CC_Classification_TF_MLP.ipynb

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      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPr5OPI8YphmEaRAn5Zxtuo",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Geek-a-Byte/Federated-Learning-Aided-Cervical-Cancer-Classification/blob/main/Cervical_Cancer_Classification_Using_MLP_tensorflow.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "## dataset link\n",
        "```\n",
        "https://drive.google.com/file/d/1OKT4Q5fZ4rDBTY_bDwLSDg5-ZF1BcBeZ/view?usp=sharing\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "PeJywYl2IEsh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import random\n",
        "import cv2\n",
        "import os\n",
        "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' \n",
        "from imutils import paths\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.utils import shuffle\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "jq_Y7kbb6vis"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S6BH8wRF5e8b",
        "outputId": "866a569a-b72c-4d87-b4ed-ee86badcef30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qIaH3sOt4Mpl"
      },
      "outputs": [],
      "source": [
        "!unzip \"/content/drive/MyDrive/CervicalCancerTrainDataset.zip\" -d \"./CervicalCancerTrainingDataset/\""
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def load(paths, verbose=-1):\n",
        "    '''expects images for each class in seperate dir, \n",
        "    e.g all digits in 0 class in the directory named 0 '''\n",
        "    data = list()\n",
        "    labels = list()\n",
        "    # loop over the input images\n",
        "    for (i, imgpath) in enumerate(paths):\n",
        "        # load the image and extract the class labels\n",
        "        im_gray = cv2.imread(imgpath, cv2.IMREAD_GRAYSCALE)\n",
        "        image = np.array(im_gray).flatten()\n",
        "        label = imgpath.split(os.path.sep)[-2] # 0 1 2 3 4 5 6 7 8 9\n",
        "        # scale the image to [0, 1] and add to list\n",
        "        data.append(image/255)\n",
        "        labels.append(label)\n",
        "        # show an update every `verbose` images\n",
        "        if verbose > 0 and i > 0 and (i + 1) % verbose == 0:\n",
        "            print(label);\n",
        "            print(\"[INFO] processed {}/{}\".format(i + 1, len(paths)))\n",
        "    # return a tuple of the data and labels\n",
        "    return data, labels"
      ],
      "metadata": {
        "id": "VIE0Mvqb6evU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#declear path to your mnist data folder\n",
        "img_path = '/content/CervicalCancerTrainingDataset'\n",
        "\n",
        "#get the path list using the path object\n",
        "image_paths = list(paths.list_images(img_path))\n",
        "\n",
        "#apply our function\n",
        "image_list, label_list = load(image_paths, verbose=10000)\n",
        "\n",
        "print(label_list)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ESmASneJ6ooT",
        "outputId": "0e3a37cc-3a95-4e22-efcf-a224db3c3848"
      },
      "execution_count": null,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
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'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic', 'Dyskeratotic']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#binarize the labels\n",
        "lb = LabelBinarizer()\n",
        "label_list = lb.fit_transform(label_list)\n",
        "\n",
        "#split data into training and test set\n",
        "X_train, X_test, y_train, y_test = train_test_split(image_list, \n",
        "                                                    label_list, \n",
        "                                                    test_size=0.1, \n",
        "                                                    random_state=42)"
      ],
      "metadata": {
        "id": "HaV5R-uU9ZTc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Trying FL"
      ],
      "metadata": {
        "id": "W8i0HLxqIkYg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow==2.2.0\n",
        "!sudo pip3 install keras"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BU5UCRSeJDn-",
        "outputId": "32241c8e-7539-456d-c579-25261eb7315e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: keras in /usr/local/lib/python3.7/dist-packages (2.9.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv2D\n",
        "from tensorflow.keras.layers import MaxPooling2D\n",
        "from tensorflow.keras.layers import Activation\n",
        "from tensorflow.keras.layers import Flatten\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.optimizers import SGD\n",
        "from tensorflow.keras import backend as K"
      ],
      "metadata": {
        "id": "g7uLp3CKI62-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def create_clients(image_list, label_list, num_clients=10, initial='clients'):\n",
        "    ''' return: a dictionary with keys clients' names and value as \n",
        "                data shards - tuple of images and label lists.\n",
        "        args: \n",
        "            image_list: a list of numpy arrays of training images\n",
        "            label_list:a list of binarized labels for each image\n",
        "            num_client: number of fedrated members (clients)\n",
        "            initials: the clients'name prefix, e.g, clients_1 \n",
        "            \n",
        "    '''\n",
        "\n",
        "    #create a list of client names\n",
        "    client_names = ['{}_{}'.format(initial, i+1) for i in range(num_clients)]\n",
        "\n",
        "    #randomize the data\n",
        "    data = list(zip(image_list, label_list))\n",
        "    random.shuffle(data)\n",
        "\n",
        "    #shard data and place at each client\n",
        "    size = len(data)//num_clients\n",
        "    shards = [data[i:i + size] for i in range(0, size*num_clients, size)]\n",
        "\n",
        "    #number of clients must equal number of shards\n",
        "    assert(len(shards) == len(client_names))\n",
        "\n",
        "    return {client_names[i] : shards[i] for i in range(len(client_names))}"
      ],
      "metadata": {
        "id": "_kwedpnMIesd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Function for preparing client data for training\n",
        "def batch_data(data_shard, bs=32):\n",
        "    '''Takes in a clients data shard and create a tfds object off it\n",
        "    args:\n",
        "        shard: a data, label constituting a client's data shard\n",
        "        bs:batch size\n",
        "    return:\n",
        "        tfds object'''\n",
        "    #seperate shard into data and labels lists\n",
        "    data, label = zip(*data_shard)\n",
        "    dataset = tf.data.Dataset.from_tensor_slices((list(data), list(label)))\n",
        "    return dataset.shuffle(len(label)).batch(bs)"
      ],
      "metadata": {
        "id": "kY5H3CetIpL-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def weight_scalling_factor(clients_trn_data, client_name):\n",
        "    client_names = list(clients_trn_data.keys())\n",
        "    #get the bs\n",
        "    bs = list(clients_trn_data[client_name])[0][0].shape[0]\n",
        "    #first calculate the total training data points across clinets\n",
        "    global_count = sum([tf.data.experimental.cardinality(clients_trn_data[client_name]).numpy() for client_name in client_names])*bs\n",
        "    # get the total number of data points held by a client\n",
        "    local_count = tf.data.experimental.cardinality(clients_trn_data[client_name]).numpy()*bs\n",
        "    return local_count/global_count\n",
        "\n",
        "def scale_model_weights(weight, scalar):\n",
        "    '''function for scaling a models weights'''\n",
        "    weight_final = []\n",
        "    steps = len(weight)\n",
        "    for i in range(steps):\n",
        "        weight_final.append(scalar * weight[i])\n",
        "    return weight_final\n",
        "\n",
        "def sum_scaled_weights(scaled_weight_list):\n",
        "    '''Return the sum of the listed scaled weights. The is equivalent to scaled avg of the weights'''\n",
        "    avg_grad = list()\n",
        "    #get the average grad accross all client gradients\n",
        "    for grad_list_tuple in zip(*scaled_weight_list):\n",
        "        layer_mean = tf.math.reduce_sum(grad_list_tuple, axis=0)\n",
        "        avg_grad.append(layer_mean)\n",
        "        \n",
        "    return avg_grad\n",
        "\n",
        "\n",
        "'''\n",
        "I ran 100 global training loops as stipulated by the comms_round and on line 48 \n",
        "tested the trained global model after each communication round our test data. \n",
        "Here is the snippet for the test logic:\n",
        "'''\n",
        "\n",
        "def test_model(X_test, Y_test,  model, comm_round):\n",
        "    cce = tf.keras.losses.CategoricalCrossentropy(from_logits=True)\n",
        "    #logits = model.predict(X_test, batch_size=100)\n",
        "    logits = model.predict(X_test)\n",
        "    loss = cce(Y_test, logits)\n",
        "    acc = accuracy_score(tf.argmax(logits, axis=1), tf.argmax(Y_test, axis=1))\n",
        "    print('comm_round: {} | global_acc: {:.3%} | global_loss: {}'.format(comm_round, acc, loss))\n",
        "    return acc, loss"
      ],
      "metadata": {
        "id": "xyihuGU-IuG2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#create clients\n",
        "clients = create_clients(X_train, y_train, num_clients=10, initial='client')\n",
        "\n",
        "'''\n",
        "While applying this function batch_data, I will process the test set as well and keep it aside for later use.\n",
        "'''\n",
        "#process and batch the training data for each client\n",
        "clients_batched = dict()\n",
        "for (client_name, data) in clients.items():\n",
        "    clients_batched[client_name] = batch_data(data)\n",
        "    \n",
        "#process and batch the test set  \n",
        "test_batched = tf.data.Dataset.from_tensor_slices((X_test, y_test)).batch(len(y_test))\n",
        "\n",
        "'''\n",
        "To build a new model, the build method will be invoked. \n",
        "It requires the input data’s shape and the number of classes as arguments. \n",
        "With MNIST, the shape parameter will be 28*28*1 = 784,while the number of classes will be 10.\n",
        "Now is the time to define an optimizer, loss function and metrics to compile our models with later on.\n",
        "'''\n",
        "comms_round = 100\n",
        "    \n",
        "#create optimizer\n",
        "lr = 0.01 \n",
        "loss='categorical_crossentropy'\n",
        "metrics = ['accuracy'] # f1_score, precision_score\n",
        "optimizer = SGD(lr=lr, \n",
        "                decay=lr / comms_round, \n",
        "                momentum=0.9\n",
        "               ) \n",
        "\n",
        "'''\n",
        "SGD is my default optimizer except when I have a reason not to use it. \n",
        "The loss function is categorical_crossentropy. \n",
        "And finally, the metric I will be using is accuracy. \n",
        "But something looks strange in the decay argument. \n",
        "What’s comms_round? \n",
        "It’s simply the number global epochs (aggregations) I will be running during training. \n",
        "So rather than decaying the learning rate with respect to the number of local epochs as you might be familiar with, \n",
        "here I want to decay with respect to the number of global aggregation. \n",
        "This is obviously an hyper parameter selection choice, \n",
        "but I found it to work pretty well while experimenting.\n",
        "'''"
      ],
      "metadata": {
        "id": "Vso_50UsIz6w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Model architecture: MLP\n",
        "class SimpleMLP:\n",
        "    @staticmethod\n",
        "    def build(shape, classes):\n",
        "        model = Sequential()\n",
        "        model.add(Dense(200, input_shape=(shape,)))\n",
        "        model.add(Activation(\"relu\"))\n",
        "        model.add(Dense(200))\n",
        "        model.add(Activation(\"relu\"))\n",
        "        model.add(Dense(classes))\n",
        "        model.add(Activation(\"softmax\"))\n",
        "        return model"
      ],
      "metadata": {
        "id": "WdbXZ1DGMJta"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#initialize global model\n",
        "smlp_global = SimpleMLP()\n",
        "global_model = smlp_global.build(4356, 5)\n",
        "        \n",
        "#commence global training loop\n",
        "for comm_round in range(comms_round):\n",
        "            \n",
        "    # get the global model's weights - will serve as the initial weights for all local models\n",
        "    global_weights = global_model.get_weights()\n",
        "    \n",
        "    #initial list to collect local model weights after scalling\n",
        "    scaled_local_weight_list = list()\n",
        "\n",
        "    #randomize client data - using keys\n",
        "    client_names= list(clients_batched.keys())\n",
        "    random.shuffle(client_names)\n",
        "        \n",
        "    #loop through each client and create new local model\n",
        "    for client in client_names:\n",
        "        smlp_local = SimpleMLP()\n",
        "        local_model = smlp_local.build(4356, 5)\n",
        "        local_model.compile(loss=loss, optimizer=optimizer, metrics=metrics)\n",
        "        \n",
        "        #set local model weight to the weight of the global model\n",
        "        local_model.set_weights(global_weights)\n",
        "        #fit local model with client's data\n",
        "        tf.autograph.experimental.do_not_convert(local_model.fit(clients_batched[client], epochs=1, verbose=0))\n",
        "        \n",
        "        #scale the model weights and add to list\n",
        "        scaling_factor = weight_scalling_factor(clients_batched, client)\n",
        "        scaled_weights = scale_model_weights(local_model.get_weights(), scaling_factor)\n",
        "        scaled_local_weight_list.append(scaled_weights)\n",
        "        \n",
        "        #clear session to free memory after each communication round\n",
        "        K.clear_session()\n",
        "        \n",
        "    #to get the average over all the local model, we simply take the sum of the scaled weights\n",
        "    average_weights = sum_scaled_weights(scaled_local_weight_list)\n",
        "\n",
        "    #update global model \n",
        "    global_model.set_weights(average_weights)\n",
        "\n",
        "    #test global model and print out metrics after each communications round\n",
        "    for(X_test, Y_test) in test_batched:\n",
        "        global_acc, global_loss = test_model(X_test, Y_test, global_model, comm_round)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 814
        },
        "id": "BciaJn59I3ol",
        "outputId": "2d964d23-63d7-49fa-953a-6552feb3404a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "13/13 [==============================] - 0s 4ms/step\n",
            "comm_round: 12 | global_acc: 34.815% | global_loss: 1.520717740058899\n",
            "13/13 [==============================] - 0s 4ms/step\n",
            "comm_round: 13 | global_acc: 47.654% | global_loss: 1.5099990367889404\n",
            "13/13 [==============================] - 0s 5ms/step\n",
            "comm_round: 14 | global_acc: 40.741% | global_loss: 1.5166752338409424\n",
            "13/13 [==============================] - 0s 4ms/step\n",
            "comm_round: 15 | global_acc: 47.901% | global_loss: 1.5078489780426025\n",
            "13/13 [==============================] - 0s 4ms/step\n",
            "comm_round: 16 | global_acc: 47.654% | global_loss: 1.5010923147201538\n",
            "13/13 [==============================] - 0s 4ms/step\n",
            "comm_round: 17 | global_acc: 47.654% | global_loss: 1.5048068761825562\n",
            "13/13 [==============================] - 0s 4ms/step\n",
            "comm_round: 18 | global_acc: 51.852% | global_loss: 1.4931693077087402\n",
            "13/13 [==============================] - 0s 4ms/step\n",
            "comm_round: 19 | global_acc: 45.679% | global_loss: 1.4883991479873657\n",
            "13/13 [==============================] - 0s 5ms/step\n",
            "comm_round: 20 | global_acc: 49.630% | global_loss: 1.4927561283111572\n",
            "13/13 [==============================] - 0s 4ms/step\n",
            "comm_round: 21 | global_acc: 50.617% | global_loss: 1.4821399450302124\n",
            "13/13 [==============================] - 0s 5ms/step\n",
            "comm_round: 22 | global_acc: 50.617% | global_loss: 1.4840084314346313\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mget_attr\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   2647\u001b[0m     \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mname\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mscope\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2648\u001b[0;31m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2649\u001b[0m   \u001b[0mReturns\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mInvalidArgumentError\u001b[0m: Operation 'SGD/Cast_1/ReadVariableOp/resource' has no attr named '_read_only_resource_inputs'.",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-136-a41c82b6f1c1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     25\u001b[0m         \u001b[0mlocal_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mglobal_weights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0;31m#fit local model with client's data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m         \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo_not_convert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocal_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclients_batched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mclient\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m         \u001b[0;31m#scale the model weights and add to list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1407\u001b[0m                 _r=1):\n\u001b[1;32m   1408\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1409\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1410\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1411\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    913\u001b[0m     \u001b[0mf_concrete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconstant\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1.\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    914\u001b[0m     \u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 915\u001b[0;31m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    916\u001b[0m     \u001b[0mNested\u001b[0m \u001b[0mstructures\u001b[0m \u001b[0mcontaining\u001b[0m \u001b[0mTensors\u001b[0m \u001b[0mmay\u001b[0m \u001b[0mbe\u001b[0m \u001b[0mspecified\u001b[0m \u001b[0mwhen\u001b[0m \u001b[0mretrieving\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    917\u001b[0m     \u001b[0mconcrete\u001b[0m \u001b[0mfunctions\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mStructures\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mmultiple\u001b[0m \u001b[0mTensors\u001b[0m \u001b[0mare\u001b[0m \u001b[0mexpanded\u001b[0m \u001b[0minto\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    961\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mconcrete\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    962\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 963\u001b[0;31m   \u001b[0;32mdef\u001b[0m \u001b[0m__get__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minstance\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mowner\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    964\u001b[0m     \u001b[0;34m\"\"\"Makes it possible to defun instance methods.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    965\u001b[0m     \u001b[0;32mdel\u001b[0m \u001b[0mowner\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_initialize\u001b[0;34m(self, args, kwds, add_initializers_to)\u001b[0m\n\u001b[1;32m    784\u001b[0m       \u001b[0mRuntimeError\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcalled\u001b[0m \u001b[0mafter\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mvariables\u001b[0m \u001b[0mhave\u001b[0m \u001b[0mbeen\u001b[0m \u001b[0minitialized\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    785\u001b[0m     \"\"\"\n\u001b[0;32m--> 786\u001b[0;31m     \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    787\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    788\u001b[0m         raise RuntimeError(\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_get_concrete_function_internal_garbage_collected\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2478\u001b[0m         \u001b[0;31m# If args are provided, they must match the input signature.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2479\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_same_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minput_signature\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2480\u001b[0;31m           raise ValueError(\"Structure of Python function inputs does not match \"\n\u001b[0m\u001b[1;32m   2481\u001b[0m                            \"input_signature.\")\n\u001b[1;32m   2482\u001b[0m         \u001b[0mflat_inputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpand_composites\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_maybe_define_function\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m   2709\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2710\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2711\u001b[0;31m   \u001b[0;32mdef\u001b[0m \u001b[0m_maybe_define_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2712\u001b[0m     \"\"\"Gets a function for these inputs, defining it if necessary.\n\u001b[1;32m   2713\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_create_graph_function\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m   2634\u001b[0m     return CacheKey(\n\u001b[1;32m   2635\u001b[0m         \u001b[0m_make_input_signature_hashable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_signature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparent_graph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2636\u001b[0;31m         \u001b[0mdevice_functions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolocation_stack\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0min_cross_replica_context\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2637\u001b[0m         xla_context_id)\n\u001b[1;32m   2638\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py\u001b[0m in \u001b[0;36mfunc_graph_from_py_func\u001b[0;34m(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, acd_record_initial_resource_uses)\u001b[0m\n\u001b[1;32m   1180\u001b[0m              \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1181\u001b[0m              flat_shapes))\n\u001b[0;32m-> 1182\u001b[0;31m     \u001b[0mshapes_iter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mflat_shapes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1183\u001b[0m   \u001b[0;32mfor\u001b[0m \u001b[0marg_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/auto_control_deps.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, unused_type, unused_value, unused_traceback)\u001b[0m\n\u001b[1;32m    462\u001b[0m       \u001b[0;31m# but that may not be as fast.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    463\u001b[0m       \u001b[0mupdated\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_acd_resource_resolvers_registry\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlookup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreads\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwrites\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 464\u001b[0;31m       \u001b[0;32mif\u001b[0m \u001b[0mupdated\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    465\u001b[0m         \u001b[0;31m# Conservatively remove any resources from `reads` that are also writes.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    466\u001b[0m         \u001b[0mreads\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreads\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdifference\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwrites\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/auto_control_deps.py\u001b[0m in \u001b[0;36m_get_resource_inputs\u001b[0;34m(op)\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/auto_control_deps_utils.py\u001b[0m in \u001b[0;36mget_read_write_resource_inputs\u001b[0;34m(op)\u001b[0m\n\u001b[1;32m    103\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mreads\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwrites\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 105\u001b[0;31m   \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    106\u001b[0m     \u001b[0mread_only_input_indices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_attr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mREAD_ONLY_RESOURCE_INPUTS_ATTR\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    107\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mget_attr\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   2650\u001b[0m     \u001b[0mthe\u001b[0m \u001b[0mname\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mop\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mequal\u001b[0m \u001b[0mto\u001b[0m \u001b[0mscope\u001b[0m \u001b[0mname\u001b[0m \u001b[0mminus\u001b[0m \u001b[0many\u001b[0m \u001b[0mtrailing\u001b[0m \u001b[0mslash\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2651\u001b[0m   \"\"\"\n\u001b[0;32m-> 2652\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"/\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2653\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2654\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    }
  ]
}