trainer.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "trainer.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "aACgFatCbWb8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "cd64afb8-0814-44ab-c012-d6dca4e16760"
      },
      "source": [
        "!pip uninstall tensorflow\n",
        "!pip install tensorflow==2.0.0"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Uninstalling tensorflow-2.2.0:\n",
            "  Would remove:\n",
            "    /usr/local/bin/estimator_ckpt_converter\n",
            "    /usr/local/bin/saved_model_cli\n",
            "    /usr/local/bin/tensorboard\n",
            "    /usr/local/bin/tf_upgrade_v2\n",
            "    /usr/local/bin/tflite_convert\n",
            "    /usr/local/bin/toco\n",
            "    /usr/local/bin/toco_from_protos\n",
            "    /usr/local/lib/python3.6/dist-packages/tensorflow-2.2.0.dist-info/*\n",
            "    /usr/local/lib/python3.6/dist-packages/tensorflow/*\n",
            "Proceed (y/n)? y\n",
            "  Successfully uninstalled tensorflow-2.2.0\n",
            "Collecting tensorflow==2.0.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/46/0f/7bd55361168bb32796b360ad15a25de6966c9c1beb58a8e30c01c8279862/tensorflow-2.0.0-cp36-cp36m-manylinux2010_x86_64.whl (86.3MB)\n",
            "\u001b[K     |████████████████████████████████| 86.3MB 71kB/s \n",
            "\u001b[?25hCollecting tensorflow-estimator<2.1.0,>=2.0.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/fc/08/8b927337b7019c374719145d1dceba21a8bb909b93b1ad6f8fb7d22c1ca1/tensorflow_estimator-2.0.1-py2.py3-none-any.whl (449kB)\n",
            "\u001b[K     |████████████████████████████████| 450kB 29.1MB/s \n",
            "\u001b[?25hRequirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (1.12.1)\n",
            "Collecting tensorboard<2.1.0,>=2.0.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/76/54/99b9d5d52d5cb732f099baaaf7740403e83fe6b0cedde940fabd2b13d75a/tensorboard-2.0.2-py3-none-any.whl (3.8MB)\n",
            "\u001b[K     |████████████████████████████████| 3.8MB 45.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (0.8.1)\n",
            "Requirement already satisfied: numpy<2.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (1.18.5)\n",
            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (3.12.2)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (1.12.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (1.1.0)\n",
            "Collecting gast==0.2.2\n",
            "  Downloading https://files.pythonhosted.org/packages/4e/35/11749bf99b2d4e3cceb4d55ca22590b0d7c2c62b9de38ac4a4a7f4687421/gast-0.2.2.tar.gz\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (1.1.2)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (0.34.2)\n",
            "Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (0.2.0)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (3.2.1)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (1.30.0)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (0.9.0)\n",
            "Requirement already satisfied: keras-applications>=1.0.8 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (1.0.8)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (1.0.1)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (49.1.0)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (0.4.1)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (2.23.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (3.2.2)\n",
            "Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (1.17.2)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.8->tensorflow==2.0.0) (2.10.0)\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->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (1.3.0)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (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->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (2020.6.20)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (3.0.4)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from markdown>=2.6.8->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (1.7.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (4.6)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (4.1.1)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (0.2.8)\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->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (3.1.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (3.1.0)\n",
            "Requirement already satisfied: pyasn1>=0.1.3 in /usr/local/lib/python3.6/dist-packages (from rsa<5,>=3.1.4; python_version >= \"3\"->google-auth<2,>=1.6.3->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (0.4.8)\n",
            "Building wheels for collected packages: gast\n",
            "  Building wheel for gast (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gast: filename=gast-0.2.2-cp36-none-any.whl size=7540 sha256=0761be160b61d60dc4a01d37c2c3a045de154316befb30e71eeaf6976178846f\n",
            "  Stored in directory: /root/.cache/pip/wheels/5c/2e/7e/a1d4d4fcebe6c381f378ce7743a3ced3699feb89bcfbdadadd\n",
            "Successfully built gast\n",
            "\u001b[31mERROR: tensorflow-probability 0.10.0 has requirement gast>=0.3.2, but you'll have gast 0.2.2 which is incompatible.\u001b[0m\n",
            "Installing collected packages: tensorflow-estimator, tensorboard, gast, tensorflow\n",
            "  Found existing installation: tensorflow-estimator 2.2.0\n",
            "    Uninstalling tensorflow-estimator-2.2.0:\n",
            "      Successfully uninstalled tensorflow-estimator-2.2.0\n",
            "  Found existing installation: tensorboard 2.2.2\n",
            "    Uninstalling tensorboard-2.2.2:\n",
            "      Successfully uninstalled tensorboard-2.2.2\n",
            "  Found existing installation: gast 0.3.3\n",
            "    Uninstalling gast-0.3.3:\n",
            "      Successfully uninstalled gast-0.3.3\n",
            "Successfully installed gast-0.2.2 tensorboard-2.0.2 tensorflow-2.0.0 tensorflow-estimator-2.0.1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "gast",
                  "tensorboard",
                  "tensorflow"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LYPlvtegnmJ3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "2c4cd05e-bb6b-4f8f-d94b-ae74c4f8f716"
      },
      "source": [
        "import json\n",
        "import os\n",
        "import sys\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import tensorflow_datasets as tfds\n",
        "from tensorflow import keras\n",
        "from google.colab import drive\n",
        "\n",
        "print(tf.__version__)\n",
        "print(sys.version)\n",
        "\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "imdb_dataset, imdb_info = tfds.load('imdb_reviews/subwords8k', with_info=True,\n",
        "                          as_supervised=True)\n",
        "\n",
        "directory = '/content/drive/My Drive/Bay Area Hacks/articles_jsons'\n",
        "\n",
        "checkpoint_path = '/content/drive/My Drive/Bay Area Hacks/checkpoint.ckpt'\n",
        "checkpoint_dir = os.path.dirname(checkpoint_path)\n",
        "cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,\n",
        "                                                 save_weights_only=True,\n",
        "                                                 verbose=1)\n",
        "\n",
        "save_path = '/content/drive/My Drive/Bay Area Hacks/saved_binary_model/my_model'\n",
        "\n",
        "null = None\n",
        " \n",
        "eval_arr = []\n",
        " \n",
        "print(\"starting\")\n",
        " \n",
        "print(\"importing all json data into numpy array\")\n",
        " \n",
        "i = 0\n",
        " \n",
        "for subdir, dirs, files in os.walk(directory):\n",
        "  for file in files:\n",
        "    # if subdir == '/content/drive/My Drive/Bay Area Hacks/articles_jsons/washingtonpost_articles_jsons':\n",
        "    #   break\n",
        "\n",
        "    print(i)\n",
        "\n",
        "    if i  > 1499:\n",
        "      break\n",
        "    if file.endswith(\".json\"):\n",
        " \n",
        "      json_data = json.load(open(subdir + '/' + file))\n",
        " \n",
        "      eval_arr.append(eval(json_data))\n",
        " \n",
        "      i+=1\n",
        " \n",
        "np_arr = np.asarray(eval_arr)\n",
        " \n",
        "print('imported all json data into numpy array')\n",
        " \n",
        "print(\"developing corpus_generator encoder\")\n",
        "\n",
        "encoder = imdb_info.features['text'].encoder\n",
        " \n",
        "print(\"developed corpus_generator encoder\")\n",
        "\n",
        "print(\"encoding numpy data\")\n",
        " \n",
        "three_quarters = int(len(np_arr) * (3 / 4))\n",
        " \n",
        "np_features_train = np.zeros((three_quarters + 1, 2**13), dtype=np.float32)\n",
        "np_labels_train = np.zeros((three_quarters + 1, 1), dtype=np.float32)\n",
        " \n",
        "np_features_test = np.zeros((len(np_arr) - three_quarters, 2**13), dtype=np.float32)\n",
        "np_labels_test = np.zeros((len(np_arr) - three_quarters, 1), dtype=np.float32)\n",
        " \n",
        "i, index = 0, 0\n",
        " \n",
        "for article in np_arr:\n",
        "  encoded_title = ''\n",
        "  encoded_text = ''\n",
        "  if ((i % 4) != 0):\n",
        "\n",
        "    index = int(i - int(i / 4))\n",
        "\n",
        "    if (article['title'] != None):\n",
        "      encoded_title = (encoder.encode(article['title']))\n",
        "    else:\n",
        "      encoded_title = (encoder.encode(' '))\n",
        " \n",
        "    if (article['text'] != None):\n",
        "      encoded_text = (encoder.encode(article['text']))\n",
        "    else:\n",
        "      encoded_text = (encoder.encode(' '))\n",
        " \n",
        "    j = 0\n",
        " \n",
        "    encoded_title.extend(encoded_text)\n",
        " \n",
        "    for title in encoded_title:\n",
        "      if (j < 2**13):\n",
        " \n",
        "        np_features_train[index][j] = title\n",
        "      j+=1\n",
        " \n",
        "    np_labels_train[index][0] = article['positive']\n",
        "    # np_labels_train[index][0] = article['negative']\n",
        "    # np_labels_train[index][0] = article['political']\n",
        "    # np_labels_train[index][0] = article['neutral']\n",
        "  else:\n",
        "\n",
        "    index = int(i * (1/4))\n",
        "\n",
        "    if (article['title'] != None):\n",
        "      encoded_title = (encoder.encode(article['title']))\n",
        "    else:\n",
        "      encoded_title = (encoder.encode(' '))\n",
        " \n",
        "    if (article['text'] != None):\n",
        "      encoded_text = (encoder.encode(article['text']))\n",
        "    else:\n",
        "      encoded_text = (encoder.encode(' '))\n",
        " \n",
        "    j = 0\n",
        " \n",
        "    encoded_title.extend(encoded_text)\n",
        " \n",
        "    for title in encoded_title:\n",
        "      if (j < 2**13):\n",
        " \n",
        "        np_features_test[index][j] = title\n",
        "      j+=1\n",
        " \n",
        "    np_labels_test[index][0] = article['positive']\n",
        "    # np_labels_test[index][0] = article['negative']\n",
        "    # np_labels_test[index][0] = article['political']\n",
        "    # np_labels_test[index][0] = article['neutral']\n",
        " \n",
        "  i+=1\n",
        " \n",
        "print(\"encoded numpy data\")\n",
        "\n",
        "print(np_features_train.shape)\n",
        "print(np_labels_train.shape)\n",
        " \n",
        "print(\"preparing for ML...\")\n",
        "\n",
        "train_dataset = tf.data.Dataset.from_tensor_slices((np_features_train, np_labels_train))\n",
        "test_dataset = tf.data.Dataset.from_tensor_slices((np_features_test, np_labels_test))\n",
        " \n",
        "train_dataset = train_dataset.shuffle(len(np_features_train))\n",
        "train_dataset = train_dataset.batch(4)\n",
        "test_dataset = test_dataset.batch(4)\n",
        " \n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(encoder.vocab_size, 64),\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64)),\n",
        "    tf.keras.layers.Dense(64, activation='relu'),\n",
        "    tf.keras.layers.Dense(1)\n",
        "  ])\n",
        " \n",
        "# model.compile(optimizer='adam',\n",
        "#     loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),\n",
        "#     metrics=['accuracy'])\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "    loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n",
        "    metrics=['accuracy'])\n",
        " \n",
        "model.fit(train_dataset, epochs=12, steps_per_epoch=8,\n",
        "          validation_data=test_dataset,\n",
        "          validation_steps=4,\n",
        "          callbacks=[cp_callback])\n",
        "\n",
        "test_loss, test_acc = model.evaluate(test_dataset)\n",
        "\n",
        "print('Test Loss: {}'.format(test_loss))\n",
        "print('Test Accuracy: {}'.format(test_acc))\n",
        "\n",
        "model.save(save_path)\n",
        "\n",
        "('saved model')\n",
        "\n",
        "('reloading and testing model')\n",
        "\n",
        "reloaded_model = tf.keras.models.load_model(save_path)\n",
        "\n",
        "reloaded_test_loss, reloaded_test_acc = reloaded_model.evaluate(test_dataset)\n",
        "\n",
        "print('Test Loss: {}'.format(reloaded_test_loss))\n",
        "print('Test Accuracy: {}'.format(reloaded_test_acc))\n",
        "\n",
        "('reloaded and tested model')\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.0.0\n",
            "3.6.9 (default, Apr 18 2020, 01:56:04) \n",
            "[GCC 8.4.0]\n",
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
            "starting\n",
            "importing all json data into numpy array\n",
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            "imported all json data into numpy array\n",
            "developing corpus_generator encoder\n",
            "developed corpus_generator encoder\n",
            "encoding numpy data\n",
            "encoded numpy data\n",
            "(1000, 8192)\n",
            "(1000, 1)\n",
            "preparing for ML...\n",
            "Train for 8 steps, validate for 4 steps\n",
            "Epoch 1/12\n",
            "7/8 [=========================>....] - ETA: 16s - loss: 0.6639 - accuracy: 0.8571\n",
            "Epoch 00001: saving model to /content/drive/My Drive/Bay Area Hacks/checkpoint.ckpt\n",
            "8/8 [==============================] - 133s 17s/step - loss: 0.6493 - accuracy: 0.8750 - val_loss: 0.5622 - val_accuracy: 0.8750\n",
            "Epoch 2/12\n",
            "7/8 [=========================>....] - ETA: 15s - loss: 0.3956 - accuracy: 0.9286\n",
            "Epoch 00002: saving model to /content/drive/My Drive/Bay Area Hacks/checkpoint.ckpt\n",
            "8/8 [==============================] - 125s 16s/step - loss: 0.5156 - accuracy: 0.8750 - val_loss: 0.3894 - val_accuracy: 0.8750\n",
            "Epoch 3/12\n",
            "7/8 [=========================>....] - ETA: 15s - loss: 0.3480 - accuracy: 0.8929\n",
            "Epoch 00003: saving model to /content/drive/My Drive/Bay Area Hacks/checkpoint.ckpt\n",
            "8/8 [==============================] - 126s 16s/step - loss: 0.3179 - accuracy: 0.9062 - val_loss: 0.3770 - val_accuracy: 0.8750\n",
            "Epoch 4/12\n",
            "7/8 [=========================>....] - ETA: 15s - loss: 0.1863 - accuracy: 0.9643\n",
            "Epoch 00004: saving model to /content/drive/My Drive/Bay Area Hacks/checkpoint.ckpt\n",
            "8/8 [==============================] - 126s 16s/step - loss: 0.3274 - accuracy: 0.9062 - val_loss: 0.3856 - val_accuracy: 0.8750\n",
            "Epoch 5/12\n",
            "7/8 [=========================>....] - ETA: 15s - loss: 0.4443 - accuracy: 0.8571\n",
            "Epoch 00005: saving model to /content/drive/My Drive/Bay Area Hacks/checkpoint.ckpt\n",
            "8/8 [==============================] - 128s 16s/step - loss: 0.4638 - accuracy: 0.8438 - val_loss: 0.3732 - val_accuracy: 0.8750\n",
            "Epoch 6/12\n",
            "7/8 [=========================>....] - ETA: 15s - loss: 0.5942 - accuracy: 0.7500\n",
            "Epoch 00006: saving model to /content/drive/My Drive/Bay Area Hacks/checkpoint.ckpt\n",
            "8/8 [==============================] - 126s 16s/step - loss: 0.5901 - accuracy: 0.7500 - val_loss: 0.4008 - val_accuracy: 0.8750\n",
            "Epoch 7/12\n",
            "7/8 [=========================>....] - ETA: 15s - loss: 0.6064 - accuracy: 0.7143\n",
            "Epoch 00007: saving model to /content/drive/My Drive/Bay Area Hacks/checkpoint.ckpt\n",
            "8/8 [==============================] - 126s 16s/step - loss: 0.5744 - accuracy: 0.7500 - val_loss: 0.4558 - val_accuracy: 0.8750\n",
            "Epoch 8/12\n",
            "7/8 [=========================>....] - ETA: 15s - loss: 0.3577 - accuracy: 0.9643\n",
            "Epoch 00008: saving model to /content/drive/My Drive/Bay Area Hacks/checkpoint.ckpt\n",
            "8/8 [==============================] - 126s 16s/step - loss: 0.3452 - accuracy: 0.9688 - val_loss: 0.3949 - val_accuracy: 0.8750\n",
            "Epoch 9/12\n",
            "7/8 [=========================>....] - ETA: 15s - loss: 0.4556 - accuracy: 0.8214\n",
            "Epoch 00009: saving model to /content/drive/My Drive/Bay Area Hacks/checkpoint.ckpt\n",
            "8/8 [==============================] - 127s 16s/step - loss: 0.4720 - accuracy: 0.8125 - val_loss: 0.3704 - val_accuracy: 0.8750\n",
            "Epoch 10/12\n",
            "7/8 [=========================>....] - ETA: 15s - loss: 0.4902 - accuracy: 0.8214\n",
            "Epoch 00010: saving model to /content/drive/My Drive/Bay Area Hacks/checkpoint.ckpt\n",
            "8/8 [==============================] - 127s 16s/step - loss: 0.5100 - accuracy: 0.8125 - val_loss: 0.3687 - val_accuracy: 0.8750\n",
            "Epoch 11/12\n",
            "7/8 [=========================>....] - ETA: 15s - loss: 0.4751 - accuracy: 0.8214\n",
            "Epoch 00011: saving model to /content/drive/My Drive/Bay Area Hacks/checkpoint.ckpt\n",
            "8/8 [==============================] - 127s 16s/step - loss: 0.4367 - accuracy: 0.8438 - val_loss: 0.3722 - val_accuracy: 0.8750\n",
            "Epoch 12/12\n",
            "7/8 [=========================>....] - ETA: 15s - loss: 0.3560 - accuracy: 0.8929\n",
            "Epoch 00012: saving model to /content/drive/My Drive/Bay Area Hacks/checkpoint.ckpt\n",
            "8/8 [==============================] - 126s 16s/step - loss: 0.3334 - accuracy: 0.9062 - val_loss: 0.3682 - val_accuracy: 0.8750\n",
            "84/84 [==============================] - 75s 890ms/step - loss: 0.4791 - accuracy: 0.8108\n",
            "Test Loss: 0.47907547812376705\n",
            "Test Accuracy: 0.8108108043670654\n",
            "INFO:tensorflow:Assets written to: /content/drive/My Drive/Bay Area Hacks/saved_binary_pol_model/my_model/assets\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: /content/drive/My Drive/Bay Area Hacks/saved_binary_pol_model/my_model/assets\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "84/84 [==============================] - 74s 883ms/step - loss: 0.4791 - accuracy: 0.8108\n",
            "Test Loss: 0.47907547812376705\n",
            "Test Accuracy: 0.8108108043670654\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic": {
              "type": "string"
            },
            "text/plain": [
              "'reloaded and tested model'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hEPwDNTIMcDX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}