DNN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "DNN.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/anujdutt9/PyTorch-DeepLearning/blob/master/DNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QWjTkLUEk5hj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "from sklearn.datasets import make_circles\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-2rGqa4un1yP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Generate a Dataset\n",
        "num_pts = 500\n",
        "centers = [[-0.5, 0.5], [0.5, -0.5]]\n",
        "X,y = make_circles(n_samples=num_pts, noise=0.1, factor=0.2 ,random_state=101)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r2o5idXRoU5t",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "4391bdbf-c130-4dcf-8795-60935cedeac9"
      },
      "source": [
        "X"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       [-2.63336313e-01,  1.04579325e-01]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RbULgfHtofm-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "outputId": "4c5056c3-55c0-4e8b-b898-3cae0cf79b1d"
      },
      "source": [
        "y"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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      "metadata": {
        "id": "ZWqew-XEof76",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Convert from Numpy to Torch Tensor\n",
        "X_data = torch.tensor(X)\n",
        "y_data = torch.tensor(y.reshape(500,1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Yv7aP7WotOG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Function to make a Scatter plot of Dataset\n",
        "def scatter_plot():\n",
        "  # Plot all values of X where y == 0 in 1st and 2nd Column of X\n",
        "  plt.scatter(X[y==0, 0], X[y==0, 1])\n",
        "\n",
        "  # Plot all values of X where y == 1 in 1st and 2nd Column of X\n",
        "  plt.scatter(X[y==1, 0], X[y==1, 1])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c-LXClKCowPX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "b2a8ed81-5157-477f-f817-f79eb204f5c9"
      },
      "source": [
        "scatter_plot()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BToS4kT1uvoAszr5UceztQLjqoCqoBJzft1CcLijaJ6iiZuTaQiFeF7ZpOzuM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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7g4vZz_2oxdO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class NN(nn.Module):\n",
        "  \n",
        "  # Constructor\n",
        "  def __init__(self, input_size, hidden_size, output_size):\n",
        "    super().__init__()\n",
        "    self.linear1 = nn.Linear(in_features=input_size, out_features=hidden_size)\n",
        "    self.linear2 = nn.Linear(in_features=hidden_size, out_features=output_size)\n",
        "  \n",
        "  # Forward Pass\n",
        "  def forward(self, x):\n",
        "    x = torch.sigmoid(self.linear1(x))\n",
        "    x = torch.sigmoid(self.linear2(x))\n",
        "    return x\n",
        "  \n",
        "  # Predict Function\n",
        "  def predict(self, x):\n",
        "    pred = self.forward(x)\n",
        "    if pred > 0.5:\n",
        "      return 1\n",
        "    else:\n",
        "      return 0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-fI0oy01pceW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "816b0d9a-baa9-41d7-9a0e-4a00d5d2117a"
      },
      "source": [
        "torch.manual_seed(2)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<torch._C.Generator at 0x7f2207979110>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oKIVYlB3pjcv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Initialize Model\n",
        "model = NN(input_size=2, hidden_size=4, output_size=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jJN1OP33prGA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "235e9ee5-19cb-4809-efd9-d2a516b1d233"
      },
      "source": [
        "model"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "NN(\n",
              "  (linear1): Linear(in_features=2, out_features=4, bias=True)\n",
              "  (linear2): Linear(in_features=4, out_features=1, bias=True)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VPWZ8ycJpsuL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "8b7a2630-d36e-4daf-fbeb-47c7a10c2a60"
      },
      "source": [
        "list(model.parameters())"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Parameter containing:\n",
              " tensor([[ 0.1622, -0.1683],\n",
              "         [ 0.1939, -0.0361],\n",
              "         [ 0.3021,  0.1683],\n",
              "         [-0.0813, -0.5717]], requires_grad=True), Parameter containing:\n",
              " tensor([ 0.1614, -0.6260,  0.0929,  0.0470], requires_grad=True), Parameter containing:\n",
              " tensor([[-0.1099,  0.4088,  0.0334,  0.2073]], requires_grad=True), Parameter containing:\n",
              " tensor([0.2116], requires_grad=True)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8y1qSydupuht",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Loss Criterion\n",
        "criterion = nn.BCELoss()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2QpnMZx4p4Ka",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Optimizer\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=0.1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AfVMlDt9p_TZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Epochs\n",
        "epochs = 1000"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hg-CIyL1qVEW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "47dd4f7a-e123-4b22-a86d-34a364efa4f6"
      },
      "source": [
        "losses = []\n",
        "\n",
        "for e in range(epochs):\n",
        "  y_pred = model.forward(X_data.type(torch.FloatTensor))\n",
        "  \n",
        "  loss = criterion(y_pred, y_data.type(torch.FloatTensor))\n",
        "  \n",
        "  print('epoch: {}'.format(e),\n",
        "       'loss: {}'.format(loss))\n",
        "  \n",
        "  \n",
        "  losses.append(loss)\n",
        "  \n",
        "  optimizer.zero_grad()\n",
        "  \n",
        "  loss.backward()\n",
        "  \n",
        "  optimizer.step()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "epoch: 0 loss: 0.7148709893226624\n",
            "epoch: 1 loss: 0.6947733759880066\n",
            "epoch: 2 loss: 0.6955791711807251\n",
            "epoch: 3 loss: 0.7015529870986938\n",
            "epoch: 4 loss: 0.7014743685722351\n",
            "epoch: 5 loss: 0.6975812911987305\n",
            "epoch: 6 loss: 0.6940743923187256\n",
            "epoch: 7 loss: 0.6930428743362427\n",
            "epoch: 8 loss: 0.694172739982605\n",
            "epoch: 9 loss: 0.695772647857666\n",
            "epoch: 10 loss: 0.6963683366775513\n",
            "epoch: 11 loss: 0.6956222653388977\n",
            "epoch: 12 loss: 0.6941139101982117\n",
            "epoch: 13 loss: 0.6926773190498352\n",
            "epoch: 14 loss: 0.6918594837188721\n",
            "epoch: 15 loss: 0.6916961669921875\n",
            "epoch: 16 loss: 0.6917821168899536\n",
            "epoch: 17 loss: 0.6915754675865173\n",
            "epoch: 18 loss: 0.6907268762588501\n",
            "epoch: 19 loss: 0.6892212629318237\n",
            "epoch: 20 loss: 0.6872692108154297\n",
            "epoch: 21 loss: 0.6851246953010559\n",
            "epoch: 22 loss: 0.682919442653656\n",
            "epoch: 23 loss: 0.6806067824363708\n",
            "epoch: 24 loss: 0.6780029535293579\n",
            "epoch: 25 loss: 0.674887478351593\n",
            "epoch: 26 loss: 0.6711087226867676\n",
            "epoch: 27 loss: 0.6666319370269775\n",
            "epoch: 28 loss: 0.6615070104598999\n",
            "epoch: 29 loss: 0.6558013558387756\n",
            "epoch: 30 loss: 0.6495347023010254\n",
            "epoch: 31 loss: 0.6426541209220886\n",
            "epoch: 32 loss: 0.635059118270874\n",
            "epoch: 33 loss: 0.6266525983810425\n",
            "epoch: 34 loss: 0.617393434047699\n",
            "epoch: 35 loss: 0.6073119044303894\n",
            "epoch: 36 loss: 0.5965033769607544\n",
            "epoch: 37 loss: 0.585098922252655\n",
            "epoch: 38 loss: 0.5732312202453613\n",
            "epoch: 39 loss: 0.5610199570655823\n",
            "epoch: 40 loss: 0.5485742688179016\n",
            "epoch: 41 loss: 0.5360053181648254\n",
            "epoch: 42 loss: 0.5234284996986389\n",
            "epoch: 43 loss: 0.5109584927558899\n",
            "epoch: 44 loss: 0.4986891448497772\n",
            "epoch: 45 loss: 0.4866773784160614\n",
            "epoch: 46 loss: 0.47493937611579895\n",
            "epoch: 47 loss: 0.4634770452976227\n",
            "epoch: 48 loss: 0.4523029029369354\n",
            "epoch: 49 loss: 0.4414536952972412\n",
            "epoch: 50 loss: 0.4309757649898529\n",
            "epoch: 51 loss: 0.4209067225456238\n",
            "epoch: 52 loss: 0.41126197576522827\n",
            "epoch: 53 loss: 0.4020371735095978\n",
            "epoch: 54 loss: 0.39321237802505493\n",
            "epoch: 55 loss: 0.38474705815315247\n",
            "epoch: 56 loss: 0.376581609249115\n",
            "epoch: 57 loss: 0.36864522099494934\n",
            "epoch: 58 loss: 0.3608599007129669\n",
            "epoch: 59 loss: 0.35315364599227905\n",
            "epoch: 60 loss: 0.3454716205596924\n",
            "epoch: 61 loss: 0.33777058124542236\n",
            "epoch: 62 loss: 0.33000993728637695\n",
            "epoch: 63 loss: 0.3221403956413269\n",
            "epoch: 64 loss: 0.3141134977340698\n",
            "epoch: 65 loss: 0.3058902621269226\n",
            "epoch: 66 loss: 0.29744598269462585\n",
            "epoch: 67 loss: 0.2887679934501648\n",
            "epoch: 68 loss: 0.2798524796962738\n",
            "epoch: 69 loss: 0.27070558071136475\n",
            "epoch: 70 loss: 0.26134493947029114\n",
            "epoch: 71 loss: 0.25179967284202576\n",
            "epoch: 72 loss: 0.2421085387468338\n",
            "epoch: 73 loss: 0.23232121765613556\n",
            "epoch: 74 loss: 0.2225024551153183\n",
            "epoch: 75 loss: 0.21273483335971832\n",
            "epoch: 76 loss: 0.20311413705348969\n",
            "epoch: 77 loss: 0.19373247027397156\n",
            "epoch: 78 loss: 0.18465574085712433\n",
            "epoch: 79 loss: 0.1759084165096283\n",
            "epoch: 80 loss: 0.167467400431633\n",
            "epoch: 81 loss: 0.15927021205425262\n",
            "epoch: 82 loss: 0.15123097598552704\n",
            "epoch: 83 loss: 0.14326846599578857\n",
            "epoch: 84 loss: 0.13534437119960785\n",
            "epoch: 85 loss: 0.12749892473220825\n",
            "epoch: 86 loss: 0.11987055838108063\n",
            "epoch: 87 loss: 0.11267239600419998\n",
            "epoch: 88 loss: 0.10611552745103836\n",
            "epoch: 89 loss: 0.1003025621175766\n",
            "epoch: 90 loss: 0.09517229348421097\n",
            "epoch: 91 loss: 0.09055835753679276\n",
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            "epoch: 94 loss: 0.07852727919816971\n",
            "epoch: 95 loss: 0.07495158910751343\n",
            "epoch: 96 loss: 0.07157357037067413\n",
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            "epoch: 98 loss: 0.06541430950164795\n",
            "epoch: 99 loss: 0.06263672560453415\n",
            "epoch: 100 loss: 0.06005874276161194\n",
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            "epoch: 109 loss: 0.04372913017868996\n",
            "epoch: 110 loss: 0.042456187307834625\n",
            "epoch: 111 loss: 0.04127059876918793\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bUPtkCIPqwP5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "708ae0f2-261a-4fee-b9f2-8cfd79a5263f"
      },
      "source": [
        "plt.plot(range(epochs), losses)\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Loss')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bvpG3c6VL3bY12+bNEgrVPC+6LkkSxceaPhkRH61YVPn86/dQPNZQav+p5CyG\nNwDHKoapC15EfCgiBiJiHcXt+LWI+AngIYrP+YaXr29dPwc8Il4A9kt6ZdL0z4EnaNBtnNgHvEFS\nd/JvvLTODbudp7nUbbsDeKukvmSU9dak7dJlfYBlHg/kvA34LvA08KtZ11PD9fonFIeWjwGPJq+3\nUdyf+iDwPeD/Af1Jf1E8E+tp4DsUz+7IfD0uc93fDHwlmb4K+HtgGPgToCNp70zmh5PlV2Vd92Wu\n6+uAoWQ7fwnoa/RtDPxn4CngceBzQEcjbmfgCxSPm4xTHBW+/3K2LfDTyfoPA++73Hp8RbOZmZU1\ny+4jMzOrgkPBzMzKHApmZlbmUDAzszKHgpmZlTkUzBKSJiQ9WvGq2d10Ja2rvAum2ULVevEuZk3j\nTES8LusizLLkkYLZRUjaK+kjkr4j6e8lXZ20r5P0teS+9g9KWpO0r5T055K+nbx+MPmonKTfT54R\n8FeSupL+d6r4PIzHJN2X0WqaAQ4Fs0pd03Yfvbti2bGIeC3wcYp3aQX4XeCzEXEt8EfAx5L2jwHf\niIjrKN6jaFfSvgHYGhHfBxwF3pG03wVcn3zOz6S1cmbV8BXNZglJJyNi8Qzte4EfiYg9yc0HX4iI\nZZJeonjP+/Gk/fmIWC5pFBiIiHMVn7EO+GoUH5qCpF8B2iLiv0h6ADhJ8fYVX4qIkymvqtmsPFIw\nq07MMn0pzlVMTzB1TO9HKd7P5gZgZ8VdQM3mnUPBrDrvrvj5d8n0NyneqRXgJ4C/TqYfBD4A5WdJ\nL53tQyW1AIMR8RDwKxRv+fyy0YrZfPFfJGZTuiQ9WjH/QESUTkvtk/QYxb/2b0vafo7i09B+meKT\n0d6XtH8QuFfS+ymOCD5A8S6YM8kBf5gEh4CPRcTRmq2R2SXyMQWzi0iOKWyKiJeyrsUsbd59ZGZm\nZR4pmJlZmUcKZmZW5lAwM7Myh4KZmZU5FMzMrMyhYGZmZQ4FMzMr+//q163WF7oO5AAAAABJRU5E\nrkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JtqfJxxDq0aR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Function to Plot Decision Boundary\n",
        "def plot_decision_boundary(X, y):\n",
        "  # Linearly spaced values between X min and X max values per column for X and Y axis\n",
        "  x_span = np.linspace(min(X[:, 0]) - 0.25, max(X[:, 0]) + 0.25)\n",
        "  y_span = np.linspace(min(X[:, 1]) - 0.25, max(X[:, 1]) + 0.25)\n",
        "  # Returns a len(x_span) by len(x_span) matrix by copying values over\n",
        "  xx, yy = np.meshgrid(x_span, y_span)\n",
        "  # Flattening out arrays\n",
        "  grid = torch.tensor(np.c_[xx.ravel(), yy.ravel()])\n",
        "  # Make Predictions\n",
        "  pred = model.forward(grid.type(torch.FloatTensor))\n",
        "  z = pred.view(xx.shape).detach().numpy()\n",
        "  # Create Contour Plot\n",
        "  plt.contourf(xx, yy, z)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Rk-DXEmtZBl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "e7f30466-6d1f-4780-ccea-3a15b9387706"
      },
      "source": [
        "# Scatter Plot with Decision Boundary\n",
        "plot_decision_boundary(X, y)\n",
        "scatter_plot()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RfQHAwwDC4v8wgN9p//xFAJ8iIhLCsJtyRqguLADKBmsyF4+qnLzsF5tr5e/n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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CW2F3Wgrtaq_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "d9f0b5d7-acbb-4945-94e7-cbb82e674447"
      },
      "source": [
        "x = 0.025\n",
        "y = 0.025\n",
        "point = torch.tensor([x, y])\n",
        "pred = model.predict(point)\n",
        "plt.plot([x], [y], 'o', markersize=10, color='red')\n",
        "print('Predicted Label: {}'.format(pred))\n",
        "plot_decision_boundary(X, y)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Predicted Label: 1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/26qsE91RFE4c+bsoOdpnxy7qUHXkf4ukh9x9vaSHovtpjrn7e6KfTRXXiQ7hou/50kb7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\nAAEi/AEgQP8PD5dL80hH2tMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}