layers.core.Dropout at 0x7fb1f781d780>,\n",
              " <tensorflow.python.keras.layers.core.Dense at 0x7fb1f78c01d0>,\n",
              " <tensorflow.python.keras.layers.core.Dropout at 0x7fb1f79799e8>,\n",
              " <tensorflow.python.keras.layers.core.Dense at 0x7fb1f77ff630>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mqF5PcaKUmHH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "outputId": "ba7f206d-dfb7-4787-88de-746403ec9e17"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_2 (Embedding)      (None, None, 128)         9438720   \n",
            "_________________________________________________________________\n",
            "bidirectional_5 (Bidirection (None, None, 256)         263168    \n",
            "_________________________________________________________________\n",
            "bidirectional_6 (Bidirection (None, 128)               164352    \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 64)                8256      \n",
            "_________________________________________________________________\n",
            "dropout_6 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_9 (Dense)              (None, 32)                2080      \n",
            "_________________________________________________________________\n",
            "dropout_7 (Dropout)          (None, 32)                0         \n",
            "_________________________________________________________________\n",
            "dense_10 (Dense)             (None, 16)                528       \n",
            "_________________________________________________________________\n",
            "dropout_8 (Dropout)          (None, 16)                0         \n",
            "_________________________________________________________________\n",
            "dense_11 (Dense)             (None, 1)                 17        \n",
            "=================================================================\n",
            "Total params: 9,877,121\n",
            "Trainable params: 9,877,121\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WKzsV6W9yRq2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m8h7FTjc2LO1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.get_layer('embedding').output"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k0XujLETOV_g",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "46d30da9-d5c2-4ce8-8fb1-68fedad7f68c"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mon Feb 24 22:51:45 2020       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 440.48.02    Driver Version: 418.67       CUDA Version: 10.1     |\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",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   46C    P0    49W / 250W |  15821MiB / 16280MiB |      0%      Default |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                       GPU Memory |\n",
            "|  GPU       PID   Type   Process name                             Usage      |\n",
            "|=============================================================================|\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SNZ49aApOnFw",
        "colab_type": "code",
        "colab": {}
      },
      "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",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "f0d4d393-b6d5-4392-d45c-2069ce7657d3"
      },
      "source": [
        "plot_graphs(history, 'accuracy')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HGTJkCImJiad972nTpjFhwgTi4uIYNWoU27dvBzjluhUAN910E2vXrq3VcqkmNB06VuJM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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qCzGm1Bao90j",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "outputId": "ac05c71e-7a9b-4fd5-9857-5cef34d06cfa"
      },
      "source": [
        "plot_graphs(history, 'loss')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PJwuK+fL3Pby7NJ1DJwqIa1GP8ZdFEx/dUINCKQtUSkBUdxoQ7iW/qITpiRm8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\nTv0/cZpOFJXBfG8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "isp05R90DC4G",
        "colab_type": "code",
        "colab": {}
      },
      "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",
        "colab_type": "code",
        "colab": {}
      },
      "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",
        "colab_type": "code",
        "colab": {}
      },
      "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",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "a4d4a646-4f45-4585-c0a1-8cb664248f6b"
      },
      "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": [
        {
          "output_type": "stream",
          "text": [
            "[62013, 51282, 38917, 68710, 69160, 28828, 66915, 55557, 73162, 48013]\n",
            "[62013, 51282, 38917, 68710, 69160, 28828, 66915, 55557, 73162, 48013, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
            "[[-2.850278]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-YCnO_394yGh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "68b3cf47-1088-4e02-f912-dffba47f1782"
      },
      "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": [
        {
          "output_type": "stream",
          "text": [
            "[28828, 66915, 55557, 32128, 66386, 46916, 45668, 55124, 53575, 66796, 66386, 56910, 51246, 45668, 64743]\n",
            "[28828, 66915, 55557, 32128, 66386, 46916, 45668, 55124, 53575, 66796, 66386, 56910, 51246, 45668, 64743, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
            "[[-4.2995567]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dg3-u8fAfFoh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "41977e9e-13cf-466f-d8fe-c33e0a550350"
      },
      "source": [
        "pred_text = ('Amazing product. Fast Delivery. Nice packing')\n",
        "predictions = predict_fn(pred_text)\n",
        "print(predictions)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[50991, 68174, 69456, 39257, 36290, 21937]\n",
            "[50991, 68174, 69456, 39257, 36290, 21937, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
            "[[7.0540037]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NM7UFDyJ4lpz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "8bdd42af-8c42-4e99-ba3a-a515af8b22f4"
      },
      "source": [
        "pred_text = ('Nice product. Packing could have been better')\n",
        "predictions = predict_fn(pred_text)\n",
        "print(predictions)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[36290, 68174, 21937, 68249, 72731, 72584, 51246]\n",
            "[36290, 68174, 21937, 68249, 72731, 72584, 51246, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
            "[[2.6154106]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2kTYTK_Uo9zJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "print(tf.distribute.get_strategy())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EwwKrOmuurZJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%reload_ext tensorboard"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1UWV9w0MtuKn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%tensorboard --logdir /tmp/logs"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1H4nWcJLt7gu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorboard import notebook\n",
        "notebook.list()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wlvO46QYuKuH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "notebook.display(port=6006, height=1000) "
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}