Lab4.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "YyvrOwVVIK_O"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import h5py\n",
        "import keras\n",
        "from keras.models import Model, clone_model\n",
        "from keras import backend as K\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import sys\n",
        "import warnings\n",
        "from tqdm import tqdm\n",
        "import os"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "CLDWMshLHL-L"
      },
      "outputs": [],
      "source": [
        "def data(filepath):\n",
        "    with h5py.File(filepath, 'r') as data:\n",
        "        x_data = np.array(data['data']).transpose((0, 2, 3, 1))\n",
        "        y_data = np.array(data['label'])\n",
        "\n",
        "    return x_data, y_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "3IVGJTsqIUDS"
      },
      "outputs": [],
      "source": [
        "clean_data_x, clean_data_y = data(os.path.join(os.getcwd(), \"data\", \"cl\", \"valid.h5\"))\n",
        "badnet_data_x, badnet_data_y = data(os.path.join(os.getcwd(), \"data\", \"bd\", \"bd_valid.h5\"))\n",
        "badnet_model = keras.models.load_model(os.path.join(os.getcwd(), \"models\", \"bd_net.h5\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IAJp5iHkI69O",
        "outputId": "6decf489-4d47-4d3e-f05e-bd77a859b96d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "361/361 [==============================] - 6s 14ms/step\n",
            "clean data accuracy: 0.9864899974019226\n"
          ]
        }
      ],
      "source": [
        "predictions = badnet_model.predict(clean_data_x)\n",
        "clean_data_label_pred = np.argmax(predictions, axis=1)\n",
        "\n",
        "# Calculating clean data accuracy\n",
        "correct_predictions = np.sum(clean_data_label_pred == clean_data_y)\n",
        "total_samples = len(clean_data_y)\n",
        "clean_accuracy = (correct_predictions / total_samples)\n",
        "print(\"clean data accuracy:\", clean_accuracy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "io5gtBn3MIWd",
        "outputId": "ebfd856b-6eff-4ea9-fe89-eecde035aef1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "361/361 [==============================] - 6s 16ms/step\n",
            "Attack success rate: 1.0\n"
          ]
        }
      ],
      "source": [
        "badnet_predictions = badnet_model.predict(badnet_data_x)\n",
        "badnet_data_label_pred = np.argmax(badnet_predictions, axis=1)\n",
        "\n",
        "# Calculating badnet attack success rate\n",
        "successful_attack = np.sum(badnet_data_label_pred == badnet_data_y)\n",
        "total_samples = len(badnet_data_y)\n",
        "attack_success_rate = (successful_attack / total_samples)\n",
        "print(\"Attack success rate:\", attack_success_rate)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "RRRWRKU0Nvc-"
      },
      "outputs": [],
      "source": [
        "# Function to calculate accuracy\n",
        "def calculate_accuracy(model):\n",
        "    predictions = model.predict(clean_data_x)\n",
        "    correct_predictions = np.sum(np.argmax(predictions, axis=1) == clean_data_y)\n",
        "    accuracy = correct_predictions / len(clean_data_y)\n",
        "    return accuracy\n",
        "\n",
        "# Function to create a copy of the model\n",
        "def copy_model(model):\n",
        "    return clone_model(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "iQtkaEnpgp6p"
      },
      "outputs": [],
      "source": [
        "def evaluate_attack_success_rate(model):\n",
        "    predictions = model.predict(badnet_data_x)\n",
        "    correct_predictions = np.sum(np.argmax(predictions, axis=1) == badnet_data_y)\n",
        "    accuracy = correct_predictions / len(badnet_data_y)\n",
        "    return accuracy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "rT883N2gWWt9"
      },
      "outputs": [],
      "source": [
        "md = keras.models.load_model(os.path.join(os.getcwd(), \"models\", \"bd_net.h5\"))\n",
        "pruned_channel_index = []\n",
        "md_saved = [False, False, False]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mkQLgac_WZkU",
        "outputId": "391f0b2c-4a57-42ca-9373-817747607fe4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "361/361 [==============================] - 5s 13ms/step\n",
            "0.9864899974019226\n"
          ]
        }
      ],
      "source": [
        "clean_data_accuracy = calculate_accuracy(md)\n",
        "print(clean_data_accuracy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Function to get the intermediate output of a layer\n",
        "def get_intermediate_output(model, layer_name, x=clean_data_x):\n",
        "    intermediate_layer = Model(inputs=model.input, outputs=model.get_layer(layer_name).output)\n",
        "    intermediate_output = intermediate_layer.predict(x)\n",
        "    return intermediate_output"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Function to calculate the average activation values for each channel\n",
        "def calculate_channel_average_activation(intermediate_output):\n",
        "    channel_average_activation = np.mean(intermediate_output, axis=(0, 1, 2))\n",
        "    return channel_average_activation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Function to prune a specific channel in a layer\n",
        "def prune_channel(model, channel_index):\n",
        "    layer = model.get_layer(index=5)\n",
        "    weights = layer.get_weights()\n",
        "    weights[0][:, :, :, channel_index] = 0\n",
        "    weights[1][channel_index] = 0\n",
        "    layer.set_weights(weights)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Function to prune the channels of a layer in descending order of activation values\n",
        "def prune_channels(model, layer_name, x, y, clean_data_accuracy):\n",
        "    pruned_model = copy_model(model)\n",
        "    pruned_model.set_weights(model.get_weights())\n",
        "\n",
        "    intermediate_output = get_intermediate_output(model, layer_name)\n",
        "    sorted_channels = np.argsort(calculate_channel_average_activation(intermediate_output))\n",
        "    \n",
        "    pruned_clean_accuracy_list = []\n",
        "    attack_success_rate_list = []\n",
        "    for channel_index in tqdm(sorted_channels):\n",
        "        prune_channel(pruned_model, channel_index)\n",
        "\n",
        "        pruned_clean_accuracy = calculate_accuracy(pruned_model)\n",
        "        pruned_clean_accuracy_list.append(pruned_clean_accuracy)\n",
        "        accuracy_drop = clean_data_accuracy - pruned_clean_accuracy\n",
        "\n",
        "        for idx, threshold in enumerate([0.02, 0.04, 0.10]):\n",
        "            if accuracy_drop >= threshold and not md_saved[idx]:\n",
        "                pruned_model.save(f\"model_{int(threshold * 100)}_percent_drop.h5\")\n",
        "                md_saved[idx] = True\n",
        "                print(f\"Model saved with {int(threshold * 100)}% accuracy drop.\")\n",
        "        \n",
        "        attack_success_rate_list.append(evaluate_attack_success_rate(pruned_model))\n",
        "\n",
        "    for channel_index, pruned_clean_accuracy, attack_success_rate in zip(sorted_channels, pruned_clean_accuracy_list, attack_success_rate_list):\n",
        "        print(f\"Pruned Channel Index: {channel_index}, Clean Accuracy: {pruned_clean_accuracy}, Attack Success Rate: {attack_success_rate}\")\n",
        "\n",
        "    return pruned_model, pruned_clean_accuracy_list, attack_success_rate_list"
      ]
    },
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      "execution_count": 15,
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        {
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            "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
          ]
        },
        {
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            "c:\\users\\raj ghodasara\\documents\\venvs\\mlcslab4\\lib\\site-packages\\keras\\src\\engine\\training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        },
        {
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        {
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          "text": [
            "Pruned Channel Index: 0, Clean Accuracy: 0.9864899974019226, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 26, Clean Accuracy: 0.9864899974019226, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 27, Clean Accuracy: 0.9864899974019226, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 30, Clean Accuracy: 0.9864899974019226, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 31, Clean Accuracy: 0.9864899974019226, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 33, Clean Accuracy: 0.9864899974019226, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 34, Clean Accuracy: 0.9864899974019226, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 36, Clean Accuracy: 0.9864899974019226, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 37, Clean Accuracy: 0.9864899974019226, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 38, Clean Accuracy: 0.9864899974019226, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 25, Clean Accuracy: 0.9864899974019226, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 39, Clean Accuracy: 0.9864899974019226, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 41, Clean Accuracy: 0.9864899974019226, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 44, Clean Accuracy: 0.9864899974019226, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 45, Clean Accuracy: 0.9864899974019226, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 47, Clean Accuracy: 0.9864899974019226, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 48, Clean Accuracy: 0.9864899974019226, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 49, Clean Accuracy: 0.9864899974019226, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 50, Clean Accuracy: 0.9864899974019226, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 53, Clean Accuracy: 0.9864899974019226, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 55, Clean Accuracy: 0.9864899974019226, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 40, Clean Accuracy: 0.9864899974019226, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 24, Clean Accuracy: 0.9864899974019226, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 59, Clean Accuracy: 0.9864899974019226, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 9, Clean Accuracy: 0.9864899974019226, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 2, Clean Accuracy: 0.9864899974019226, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 12, Clean Accuracy: 0.9864899974019226, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 13, Clean Accuracy: 0.9864899974019226, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 17, Clean Accuracy: 0.9864899974019226, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 14, Clean Accuracy: 0.9864899974019226, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 15, Clean Accuracy: 0.9864899974019226, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 23, Clean Accuracy: 0.9864899974019226, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 6, Clean Accuracy: 0.9864899974019226, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 51, Clean Accuracy: 0.9864033948211657, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 32, Clean Accuracy: 0.9864033948211657, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 22, Clean Accuracy: 0.9863167922404088, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 21, Clean Accuracy: 0.9865765999826794, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 20, Clean Accuracy: 0.9864899974019226, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 19, Clean Accuracy: 0.9860569844981381, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 43, Clean Accuracy: 0.9857105741751104, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 58, Clean Accuracy: 0.9853641638520828, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 3, Clean Accuracy: 0.9819000606218066, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 42, Clean Accuracy: 0.9765307006148783, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 1, Clean Accuracy: 0.9750584567420109, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 29, Clean Accuracy: 0.9575647354291158, Attack Success Rate: 1.0\n",
            "Pruned Channel Index: 16, Clean Accuracy: 0.9520221702606738, Attack Success Rate: 0.9999133974192431\n",
            "Pruned Channel Index: 56, Clean Accuracy: 0.947172425738287, Attack Success Rate: 0.9999133974192431\n",
            "Pruned Channel Index: 46, Clean Accuracy: 0.9209318437689443, Attack Success Rate: 0.9999133974192431\n",
            "Pruned Channel Index: 5, Clean Accuracy: 0.9149562656967177, Attack Success Rate: 0.9999133974192431\n",
            "Pruned Channel Index: 8, Clean Accuracy: 0.9101931237550879, Attack Success Rate: 0.9998267948384861\n",
            "Pruned Channel Index: 11, Clean Accuracy: 0.8917467740538668, Attack Success Rate: 0.8073958603966398\n",
            "Pruned Channel Index: 54, Clean Accuracy: 0.8443751623798389, Attack Success Rate: 0.77015675067117\n",
            "Pruned Channel Index: 10, Clean Accuracy: 0.7648739932449987, Attack Success Rate: 0.35714904304148265\n",
            "Pruned Channel Index: 28, Clean Accuracy: 0.548627349095003, Attack Success Rate: 0.06954187234779596\n",
            "Pruned Channel Index: 35, Clean Accuracy: 0.2708928726076037, Attack Success Rate: 0.004243526457088421\n",
            "Pruned Channel Index: 18, Clean Accuracy: 0.1387373343725643, Attack Success Rate: 0.0\n",
            "Pruned Channel Index: 4, Clean Accuracy: 0.07101411622066338, Attack Success Rate: 0.0\n",
            "Pruned Channel Index: 7, Clean Accuracy: 0.015501861955486274, Attack Success Rate: 0.0\n",
            "Pruned Channel Index: 52, Clean Accuracy: 0.007188014202823244, Attack Success Rate: 0.0\n",
            "Pruned Channel Index: 57, Clean Accuracy: 0.000779423226812159, Attack Success Rate: 0.0\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "md = keras.models.load_model(os.path.join(os.getcwd(), \"models\", \"bd_net.h5\"))\n",
        "threshold_list = [0.02, 0.04, 0.10]\n",
        "pruned_model, clean_accuracy_list, attack_success_rate_list = prune_channels(md, 'pool_3', clean_data_x, clean_data_y, clean_data_accuracy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Define the fraction of channels pruned\n",
        "fraction_pruned = np.arange(0, 60) / 60"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Table of accuracy on clean test data and the attack success rate (on backdoored test data) as a function of the fraction of channels pruned (X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "+-------------------------------+----------------------------+-----------------------------------------------+\n",
            "| Fraction of Channels Pruned   |   Clean Test Data Accuracy |   Attack Success Rate on backdoored test data |\n",
            "+===============================+============================+===============================================+\n",
            "| 1/60                          |                0.98649     |                                    1          |\n",
            "+-------------------------------+----------------------------+-----------------------------------------------+\n",
            "| 2/60                          |                0.98649     |                                    1          |\n",
            "+-------------------------------+----------------------------+-----------------------------------------------+\n",
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            "+-------------------------------+----------------------------+-----------------------------------------------+\n",
            "| 23/60                         |                0.98649     |                                    1          |\n",
            "+-------------------------------+----------------------------+-----------------------------------------------+\n",
            "| 24/60                         |                0.98649     |                                    1          |\n",
            "+-------------------------------+----------------------------+-----------------------------------------------+\n",
            "| 25/60                         |                0.98649     |                                    1          |\n",
            "+-------------------------------+----------------------------+-----------------------------------------------+\n",
            "| 26/60                         |                0.98649     |                                    1          |\n",
            "+-------------------------------+----------------------------+-----------------------------------------------+\n",
            "| 27/60                         |                0.98649     |                                    1          |\n",
            "+-------------------------------+----------------------------+-----------------------------------------------+\n",
            "| 28/60                         |                0.98649     |                                    1          |\n",
            "+-------------------------------+----------------------------+-----------------------------------------------+\n",
            "| 29/60                         |                0.98649     |                                    1          |\n",
            "+-------------------------------+----------------------------+-----------------------------------------------+\n",
            "| 30/60                         |                0.98649     |                                    1          |\n",
            "+-------------------------------+----------------------------+-----------------------------------------------+\n",
            "| 31/60                         |                0.98649     |                                    1          |\n",
            "+-------------------------------+----------------------------+-----------------------------------------------+\n",
            "| 32/60                         |                0.98649     |                                    1          |\n",
            "+-------------------------------+----------------------------+-----------------------------------------------+\n",
            "| 33/60                         |                0.98649     |                                    1          |\n",
            "+-------------------------------+----------------------------+-----------------------------------------------+\n",
            "| 34/60                         |                0.986403    |                                    1          |\n",
            "+-------------------------------+----------------------------+-----------------------------------------------+\n",
            "| 35/60                         |                0.986403    |                                    1          |\n",
            "+-------------------------------+----------------------------+-----------------------------------------------+\n",
            "| 36/60                         |                0.986317    |                                    1          |\n",
            "+-------------------------------+----------------------------+-----------------------------------------------+\n",
            "| 37/60                         |                0.986577    |                                    1          |\n",
            "+-------------------------------+----------------------------+-----------------------------------------------+\n",
            "| 38/60                         |                0.98649     |                                    1          |\n",
            "+-------------------------------+----------------------------+-----------------------------------------------+\n",
            "| 39/60                         |                0.986057    |                                    1          |\n",
            "+-------------------------------+----------------------------+-----------------------------------------------+\n",
            "| 40/60                         |                0.985711    |                                    1          |\n",
            "+-------------------------------+----------------------------+-----------------------------------------------+\n",
            "| 41/60                         |                0.985364    |                                    1          |\n",
            "+-------------------------------+----------------------------+-----------------------------------------------+\n",
            "| 42/60                         |                0.9819      |                                    1          |\n",
            "+-------------------------------+----------------------------+-----------------------------------------------+\n",
            "| 43/60                         |                0.976531    |                                    1          |\n",
            "+-------------------------------+----------------------------+-----------------------------------------------+\n",
            "| 44/60                         |                0.975058    |                                    1          |\n",
            "+-------------------------------+----------------------------+-----------------------------------------------+\n",
            "| 45/60                         |                0.957565    |                                    1          |\n",
            "+-------------------------------+----------------------------+-----------------------------------------------+\n",
            "| 46/60                         |                0.952022    |                                    0.999913   |\n",
            "+-------------------------------+----------------------------+-----------------------------------------------+\n",
            "| 47/60                         |                0.947172    |                                    0.999913   |\n",
            "+-------------------------------+----------------------------+-----------------------------------------------+\n",
            "| 48/60                         |                0.920932    |                                    0.999913   |\n",
            "+-------------------------------+----------------------------+-----------------------------------------------+\n",
            "| 49/60                         |                0.914956    |                                    0.999913   |\n",
            "+-------------------------------+----------------------------+-----------------------------------------------+\n",
            "| 50/60                         |                0.910193    |                                    0.999827   |\n",
            "+-------------------------------+----------------------------+-----------------------------------------------+\n",
            "| 51/60                         |                0.891747    |                                    0.807396   |\n",
            "+-------------------------------+----------------------------+-----------------------------------------------+\n",
            "| 52/60                         |                0.844375    |                                    0.770157   |\n",
            "+-------------------------------+----------------------------+-----------------------------------------------+\n",
            "| 53/60                         |                0.764874    |                                    0.357149   |\n",
            "+-------------------------------+----------------------------+-----------------------------------------------+\n",
            "| 54/60                         |                0.548627    |                                    0.0695419  |\n",
            "+-------------------------------+----------------------------+-----------------------------------------------+\n",
            "| 55/60                         |                0.270893    |                                    0.00424353 |\n",
            "+-------------------------------+----------------------------+-----------------------------------------------+\n",
            "| 56/60                         |                0.138737    |                                    0          |\n",
            "+-------------------------------+----------------------------+-----------------------------------------------+\n",
            "| 57/60                         |                0.0710141   |                                    0          |\n",
            "+-------------------------------+----------------------------+-----------------------------------------------+\n",
            "| 58/60                         |                0.0155019   |                                    0          |\n",
            "+-------------------------------+----------------------------+-----------------------------------------------+\n",
            "| 59/60                         |                0.00718801  |                                    0          |\n",
            "+-------------------------------+----------------------------+-----------------------------------------------+\n",
            "| 60/60                         |                0.000779423 |                                    0          |\n",
            "+-------------------------------+----------------------------+-----------------------------------------------+\n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from tabulate import tabulate\n",
        "\n",
        "# Create a list of tuples containing the percent of channels pruned, clean accuracy, and attack success rate\n",
        "data_table = []\n",
        "for i in range(len(fraction_pruned)):\n",
        "    data_table.append((\"{}/60\".format(i+1), clean_accuracy_list[i], attack_success_rate_list[i]))\n",
        "\n",
        "# Print the table\n",
        "table = tabulate(data_table, headers=['Fraction of Channels Pruned', 'Clean Test Data Accuracy', 'Attack Success Rate on backdoored test data'], tablefmt='grid')\n",
        "print(table)\n",
        "\n",
        "# # Save the table as a JPEG image\n",
        "# plt.figure(figsize=(10, 6))\n",
        "# plt.axis('off')\n",
        "# plt.table(cellText=data_table, colLabels=['Fraction of Channels Pruned', 'Clean Test Data Accuracy', 'Attack Success Rate on backdoored test data'], loc='center')\n",
        "# plt.savefig('table.jpg', bbox_inches='tight', pad_inches=0.5)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot the validation accuracy\n",
        "plt.plot(fraction_pruned, clean_accuracy_list, label='Validation Accuracy')\n",
        "\n",
        "# Plot the attack success rate\n",
        "plt.plot(fraction_pruned, attack_success_rate_list, label='Attack Success Rate')\n",
        "\n",
        "# Add labels and title to the plot\n",
        "plt.xlabel('Fraction of Channels Pruned')\n",
        "plt.ylabel('Accuracy / Attack Success Rate')\n",
        "plt.title('Fraction of Channels Pruned vs. Accuracy / Attack Success Rate')\n",
        "\n",
        "# Add legend to the plot\n",
        "plt.legend()\n",
        "\n",
        "# Save the plot as an image\n",
        "# plt.savefig('plot.png')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {},
      "outputs": [],
      "source": [
        "class G(keras.Model):\n",
        "    def __init__(self, B, B_prime):\n",
        "        super(G, self).__init__()\n",
        "        self.B = B\n",
        "        self.B_prime = B_prime\n",
        "\n",
        "    def predict(self, data):\n",
        "        y = np.argmax(self.B(data), axis=1)\n",
        "        y_prime = np.argmax(self.B_prime(data), axis=1)\n",
        "        pred = np.where(y == y_prime, y, 1283)\n",
        "        return pred\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "QXTu8n4vhVnD"
      },
      "outputs": [],
      "source": [
        "clean_test_data_x, clean_test_data_y = data(os.path.join(os.getcwd(), \"data\", \"cl\", \"test.h5\"))\n",
        "badnet_test_data_x, badnet_test_data_y = data(os.path.join(os.getcwd(), \"data\", \"bd\", \"bd_test.h5\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n",
            "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n",
            "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
          ]
        }
      ],
      "source": [
        "# load the models\n",
        "model_2_percent = keras.models.load_model(os.path.join(os.getcwd(), \"model_2_percent_drop.h5\"))\n",
        "model_4_percent = keras.models.load_model(os.path.join(os.getcwd(), \"model_4_percent_drop.h5\"))\n",
        "model_10_percent = keras.models.load_model(os.path.join(os.getcwd(), \"model_10_percent_drop.h5\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {},
      "outputs": [],
      "source": [
        "def calculate_test_accuracy(model):\n",
        "    predictions = model.predict(clean_test_data_x)\n",
        "    correct_predictions = np.sum(np.argmax(predictions, axis=1) == clean_test_data_y)\n",
        "    accuracy = correct_predictions / len(clean_test_data_y)\n",
        "    return accuracy\n",
        "\n",
        "def evaluate_test_attack_success_rate(model):\n",
        "    predictions = model.predict(badnet_test_data_x)\n",
        "    correct_predictions = np.sum(np.argmax(predictions, axis=1) == badnet_test_data_y)\n",
        "    accuracy = correct_predictions / len(badnet_test_data_y)\n",
        "    return accuracy"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Repaired Models"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "401/401 [==============================] - 7s 18ms/step\n",
            "401/401 [==============================] - 7s 17ms/step\n",
            "401/401 [==============================] - 7s 17ms/step\n",
            "Test Accuracy - 2% Model: 0.9590023382696804\n",
            "Test Accuracy - 4% Model: 0.9229150428682775\n",
            "Test Accuracy - 10% Model: 0.8454403741231489\n"
          ]
        }
      ],
      "source": [
        "# Calculate test accuracies\n",
        "test_accuracy_2_percent = calculate_test_accuracy(model_2_percent)\n",
        "test_accuracy_4_percent = calculate_test_accuracy(model_4_percent)\n",
        "test_accuracy_10_percent = calculate_test_accuracy(model_10_percent)\n",
        "\n",
        "# Print test accuracies\n",
        "print(\"Test Accuracy - 2% Model:\", test_accuracy_2_percent)\n",
        "print(\"Test Accuracy - 4% Model:\", test_accuracy_4_percent)\n",
        "print(\"Test Accuracy - 10% Model:\", test_accuracy_10_percent)\n",
        "\n",
        "# Test accuracies\n",
        "test_accuracies = [test_accuracy_2_percent, test_accuracy_4_percent, test_accuracy_10_percent]\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "401/401 [==============================] - 6s 16ms/step\n",
            "401/401 [==============================] - 7s 18ms/step\n",
            "401/401 [==============================] - 7s 17ms/step\n",
            "Attack Success Rate - 2% Model: 1.0\n",
            "Attack Success Rate - 4% Model: 0.9998441153546376\n",
            "Attack Success Rate - 10% Model: 0.7720966484801247\n"
          ]
        }
      ],
      "source": [
        "# Calculate attack success rates\n",
        "attack_success_rate_2_percent = evaluate_test_attack_success_rate(model_2_percent)\n",
        "attack_success_rate_4_percent = evaluate_test_attack_success_rate(model_4_percent)\n",
        "attack_success_rate_10_percent = evaluate_test_attack_success_rate(model_10_percent)\n",
        "\n",
        "# Print attack success rates\n",
        "print(\"Attack Success Rate - 2% Model:\", attack_success_rate_2_percent)\n",
        "print(\"Attack Success Rate - 4% Model:\", attack_success_rate_4_percent)\n",
        "print(\"Attack Success Rate - 10% Model:\", attack_success_rate_10_percent)\n",
        "\n",
        "# Attack success rates\n",
        "attack_success_rates = [attack_success_rate_2_percent, attack_success_rate_4_percent, attack_success_rate_10_percent]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Labels for x-axis\n",
        "labels = ['2%', '4%', '10%']\n",
        "\n",
        "# Plotting the test accuracies\n",
        "plt.bar(labels, test_accuracies, label='Test Accuracy')\n",
        "\n",
        "# Adding labels and title to the plot\n",
        "plt.xlabel('Fraction of Channels Pruned')\n",
        "plt.ylabel('Test Accuracy')\n",
        "plt.title('Test Accuracy vs. Fraction of Channels Pruned')\n",
        "\n",
        "# Adding legend to the plot\n",
        "plt.legend()\n",
        "\n",
        "# Displaying the plot\n",
        "plt.show()\n",
        "\n",
        "# Plotting the attack success rates\n",
        "plt.bar(labels, attack_success_rates, label='Attack Success Rate')\n",
        "\n",
        "# Adding labels and title to the plot\n",
        "plt.xlabel('Fraction of Channels Pruned')\n",
        "plt.ylabel('Attack Success Rate')\n",
        "plt.title('Attack Success Rate vs. Fraction of Channels Pruned')\n",
        "\n",
        "# Adding legend to the plot\n",
        "plt.legend()\n",
        "\n",
        "# Displaying the plot\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Goodnet models"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {},
      "outputs": [],
      "source": [
        "goodnet_2_percent = G(badnet_model, model_2_percent)\n",
        "goodnet_4_percent = G(badnet_model, model_4_percent)\n",
        "goodnet_10_percent = G(badnet_model, model_10_percent)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {},
      "outputs": [],
      "source": [
        "def calculate_goodnet_test_accuracy(model):\n",
        "    predictions = model.predict(clean_test_data_x)\n",
        "    correct_predictions = np.sum(predictions == clean_test_data_y)\n",
        "    accuracy = correct_predictions / len(clean_test_data_y)\n",
        "    return accuracy\n",
        "\n",
        "def evaluate_goodnet_test_attack_success_rate(model):\n",
        "    predictions = model.predict(badnet_test_data_x)\n",
        "    correct_predictions = np.sum(predictions == badnet_test_data_y)\n",
        "    accuracy = correct_predictions / len(badnet_test_data_y)\n",
        "    return accuracy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test Accuracy - 2% Goodnet Model: 0.9574434918160561\n",
            "Test Accuracy - 4% Goodnet Model: 0.921278254091972\n",
            "Test Accuracy - 10% Goodnet Model: 0.843335931410756\n"
          ]
        }
      ],
      "source": [
        "# Calculate test accuracies for goodnet models\n",
        "goodnet_test_accuracy_2_percent = calculate_goodnet_test_accuracy(goodnet_2_percent)\n",
        "goodnet_test_accuracy_4_percent = calculate_goodnet_test_accuracy(goodnet_4_percent)\n",
        "goodnet_test_accuracy_10_percent = calculate_goodnet_test_accuracy(goodnet_10_percent)\n",
        "\n",
        "# Print test accuracies for goodnet models\n",
        "print(\"Test Accuracy - 2% Goodnet Model:\", goodnet_test_accuracy_2_percent)\n",
        "print(\"Test Accuracy - 4% Goodnet Model:\", goodnet_test_accuracy_4_percent)\n",
        "print(\"Test Accuracy - 10% Goodnet Model:\", goodnet_test_accuracy_10_percent)\n",
        "\n",
        "# Test accuracies for goodnet models\n",
        "goodnet_test_accuracies = [goodnet_test_accuracy_2_percent, goodnet_test_accuracy_4_percent, goodnet_test_accuracy_10_percent]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Attack Success Rate - 2% Goodnet Model: 1.0\n",
            "Attack Success Rate - 4% Goodnet Model: 0.9998441153546376\n",
            "Attack Success Rate - 10% Goodnet Model: 0.7720966484801247\n"
          ]
        }
      ],
      "source": [
        "# Attack success rates for goodnet models\n",
        "goodnet_attack_success_rate_2_percent = evaluate_goodnet_test_attack_success_rate(goodnet_2_percent)\n",
        "goodnet_attack_success_rate_4_percent = evaluate_goodnet_test_attack_success_rate(goodnet_4_percent)\n",
        "goodnet_attack_success_rate_10_percent = evaluate_goodnet_test_attack_success_rate(goodnet_10_percent)\n",
        "\n",
        "# Print attack success rates for goodnet models\n",
        "print(\"Attack Success Rate - 2% Goodnet Model:\", goodnet_attack_success_rate_2_percent)\n",
        "print(\"Attack Success Rate - 4% Goodnet Model:\", goodnet_attack_success_rate_4_percent)\n",
        "print(\"Attack Success Rate - 10% Goodnet Model:\", goodnet_attack_success_rate_10_percent)\n",
        "\n",
        "# Attack success rates for goodnet models\n",
        "goodnet_attack_success_rates = [goodnet_attack_success_rate_2_percent, goodnet_attack_success_rate_4_percent, goodnet_attack_success_rate_10_percent]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Labels for x-axis\n",
        "labels = ['2%', '4%', '10%']\n",
        "\n",
        "# Plotting the test accuracies for goodnet models\n",
        "plt.bar(labels, goodnet_test_accuracies, label='Test Accuracy')\n",
        "\n",
        "# Adding labels and title to the plot\n",
        "plt.xlabel('Fraction of Channels Pruned')\n",
        "plt.ylabel('Test Accuracy')\n",
        "plt.title('Test Accuracy vs. Fraction of Channels Pruned (Goodnet Models)')\n",
        "\n",
        "# Adding legend to the plot\n",
        "plt.legend()\n",
        "\n",
        "# Displaying the plot\n",
        "plt.show()\n",
        "\n",
        "# Plotting the attack success rates for goodnet models\n",
        "plt.bar(labels, goodnet_attack_success_rates, label='Attack Success Rate')\n",
        "\n",
        "# Adding labels and title to the plot\n",
        "plt.xlabel('Fraction of Channels Pruned')\n",
        "plt.ylabel('Attack Success Rate')\n",
        "plt.title('Attack Success Rate vs. Fraction of Channels Pruned (Goodnet Models)')\n",
        "\n",
        "# Adding legend to the plot\n",
        "plt.legend()\n",
        "\n",
        "# Displaying the plot\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Repaired model table\n",
            "+--------------------+----------------------------+-----------------------+\n",
            "| Model Percentage   |   Clean Data Test Accuracy |   Attack Success Rate |\n",
            "+====================+============================+=======================+\n",
            "| 2%                 |                   0.959002 |              1        |\n",
            "+--------------------+----------------------------+-----------------------+\n",
            "| 4%                 |                   0.922915 |              0.999844 |\n",
            "+--------------------+----------------------------+-----------------------+\n",
            "| 10%                |                   0.84544  |              0.772097 |\n",
            "+--------------------+----------------------------+-----------------------+\n"
          ]
        }
      ],
      "source": [
        "from tabulate import tabulate\n",
        "\n",
        "# Create a list of tuples containing the model percentage, clean data test accuracy, and attack success rate\n",
        "regular_data_table = [\n",
        "    ('2%', test_accuracy_2_percent, attack_success_rate_2_percent),\n",
        "    ('4%', test_accuracy_4_percent, attack_success_rate_4_percent),\n",
        "    ('10%', test_accuracy_10_percent, attack_success_rate_10_percent)\n",
        "]\n",
        "\n",
        "# Print the table\n",
        "table = tabulate(data_table, headers=['Model Percentage', 'Clean Data Test Accuracy', 'Attack Success Rate'], tablefmt='grid')\n",
        "print(\"Repaired model table\")\n",
        "print(table)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Goodnet model table\n",
            "+--------------------+----------------------------+-----------------------+\n",
            "| Model Percentage   |   Clean Data Test Accuracy |   Attack Success Rate |\n",
            "+====================+============================+=======================+\n",
            "| 2%                 |                   0.957443 |              1        |\n",
            "+--------------------+----------------------------+-----------------------+\n",
            "| 4%                 |                   0.921278 |              0.999844 |\n",
            "+--------------------+----------------------------+-----------------------+\n",
            "| 10%                |                   0.843336 |              0.772097 |\n",
            "+--------------------+----------------------------+-----------------------+\n"
          ]
        }
      ],
      "source": [
        "# Create a list of tuples containing the model percentage, clean data test accuracy, and attack success rate\n",
        "goodnet_data_table = [\n",
        "    ('2%', goodnet_test_accuracy_2_percent, goodnet_attack_success_rate_2_percent),\n",
        "    ('4%', goodnet_test_accuracy_4_percent, goodnet_attack_success_rate_4_percent),\n",
        "    ('10%', goodnet_test_accuracy_10_percent, goodnet_attack_success_rate_10_percent)\n",
        "]\n",
        "\n",
        "# Print the table\n",
        "table = tabulate(goodnet_data_table, headers=['Model Percentage', 'Clean Data Test Accuracy', 'Attack Success Rate'], tablefmt='grid')\n",
        "print(\"Goodnet model table\")\n",
        "print(table)\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "mlcslab4",
      "language": "python",
      "name": "mlcslab4"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.10"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}