under_working.ipynb

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          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Lagstill/Federated-ML/blob/main/under_working.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VCkDyqYdWjUn"
      },
      "source": [
        "import tensorflow as tf\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xlvk3Ro1WjUq"
      },
      "source": [
        "import sys\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sb\n",
        "%matplotlib inline\n",
        "\n",
        "from scipy.io import loadmat\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3AUoWJWoWjUr"
      },
      "source": [
        "import torch as th\n",
        "import numpy as np\n",
        "from torchvision import datasets, transforms\n",
        "import torchvision.datasets as datasets\n",
        "from torch.utils.data import Subset\n",
        "from torch import nn\n",
        "import torch.nn.functional as F\n",
        "from torch import optim\n",
        "import syft as sy\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WINRERrjWjUs"
      },
      "source": [
        "fashion_mnist = tf.keras.datasets.fashion_mnist\n",
        "\n",
        "(train_images, train_labels),(test_images,test_labels) = fashion_mnist.load_data()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i_ppsZpuWjUs",
        "outputId": "465b55da-959a-4e39-eddd-cdede570481c"
      },
      "source": [
        "train_images.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l8aGqZ2QWjUt",
        "outputId": "83e605ef-d8a9-4ca3-e1c3-973b902b34bd"
      },
      "source": [
        "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
        "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n",
        "\n",
        "for i in range (0,10):\n",
        "    print(i,\"=====>\",class_names[i])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0 =====> T-shirt/top\n",
            "1 =====> Trouser\n",
            "2 =====> Pullover\n",
            "3 =====> Dress\n",
            "4 =====> Coat\n",
            "5 =====> Sandal\n",
            "6 =====> Shirt\n",
            "7 =====> Sneaker\n",
            "8 =====> Bag\n",
            "9 =====> Ankle boot\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2f7DgIrJWjUu",
        "outputId": "6e211044-ee11-4174-b6fb-f32cd6ab54c9"
      },
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "for i in range(36):\n",
        "    plt.subplot(6,6,i+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.grid(False)\n",
        "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
        "    plt.xlabel(class_names[train_labels[i]])\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 36 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4gKZ2CEtWjUu",
        "outputId": "45bcc7df-b0e0-49b3-bd5e-f5482b57b972"
      },
      "source": [
        "train_labels"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([9, 0, 0, ..., 3, 0, 5], dtype=uint8)"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SQl9tVsvWjUv"
      },
      "source": [
        "train1 = train_images[:200]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sgR2AHL6WjUw",
        "outputId": "f3d34fe7-1f90-4579-eb6b-6f8ddefbbb58"
      },
      "source": [
        "train_images"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[[0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        ...,\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0]],\n",
              "\n",
              "       [[0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        ...,\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0]],\n",
              "\n",
              "       [[0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        ...,\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0]],\n",
              "\n",
              "       ...,\n",
              "\n",
              "       [[0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        ...,\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0]],\n",
              "\n",
              "       [[0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        ...,\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0]],\n",
              "\n",
              "       [[0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        ...,\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0]]], dtype=uint8)"
            ]
          },
          "execution_count": 67,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zHKnA99rWjUw"
      },
      "source": [
        "label1=train_labels[:200]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lZzxY5KeWjUw"
      },
      "source": [
        "df = pd.DataFrame(train_labels)\n",
        "df=df.rename(columns={0: \"label\"})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lHwAx0B0WjUx",
        "outputId": "f7e60e6b-36f0-4394-b0a8-769eb2bdaf97"
      },
      "source": [
        "ax = sns.countplot(x=\"label\", data=df)"
      ],
      "execution_count": null,
      "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"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mW6_JqqSWjUx",
        "outputId": "f01b8cae-ae2c-4880-fe00-d54988c7d609"
      },
      "source": [
        "y_value=np.zeros((1,10))\n",
        "for i in range (10):\n",
        "    print(\"occurance of \",i,\"=\",np.count_nonzero(train_labels==i))\n",
        "    y_value[0,i-1]= np.count_nonzero(train_labels==i)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "occurance of  0 = 6000\n",
            "occurance of  1 = 6000\n",
            "occurance of  2 = 6000\n",
            "occurance of  3 = 6000\n",
            "occurance of  4 = 6000\n",
            "occurance of  5 = 6000\n",
            "occurance of  6 = 6000\n",
            "occurance of  7 = 6000\n",
            "occurance of  8 = 6000\n",
            "occurance of  9 = 6000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NvrfZMFAWjUx"
      },
      "source": [
        "def normalize(data):\n",
        "    mean = np.mean(data, axis=1, keepdims=True)\n",
        "    std = np.std(data, axis=1, keepdims=True)\n",
        "    data_normalized = (data - mean)/std\n",
        "    return data_normalized"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gwIPwVjwWjUx",
        "outputId": "a7a8582f-e76c-420a-8799-4978cfcb867d"
      },
      "source": [
        "mnist_data_normalized = normalize(train_images)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\kpdla\\AppData\\Local\\Temp/ipykernel_14556/2098459775.py:4: RuntimeWarning: invalid value encountered in true_divide\n",
            "  data_normalized = (data - mean)/std\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L2kmBw-vWjUy"
      },
      "source": [
        "train1 =train1.reshape(-1, 28**2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aaCEsmRVWjUy"
      },
      "source": [
        "label1 = label1.astype('float64')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LKhhXBHKWjUy"
      },
      "source": [
        "train1 = train1.astype('float64')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nQJqW_g0WjUy",
        "outputId": "b33adade-5227-4448-c457-8d65bfc05a65"
      },
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(train1,label1, test_size=0.20, random_state=42)\n",
        "Y_train = Y_train.reshape(Y_train.shape[0],1)\n",
        "Y_test = Y_test.reshape(Y_test.shape[0],1)\n",
        "print(\"The shape of the training set feature matrix is:\", X_train.shape)\n",
        "print(\"The shape of the training label vector is:\", Y_train.shape)\n",
        "print(\"The shape of the test set feature matrix is:\", X_test.shape)\n",
        "print(\"The shape of the test label vector is:\", Y_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The shape of the training set feature matrix is: (160, 784)\n",
            "The shape of the training label vector is: (160, 1)\n",
            "The shape of the test set feature matrix is: (40, 784)\n",
            "The shape of the test label vector is: (40, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lxtGKzIRWjUz"
      },
      "source": [
        "Y_train_0=(Y_train==0).astype(int)\n",
        "Y_train_1=(Y_train==1).astype(int)\n",
        "Y_train_2=(Y_train==2).astype(int)\n",
        "Y_train_3=(Y_train==3).astype(int)\n",
        "Y_train_4=(Y_train==4).astype(int)\n",
        "Y_train_5=(Y_train==5).astype(int)\n",
        "Y_train_6=(Y_train==6).astype(int)\n",
        "Y_train_7=(Y_train==7).astype(int)\n",
        "Y_train_8=(Y_train==8).astype(int)\n",
        "Y_train_9=(Y_train==9).astype(int)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WKIoa_80WjUz"
      },
      "source": [
        "Y_test_0=(Y_test==0).astype(int)\n",
        "Y_test_1=(Y_test==1).astype(int)\n",
        "Y_test_2=(Y_test==2).astype(int)\n",
        "Y_test_3=(Y_test==3).astype(int)\n",
        "Y_test_4=(Y_test==4).astype(int)\n",
        "Y_test_5=(Y_test==5).astype(int)\n",
        "Y_test_6=(Y_test==6).astype(int)\n",
        "Y_test_7=(Y_test==7).astype(int)\n",
        "Y_test_8=(Y_test==8).astype(int)\n",
        "Y_test_9=(Y_test==9).astype(int)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pPL5V1cDWjU0"
      },
      "source": [
        "def initializer(nbr_features):\n",
        "    W = np.zeros((nbr_features,1))\n",
        "    B = 0\n",
        "    return W, B"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nK3l69dOWjU0"
      },
      "source": [
        "# Creating a Sigmoid function\n",
        "\n",
        "def sigmoid(x):\n",
        "    s = 1/(1+np.exp(-x))\n",
        "    return s\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z4Slp-d_WjU0"
      },
      "source": [
        "def ForwardBackProp(X, Y, W, B):\n",
        "    m = X.shape[0] \n",
        "    dw = np.zeros((W.shape[0],1))\n",
        "    dB = 0\n",
        "    \n",
        "    Z = np.dot(X,W)+B\n",
        "    Yhat = sigmoid(Z) \n",
        "    J = -(1/m+1)*(np.dot(Y.T,np.log(Yhat))+np.dot((1-Y).T,np.log(1-Yhat)))\n",
        "    dW = (1/m)*np.dot(X.T,(Yhat-Y))\n",
        "    dB = (1/m)*np.sum(Yhat-Y)\n",
        "    return J, dW, dB\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d6G5d9K3WjU0"
      },
      "source": [
        "# Creating a prediction function which predicts the labels of the input images\n",
        "\n",
        "def predict(X,W,B):\n",
        "    Yhat_prob = sigmoid(np.dot(X,W)+B)\n",
        "    Yhat = np.round(Yhat_prob).astype(int)\n",
        "    return Yhat, Yhat_prob"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3rN_PaA0WjU0"
      },
      "source": [
        "def gradient_descent(X, Y, W, B, alpha, max_iter):\n",
        "    i=0\n",
        "    RMSE = 1\n",
        "    cost_history=[]\n",
        "    \n",
        "    # setup toolbar\n",
        "    toolbar_width = 20\n",
        "    sys.stdout.write(\"[%s]\" % (\"\" * toolbar_width))\n",
        "    sys.stdout.flush()\n",
        "    sys.stdout.write(\"\\b\" * (toolbar_width+1)) # return to start of line, after '['\n",
        "    \n",
        "    while (i<max_iter)&(RMSE>10e-6):\n",
        "        J, dW, dB = ForwardBackProp(X,Y,W,B)\n",
        "        W = W - alpha*dW\n",
        "        B = B - alpha*dB\n",
        "        cost_history.append(J)\n",
        "        Yhat, _ = predict(X,W,B)\n",
        "        RMSE = np.sqrt(np.mean(Yhat-Y)**2)\n",
        "        i+=1\n",
        "        if i%50==0:\n",
        "            sys.stdout.write(\"=\")\n",
        "            sys.stdout.flush()\n",
        "    \n",
        "    sys.stdout.write(\"]\\n\") # this ends the progress bar\n",
        "    return cost_history, W, B, i"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YgcBNmeNWjU1"
      },
      "source": [
        "# Creating the model function which trains a model and return its parameters. \n",
        "\n",
        "def LogRegModel(X_train, X_test, Y_train, Y_test, alpha, max_iter):\n",
        "    \n",
        "    nbr_features = X_train.shape[1]\n",
        "    W, B = initializer(nbr_features)\n",
        "    cost_history, W, B, i = gradient_descent(X_train, Y_train, W, B, alpha, max_iter)\n",
        "    Yhat_train, _ = predict(X_train, W, B)\n",
        "    Yhat, _ = predict(X_test, W, B)\n",
        "    \n",
        "    train_accuracy = accuracy_score(Y_train, Yhat_train)\n",
        "    test_accuracy = accuracy_score(Y_test, Yhat)\n",
        "    conf_matrix = confusion_matrix(Y_test, Yhat, normalize='true')\n",
        "    \n",
        "    model = {\"weights\": W,\n",
        "            \"bias\": B,\n",
        "            \"train_accuracy\": train_accuracy,\n",
        "            \"test_accuracy\": test_accuracy,\n",
        "            \"confusion_matrix\": conf_matrix,\n",
        "            \"cost_history\": cost_history}\n",
        "    return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W-93KjqgWjU1",
        "outputId": "ae00a092-fdde-497e-c917-b7151507b65e"
      },
      "source": [
        "# Testing the model function by training a classifier for the digit '0'\n",
        "\n",
        "print('Progress bar: 1 step each 50 iteration')\n",
        "model_0 = LogRegModel(X_train, X_test, Y_train_0, Y_test_0, alpha=0.01, max_iter=1000)\n",
        "print('Training completed!')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Progress bar: 1 step each 50 iteration\n",
            "[]\n",
            "Training completed!\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\kpdla\\AppData\\Local\\Temp/ipykernel_14556/2291245578.py:4: RuntimeWarning: overflow encountered in exp\n",
            "  s = 1/(1+np.exp(-x))\n",
            "C:\\Users\\kpdla\\AppData\\Local\\Temp/ipykernel_14556/3707342468.py:8: RuntimeWarning: divide by zero encountered in log\n",
            "  J = -(1/m+1)*(np.dot(Y.T,np.log(Yhat))+np.dot((1-Y).T,np.log(1-Yhat)))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TpySRVrPWjU1",
        "outputId": "7acad52e-4926-49d6-cb05-71b28c8dd5db"
      },
      "source": [
        "print('The training accuracy of the model',model_0['train_accuracy'])\n",
        "print('The test accuracy of the model',model_0['test_accuracy'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The training accuracy of the model 0.95\n",
            "The test accuracy of the model 0.875\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BhuHltPuWjU1",
        "outputId": "2c763f8e-7953-4902-8781-c41f824967cf"
      },
      "source": [
        "models_list=[]\n",
        "models_name_list=['model_0','model_1','model_2','model_3','model_4','model_5','model_6',\n",
        "                 'model_7','model_8','model_9']\n",
        "Y_train_list=[Y_train_0,Y_train_1,Y_train_2,Y_train_3,Y_train_4,Y_train_5,Y_train_6,\n",
        "             Y_train_7,Y_train_8,Y_train_9]\n",
        "Y_test_list = [Y_test_0,Y_test_1,Y_test_2,Y_test_3,Y_test_4,Y_test_5,Y_test_6,Y_test_7,\n",
        "              Y_test_8,Y_test_9]\n",
        "print('Training of a classifier for each digit:')\n",
        "for i in range(10):\n",
        "    print('Training of the model: ', models_name_list[i],', to recognize the digit: ',i)\n",
        "    print('Training progress bar: 1 step each 50 iteration')\n",
        "    model = LogRegModel(X_train, X_test, Y_train_list[i], Y_test_list[i], alpha=0.01, max_iter=1000)\n",
        "    print('Training completed!')\n",
        "    print('Accuracy:', model['test_accuracy'])\n",
        "    print('-'*60)\n",
        "    models_list.append(model)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training of a classifier for each digit:\n",
            "Training of the model:  model_0 , to recognize the digit:  0\n",
            "Training progress bar: 1 step each 50 iteration\n",
            "[]\n",
            "Training completed!\n",
            "Accuracy: 0.875\n",
            "------------------------------------------------------------\n",
            "Training of the model:  model_1 , to recognize the digit:  1\n",
            "Training progress bar: 1 step each 50 iteration\n",
            "[]\n",
            "Training completed!\n",
            "Accuracy: 1.0\n",
            "------------------------------------------------------------\n",
            "Training of the model:  model_2 , to recognize the digit:  2\n",
            "Training progress bar: 1 step each 50 iteration\n",
            "[]\n",
            "Training completed!\n",
            "Accuracy: 0.95\n",
            "------------------------------------------------------------\n",
            "Training of the model:  model_3 , to recognize the digit:  3\n",
            "Training progress bar: 1 step each 50 iteration\n",
            "[]\n",
            "Training completed!\n",
            "Accuracy: 0.975\n",
            "------------------------------------------------------------\n",
            "Training of the model:  model_4 , to recognize the digit:  4\n",
            "Training progress bar: 1 step each 50 iteration\n",
            "[]\n",
            "Training completed!\n",
            "Accuracy: 0.875\n",
            "------------------------------------------------------------\n",
            "Training of the model:  model_5 , to recognize the digit:  5\n",
            "Training progress bar: 1 step each 50 iteration\n",
            "[]\n",
            "Training completed!\n",
            "Accuracy: 0.9\n",
            "------------------------------------------------------------\n",
            "Training of the model:  model_6 , to recognize the digit:  6\n",
            "Training progress bar: 1 step each 50 iteration\n",
            "[=]\n",
            "Training completed!\n",
            "Accuracy: 0.9\n",
            "------------------------------------------------------------\n",
            "Training of the model:  model_7 , to recognize the digit:  7\n",
            "Training progress bar: 1 step each 50 iteration\n",
            "[]\n",
            "Training completed!\n",
            "Accuracy: 0.95\n",
            "------------------------------------------------------------\n",
            "Training of the model:  model_8 , to recognize the digit:  8\n",
            "Training progress bar: 1 step each 50 iteration\n",
            "[]\n",
            "Training completed!\n",
            "Accuracy: 0.875\n",
            "------------------------------------------------------------\n",
            "Training of the model:  model_9 , to recognize the digit:  9\n",
            "Training progress bar: 1 step each 50 iteration\n",
            "[]\n",
            "Training completed!\n",
            "Accuracy: 0.9\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\kpdla\\AppData\\Local\\Temp/ipykernel_14556/2291245578.py:4: RuntimeWarning: overflow encountered in exp\n",
            "  s = 1/(1+np.exp(-x))\n",
            "C:\\Users\\kpdla\\AppData\\Local\\Temp/ipykernel_14556/3707342468.py:8: RuntimeWarning: divide by zero encountered in log\n",
            "  J = -(1/m+1)*(np.dot(Y.T,np.log(Yhat))+np.dot((1-Y).T,np.log(1-Yhat)))\n",
            "C:\\Users\\kpdla\\AppData\\Local\\Temp/ipykernel_14556/2291245578.py:4: RuntimeWarning: overflow encountered in exp\n",
            "  s = 1/(1+np.exp(-x))\n",
            "C:\\Users\\kpdla\\AppData\\Local\\Temp/ipykernel_14556/3707342468.py:8: RuntimeWarning: divide by zero encountered in log\n",
            "  J = -(1/m+1)*(np.dot(Y.T,np.log(Yhat))+np.dot((1-Y).T,np.log(1-Yhat)))\n",
            "C:\\Users\\kpdla\\AppData\\Local\\Temp/ipykernel_14556/2291245578.py:4: RuntimeWarning: overflow encountered in exp\n",
            "  s = 1/(1+np.exp(-x))\n",
            "C:\\Users\\kpdla\\AppData\\Local\\Temp/ipykernel_14556/3707342468.py:8: RuntimeWarning: divide by zero encountered in log\n",
            "  J = -(1/m+1)*(np.dot(Y.T,np.log(Yhat))+np.dot((1-Y).T,np.log(1-Yhat)))\n",
            "C:\\Users\\kpdla\\AppData\\Local\\Temp/ipykernel_14556/2291245578.py:4: RuntimeWarning: overflow encountered in exp\n",
            "  s = 1/(1+np.exp(-x))\n",
            "C:\\Users\\kpdla\\AppData\\Local\\Temp/ipykernel_14556/3707342468.py:8: RuntimeWarning: divide by zero encountered in log\n",
            "  J = -(1/m+1)*(np.dot(Y.T,np.log(Yhat))+np.dot((1-Y).T,np.log(1-Yhat)))\n",
            "C:\\Users\\kpdla\\AppData\\Local\\Temp/ipykernel_14556/2291245578.py:4: RuntimeWarning: overflow encountered in exp\n",
            "  s = 1/(1+np.exp(-x))\n",
            "C:\\Users\\kpdla\\AppData\\Local\\Temp/ipykernel_14556/3707342468.py:8: RuntimeWarning: divide by zero encountered in log\n",
            "  J = -(1/m+1)*(np.dot(Y.T,np.log(Yhat))+np.dot((1-Y).T,np.log(1-Yhat)))\n",
            "C:\\Users\\kpdla\\AppData\\Local\\Temp/ipykernel_14556/2291245578.py:4: RuntimeWarning: overflow encountered in exp\n",
            "  s = 1/(1+np.exp(-x))\n",
            "C:\\Users\\kpdla\\AppData\\Local\\Temp/ipykernel_14556/3707342468.py:8: RuntimeWarning: divide by zero encountered in log\n",
            "  J = -(1/m+1)*(np.dot(Y.T,np.log(Yhat))+np.dot((1-Y).T,np.log(1-Yhat)))\n",
            "C:\\Users\\kpdla\\AppData\\Local\\Temp/ipykernel_14556/2291245578.py:4: RuntimeWarning: overflow encountered in exp\n",
            "  s = 1/(1+np.exp(-x))\n",
            "C:\\Users\\kpdla\\AppData\\Local\\Temp/ipykernel_14556/3707342468.py:8: RuntimeWarning: divide by zero encountered in log\n",
            "  J = -(1/m+1)*(np.dot(Y.T,np.log(Yhat))+np.dot((1-Y).T,np.log(1-Yhat)))\n",
            "C:\\Users\\kpdla\\AppData\\Local\\Temp/ipykernel_14556/2291245578.py:4: RuntimeWarning: overflow encountered in exp\n",
            "  s = 1/(1+np.exp(-x))\n",
            "C:\\Users\\kpdla\\AppData\\Local\\Temp/ipykernel_14556/3707342468.py:8: RuntimeWarning: divide by zero encountered in log\n",
            "  J = -(1/m+1)*(np.dot(Y.T,np.log(Yhat))+np.dot((1-Y).T,np.log(1-Yhat)))\n",
            "C:\\Users\\kpdla\\AppData\\Local\\Temp/ipykernel_14556/2291245578.py:4: RuntimeWarning: overflow encountered in exp\n",
            "  s = 1/(1+np.exp(-x))\n",
            "C:\\Users\\kpdla\\AppData\\Local\\Temp/ipykernel_14556/3707342468.py:8: RuntimeWarning: divide by zero encountered in log\n",
            "  J = -(1/m+1)*(np.dot(Y.T,np.log(Yhat))+np.dot((1-Y).T,np.log(1-Yhat)))\n",
            "C:\\Users\\kpdla\\AppData\\Local\\Temp/ipykernel_14556/2291245578.py:4: RuntimeWarning: overflow encountered in exp\n",
            "  s = 1/(1+np.exp(-x))\n",
            "C:\\Users\\kpdla\\AppData\\Local\\Temp/ipykernel_14556/3707342468.py:8: RuntimeWarning: divide by zero encountered in log\n",
            "  J = -(1/m+1)*(np.dot(Y.T,np.log(Yhat))+np.dot((1-Y).T,np.log(1-Yhat)))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CYxop6ioWjU2",
        "outputId": "6292ce2e-b920-4b72-8be5-c61a95302eb0"
      },
      "source": [
        "accuracy_list=[]\n",
        "for i in range(len(models_list)):\n",
        "    accuracy_list.append(models_list[i]['test_accuracy'])\n",
        "ove_vs_all_accuracy=np.mean(accuracy_list)\n",
        "print('The accuracy of the Onve-Vs-All model is:', ove_vs_all_accuracy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The accuracy of the Onve-Vs-All model is: 0.9200000000000002\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8rqaYzG6WjU2"
      },
      "source": [
        "def one_vs_all(data, models_list):\n",
        "    pred_matrix = np.zeros((data.shape[0],10))\n",
        "    for i in range(len(models_list)):\n",
        "        W = models_list[i]['weights']\n",
        "        B = models_list[i]['bias']\n",
        "        Yhat, Yhat_prob = predict(data,W,B)\n",
        "        pred_matrix[:,i] = Yhat_prob.T\n",
        "    max_prob_vec = np.amax(pred_matrix, axis=1, keepdims=True)\n",
        "    pred_matrix_max_prob = (pred_matrix == max_prob_vec).astype(int)\n",
        "    labels=[]\n",
        "    for j in range(pred_matrix_max_prob.shape[0]):\n",
        "        idx = np.where(pred_matrix_max_prob[j,:]==1)\n",
        "        labels.append(idx)\n",
        "    labels = np.vstack(labels).flatten()\n",
        "    return labels"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0IVVIi21WjU2",
        "outputId": "308a4a60-6716-4d3c-89f9-ee32f5f950e7"
      },
      "source": [
        "# Testing the one-vs-all function on 6 random examples \n",
        "\n",
        "examples_number = 6 \n",
        "index_random_sample = np.random.randint(70, size=(1,examples_number))\n",
        "example = train1[index_random_sample].reshape(examples_number ,784)\n",
        "true_labels = label1[index_random_sample].flatten().astype(int)\n",
        "label = one_vs_all(example, models_list)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\kpdla\\AppData\\Local\\Temp/ipykernel_14556/2291245578.py:4: RuntimeWarning: overflow encountered in exp\n",
            "  s = 1/(1+np.exp(-x))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "57vqr_6-WjU2",
        "outputId": "a057c6a9-e924-4e52-ba64-ef9fe940a5ea"
      },
      "source": [
        "plt.figure(figsize=(14,8))\n",
        "for i in range(examples_number):\n",
        "    image = example[i].reshape(28,28)\n",
        "    plt.subplot(2,3,i+1)\n",
        "    plt.imshow(image)\n",
        "    title = f\"True label is: {true_labels[i]}, predicted as: {label[i]}\"\n",
        "    plt.title(title)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x576 with 6 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nIyBkIi2WjU2",
        "outputId": "6455c4d5-943c-4f4d-fb4c-c166a61d5e8a"
      },
      "source": [
        "import syft as sy\n",
        "hook = sy.TorchHook(th)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "ename": "AttributeError",
          "evalue": "module 'syft' has no attribute 'TorchHook'",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_16424/3565910924.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0msyft\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0msy\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mhook\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTorchHook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[1;31mAttributeError\u001b[0m: module 'syft' has no attribute 'TorchHook'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rG8pZ2qKWjU2",
        "outputId": "c93789e7-5ade-4386-d547-04405a372aef"
      },
      "source": [
        "pip install syft --user"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: syft in d:\\anaconda\\lib\\site-packages (0.5.0)\n",
            "Requirement already satisfied: cachetools in d:\\anaconda\\lib\\site-packages (from syft) (4.2.4)\n",
            "Requirement already satisfied: pyarrow in d:\\anaconda\\lib\\site-packages (from syft) (6.0.0)\n",
            "Requirement already satisfied: dpcontracts in d:\\anaconda\\lib\\site-packages (from syft) (0.6.0)\n",
            "Requirement already satisfied: sqlitedict in d:\\anaconda\\lib\\site-packages (from syft) (1.7.0)\n",
            "Requirement already satisfied: websocket-client in d:\\anaconda\\lib\\site-packages (from syft) (1.2.1)\n",
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            "Collecting torchvision<=0.9.1,>=0.5\n",
            "  Using cached torchvision-0.9.1-cp38-cp38-win_amd64.whl (852 kB)\n",
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            "Note: you may need to restart the kernel to use updated packages.\n",
            "Installing collected packages: torchvision\n",
            "Successfully installed torchvision-0.9.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HGBI1s8hWjU3"
      },
      "source": [
        "import syft as sy"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "MSG3yDIKWjU3",
        "outputId": "562b7f0e-446b-45e5-9d5f-347526627e7e"
      },
      "source": [
        "pip install syft==0.2.9 --user"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Requirement already satisfied: testpath in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook==5.7.8->syft==0.2.9) (0.5.0)\n",
            "Requirement already satisfied: jsonschema!=2.5.0,>=2.4 in /usr/local/lib/python3.7/dist-packages (from nbformat->notebook==5.7.8->syft==0.2.9) (2.6.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert->notebook==5.7.8->syft==0.2.9) (21.2)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert->notebook==5.7.8->syft==0.2.9) (0.5.1)\n",
            "Requirement already satisfied: pyparsing<3,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->bleach->nbconvert->notebook==5.7.8->syft==0.2.9) (2.4.7)\n",
            "Building wheels for collected packages: openmined.threepio, psutil, tornado, phe\n",
            "  Building wheel for openmined.threepio (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for openmined.threepio: filename=openmined.threepio-0.2.0-py3-none-any.whl size=80095 sha256=03e79af3a6bde93a7eae23451d67df6ae8dedfa001aa65c43caddcf0e7fa82fa\n",
            "  Stored in directory: /root/.cache/pip/wheels/97/3d/ce/4ca4386006e622cb87d5116e5e65026ec021d3cf906a9b3d5d\n",
            "  Building wheel for psutil (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for psutil: filename=psutil-5.7.0-cp37-cp37m-linux_x86_64.whl size=276512 sha256=208ce0e1b9c86a0e68bce9748e84727ed4326d81e1bdf4d74905bd474b22160e\n",
            "  Stored in directory: /root/.cache/pip/wheels/b6/e7/50/aee9cc966163d74430f13f208171dee22f11efa4a4a826661c\n",
            "  Building wheel for tornado (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for tornado: filename=tornado-4.5.3-cp37-cp37m-linux_x86_64.whl size=434043 sha256=76dbfda7f996101b1e4e006b331209c05827d8953ab0bad8d04b597f095b5574\n",
            "  Stored in directory: /root/.cache/pip/wheels/a2/45/43/36ec7a893e16c1212a6b1505ded0a2d73cf8e863a0227c8e04\n",
            "  Building wheel for phe (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for phe: filename=phe-1.4.0-py2.py3-none-any.whl size=37362 sha256=547a3a8cb3727ed06864b518dbce76559f68e1122a76bc1297fb27ad9be9a9cc\n",
            "  Stored in directory: /root/.cache/pip/wheels/bb/ac/9b/b07a04fe6bb1418ab4ee06d6652757aef848b80363c4dac507\n",
            "Successfully built openmined.threepio psutil tornado phe\n",
            "Installing collected packages: tornado, python-engineio, netifaces, idna, bidict, torch, requests, python-socketio, pylibsrtp, pyee, numpy, cryptography, crc32c, av, aioice, websockets, websocket-client, torchvision, tblib, syft-proto, shaloop, RestrictedPython, requests-toolbelt, psutil, phe, openmined.threepio, notebook, lz4, importlib-resources, flask-socketio, aiortc, syft\n",
            "\u001b[33m  WARNING: The scripts convert-caffe2-to-onnx and convert-onnx-to-caffe2 are installed in '/root/.local/bin' which is not on PATH.\n",
            "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\n",
            "\u001b[33m  WARNING: The scripts f2py, f2py3 and f2py3.7 are installed in '/root/.local/bin' which is not on PATH.\n",
            "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\n",
            "\u001b[33m  WARNING: The script pyav is installed in '/root/.local/bin' which is not on PATH.\n",
            "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\n",
            "\u001b[33m  WARNING: The script pheutil is installed in '/root/.local/bin' which is not on PATH.\n",
            "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\n",
            "\u001b[33m  WARNING: The scripts jupyter-bundlerextension, jupyter-nbextension, jupyter-notebook and jupyter-serverextension are installed in '/root/.local/bin' which is not on PATH.\n",
            "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "torchtext 0.11.0 requires torch==1.10.0, but you have torch 1.4.0 which is incompatible.\n",
            "google-colab 1.0.0 requires notebook~=5.3.0; python_version >= \"3.0\", but you have notebook 5.7.8 which is incompatible.\n",
            "google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.22.0 which is incompatible.\n",
            "google-colab 1.0.0 requires tornado~=5.1.0; python_version >= \"3.0\", but you have tornado 4.5.3 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n",
            "bokeh 2.3.3 requires tornado>=5.1, but you have tornado 4.5.3 which is incompatible.\n",
            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Successfully installed RestrictedPython-5.1 aioice-0.6.18 aiortc-0.9.28 av-8.0.3 bidict-0.21.4 crc32c-2.2.post0 cryptography-35.0.0 flask-socketio-4.2.1 idna-2.8 importlib-resources-1.5.0 lz4-3.0.2 netifaces-0.11.0 notebook-5.7.8 numpy-1.18.5 openmined.threepio-0.2.0 phe-1.4.0 psutil-5.7.0 pyee-8.2.2 pylibsrtp-0.6.8 python-engineio-4.3.0 python-socketio-5.4.1 requests-2.22.0 requests-toolbelt-0.9.1 shaloop-0.2.1a11 syft-0.2.9 syft-proto-0.5.3 tblib-1.6.0 torch-1.4.0 torchvision-0.5.0 tornado-4.5.3 websocket-client-0.57.0 websockets-8.1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "numpy",
                  "psutil",
                  "tornado"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YiAAuhEsWjU4"
      },
      "source": [
        "hook = sy.TorchHook(th)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8xwNRo7GWjU4"
      },
      "source": [
        "import torch as th\n",
        "import numpy as np\n",
        "from torchvision import datasets, transforms\n",
        "import torchvision.datasets as datasets\n",
        "from torch.utils.data import Subset\n",
        "from torch import nn\n",
        "import torch.nn.functional as F\n",
        "from torch import optim\n",
        "import syft as sy\n",
        "import helper"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yvugB6mDZZVO"
      },
      "source": [
        "\n",
        "#Method to create 10 virtual workers and move to a list of workers\n",
        "def create_workers():\n",
        "  workers = []\n",
        "  cartman = sy.VirtualWorker(hook, id = \"cartman\")\n",
        "  workers.append(cartman)\n",
        "  kyle = sy.VirtualWorker(hook, id = \"kyle\")\n",
        "  workers.append(kyle)\n",
        "  kenny = sy.VirtualWorker(hook, id = \"kenny\")\n",
        "  workers.append(kenny)\n",
        "  stan = sy.VirtualWorker(hook, id = \"stan\")\n",
        "  workers.append(stan)\n",
        "  butters = sy.VirtualWorker(hook, id = \"butters\")\n",
        "  workers.append(butters)\n",
        "  wendy = sy.VirtualWorker(hook, id = \"wendy\")\n",
        "  workers.append(wendy)\n",
        "  heidi = sy.VirtualWorker(hook, id = \"heidi\")\n",
        "  workers.append(heidi)\n",
        "  bebe = sy.VirtualWorker(hook, id = \"bebe\")\n",
        "  workers.append(bebe)\n",
        "  nichole = sy.VirtualWorker(hook, id = \"nichole\")\n",
        "  workers.append(nichole)\n",
        "  patty = sy.VirtualWorker(hook, id = \"patty\")\n",
        "  workers.append(patty)\n",
        "  return workers"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xbn0E5CIZqxp"
      },
      "source": [
        "\n",
        "#Method to clear out every tensor stored in the list of virtual workers\n",
        "def clear_workers(workers):\n",
        "  for worker in workers:\n",
        "    worker.clear_objects()"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lbWm2F4SZuIF"
      },
      "source": [
        "\n",
        "#Method to split the mnist test dataset into the various workers and also to load the mnist test dataset into a test loader\n",
        "def create_federated_and_test_loaders(workers, trainset, testset):\n",
        "  federated_train_loader = sy.FederatedDataLoader(\n",
        "      trainset.federate(workers), \n",
        "      batch_size=32, shuffle=True)\n",
        "\n",
        "  test_loader = th.utils.data.DataLoader(testset, batch_size=64, shuffle=False)\n",
        "  return federated_train_loader, test_loader"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R-Pu6THcZ5rQ"
      },
      "source": [
        "#Method to bind remote optimizer functions to the Initial states of the remote models \n",
        "def create_models(workers, lr):\n",
        "  remoteModels = list()\n",
        "  remoteOptimizers = list()\n",
        "  for worker in workers:\n",
        "    model = classifier()\n",
        "    model = model.send(worker)\n",
        "    remoteOptimizers.append(optim.SGD(model.parameters(), lr))\n",
        "    remoteModels.append(model)\n",
        "  return remoteModels, remoteOptimizers"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GKmFvvADZ5wI"
      },
      "source": [
        "#Method to train models on the virtual workers without moving any gradients to the central model until the gradients have been collated.\n",
        "def create_train_federated_models(workers, loader, lr = 0.12, epoch = 5):\n",
        "  #sends model to first virtual worker\n",
        "  virtual_models, virtual_optimizers = create_models(workers, lr)\n",
        "#   virtual_model = classifier().send(workers[0])\n",
        "#   optimizer = optim.SGD(virtual_model.parameters(), lr)\n",
        "  criterion = nn.NLLLoss()\n",
        "  for n in range(epoch):\n",
        "    \n",
        "    #Integer to keep up with first index.\n",
        "    i = 0\n",
        "    \n",
        "    #Integer to keep up with current worker while training\n",
        "    j = 0\n",
        "    \n",
        "    #Integer to count number of mini-batches per worker\n",
        "    n_mbatch = 0\n",
        "    \n",
        "    #Variable to keep up with current worker while looping\n",
        "    dbLoc = None\n",
        "    \n",
        "    #Variable to store cummulative loss.\n",
        "    cum_loss = 0\n",
        "    \n",
        "    \n",
        "    for batch_idx, (imgs, labels) in enumerate(loader):\n",
        "      \n",
        "      #condition to set dbLoc to the first worker\n",
        "      if i == 0:\n",
        "        i = 2\n",
        "        dbLoc = imgs.location\n",
        "        \n",
        "      #condition to change dbLoc if img is stored on a different worker and also calculate loss\n",
        "      if dbLoc is not imgs.location:\n",
        "        print(\"The total loss for {0} for epoch {2} is {1}\".format(workers[j].id, cum_loss / n_mbatch, n+1))\n",
        "        dbLoc = imgs.location\n",
        "        j += 1\n",
        "        \n",
        "        #Moving the model to a new worker\n",
        "#         virtual_model.move(dbLoc)\n",
        "        \n",
        "        #Resetting the cummulative loss and batch count to zero for new worker\n",
        "        cum_loss = 0\n",
        "        n_mbatch = 0\n",
        "\n",
        "      virtual_optimizers[j].zero_grad()\n",
        "      output = virtual_models[j].forward(imgs)\n",
        "      loss = criterion(output, labels)\n",
        "      loss.backward()\n",
        "      virtual_optimizers[j].step()\n",
        "      cum_loss +=  loss.get().item()\n",
        "      n_mbatch += 1\n",
        "    print(\"The total loss for {0} is {1}\".format(workers[j].id, cum_loss / n_mbatch))\n",
        "  return virtual_models"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-p6DPErbaIWp"
      },
      "source": [
        "#Method to return the model to the central machine\n",
        "def create_central_model(model):\n",
        "  return model.get()"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wmMYs_htaKbd"
      },
      "source": [
        "\n",
        "#Method to convert the gradients of the remote models to their integar representation, encrypt and distribute among the provided workers\n",
        "def share_gradients(models, workers):\n",
        "  for model in models:\n",
        "    model.fc1.weight.data = model.fc1.weight.data.fix_prec().share(*workers)\n",
        "    model.fc1.bias.data = model.fc1.bias.data.fix_prec().share(*workers)\n",
        "    model.fc2.weight.data = model.fc2.weight.data.fix_prec().share(*workers)\n",
        "    model.fc2.bias.data = model.fc2.bias.data.fix_prec().share(*workers)\n",
        "    model.fc3.weight.data = model.fc3.weight.data.fix_prec().share(*workers)\n",
        "    model.fc3.bias.data = model.fc3.bias.data.fix_prec().share(*workers)\n",
        "    model.fc4.weight.data = model.fc4.weight.data.fix_prec().share(*workers)\n",
        "    model.fc4.bias.data = model.fc4.bias.data.fix_prec().share(*workers)\n",
        "    model.fc5.weight.data = model.fc5.weight.data.fix_prec().share(*workers)\n",
        "    model.fc5.bias.data = model.fc5.bias.data.fix_prec().share(*workers)   \n",
        "  return models"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Na0iuTqAaNET"
      },
      "source": [
        "#Method to aggregate the gradients accross the shared devices and return the parameters\n",
        "def aggregate_grads(models):\n",
        "  fc1_weight = list()\n",
        "  fc1_bias = list()\n",
        "  fc2_weight = list()\n",
        "  fc2_bias = list()\n",
        "  fc3_weight = list()\n",
        "  fc3_bias = list()\n",
        "  fc4_weight = list()\n",
        "  fc4_bias = list()\n",
        "  fc5_weight = list()\n",
        "  fc5_bias = list()\n",
        "  for model in models:\n",
        "    fc1_weight.append(model.fc1.weight.data.clone().get())\n",
        "    fc1_bias.append(model.fc1.bias.data.clone().get())\n",
        "    fc2_weight.append(model.fc2.weight.data.clone().get())\n",
        "    fc2_bias.append(model.fc2.bias.data.clone().get())\n",
        "    fc3_weight.append(model.fc3.weight.data.clone().get())\n",
        "    fc3_bias.append(model.fc3.bias.data.clone().get())\n",
        "    fc4_weight.append(model.fc4.weight.data.clone().get())\n",
        "    fc4_bias.append(model.fc4.bias.data.clone().get())\n",
        "    fc5_weight.append(model.fc5.weight.data.clone().get())\n",
        "    fc5_bias.append(model.fc5.bias.data.clone().get())\n",
        "  params = {}\n",
        "  params[\"fc1.weight\"] = (sum(fc1_weight) / len(fc1_weight)).get().float_prec()\n",
        "  params[\"fc1.bias\"] = (sum(fc1_bias) / len(fc1_bias)).get().float_prec()\n",
        "  params[\"fc2.weight\"] = (sum(fc2_weight) / len(fc2_weight)).get().float_prec()\n",
        "  params[\"fc2.bias\"] = (sum(fc2_bias) / len(fc2_bias)).get().float_prec()\n",
        "  params[\"fc3.weight\"] = (sum(fc3_weight) / len(fc3_weight)).get().float_prec()\n",
        "  params[\"fc3.bias\"] = (sum(fc3_bias) / len(fc3_bias)).get().float_prec()\n",
        "  params[\"fc4.weight\"] = (sum(fc4_weight) / len(fc4_weight)).get().float_prec()\n",
        "  params[\"fc4.bias\"] = (sum(fc4_bias) / len(fc4_bias)).get().float_prec()\n",
        "  params[\"fc5.weight\"] = (sum(fc5_weight) / len(fc5_weight)).get().float_prec()\n",
        "  params[\"fc5.bias\"] = (sum(fc5_bias) / len(fc5_bias)).get().float_prec()\n",
        "  return params"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qc7r2EO_aOiS"
      },
      "source": [
        "\n",
        "#Method to update the central model with the aggregated parameters from the remote models\n",
        "def update_central_model(params):\n",
        "  model = classifier()\n",
        "  model.fc1.weight.data = params[\"fc1.weight\"]\n",
        "  model.fc1.bias.data = params[\"fc1.bias\"]\n",
        "  model.fc2.weight.data = params[\"fc2.weight\"]\n",
        "  model.fc2.bias.data = params[\"fc2.bias\"]\n",
        "  model.fc3.weight.data = params[\"fc3.weight\"]\n",
        "  model.fc3.bias.data = params[\"fc3.bias\"]\n",
        "  model.fc4.weight.data = params[\"fc4.weight\"]\n",
        "  model.fc4.bias.data = params[\"fc4.bias\"]\n",
        "  model.fc5.weight.data = params[\"fc5.weight\"]\n",
        "  model.fc5.bias.data = params[\"fc5.bias\"]  \n",
        "  return model"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ebq7xaqcaZDz"
      },
      "source": [
        "#Classifier for creating the models\n",
        "class classifier(nn.Module):\n",
        "  def __init__(self):\n",
        "    super().__init__() \n",
        "    self.fc1 = nn.Linear(784, 256)\n",
        "    self.fc2 = nn.Linear(256, 128)\n",
        "    self.fc3 = nn.Linear(128, 64)\n",
        "    self.fc4 = nn.Linear(64, 32)\n",
        "    self.fc5 = nn.Linear(32, 10)\n",
        "    \n",
        "  def forward(self, x):\n",
        "    x = x.view(x.shape[0], -1)\n",
        "    x = F.relu(self.fc1(x))\n",
        "    x = F.relu(self.fc2(x))\n",
        "    x = F.relu(self.fc3(x))\n",
        "    x = F.relu(self.fc4(x))\n",
        "    x = F.log_softmax(self.fc5(x), dim = 1)   \n",
        "    return x"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333,
          "referenced_widgets": [
            "7feeef269120422592ee887b2ec00d23",
            "a916444468ad423aad0ea2a867e22def",
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            "b725178a28814a2491c9e8714be20c61",
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            "8324a22996684551a583271c11a29d03"
          ]
        },
        "id": "pTeFiGM4adIf",
        "outputId": "4b17d8ce-261c-4a16-9be0-f72634676166"
      },
      "source": [
        "# Application of transforms to normalize the mnist data\n",
        "transform = transforms.Compose([transforms.ToTensor(),\n",
        "                                transforms.Normalize((0.5,), (0.5,))])\n",
        "mnist_trainset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)\n",
        "mnist_testset = datasets.MNIST(root='./data', train=False, download=True, transform=transform)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7feeef269120422592ee887b2ec00d23",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "0it [00:00, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e56002908e984dcf896478654b104676",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "0it [00:00, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4e5a19f1a4f44848be5ec795c1acd738",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "0it [00:00, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f607a05efd564e27a7e3107ad0005631",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "0it [00:00, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "Processing...\n",
            "Done!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1RSKDVWwadKS"
      },
      "source": [
        "workers = create_workers()\n",
        "clear_workers(workers)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OcipIwTjadMl"
      },
      "source": [
        "federatedset, testset= create_federated_and_test_loaders(workers, mnist_trainset, mnist_testset)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 975
        },
        "id": "_UsHeJxNadP_",
        "outputId": "338ae26b-a030-432b-b6ca-5fc14cee62ff"
      },
      "source": [
        "remoteModels = create_train_federated_models(workers, federatedset, lr = 0.12, epoch = 6)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The total loss for cartman for epoch 1 is 1.9477787385595606\n",
            "The total loss for kyle for epoch 1 is 1.8399215407193976\n",
            "The total loss for kenny for epoch 1 is 1.8249504978352404\n",
            "The total loss for stan for epoch 1 is 1.8632983977490283\n",
            "The total loss for butters for epoch 1 is 1.8596898155643584\n",
            "The total loss for wendy for epoch 1 is 1.9884119464996013\n",
            "The total loss for heidi for epoch 1 is 1.8903710157313245\n",
            "The total loss for bebe for epoch 1 is 1.813361494465077\n",
            "The total loss for nichole for epoch 1 is 1.9305312481332333\n",
            "The total loss for patty is 1.8648720516803416\n",
            "The total loss for cartman for epoch 2 is 0.9526580378730246\n",
            "The total loss for kyle for epoch 2 is 0.8320544186107656\n",
            "The total loss for kenny for epoch 2 is 1.4729449184651071\n",
            "The total loss for stan for epoch 2 is 0.9313496928265754\n",
            "The total loss for butters for epoch 2 is 0.8848136994749942\n",
            "The total loss for wendy for epoch 2 is 1.0599777519385865\n",
            "The total loss for heidi for epoch 2 is 0.9350778928145449\n",
            "The total loss for bebe for epoch 2 is 0.8564889576840908\n",
            "The total loss for nichole for epoch 2 is 0.914997380306112\n",
            "The total loss for patty is 0.7944775625587778\n",
            "The total loss for cartman for epoch 3 is 0.5297578865701847\n",
            "The total loss for kyle for epoch 3 is 0.49647902649767855\n",
            "The total loss for kenny for epoch 3 is 0.7906437217396625\n",
            "The total loss for stan for epoch 3 is 0.5794438903636121\n",
            "The total loss for butters for epoch 3 is 0.5389006232961695\n",
            "The total loss for wendy for epoch 3 is 0.6592415873199067\n",
            "The total loss for heidi for epoch 3 is 0.5658078579668034\n",
            "The total loss for bebe for epoch 3 is 0.6154995536074993\n",
            "The total loss for nichole for epoch 3 is 0.5273655457223984\n",
            "The total loss for patty is 0.41380153457693597\n",
            "The total loss for cartman for epoch 4 is 0.38909399402426914\n",
            "The total loss for kyle for epoch 4 is 0.39490476456728385\n",
            "The total loss for kenny for epoch 4 is 0.5358767670361285\n",
            "The total loss for stan for epoch 4 is 0.4089753421697211\n",
            "The total loss for butters for epoch 4 is 0.45693623730318345\n",
            "The total loss for wendy for epoch 4 is 0.44835079104659403\n",
            "The total loss for heidi for epoch 4 is 0.38514181646577855\n",
            "The total loss for bebe for epoch 4 is 0.4102823236521254\n",
            "The total loss for nichole for epoch 4 is 0.4180928303642159\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-21-99f531d4081a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mremoteModels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcreate_train_federated_models\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mworkers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfederatedset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.12\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-12-11bf5bccad4a>\u001b[0m in \u001b[0;36mcreate_train_federated_models\u001b[0;34m(workers, loader, lr, epoch)\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mbatch_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mimgs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloader\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     27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m       \u001b[0;31m#condition to set dbLoc to the first worker\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/root/.local/lib/python3.7/site-packages/syft/frameworks/torch/fl/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    248\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    249\u001b[0m             \u001b[0miterator\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterators\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[0m\n\u001b[0;32m--> 250\u001b[0;31m             \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\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    251\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    252\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/root/.local/lib/python3.7/site-packages/syft/frameworks/torch/fl/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__next__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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--> 102\u001b[0;31m         \u001b[0mbatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_batch\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    103\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mbatch\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/root/.local/lib/python3.7/site-packages/syft/frameworks/torch/fl/dataloader.py\u001b[0m in \u001b[0;36m_get_batch\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     83\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[1;32m     84\u001b[0m             \u001b[0mindices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample_iter\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mworker\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---> 85\u001b[0;31m             \u001b[0mbatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcollate_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfederated_dataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mworker\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mindices\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     86\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m         \u001b[0;31m# All the data for this worker has been used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/root/.local/lib/python3.7/site-packages/syft/frameworks/torch/fl/dataloader.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     83\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[1;32m     84\u001b[0m             \u001b[0mindices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample_iter\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mworker\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---> 85\u001b[0;31m             \u001b[0mbatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcollate_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfederated_dataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mworker\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mindices\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     86\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m         \u001b[0;31m# All the data for this worker has been used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/root/.local/lib/python3.7/site-packages/syft/frameworks/torch/fl/dataset.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, index)\u001b[0m\n\u001b[1;32m     55\u001b[0m             \u001b[0mdata_elem\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_elem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\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     56\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 57\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mdata_elem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtargets\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\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     58\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransform\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/root/.local/lib/python3.7/site-packages/syft/generic/frameworks/hook/hook.py\u001b[0m in \u001b[0;36moverloaded_native_method\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    207\u001b[0m                 \u001b[0;31m# Send the new command to the appropriate class and get the response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    208\u001b[0m                 \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_self\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 209\u001b[0;31m                 \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnew_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mnew_kwargs\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    210\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    211\u001b[0m                 \u001b[0;31m# For inplace methods, just directly return self\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/root/.local/lib/python3.7/site-packages/syft/generic/frameworks/hook/pointers.py\u001b[0m in \u001b[0;36moverloaded_pointer_method\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     82\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m             \u001b[0;31m# Send the command\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 84\u001b[0;31m             \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mowner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend_command\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattr\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[0m\n\u001b[0m\u001b[1;32m     85\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m             \u001b[0;31m# For inplace methods, just directly return self\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/root/.local/lib/python3.7/site-packages/syft/workers/base.py\u001b[0m in \u001b[0;36msend_command\u001b[0;34m(self, recipient, cmd_name, target, args_, kwargs_, return_ids, return_value)\u001b[0m\n\u001b[1;32m    523\u001b[0m                 \u001b[0mcmd_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\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[0mreturn_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    524\u001b[0m             )\n\u001b[0;32m--> 525\u001b[0;31m             \u001b[0mret_val\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend_msg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrecipient\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    526\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mResponseSignatureError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    527\u001b[0m             \u001b[0mret_val\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[0;32m/root/.local/lib/python3.7/site-packages/syft/workers/base.py\u001b[0m in \u001b[0;36msend_msg\u001b[0;34m(self, message, location)\u001b[0m\n\u001b[1;32m    311\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    312\u001b[0m         \u001b[0;31m# Step 1: serialize the message to a binary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 313\u001b[0;31m         \u001b[0mbin_message\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mserde\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mserialize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mworker\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\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    314\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    315\u001b[0m         \u001b[0;31m# Step 2: send the message and wait for a response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/root/.local/lib/python3.7/site-packages/syft/serde/serde.py\u001b[0m in \u001b[0;36mserialize\u001b[0;34m(obj, worker, simplified, force_full_simplification, strategy)\u001b[0m\n\u001b[1;32m     45\u001b[0m         \u001b[0mstrategy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmsgpack_serialize\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 47\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mstrategy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mworker\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msimplified\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforce_full_simplification\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     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/root/.local/lib/python3.7/site-packages/syft/serde/msgpack/serde.py\u001b[0m in \u001b[0;36mserialize\u001b[0;34m(obj, worker, simplified, force_full_simplification)\u001b[0m\n\u001b[1;32m    340\u001b[0m         \u001b[0mworker\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msyft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlocal_worker\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    341\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 342\u001b[0;31m     \u001b[0msimple_objects\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_serialize_msgpack_simple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mworker\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msimplified\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforce_full_simplification\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    343\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_serialize_msgpack_binary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msimple_objects\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    344\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/root/.local/lib/python3.7/site-packages/syft/serde/msgpack/serde.py\u001b[0m in \u001b[0;36m_serialize_msgpack_simple\u001b[0;34m(obj, worker, simplified, force_full_simplification)\u001b[0m\n\u001b[1;32m    280\u001b[0m             \u001b[0msimple_objects\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_force_full_simplify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mworker\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    281\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 282\u001b[0;31m             \u001b[0msimple_objects\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_simplify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mworker\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\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    283\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    284\u001b[0m         \u001b[0msimple_objects\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/root/.local/lib/python3.7/site-packages/syft/serde/msgpack/serde.py\u001b[0m in \u001b[0;36m_simplify\u001b[0;34m(worker, obj, **kwargs)\u001b[0m\n\u001b[1;32m    426\u001b[0m         result = (\n\u001b[1;32m    427\u001b[0m             \u001b[0mmsgpack_global_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msimplifiers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcurrent_type\u001b[0m\u001b[0;34m]\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[0;32m--> 428\u001b[0;31m             \u001b[0mmsgpack_global_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msimplifiers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcurrent_type\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[0mworker\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\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[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    429\u001b[0m         )\n\u001b[1;32m    430\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/root/.local/lib/python3.7/site-packages/syft/messaging/message.py\u001b[0m in \u001b[0;36msimplify\u001b[0;34m(worker, ptr)\u001b[0m\n\u001b[1;32m    146\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msimplify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    147\u001b[0m         \"\"\"\n\u001b[0;32m--> 148\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0msy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mserde\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmsgpack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mserde\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_simplify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mworker\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mptr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m)\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    149\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/root/.local/lib/python3.7/site-packages/syft/serde/msgpack/serde.py\u001b[0m in \u001b[0;36m_simplify\u001b[0;34m(worker, obj, **kwargs)\u001b[0m\n\u001b[1;32m    426\u001b[0m         result = (\n\u001b[1;32m    427\u001b[0m             \u001b[0mmsgpack_global_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msimplifiers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcurrent_type\u001b[0m\u001b[0;34m]\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[0;32m--> 428\u001b[0;31m             \u001b[0mmsgpack_global_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msimplifiers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcurrent_type\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[0mworker\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\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[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    429\u001b[0m         )\n\u001b[1;32m    430\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/root/.local/lib/python3.7/site-packages/syft/execution/computation.py\u001b[0m in \u001b[0;36msimplify\u001b[0;34m(worker, action)\u001b[0m\n\u001b[1;32m     45\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msimplify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mworker\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m         \"\"\"\n\u001b[0;32m---> 47\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mAction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msimplify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mworker\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maction\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     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/root/.local/lib/python3.7/site-packages/syft/execution/action.py\u001b[0m in \u001b[0;36msimplify\u001b[0;34m(worker, action)\u001b[0m\n\u001b[1;32m    137\u001b[0m             \u001b[0msy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mserde\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmsgpack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mserde\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_simplify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mworker\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\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    138\u001b[0m             \u001b[0msy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mserde\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmsgpack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mserde\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_simplify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mworker\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtarget\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--> 139\u001b[0;31m             \u001b[0msy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mserde\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmsgpack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mserde\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_simplify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mworker\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maction\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[0m\u001b[1;32m    140\u001b[0m             \u001b[0msy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mserde\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmsgpack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mserde\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_simplify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mworker\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maction\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[1;32m    141\u001b[0m             \u001b[0msy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mserde\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmsgpack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mserde\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_simplify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mworker\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturn_ids\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/root/.local/lib/python3.7/site-packages/syft/serde/msgpack/serde.py\u001b[0m in \u001b[0;36m_simplify\u001b[0;34m(worker, obj, **kwargs)\u001b[0m\n\u001b[1;32m    425\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcurrent_type\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmsgpack_global_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msimplifiers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    426\u001b[0m         result = (\n\u001b[0;32m--> 427\u001b[0;31m             \u001b[0mmsgpack_global_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msimplifiers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcurrent_type\u001b[0m\u001b[0;34m]\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    428\u001b[0m             \u001b[0mmsgpack_global_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msimplifiers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcurrent_type\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[0mworker\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    429\u001b[0m         )\n",
            "\u001b[0;32m/root/.local/lib/python3.7/site-packages/syft/serde/msgpack/serde.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     70\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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     71\u001b[0m             \u001b[0mself\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\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---> 72\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped_func\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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     73\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MsYtc5-3a9QC"
      },
      "source": [
        "remoteModels = share_gradients(remoteModels, workers[5:8])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I9RgRquqa9Rv"
      },
      "source": [
        "params  = aggregate_grads(remoteModels)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xU0eQi6Ka9VI"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}