t81_558_class_09_3_transfer_nlp.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pQwmBz1GumJR"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_09_3_transfer_nlp.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KBQzVEtJumJT"
   },
   "source": [
    "# T81-558: Applications of Deep Neural Networks\n",
    "**Module 9: Transfer Learning**\n",
    "* Instructor: [Jeff Heaton](https://sites.wustl.edu/jeffheaton/), McKelvey School of Engineering, [Washington University in St. Louis](https://engineering.wustl.edu/Programs/Pages/default.aspx)\n",
    "* For more information visit the [class website](https://sites.wustl.edu/jeffheaton/t81-558/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Cwz1jhRaumJU"
   },
   "source": [
    "# Module 9 Material\n",
    "\n",
    "* Part 9.1: Introduction to Keras Transfer Learning [[Video]](https://www.youtube.com/watch?v=AtoeoNwmd7w&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_09_1_keras_transfer.ipynb)\n",
    "* Part 9.2: Keras Transfer Learning for Computer Vision [[Video]](https://www.youtube.com/watch?v=nXcz0V5SfYw&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_09_2_keras_xfer_cv.ipynb)\n",
    "* **Part 9.3: Transfer Learning for NLP with Keras** [[Video]](https://www.youtube.com/watch?v=PyRsjwLHgAU&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_09_3_transfer_nlp.ipynb)\n",
    "* Part 9.4: Transfer Learning for Facial Feature Recognition [[Video]](https://www.youtube.com/watch?v=uUZg33DfCls&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_09_4_facial_points.ipynb)\n",
    "* Part 9.5: Transfer Learning for Style Transfer [[Video]](https://www.youtube.com/watch?v=pLWIaQwkJwU&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_09_5_style_transfer.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WPEQF9auumJU"
   },
   "source": [
    "# Google CoLab Instructions\n",
    "\n",
    "The following code ensures that Google CoLab is running the correct version of TensorFlow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rUpegFqQumJV",
    "outputId": "213c9008-d443-4819-dfc6-f26be8c665f9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: using Google CoLab\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    %tensorflow_version 2.x\n",
    "    COLAB = True\n",
    "    print(\"Note: using Google CoLab\")\n",
    "except:\n",
    "    print(\"Note: not using Google CoLab\")\n",
    "    COLAB = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KtQUhDWNumJW"
   },
   "source": [
    "# Part 9.3: Transfer Learning for NLP with Keras\n",
    "\n",
    "You will commonly use transfer learning with Natural Language Processing (NLP). Word embeddings are a common means of transfer learning in NLP where network layers map words to vectors. Third parties trained neural networks on a large corpus of text to learn these embeddings. We will use these vectors as the input to the neural network rather than the actual characters of words.  \n",
    "\n",
    "This course has an entire module covering NLP; however, we use word embeddings to perform sentiment analysis in this module. We will specifically attempt to classify if a text sample is speaking in a positive or negative tone.\n",
    "\n",
    "The following three sources were helpful for the creation of this section.\n",
    "\n",
    "* Universal sentence encoder [[Cite:cer2018universal]](https://arxiv.org/abs/1803.11175). arXiv preprint arXiv:1803.11175)\n",
    "* Deep Transfer Learning for Natural Language Processing: Text Classification with Universal Embeddings [[Cite:howard2018universal]](https://towardsdatascience.com/deep-transfer-learning-for-natural-language-processing-text-classification-with-universal-1a2c69e5baa9)\n",
    "* [Keras Tutorial: How to Use Google's Universal Sentence Encoder for Spam Classification](http://hunterheidenreich.com/blog/google-universal-sentence-encoder-in-keras/)\n",
    "\n",
    "These examples use TensorFlow Hub, which allows pretrained models to be loaded into TensorFlow easily. To install TensorHub use the following commands."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eMp-8-OIumJW",
    "outputId": "e97f541b-b98e-455c-e735-bf988555ad95"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tensorflow_hub in /usr/local/lib/python3.7/dist-packages (0.12.0)\n",
      "Requirement already satisfied: numpy>=1.12.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow_hub) (1.19.5)\n",
      "Requirement already satisfied: protobuf>=3.8.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow_hub) (3.17.3)\n",
      "Requirement already satisfied: six>=1.9 in /usr/local/lib/python3.7/dist-packages (from protobuf>=3.8.0->tensorflow_hub) (1.15.0)\n"
     ]
    }
   ],
   "source": [
    "# HIDE OUTPUT\n",
    "!pip install tensorflow_hub"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1jSQ2owPumJX"
   },
   "source": [
    "It is also necessary to install TensorFlow Datasets, which you can install with the following command."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Z56Ce_B7umJX",
    "outputId": "a3fe3a71-8ed3-45ec-e60e-918e5bb38a1b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tensorflow_datasets in /usr/local/lib/python3.7/dist-packages (4.0.1)\n",
      "Requirement already satisfied: importlib-resources in /usr/local/lib/python3.7/dist-packages (from tensorflow_datasets) (5.4.0)\n",
      "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow_datasets) (3.17.3)\n",
      "Requirement already satisfied: attrs>=18.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow_datasets) (21.4.0)\n",
      "Requirement already satisfied: termcolor in /usr/local/lib/python3.7/dist-packages (from tensorflow_datasets) (1.1.0)\n",
      "Requirement already satisfied: promise in /usr/local/lib/python3.7/dist-packages (from tensorflow_datasets) (2.3)\n",
      "Requirement already satisfied: dill in /usr/local/lib/python3.7/dist-packages (from tensorflow_datasets) (0.3.4)\n",
      "Requirement already satisfied: absl-py in /usr/local/lib/python3.7/dist-packages (from tensorflow_datasets) (1.0.0)\n",
      "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from tensorflow_datasets) (1.15.0)\n",
      "Requirement already satisfied: tensorflow-metadata in /usr/local/lib/python3.7/dist-packages (from tensorflow_datasets) (1.6.0)\n",
      "Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from tensorflow_datasets) (0.16.0)\n",
      "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow_datasets) (2.23.0)\n",
      "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from tensorflow_datasets) (4.62.3)\n",
      "Requirement already satisfied: dm-tree in /usr/local/lib/python3.7/dist-packages (from tensorflow_datasets) (0.1.6)\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from tensorflow_datasets) (1.19.5)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->tensorflow_datasets) (3.0.4)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->tensorflow_datasets) (1.24.3)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->tensorflow_datasets) (2.10)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->tensorflow_datasets) (2021.10.8)\n",
      "Requirement already satisfied: zipp>=3.1.0 in /usr/local/lib/python3.7/dist-packages (from importlib-resources->tensorflow_datasets) (3.7.0)\n",
      "Requirement already satisfied: googleapis-common-protos<2,>=1.52.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow-metadata->tensorflow_datasets) (1.54.0)\n"
     ]
    }
   ],
   "source": [
    "# HIDE OUTPUT\n",
    "!pip install tensorflow_datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0UgVyHXxumJY"
   },
   "source": [
    "Movie reviews are a good source of training data for sentiment analysis. These reviews are textual, and users give them a star rating which indicates if the viewer had a positive or negative experience with the movie. Load the Internet Movie DataBase (IMDB) reviews data set. This example is based on a TensorFlow example that you can [find here](https://colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/tf2_text_classification.ipynb#scrollTo=2ew7HTbPpCJH)."
   ]
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    "id": "NrZOixq-umJY",
    "outputId": "ac74b049-3050-4dc5-b491-69bc23dce46f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mDownloading and preparing dataset imdb_reviews/plain_text/1.0.0 (download: 80.23 MiB, generated: Unknown size, total: 80.23 MiB) to /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0...\u001b[0m\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "342c8ecc48764a19a7d84fca7cfd8b0c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dl Completed...: 0 url [00:00, ? url/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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      },
      "text/plain": [
       "Dl Size...: 0 MiB [00:00, ? MiB/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "78c16501daf448f68db6acd6ac22fe44",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shuffling and writing examples to /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incomplete0GRP97/imdb_reviews-train.tfrecord\n"
     ]
    },
    {
     "data": {
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       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ce9542b8402444b7b82f51033bc0c286",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shuffling and writing examples to /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incomplete0GRP97/imdb_reviews-test.tfrecord\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6b0946b06bc0428e8ae91155fa4a482a",
       "version_major": 2,
       "version_minor": 0
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     },
     "metadata": {},
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    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "279c293b01a846118b31684e451c3499",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shuffling and writing examples to /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incomplete0GRP97/imdb_reviews-unsupervised.tfrecord\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "624ebbef587d47a7916432035e7666b6",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Dataset is using deprecated text encoder API which will be removed soon. Please use the plain_text version of the dataset and migrate to `tensorflow_text`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mDataset imdb_reviews downloaded and prepared to /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0. Subsequent calls will reuse this data.\u001b[0m\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow_datasets/core/dataset_builder.py:598: get_single_element (from tensorflow.python.data.experimental.ops.get_single_element) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.data.Dataset.get_single_element()`.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow_datasets/core/dataset_builder.py:598: get_single_element (from tensorflow.python.data.experimental.ops.get_single_element) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.data.Dataset.get_single_element()`.\n"
     ]
    }
   ],
   "source": [
    "# HIDE OUTPUT\n",
    "import tensorflow as tf\n",
    "import tensorflow_hub as hub\n",
    "import tensorflow_datasets as tfds\n",
    "\n",
    "train_data, test_data = tfds.load(name=\"imdb_reviews\", \n",
    "                                  split=[\"train\", \"test\"], \n",
    "                                  batch_size=-1, as_supervised=True)\n",
    "\n",
    "train_examples, train_labels = tfds.as_numpy(train_data)\n",
    "test_examples, test_labels = tfds.as_numpy(test_data)\n",
    "\n",
    "# /Users/jheaton/tensorflow_datasets/imdb_reviews/plain_text/0.1.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "B95Ek4cIumJZ"
   },
   "source": [
    "Load a pretrained embedding model called [gnews-swivel-20dim](https://tfhub.dev/google/tf2-preview/gnews-swivel-20dim/1).  Google trained this network on GNEWS data and can convert raw text into vectors.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "6_2X_-SlumJZ"
   },
   "outputs": [],
   "source": [
    "model = \"https://tfhub.dev/google/tf2-preview/gnews-swivel-20dim/1\"\n",
    "hub_layer = hub.KerasLayer(model, output_shape=[20], input_shape=[], \n",
    "                           dtype=tf.string, trainable=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vPa3iHUoumJZ"
   },
   "source": [
    "The following code displays three movie reviews.  This display allows you to see the actual data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HSIAeshoumJZ",
    "outputId": "646ec7e4-db4e-465b-94ab-9e03b5485493"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([b\"This was an absolutely terrible movie. Don't be lured in by Christopher Walken or Michael Ironside. Both are great actors, but this must simply be their worst role in history. Even their great acting could not redeem this movie's ridiculous storyline. This movie is an early nineties US propaganda piece. The most pathetic scenes were those when the Columbian rebels were making their cases for revolutions. Maria Conchita Alonso appeared phony, and her pseudo-love affair with Walken was nothing but a pathetic emotional plug in a movie that was devoid of any real meaning. I am disappointed that there are movies like this, ruining actor's like Christopher Walken's good name. I could barely sit through it.\",\n",
       "       b'I have been known to fall asleep during films, but this is usually due to a combination of things including, really tired, being warm and comfortable on the sette and having just eaten a lot. However on this occasion I fell asleep because the film was rubbish. The plot development was constant. Constantly slow and boring. Things seemed to happen, but with no explanation of what was causing them or why. I admit, I may have missed part of the film, but i watched the majority of it and everything just seemed to happen of its own accord without any real concern for anything else. I cant recommend this film at all.',\n",
       "       b'Mann photographs the Alberta Rocky Mountains in a superb fashion, and Jimmy Stewart and Walter Brennan give enjoyable performances as they always seem to do. <br /><br />But come on Hollywood - a Mountie telling the people of Dawson City, Yukon to elect themselves a marshal (yes a marshal!) and to enforce the law themselves, then gunfighters battling it out on the streets for control of the town? <br /><br />Nothing even remotely resembling that happened on the Canadian side of the border during the Klondike gold rush. Mr. Mann and company appear to have mistaken Dawson City for Deadwood, the Canadian North for the American Wild West.<br /><br />Canadian viewers be prepared for a Reefer Madness type of enjoyable howl with this ludicrous plot, or, to shake your head in disgust.'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_examples[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lK53sJ5iumJZ"
   },
   "source": [
    "The embedding layer can convert each to 20-number vectors, which the neural network receives as input in place of the actual words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6kr2SLKSumJa",
    "outputId": "e3b3671d-e40c-4679-8554-65866a1fa862"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(3, 20), dtype=float32, numpy=\n",
       "array([[ 1.7657859 , -3.882232  ,  3.913424  , -1.5557289 , -3.3362343 ,\n",
       "        -1.7357956 , -1.9954445 ,  1.298955  ,  5.081597  , -1.1041285 ,\n",
       "        -2.0503852 , -0.7267516 , -0.6567596 ,  0.24436145, -3.7208388 ,\n",
       "         2.0954835 ,  2.2969332 , -2.0689783 , -2.9489715 , -1.1315986 ],\n",
       "       [ 1.8804485 , -2.5852385 ,  3.4066994 ,  1.0982676 , -4.056685  ,\n",
       "        -4.891284  , -2.7855542 ,  1.3874227 ,  3.8476458 , -0.9256539 ,\n",
       "        -1.896706  ,  1.2113281 ,  0.11474716,  0.76209456, -4.8791065 ,\n",
       "         2.906149  ,  4.7087674 , -2.3652055 , -3.5015903 , -1.6390051 ],\n",
       "       [ 0.71152216, -0.63532174,  1.7385626 , -1.1168287 , -0.54515934,\n",
       "        -1.1808155 ,  0.09504453,  1.4653089 ,  0.66059506,  0.79308075,\n",
       "        -2.2268343 ,  0.07446616, -1.4075902 , -0.706454  , -1.907037  ,\n",
       "         1.4419788 ,  1.9551864 , -0.42660046, -2.8022065 ,  0.43727067]],\n",
       "      dtype=float32)>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hub_layer(train_examples[:3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fH8nB4z5umJa"
   },
   "source": [
    "We add additional layers to classify the movie reviews as either positive or negative."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ezL-ysehumJa",
    "outputId": "6e955403-426a-41f4-a817-b29f62b17ef2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " keras_layer (KerasLayer)    (None, 20)                400020    \n",
      "                                                                 \n",
      " dense (Dense)               (None, 16)                336       \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 17        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 400,373\n",
      "Trainable params: 400,373\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = tf.keras.Sequential()\n",
    "model.add(hub_layer)\n",
    "model.add(tf.keras.layers.Dense(16, activation='relu'))\n",
    "model.add(tf.keras.layers.Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3z685otDumJa"
   },
   "source": [
    "We are now ready to compile the neural network. For this application, we use the adam training method for binary classification. We also save the initial random weights for later to start over easily."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "6ACYutHrumJa"
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "init_weights = model.get_weights()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "G4rLPk0QumJa"
   },
   "source": [
    "Before fitting, we split the training data into the train and validation sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "aZLYInpaumJa"
   },
   "outputs": [],
   "source": [
    "x_val = train_examples[:10000]\n",
    "partial_x_train = train_examples[10000:]\n",
    "\n",
    "y_val = train_labels[:10000]\n",
    "partial_y_train = train_labels[10000:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pY3B9wM-oruO"
   },
   "source": [
    "We can now fit the neural network. This fitting will run for 40 epochs and allow us to evaluate the effectiveness of the neural network, as measured by the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EqDAQls1umJb",
    "outputId": "cdb29323-1fef-44ae-edff-34d98dd23613"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      "30/30 [==============================] - 4s 80ms/step - loss: 1.5554 - accuracy: 0.5515 - val_loss: 0.8048 - val_accuracy: 0.5865\n",
      "Epoch 2/40\n",
      "30/30 [==============================] - 2s 73ms/step - loss: 0.7600 - accuracy: 0.6011 - val_loss: 0.7107 - val_accuracy: 0.6230\n",
      "Epoch 3/40\n",
      "30/30 [==============================] - 2s 82ms/step - loss: 0.6541 - accuracy: 0.6561 - val_loss: 0.6263 - val_accuracy: 0.6662\n",
      "Epoch 4/40\n",
      "30/30 [==============================] - 2s 70ms/step - loss: 0.5864 - accuracy: 0.6953 - val_loss: 0.5818 - val_accuracy: 0.6978\n",
      "Epoch 5/40\n",
      "30/30 [==============================] - 2s 77ms/step - loss: 0.5493 - accuracy: 0.7248 - val_loss: 0.5551 - val_accuracy: 0.7190\n",
      "Epoch 6/40\n",
      "30/30 [==============================] - 2s 77ms/step - loss: 0.5222 - accuracy: 0.7452 - val_loss: 0.5336 - val_accuracy: 0.7338\n",
      "Epoch 7/40\n",
      "30/30 [==============================] - 1s 37ms/step - loss: 0.4990 - accuracy: 0.7618 - val_loss: 0.5146 - val_accuracy: 0.7477\n",
      "Epoch 8/40\n",
      "30/30 [==============================] - 1s 36ms/step - loss: 0.4765 - accuracy: 0.7768 - val_loss: 0.4967 - val_accuracy: 0.7637\n",
      "Epoch 9/40\n",
      "30/30 [==============================] - 1s 37ms/step - loss: 0.4551 - accuracy: 0.7925 - val_loss: 0.4798 - val_accuracy: 0.7739\n",
      "Epoch 10/40\n",
      "30/30 [==============================] - 1s 37ms/step - loss: 0.4335 - accuracy: 0.8062 - val_loss: 0.4629 - val_accuracy: 0.7864\n",
      "Epoch 11/40\n",
      "30/30 [==============================] - 1s 36ms/step - loss: 0.4129 - accuracy: 0.8191 - val_loss: 0.4466 - val_accuracy: 0.7971\n",
      "Epoch 12/40\n",
      "30/30 [==============================] - 1s 36ms/step - loss: 0.3915 - accuracy: 0.8315 - val_loss: 0.4309 - val_accuracy: 0.8086\n",
      "Epoch 13/40\n",
      "30/30 [==============================] - 1s 37ms/step - loss: 0.3710 - accuracy: 0.8431 - val_loss: 0.4159 - val_accuracy: 0.8180\n",
      "Epoch 14/40\n",
      "30/30 [==============================] - 1s 38ms/step - loss: 0.3510 - accuracy: 0.8544 - val_loss: 0.4017 - val_accuracy: 0.8262\n",
      "Epoch 15/40\n",
      "30/30 [==============================] - 1s 38ms/step - loss: 0.3310 - accuracy: 0.8643 - val_loss: 0.3883 - val_accuracy: 0.8315\n",
      "Epoch 16/40\n",
      "30/30 [==============================] - 1s 37ms/step - loss: 0.3118 - accuracy: 0.8740 - val_loss: 0.3754 - val_accuracy: 0.8385\n",
      "Epoch 17/40\n",
      "30/30 [==============================] - 1s 37ms/step - loss: 0.2932 - accuracy: 0.8846 - val_loss: 0.3638 - val_accuracy: 0.8454\n",
      "Epoch 18/40\n",
      "30/30 [==============================] - 1s 37ms/step - loss: 0.2747 - accuracy: 0.8930 - val_loss: 0.3533 - val_accuracy: 0.8495\n",
      "Epoch 19/40\n",
      "30/30 [==============================] - 1s 38ms/step - loss: 0.2568 - accuracy: 0.9030 - val_loss: 0.3434 - val_accuracy: 0.8539\n",
      "Epoch 20/40\n",
      "30/30 [==============================] - 1s 37ms/step - loss: 0.2396 - accuracy: 0.9094 - val_loss: 0.3360 - val_accuracy: 0.8567\n",
      "Epoch 21/40\n",
      "30/30 [==============================] - 1s 37ms/step - loss: 0.2246 - accuracy: 0.9183 - val_loss: 0.3298 - val_accuracy: 0.8605\n",
      "Epoch 22/40\n",
      "30/30 [==============================] - 1s 38ms/step - loss: 0.2104 - accuracy: 0.9248 - val_loss: 0.3234 - val_accuracy: 0.8633\n",
      "Epoch 23/40\n",
      "30/30 [==============================] - 1s 37ms/step - loss: 0.1971 - accuracy: 0.9295 - val_loss: 0.3192 - val_accuracy: 0.8663\n",
      "Epoch 24/40\n",
      "30/30 [==============================] - 1s 37ms/step - loss: 0.1856 - accuracy: 0.9359 - val_loss: 0.3173 - val_accuracy: 0.8678\n",
      "Epoch 25/40\n",
      "30/30 [==============================] - 1s 37ms/step - loss: 0.1739 - accuracy: 0.9417 - val_loss: 0.3147 - val_accuracy: 0.8704\n",
      "Epoch 26/40\n",
      "30/30 [==============================] - 1s 37ms/step - loss: 0.1631 - accuracy: 0.9464 - val_loss: 0.3144 - val_accuracy: 0.8713\n",
      "Epoch 27/40\n",
      "30/30 [==============================] - 1s 37ms/step - loss: 0.1538 - accuracy: 0.9508 - val_loss: 0.3134 - val_accuracy: 0.8725\n",
      "Epoch 28/40\n",
      "30/30 [==============================] - 1s 37ms/step - loss: 0.1454 - accuracy: 0.9540 - val_loss: 0.3158 - val_accuracy: 0.8723\n",
      "Epoch 29/40\n",
      "30/30 [==============================] - 1s 39ms/step - loss: 0.1372 - accuracy: 0.9573 - val_loss: 0.3174 - val_accuracy: 0.8739\n",
      "Epoch 30/40\n",
      "30/30 [==============================] - 1s 38ms/step - loss: 0.1287 - accuracy: 0.9605 - val_loss: 0.3174 - val_accuracy: 0.8748\n",
      "Epoch 31/40\n",
      "30/30 [==============================] - 1s 37ms/step - loss: 0.1211 - accuracy: 0.9634 - val_loss: 0.3202 - val_accuracy: 0.8752\n",
      "Epoch 32/40\n",
      "30/30 [==============================] - 1s 38ms/step - loss: 0.1140 - accuracy: 0.9657 - val_loss: 0.3226 - val_accuracy: 0.8745\n",
      "Epoch 33/40\n",
      "30/30 [==============================] - 1s 37ms/step - loss: 0.1067 - accuracy: 0.9683 - val_loss: 0.3272 - val_accuracy: 0.8738\n",
      "Epoch 34/40\n",
      "30/30 [==============================] - 1s 38ms/step - loss: 0.1001 - accuracy: 0.9707 - val_loss: 0.3323 - val_accuracy: 0.8737\n",
      "Epoch 35/40\n",
      "30/30 [==============================] - 1s 38ms/step - loss: 0.0934 - accuracy: 0.9734 - val_loss: 0.3352 - val_accuracy: 0.8737\n",
      "Epoch 36/40\n",
      "30/30 [==============================] - 1s 38ms/step - loss: 0.0871 - accuracy: 0.9761 - val_loss: 0.3403 - val_accuracy: 0.8740\n",
      "Epoch 37/40\n",
      "30/30 [==============================] - 1s 38ms/step - loss: 0.0817 - accuracy: 0.9788 - val_loss: 0.3449 - val_accuracy: 0.8729\n",
      "Epoch 38/40\n",
      "30/30 [==============================] - 1s 36ms/step - loss: 0.0765 - accuracy: 0.9805 - val_loss: 0.3508 - val_accuracy: 0.8739\n",
      "Epoch 39/40\n",
      "30/30 [==============================] - 1s 37ms/step - loss: 0.0711 - accuracy: 0.9820 - val_loss: 0.3562 - val_accuracy: 0.8738\n",
      "Epoch 40/40\n",
      "30/30 [==============================] - 1s 37ms/step - loss: 0.0661 - accuracy: 0.9847 - val_loss: 0.3626 - val_accuracy: 0.8728\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(partial_x_train,\n",
    "                    partial_y_train,\n",
    "                    epochs=40,\n",
    "                    batch_size=512,\n",
    "                    validation_data=(x_val, y_val),\n",
    "                    verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C8cjFPa1uhMA"
   },
   "source": [
    "## Benefits of Early Stopping\n",
    "While we used a validation set, we fit the neural network without early stopping. This dataset is complex enough to allow us to see the benefit of early stopping. We will examine how accuracy and loss progressed for training and validation sets. Loss measures the degree to which the neural network was confident in incorrect answers. Accuracy is the percentage of correct classifications, regardless of the neural network's confidence.\n",
    "\n",
    "We begin by looking at the loss as we fit the neural network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "sDBXJsl5umJb",
    "outputId": "239a4a13-6564-406b-8e25-104c84dd8446"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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z+eWXA9ClSxdeeeUVbrvtNm6++WbuvPPOtN8v38NVx1kjmAmMylTAzJoDPwGeiDEOEWkCkpuHkpuFHnjgAQYNGsTAgQNZsmRJlWacVM8++yynn346xcXFdOjQgVNOOWX3tsWLF3PMMcfQt29fZs2axZIlSzLG8+abb9K7d28OOeQQAM4//3zmz5+/e/sZZ5wBwODBg3cPVJfOc889x3nnnQdUP1z19OnTWbduHS1atGDIkCHcfffdTJ06lddee4327dtn3Hcu4nx4/XwzK81S7BLgj8CQuOIQkbqV6Zd7nE499VSuuOIKXnnlFTZv3szgwYN59913ufnmm3n55ZfZd999GT9+fNrhp7MZP348c+bMoX///sycOZN58+bVKt7EUNa1GcZ68uTJnHTSSTz66KMMHz6cxx9/fPdw1Y888gjjx4/nyiuvZNy4cbWKNW99BGbWAzgduD2HshPNrNzMylevXh1/cCLS4LRr145jjz2WCRMm7K4NbNiwgbZt29KxY0c+/vhjHnvssYz7GDFiBHPmzGHLli1s3LiRv/71r7u3bdy4ke7du7N9+/bdQ0cDtG/fno0bN+6xr0MPPZTly5ezbNkyAO69916+9KUv1ei75Xu46nyONXQLcI277zKzjAXdfQYwA8Loo/UQm4g0QGPGjOH000/f3USUGLb5sMMO48ADD2T48OEZ3z9o0CC+9rWv0b9/f/bbbz+GDKlsjPjhD3/IsGHD6Nq1K8OGDdt98j/33HO58MILmT59+u5OYoDWrVtz9913c/bZZ7Njxw6GDBnCxRdfXKPvlXiWcr9+/SguLq4yXPXcuXNp1qwZffr0YfTo0cyePZuf/exnFBUV0a5duzp5gE2sw1BHTUMPV9dZbGbvAokM0AXYDEx09zmZ9qlhqEXqn4ahblz2dhjqvNUI3L13Yt7MZhISRsYkICIidS/Oy0d/D4wEuphZBXADUATg7r+K63NFRGTvxHnVUG63/IWy4+OKQ0TqhruTrT9P8q8mzf26s1hEsmrdujVr166t0UlG6o+7s3btWlq3br1X79MTykQkq549e1JRUYEu3274WrduTc+ePffqPUoEIpJVUVERvXv3zl5QGiU1DYmIFDglAhGRAqdEICJS4JQIREQKnBKBiEiBUyIQESlwSgQiIgVOiUBEpMApEYiIFDglAhGRAqdEICJS4JQIREQKnBKBiEiBUyIQESlwsSUCM7vLzFaZ2eI028ea2SIze83Mnjez/nHFIiIi6cVZI5gJjMqw/V3gS+7eF/ghMCPGWEREJI04n1k838xKM2x/PmnxRWDvHqkjIiJ1oqH0EVwAPJZuo5lNNLNyMyvXo/JEROpW3hOBmR1LSATXpCvj7jPcvczdy7p27Vp/wYmIFIC8PrPYzPoBdwKj3X1tPmMRESlUeasRmFkv4E/Aee7+Vr7iEBEpdLHVCMzs98BIoIuZVQA3AEUA7v4r4HqgM3CbmQHscPeyuOIREZHqxXnV0Jgs278JfDOuzxcRkdzkvbNYRETyS4lARKTAKRGIiBQ4JQIRkQKnRCAiUuCUCERECpwSgYhIgVMiEBEpcEoEIiIFTolARKTAKRGIiBQ4JQIRkQKnRCAiUuCUCERECpwSgYhIgVMiEBEpcEoEIiIFTolARKTAxZYIzOwuM1tlZovTbDczm25my8xskZkNiisWERFJL84awUxgVIbto4GDo2kicHuMsYiISBqxJQJ3nw98kqHIqcBvPXgR2MfMuscVj4iIVC+ffQQ9gPeTliuidXsws4lmVm5m5atXr66X4ERECkWj6Cx29xnuXubuZV27ds13OCIiTUo+E8EHwIFJyz2jdSIiUo/ymQgeAsZFVw8dCax395V5jEdEpCC1iGvHZvZ7YCTQxcwqgBuAIgB3/xXwKHAisAzYDHwjrlhERCS92BKBu4/Jst2Bb8f1+SIikptG0VksIiLxUSIQESlwSgQiIgVOiUBEpMApEYiIFDglAhGRAqdEICJS4JQIREQKnBKBiEiBUyIQESlwSgQiIgUup0RgZm3NrFk0f4iZnWJmRfGGJiIi9SHXGsF8oLWZ9QCeAM4jPJNYREQauVwTgbn7ZuAM4DZ3PxvoE19YIiJSX3JOBGZ2FDAWeCRa1zyekEREpD7lmgguB64F/uzuS8zsIGBufGGJiEh9yenBNO7+DPAMQNRpvMbdL40zMBERqR+5XjX0OzPrYGZtgcXA62Z2VQ7vG2Vmb5rZMjObXM32XmY218xeNbNFZnbi3n8FERGpjVybhg539w3AacBjQG/ClUNpmVlz4FZgNHA4MMbMDk8pdh3wgLsPBM4FbtuL2Pfa1q1x7l1EpHHKNREURfcNnAY85O7bAc/ynqHAMnd/x90/A2YDp6aUcaBDNN8R+DDHePbaX/4CpaWwYkVcnyAi0jjlmgh+DSwH2gLzzawE2JDlPT2A95OWK6J1yaYC/2VmFcCjwCU5xrPX+veHjRvhO98Bz5bCREQKSE6JwN2nu3sPdz/RgxXAsXXw+WOAme7eEzgRuDdxB3MyM5toZuVmVr569eoafVBpKdx4Izz8MPz5z7WKWUSkScm1s7ijmf08cTI2s/8h1A4y+QA4MGm5Z7Qu2QXAAwDu/gLQGuiSuiN3n+HuZe5e1rVr11xCrtZll4WawSWXwIZs9RkRkQKRa9PQXcBG4Jxo2gDcneU9LwMHm1lvM2tJ6Ax+KKXMe8DxAGb2BUIiqNlP/hwUFcGvfw0rV8J111WunzUr1BiaNQuvs2bFFYGISMOT030EwOfc/cyk5RvNbGGmN7j7DjP7DvA44S7ku6Kb0W4Cyt39IeC7wB1mdgWh43i8e7wt+MOGwbe+Bb/8JZx3Hrz1FkycCJs3h+0rVoRlgLFj44xERKRhsFzOu2b2AnCVuz8XLQ8Hbnb3o2KObw9lZWVeXl5eq32sXw9f+AJ06waffALvvbdnmZISWL68Vh8jItJgmNkCdy+rbluuNYKLgd+aWcdo+T/A+XURXD507AjTp8PZZ6cvU11yEBFpinK9auhf7t4f6Af0i24AOy7WyGJ25plw8slgVv32Xr3qNx4RkXzZqyeUufuG6A5jgCtjiKfemIV+gpYtoXnKOKrFxTBtWn7iEhGpb7V5VGWa39KNR0lJOOHv3Aldu4bkUFICM2aoo1hECkeufQTVaRL35152Gdx7L6xZA+vWQYcO2d8jItKUZKwRmNlGM9tQzbQROKCeYoxVixahBvDhh1XvLRARKRQZE4G7t3f3DtVM7d29NrWJBmXoUPj2t0OfwTPP5DsaEZH6VZs+giblRz+Cz30Oxo0L9xmIiBQKJYJIu3ahr+CDD+BSPXtNRAqIEkGSI4+EKVPgt7+FBx/MdzQiIvVDiSDFddfBkCFw0UWhA1lEpKlTIkhRVBSaiLZsgQkTwkNsNDqpiDRlTebKn7p06KFw883hSqJvfAP+8AeNTioiTZdqBGlMmgSjRoX+gkQSSNi8OfQliIg0BUoEaZjBXXelf76xRicVkaZCiSCD7t3DGETV0eikItJUKBFk8YtfaHRSEWnalAiyGDsWbr+9Mhnst59GJxWRpiXWRGBmo8zsTTNbZmaT05Q5x8xeN7MlZva7OOOpqQsvhDfegL59YfXqML9jR76jEhGpG7ElAjNrDtwKjAYOB8aY2eEpZQ4GrgWGu3sf4PK44qmtgw+GF18M9xZMmwZf/jKsXJnvqEREai/OGsFQYJm7v+PunwGzgVNTylwI3Oru/wFw91UxxlNrxcVw550wcyb8858wcCDMnasbzkSkcYvzhrIewPtJyxXAsJQyhwCY2T+A5sBUd/9b6o7MbCIwEaBXA7hc5/zzYfBgOPtsOP748EyD7dvDNt1wJiKNTb47i1sABwMjgTHAHWa2T2ohd5/h7mXuXtY13fWc9eyII+Dll6FNm8okkKAbzkSkMYkzEXwAHJi03DNal6wCeMjdt7v7u8BbhMTQKLRrt+ddxwm64UxEGos4E8HLwMFm1tvMWgLnAg+llJlDqA1gZl0ITUXvxBhTnSspqX5969bw7LPp70wWEWkoYksE7r4D+A7wOPAG8IC7LzGzm8zslKjY48BaM3sdmAtc5e5r44opDtOmhU7kZC1ahPsORowIzzi47LKQMNSZLCINkXkj+8laVlbm5eXl+Q6jilmzQp/Ae++FoSemTYPTT4d77oEbb4SPP65avrhYN6WJSP0yswXuXlbtNiWCeJWUVN9f0LkzvPMOdOhQ/zGJSOHJlAjyfdVQk/f++9WvX7sW9t8fjj4aunULo52q2UhE8kGJIGbpbntIJIEXXoBV0W10K1bABReEJiURkfqiRBCz6jqTi4vDE9CWLduz/LZtYRiLCRPg4YfDsohInJQIYjZ2bOgYLikJzT8lJZUdxenuNdi1C/70J/jqV2GffaBt2/DeXr3UdCQidU+JoB6MHQvLl4cT/PLllVcLpWs2KikJzUVXXRXuWk7ctPb++zBuHIwfDxUV9RC4iBQEJYI8StdsNG0atGwJDzwAO3dW3b5rV+hDOPDAMN7RjTfCq6/qxjURqTklgjzK1GwEmYep+PGPw93LN94IgwaFm9Xat4fLL4cNG+onfhFpGnQfQQNWWhquJEpVUhKamGbNCg/N2bKl6vbmzeGYY+DEE2H0aOjTJyQaESlcuo+gkcrUdAThbubUJAChc3ntWrj66vBUtZISuOgi+MtfYNOm+OMWkcZFiaABq2nT0caNsGhR6Fy+4AJYsya877TTwlVIJ5wAP/85LF2qvgURUdNQo5ZL09HEiVWHym7RItzMlrjqqLQ0NCGNGgXHHhuG1haRpkdNQ01ULk1Hqc9L2LEj9CEsXw633w79+oVHb55yCnTqFJLBj38crkTatas+voWI5JsSQSNW06aj994LZS++GM45B7p0CevbtAkD4V17bbgSqXt3OO88uO8++Oij+vlOIlL/1DTUhNWk6ai4GH7yk3Ap6uOPw9//HvoYIDye88tfDn0MI0aoGUmkMdEw1AUq3Yk+UWvIliggNA+9+io8+WSYnn02jH/UogUcdVRICiecAEOGQFFRfXwrEakJ9REUqNo0HUFIJAcdFE7yt98ehrb4z39CQvje98Klq1OnwvDhsO++ocP5Jz+Bl14KfREi0jjEWiMws1HA/wLNgTvd/cdpyp0JPAgMcfeMP/dVI6g7mWoE06Zlrk0kfPIJzJ1bOb3+eljfoUO4qe3YY8PUv3/opBaR/MhL05CZNQfeAr4MVBAeZj/G3V9PKdceeARoCXxHiaD+ZGo6mjIle7NRdT7+GObNg6efDonh3/8O6zt0CDWHESPgS18K4yS1bFmX30ZEMslX09BQYJm7v+PunwGzgVOrKfdD4CfA1hhjkWrUZIjs5PWzZoVaRbNmlU9X69YNvvY1+PWv4a23wk1ts2bBmDEhgVx7bXggzz77wPHHh7GSnnoq3AQnIvnRIsZ99wCSH9RYAQxLLmBmg4AD3f0RM7sqxlgkjbFjqzb1JPTqVX2NIDF0dmptYsWKsJzYZ0LPnvD1r4cJwvDazz0H8+eH6cYbw93NzZqF5qPhw0OiGD48jLCqMZJE4pe3zmIzawb8HPhuDmUnmlm5mZWvXr06/uCkRjerbd4c1idUV2PYbz844wy45RZ45ZXQx/DYY+F9++4Ld98dkkZJSUgE55wDv/hFeKTnVtUZRWIRZx/BUcBUd/9KtHwtgLv/KFruCLwNJIZB2x/4BDglUz+B+gjqz6xZ4QT93nuhJjBtWuWv/WbNqh+nyCxccprt0tV0duwI4yQ9/zz84x/hNdEcVVQEAwfCkUfCsGHhtXdv1RpEcpGvzuIWhM7i44EPCJ3FX3f3JWnKzwO+p87ixiHbPQi53KOQqw8/hH/+M0wvvggvv1yZYLp2DR3PyZOalET2lCkRxNZH4O47zOw7wOOEy0fvcvclZnYTUO7uD8X12RK/dJeXJpqOcu1sTlfjSHbAAXD66WGCUGtYvLgyMSxYEO6ATjzNrUuXMETG4MHhtV8/+NzndPmqNE7usH59GCiyQ4f0j7itDd1ZLDWW6URe0+EtsjUdpbNlS2hSWrCgclqypPLGtjZtwgN6+vULU9++4TUxzpJIPmzbFi65/uij8CJlTMkAAA1ASURBVPrhh+GE//774TUxffppKD95MvzoRzX7LA0xIfWuLoa3qK2tW0PN4bXXQpJIvCZfb9CtW0gQqdO++9ZNDFJ43MMd+CtXhunDDytfP/qo8qT/0Ufhl36qZs1CLbhnzz2nAQPg0ENrFpcSgeRFbTqbs72/Nj7+uDIpLF4cag6vv1716W3du4eEcMghoUO6tLTytVMn9UEUEvfwHPBVq8K0enUYiHHNmqrza9aE7StXhl/6qdq3D/+u9t8//ABJvCbPJ7a3iKHRXolAGpz6bjrKZteuUB1fsqQyOSxZAm+/DevWVS3bvn2Iv7Q0/Err0aPyNTF16FD3MUrNuYdEv2pV+LW+aVO4iTF5SqxLnNCTp88+q36/xcWheTExde0aTuYHHFD1tXv3/I/Wq0QgDU5tm47iqi1UZ9268JnLl8O771Z9ragIJ5ZU7dpVJoh0U+fOqlnUlHvlSTv1F/rq1XueyFetyu0+lLZtwwl9v/2qn7p2DVPixJ96r01DpkQgDVJNm47uvbd+awvZbN4c2n8/+KDqVFFR+bpyZeVVTQktW4ZmptSpc+fw2qEDtG6dfiouDgmnbdvw2rJl/SUWd9i+PTSBJKbPPqucT/6uqX9H99C5v2lT6ATdtKnq9OmnldPmzXu+JhLA9u3Vx9ay5Z4n7+TlffcNtbrUqW3bpn1lmRKBNDqZagQQf0dzXdu5M/RNJF8J8uGHoTaxdm24wzp5SlwlsjdatKhMCm3bhmWzkFTNqs43axa2J6aioqrzEE7WqSfkxPyWLXV7fBKaNauMv7i48jV5vl27PZtjkufbt1dNqzp5uY9ApDYy3adw3nnVv6cm9yjUl+bNQ3vxAQfA0KHZy2/bFjoot20LTRqp05YtlSfn5F/SyfM7doRf3+6hDyR5fteukJx27Kj8ZZ+Y37EjbE+ceLt1qzwRJ9a1aQOtWlWdWrasnE/t7Ew9MbdpE07oqVOrVjqJ54MSgTRIiZN2dSfzdENk7+2AeA1Zq1bh161IfdATyqTBGjs2NPXs2hVeEyfxuAbEEylUSgTS6NTFIzgnTgw1BffKGoOSgRQqdRZLk1OfA+KJNBZ6eL0UlGxNRzV9+ppIU6VEIE1OtqajdKM3pnY2q+lICoUSgTRJ6TqaQZ3NIqmUCKTgqLNZpColAilImWoM2ZqOVGOQpkaJQCRFbTubVWOQxkaJQCRFbTubs9UYVFuQhibWRGBmo8zsTTNbZmaTq9l+pZm9bmaLzOwpMyuJMx6RXNWmszlTjUG1BWmIYksEZtYcuBUYDRwOjDGzw1OKvQqUuXs/4EHgp3HFI1JXalNjUP+CNERx1giGAsvc/R13/wyYDZyaXMDd57p74r/Fi0DPGOMRqTM1rTHURf+CEoXUtTgTQQ/g/aTlimhdOhcAj1W3wcwmmlm5mZWvTn7yuEgDlKnGUBf9C2pakrrWIDqLzey/gDLgZ9Vtd/cZ7l7m7mVdNTavNAI1HTk1W41BTUsShzgTwQfAgUnLPaN1VZjZCcAU4BR33xZjPCJ5V9srktS0JLFw91gmwkNv3gF6Ay2BfwF9UsoMBN4GDs51v4MHD3aRpuq++9yLixPPEgtTcXFY7+5eUlJ1W2IqKclte7b933dfKGsWXhPrpfEDyj3d+TrdhrqYgBOBt6KT/ZRo3U2EX/8ATwIfAwuj6aFs+1QikKYu08k424ncrPpEYBa2Z0oU2fadLTZp2PKWCOKYlAik0GU6GWerEWRKFLWtTWSLTfIrUyLQg2lEmpDU5zVD6IxO9ENkeijPe++F03sqs9Dpne2BPtk+W/JLD6YRKRDZOqMzXbVU245qXdHUiKWrKjTUSU1DIrWTrvmmth3V2fon1LSUX6iPQERyUZuOal3R1LApEYhInWjMVzQVeiJRIhCRetFQr2hSs5QSgYg0ALVpWqpNbSKX7YWQKJQIRKRBqGnTUm07qmubSJpC/4USgYg0CnFd0VTbRNIU+i+UCESk0atNR3Wcl8Y2lv4LJQIRafJq86u7sfdf5CJTItAQEyIihLucp0wJd0r36hXutk7ckZ1p+IwpUzIPvdGsWTh9p0oM3ZFte7ahPXKlISZERLLI9PjRTEN3ZHvYULahO2o7tEddUCIQEclBukRRm/GdctmeLVHUBSUCEZFaqmltIpft2RJFXVAfgYhIA5ep/yJXmfoIWtRFkCIiEp+xY+N9poOahkREClysicDMRpnZm2a2zMwmV7O9lZndH23/p5mVxhmPiIjsKbZEYGbNgVuB0cDhwBgzOzyl2AXAf9z988AvgJ/EFY+IiFQvzhrBUGCZu7/j7p8Bs4FTU8qcCtwTzT8IHG9mFmNMIiKSIs5E0AN4P2m5IlpXbRl33wGsBzqn7sjMJppZuZmVr169OqZwRUQKU6O4asjdZwAzAMxstZlVc8P1bl2ANfUS2N5TbDWj2GpGsdVMU42tJN2GOBPBB8CBScs9o3XVlakwsxZAR2Btpp26e9dM282sPN21svmm2GpGsdWMYquZQowtzqahl4GDzay3mbUEzgUeSinzEHB+NH8W8LQ3tjvcREQaudhqBO6+w8y+AzwONAfucvclZnYTYTjUh4DfAPea2TLgE0KyEBGRehRrH4G7Pwo8mrLu+qT5rcDZdfyxM+p4f3VJsdWMYqsZxVYzBRdboxtrSERE6paGmBARKXBKBCIiBa7JJIJs4xrlk5ktN7PXzGyhmeV1DG0zu8vMVpnZ4qR1nczs72b27+h13wYU21Qz+yA6dgvN7MQ8xXagmc01s9fNbImZXRatz/uxyxBb3o+dmbU2s5fM7F9RbDdG63tH44sti8Yba9mAYptpZu8mHbcB9R1bUozNzexVM3s4Wo7nuKV7mHFjmghXJb0NHAS0BP4FHJ7vuJLiWw50yXccUSwjgEHA4qR1PwUmR/OTgZ80oNimAt9rAMetOzAomm8PvEUYQyvvxy5DbHk/doAB7aL5IuCfwJHAA8C50fpfAZMaUGwzgbPy/W8uiutK4HfAw9FyLMetqdQIchnXSAB3n0+4VDdZ8phP9wCn1WtQkTSxNQjuvtLdX4nmNwJvEIZIyfuxyxBb3nmwKVosiiYHjiOMLwb5O27pYmsQzKwncBJwZ7RsxHTcmkoiyGVco3xy4AkzW2BmE/MdTDW6ufvKaP4joFs+g6nGd8xsUdR0lJdmq2TRcOkDCb8gG9SxS4kNGsCxi5o3FgKrgL8Tau/rPIwvBnn8/5oam7snjtu06Lj9wsxa5SM24BbgamBXtNyZmI5bU0kEDd0X3X0QYUjub5vZiHwHlI6HOmeD+VUE3A58DhgArAT+J5/BmFk74I/A5e6+IXlbvo9dNbE1iGPn7jvdfQBhmJmhwGH5iKM6qbGZ2RHAtYQYhwCdgGvqOy4zOxlY5e4L6uPzmkoiyGVco7xx9w+i11XAnwn/GRqSj82sO0D0uirP8ezm7h9H/1l3AXeQx2NnZkWEE+0sd/9TtLpBHLvqYmtIxy6KZx0wFzgK2CcaXwwawP/XpNhGRU1t7u7bgLvJz3EbDpxiZssJTd3HAf9LTMetqSSCXMY1ygsza2tm7RPzwP8BFmd+V71LHvPpfOAveYylisRJNnI6eTp2Ufvsb4A33P3nSZvyfuzSxdYQjp2ZdTWzfaL5NsCXCX0Ycwnji0H+jlt1sS1NSuxGaIOv9+Pm7te6e093LyWcz55297HEddzy3SteVxNwIuFqibeBKfmOJymugwhXMf0LWJLv2IDfE5oJthPaGC8gtD0+BfwbeBLo1IBiuxd4DVhEOOl2z1NsXyQ0+ywCFkbTiQ3h2GWILe/HDugHvBrFsBi4Plp/EPASsAz4A9CqAcX2dHTcFgP3EV1ZlK8JGEnlVUOxHDcNMSEiUuCaStOQiIjUkBKBiEiBUyIQESlwSgQiIgVOiUBEpMApEYhEzGxn0oiTC60OR7E1s9LkUVVFGpJYH1Up0shs8TDcgEhBUY1AJAsLz5P4qYVnSrxkZp+P1pea2dPR4GRPmVmvaH03M/tzNM79v8zs6GhXzc3sjmjs+yeiu1kxs0ujZwksMrPZefqaUsCUCEQqtUlpGvpa0rb17t4X+CVhVEiA/wfc4+79gFnA9Gj9dOAZd+9PeL7Ckmj9wcCt7t4HWAecGa2fDAyM9nNxXF9OJB3dWSwSMbNN7t6umvXLgePc/Z1ocLeP3L2zma0hDNuwPVq/0t27mNlqoKeHQcsS+yglDHN8cLR8DVDk7v9tZn8DNgFzgDleOUa+SL1QjUAkN55mfm9sS5rfSWUf3UnArYTaw8tJo0uK1AslApHcfC3p9YVo/nnCyJAAY4Fno/mngEmw+8EnHdPt1MyaAQe6+1zCuPcdgT1qJSJx0i8PkUptoqdVJfzN3ROXkO5rZosIv+rHROsuAe42s6uA1cA3ovWXATPM7ALCL/9JhFFVq9McuC9KFgZM9zA2vki9UR+BSBZRH0GZu6/JdywicVDTkIhIgVONQESkwKlGICJS4JQIREQKnBKBiEiBUyIQESlwSgQiIgXu/wOCgX2OmjARtgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "history_dict = history.history\n",
    "acc = history_dict['accuracy']\n",
    "val_acc = history_dict['val_accuracy']\n",
    "loss = history_dict['loss']\n",
    "val_loss = history_dict['val_loss']\n",
    "\n",
    "epochs = range(1, len(acc) + 1)\n",
    "\n",
    "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "j2cQ3mocxVgp"
   },
   "source": [
    "We can see that training and validation loss are similar early in the fitting. However, as fitting continues and overfitting sets in, training and validation loss diverge from each other. Training loss continues to fall consistently. However, once overfitting happens, the validation loss no longer falls and eventually begins to increase a bit. Early stopping, which we saw earlier in this course, can prevent some overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "hU02g4gjumJb",
    "outputId": "125c6e89-6571-477a-94e5-25173bcf8380"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()   # clear figure\n",
    "\n",
    "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
    "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BagkzLu4yxyC"
   },
   "source": [
    "The accuracy graph tells a similar story. Now let's repeat the fitting with early stopping. We begin by creating an early stopping monitor and restoring the network's weights to random. Once this is complete, we can fit the neural network with the early stopping monitor enabled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Kh4uYTcwzEtG",
    "outputId": "15072a6d-b941-452b-db44-c17c78eb668d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      "30/30 [==============================] - 1s 38ms/step - loss: 1.1912 - accuracy: 0.5643 - val_loss: 0.7129 - val_accuracy: 0.6332\n",
      "Epoch 2/40\n",
      "30/30 [==============================] - 1s 37ms/step - loss: 0.6202 - accuracy: 0.6749 - val_loss: 0.5862 - val_accuracy: 0.6922\n",
      "Epoch 3/40\n",
      "30/30 [==============================] - 1s 37ms/step - loss: 0.5531 - accuracy: 0.7251 - val_loss: 0.5525 - val_accuracy: 0.7208\n",
      "Epoch 4/40\n",
      "30/30 [==============================] - 1s 36ms/step - loss: 0.5228 - accuracy: 0.7483 - val_loss: 0.5308 - val_accuracy: 0.7403\n",
      "Epoch 5/40\n",
      "30/30 [==============================] - 1s 38ms/step - loss: 0.4981 - accuracy: 0.7671 - val_loss: 0.5106 - val_accuracy: 0.7569\n",
      "Epoch 6/40\n",
      "30/30 [==============================] - 1s 38ms/step - loss: 0.4754 - accuracy: 0.7823 - val_loss: 0.4925 - val_accuracy: 0.7690\n",
      "Epoch 7/40\n",
      "30/30 [==============================] - 1s 37ms/step - loss: 0.4542 - accuracy: 0.7959 - val_loss: 0.4759 - val_accuracy: 0.7803\n",
      "Epoch 8/40\n",
      "30/30 [==============================] - 1s 37ms/step - loss: 0.4342 - accuracy: 0.8089 - val_loss: 0.4619 - val_accuracy: 0.7883\n",
      "Epoch 9/40\n",
      "30/30 [==============================] - 1s 38ms/step - loss: 0.4143 - accuracy: 0.8215 - val_loss: 0.4455 - val_accuracy: 0.7998\n",
      "Epoch 10/40\n",
      "30/30 [==============================] - 1s 37ms/step - loss: 0.3960 - accuracy: 0.8318 - val_loss: 0.4319 - val_accuracy: 0.8090\n",
      "Epoch 11/40\n",
      "30/30 [==============================] - 1s 37ms/step - loss: 0.3784 - accuracy: 0.8390 - val_loss: 0.4190 - val_accuracy: 0.8166\n",
      "Epoch 12/40\n",
      "30/30 [==============================] - 1s 37ms/step - loss: 0.3613 - accuracy: 0.8483 - val_loss: 0.4068 - val_accuracy: 0.8237\n",
      "Epoch 13/40\n",
      "30/30 [==============================] - 1s 37ms/step - loss: 0.3453 - accuracy: 0.8570 - val_loss: 0.3962 - val_accuracy: 0.8291\n",
      "Epoch 14/40\n",
      "30/30 [==============================] - 1s 38ms/step - loss: 0.3301 - accuracy: 0.8644 - val_loss: 0.3861 - val_accuracy: 0.8360\n",
      "Epoch 15/40\n",
      "30/30 [==============================] - 1s 39ms/step - loss: 0.3157 - accuracy: 0.8735 - val_loss: 0.3778 - val_accuracy: 0.8366\n",
      "Epoch 16/40\n",
      "30/30 [==============================] - 1s 38ms/step - loss: 0.3022 - accuracy: 0.8784 - val_loss: 0.3690 - val_accuracy: 0.8419\n",
      "Epoch 17/40\n",
      "30/30 [==============================] - 1s 38ms/step - loss: 0.2888 - accuracy: 0.8866 - val_loss: 0.3612 - val_accuracy: 0.8465\n",
      "Epoch 18/40\n",
      "30/30 [==============================] - 1s 39ms/step - loss: 0.2765 - accuracy: 0.8917 - val_loss: 0.3546 - val_accuracy: 0.8500\n",
      "Epoch 19/40\n",
      "30/30 [==============================] - 1s 36ms/step - loss: 0.2645 - accuracy: 0.8961 - val_loss: 0.3490 - val_accuracy: 0.8524\n",
      "Epoch 20/40\n",
      "30/30 [==============================] - 1s 36ms/step - loss: 0.2533 - accuracy: 0.9024 - val_loss: 0.3436 - val_accuracy: 0.8554\n",
      "Epoch 21/40\n",
      "30/30 [==============================] - 1s 38ms/step - loss: 0.2433 - accuracy: 0.9065 - val_loss: 0.3386 - val_accuracy: 0.8567\n",
      "Epoch 22/40\n",
      "30/30 [==============================] - 1s 37ms/step - loss: 0.2333 - accuracy: 0.9108 - val_loss: 0.3348 - val_accuracy: 0.8590\n",
      "Epoch 23/40\n",
      "30/30 [==============================] - 1s 38ms/step - loss: 0.2231 - accuracy: 0.9165 - val_loss: 0.3312 - val_accuracy: 0.8615\n",
      "Epoch 24/40\n",
      "30/30 [==============================] - 1s 50ms/step - loss: 0.2142 - accuracy: 0.9206 - val_loss: 0.3287 - val_accuracy: 0.8630\n",
      "Epoch 25/40\n",
      "30/30 [==============================] - 1s 48ms/step - loss: 0.2054 - accuracy: 0.9247 - val_loss: 0.3264 - val_accuracy: 0.8639\n",
      "Epoch 26/40\n",
      "30/30 [==============================] - 1s 37ms/step - loss: 0.1972 - accuracy: 0.9299 - val_loss: 0.3247 - val_accuracy: 0.8660\n",
      "Epoch 27/40\n",
      "30/30 [==============================] - 1s 39ms/step - loss: 0.1891 - accuracy: 0.9327 - val_loss: 0.3225 - val_accuracy: 0.8668\n",
      "Epoch 28/40\n",
      "30/30 [==============================] - 1s 39ms/step - loss: 0.1818 - accuracy: 0.9354 - val_loss: 0.3231 - val_accuracy: 0.8656\n",
      "Epoch 29/40\n",
      "30/30 [==============================] - 1s 37ms/step - loss: 0.1746 - accuracy: 0.9384 - val_loss: 0.3208 - val_accuracy: 0.8685\n",
      "Epoch 30/40\n",
      "30/30 [==============================] - 1s 36ms/step - loss: 0.1671 - accuracy: 0.9419 - val_loss: 0.3203 - val_accuracy: 0.8694\n",
      "Epoch 31/40\n",
      "30/30 [==============================] - 1s 36ms/step - loss: 0.1605 - accuracy: 0.9445 - val_loss: 0.3210 - val_accuracy: 0.8688\n",
      "Epoch 32/40\n",
      "30/30 [==============================] - 1s 37ms/step - loss: 0.1539 - accuracy: 0.9480 - val_loss: 0.3209 - val_accuracy: 0.8699\n",
      "Epoch 33/40\n",
      "30/30 [==============================] - 1s 39ms/step - loss: 0.1475 - accuracy: 0.9508 - val_loss: 0.3220 - val_accuracy: 0.8700\n",
      "Epoch 34/40\n",
      "29/30 [============================>.] - ETA: 0s - loss: 0.1419 - accuracy: 0.9528Restoring model weights from the end of the best epoch: 29.\n",
      "30/30 [==============================] - 1s 38ms/step - loss: 0.1414 - accuracy: 0.9531 - val_loss: 0.3231 - val_accuracy: 0.8704\n",
      "Epoch 00034: early stopping\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "\n",
    "monitor = EarlyStopping(monitor='val_loss', min_delta=1e-3, \n",
    "        patience=5, verbose=1, mode='auto',\n",
    "        restore_best_weights=True)\n",
    "\n",
    "model.set_weights(init_weights)\n",
    "\n",
    "history = model.fit(partial_x_train,\n",
    "                    partial_y_train,\n",
    "                    epochs=40,\n",
    "                    batch_size=512,\n",
    "                    callbacks=[monitor],\n",
    "                    validation_data=(x_val, y_val),\n",
    "                    verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pQsyC5VHmfuX"
   },
   "source": [
    "The training history chart is now shorter because we stopped earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "hyQUkk1kz22U",
    "outputId": "d0651549-b9b0-4d67-e917-27a08e509a41"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "history_dict = history.history\n",
    "acc = history_dict['accuracy']\n",
    "val_acc = history_dict['val_accuracy']\n",
    "loss = history_dict['loss']\n",
    "val_loss = history_dict['val_loss']\n",
    "\n",
    "epochs = range(1, len(acc) + 1)\n",
    "\n",
    "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "U8CoFEPUmpPO"
   },
   "source": [
    "Finally, we evaluate the accuracy for the best neural network before early stopping occured."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ccmjDt8KkBgF",
    "outputId": "58cc12d6-0016-447c-80c7-e3185f601618"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8685\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "import numpy as np\n",
    "\n",
    "pred = model.predict(x_val)\n",
    "# Use 0.5 as the threshold\n",
    "predict_classes = pred.flatten()>0.5\n",
    "\n",
    "correct = accuracy_score(y_val,predict_classes)\n",
    "print(f\"Accuracy: {correct}\")"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
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