TransferLearning_and_Data.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of Retry.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/taasinsaquib/ALL-Augmentations/blob/main/TransferLearning_and_Data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uBBRRJ3wc13_"
      },
      "source": [
        "# Move Files Around\n",
        "* DON'T run this section"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uUkpCIq4Ua2z"
      },
      "source": [
        "## Fixing a Mistake where I copied to the wrong place"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z6_Em_dHKT0F",
        "outputId": "1c46dec0-80c8-4d63-ace2-caa788bfad7b"
      },
      "source": [
        "!find C-NMC_Leukemia/training_data/val/hem -maxdepth 1 -mindepth 1 -type f -ls -exec grep 'Dec' {} \\; | wc -l"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1514\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1_A1pT3HOaH3",
        "outputId": "052e9279-0473-43b6-9631-2fd22b81f4a8"
      },
      "source": [
        "!find C-NMC_Leukemia/training_data/val/hem -maxdepth 1 -mindepth 1 -type f -ls -exec grep 'Aug' {} \\; | wc -l"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1512\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HI1zqmDuO-Il",
        "outputId": "0b50446e-3877-415f-c648-4a8ee1f68aaf"
      },
      "source": [
        "!ls -l C-NMC_Leukemia/training_data/aug/all | wc -l"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hEAvVwIlPHkx",
        "outputId": "1d9400df-5921-4124-c918-0fdde372a9ed"
      },
      "source": [
        "from pathlib import Path\n",
        "import time\n",
        "\n",
        "paths = sorted(Path(val_dst).iterdir(), key=os.path.getmtime)\n",
        "count = 0\n",
        "for p in paths:\n",
        "  if \"Aug\" in time.ctime(os.path.getmtime(p)):\n",
        "    count+=1\n",
        "  else:\n",
        "    os.remove(p)\n",
        "  \n",
        "print(count)\n",
        "# print(len(paths))\n",
        "# print(paths)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "680\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KS3LSIGaNnrl"
      },
      "source": [
        "## Consolidate Training Folds"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y2ZLYAU8Nlz3"
      },
      "source": [
        "import os\n",
        "import shutil\n",
        "from tqdm import tqdm"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sds-n2GCPCRl"
      },
      "source": [
        "def moveFiles(src, dest, filenames):\n",
        "  for file_name in filenames:\n",
        "    shutil.move(os.path.join(src, file_name), dest)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DFSt3IQ6ODa2"
      },
      "source": [
        "path='/content/drive/My Drive/CS M226/C-NMC_Leukemia/training_data/'\n",
        "fold0 = path+'fold_0/'\n",
        "fold1 = path+'fold_1/'\n",
        "fold2 = path+'fold_2/'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JN-CiRYKN0cB"
      },
      "source": [
        "dest_hem = fold0 + 'hem/'\n",
        "src_hem  = fold1 + 'hem/'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3LmPJnjjP8tn",
        "outputId": "337782e6-61ba-4af4-f0d5-43c2bb61b564"
      },
      "source": [
        "file_names = os.listdir(dest_hem)\n",
        "print(len(file_names))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2709\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z0SD32pD77LQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5557cdf9-a23c-429a-c5fc-a5ea63dad93a"
      },
      "source": [
        "print(len(os.listdir(dest_hem)))\n",
        "# moveFiles(src_hem, dest_hem, file_names)\n",
        "print(len(os.listdir(dest_hem)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2709\n",
            "2709\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HuwhWGJo7mIN"
      },
      "source": [
        "dest_all = fold0 + 'all/'\n",
        "src_all  = fold2 + 'all/'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GBnm90oG_ZcF",
        "outputId": "406c29fc-7304-4fcf-9ca5-55f721514cd6"
      },
      "source": [
        "file_names = os.listdir(dest_all)\n",
        "print(len(file_names))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5822\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WCAvYXiFBuZ7",
        "outputId": "84290902-c21a-43f7-b499-e1200a66168b"
      },
      "source": [
        "print(len(os.listdir(dest_all)))\n",
        "# moveFiles(src_all, dest_all, file_names)\n",
        "print(len(os.listdir(dest_all)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5822\n",
            "5822\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TFd_aYp_c5Tp"
      },
      "source": [
        "## Create New Validation Folder"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-myXnYRuc9CT"
      },
      "source": [
        "# f = os.listdir('/content/drive/My Drive/CS M226/C-NMC_Leukemia/training_data/train/all/')\n",
        "# print(len(f))\n",
        "\n",
        "# ALL 7272, 0.2 = 1450\n",
        "# HEM 3389, 0.2 = 680"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Iwu1hzUOd4kB"
      },
      "source": [
        "all_files = os.listdir(dest_all)\n",
        "hem_files = os.listdir(dest_hem)\n",
        "\n",
        "all_indices = np.random.choice(all_files, 1450, replace=False)\n",
        "hem_indices = np.random.choice(hem_files, 680, replace=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YlzrNRbQeX0j",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ef6f35e8-251d-4565-8d1f-871995dd079f"
      },
      "source": [
        "val_src = dest_hem\n",
        "val_dst = path + 'val/' + 'hem'\n",
        "\n",
        "print(len(os.listdir(val_dst)))\n",
        "# moveFiles(val_src, val_dst, hem_indices)\n",
        "print(len(os.listdir(val_dst)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1450\n",
            "1450\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2JorLAjL2jrI"
      },
      "source": [
        "## Create New Augmentation Folder"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oi-LVK9sGcC-"
      },
      "source": [
        "# ALL 5717, 0.3=1715\n",
        "# HEM 2709, 0.3=812"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0lRWqNzq2vxV"
      },
      "source": [
        "def copyFiles(src, dest, filenames):\n",
        "  for file_name in tqdm(filenames):\n",
        "    shutil.copy(os.path.join(src, file_name), dest)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8eeiayHXF79-",
        "outputId": "cbef555f-0b37-4614-b21b-0c52f598998d"
      },
      "source": [
        "all_files = os.listdir(dest_all)\n",
        "hem_files = os.listdir(dest_hem)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5822\n",
            "2709\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dt2GMSpGS6Vp"
      },
      "source": [
        "all_indices_aug = np.random.choice(all_files, 1715, replace=False)\n",
        "hem_indices_aug = np.random.choice(hem_files, 812, replace=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-jRKFb802iwc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0031b1d9-0e94-43d3-c632-d1f1b15b2c6b"
      },
      "source": [
        "aug_src = dest_hem\n",
        "aug_dest = path + 'aug/' + 'hem'\n",
        "\n",
        "print(len(os.listdir(aug_src)))\n",
        "# copyFiles(aug_src, aug_dest, hem_indices_aug)\n",
        "print(len(os.listdir(aug_dest)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2709\n",
            "812\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HIS260Pf7fDM"
      },
      "source": [
        "# Connect to Drive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K5DDo90Y7R8V",
        "outputId": "062f6a8f-bfb9-4ce1-f5fc-aa4a5ed6863d"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sO7rj-bV7jH5",
        "outputId": "89a2efc3-2af5-4606-d4f0-9495c552eb5c"
      },
      "source": [
        "%cd drive/My\\ Drive/CS\\ M226"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/.shortcut-targets-by-id/1r7MQOXDGZY7wBy0tWFKzg9Q9qokYEg0J/CS M226\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hn_2ggwP7tN2"
      },
      "source": [
        "# Load Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aZpyDdDT7w6V"
      },
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, GlobalAveragePooling2D\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
        "from tensorflow.keras.layers.experimental import preprocessing\n",
        "#from cnnArchitectures.Xception import get_xception_model\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "import os\n",
        "import datetime\n",
        "\n",
        "%matplotlib inline\n",
        "%reload_ext tensorboard"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jI7Qm5au8ELt"
      },
      "source": [
        "# Set up dataset parameters\n",
        "BATCH_SIZE = 16\n",
        "IMAGE_SIZE = (256, 256)\n",
        "SEED = 322\n",
        "PREFETCH_BUFFER_SIZE = 400\n",
        "SHUFFLE_BUFFER_SIZE = 1000\n",
        "CACHE_DIR = \"caches/ds_cache\"\n",
        "ds_params = dict(\n",
        "    labels=\"inferred\",\n",
        "    label_mode=\"categorical\",\n",
        "    class_names=[\"all\", \"hem\"],\n",
        "    color_mode=\"rgb\",\n",
        "    batch_size=BATCH_SIZE,\n",
        "    image_size=IMAGE_SIZE,\n",
        "    seed=SEED\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "APhI4OnM74na"
      },
      "source": [
        "def get_cnn_model_1(input_shape):\n",
        "\n",
        "    kernel_initializer = 'lecun_uniform'\n",
        "    bias_initializer = 'lecun_uniform'\n",
        "    kernel_regularizer = None\n",
        "    activation = \"selu\"\n",
        "\n",
        "    model = tf.keras.Sequential()\n",
        "    model.add(Conv2D(32, (3, 3), input_shape=input_shape, \n",
        "                     data_format=\"channels_last\", kernel_initializer=kernel_initializer, \n",
        "                     bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer\n",
        "                    ))\n",
        "    model.add(Activation(activation))\n",
        "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "    model.add(Conv2D(64, (3, 3), data_format=\"channels_last\", kernel_initializer=kernel_initializer, \n",
        "                     bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer\n",
        "                    ))\n",
        "    model.add(Activation(activation))\n",
        "    model.add(MaxPooling2D(pool_size=(3, 3)))\n",
        "\n",
        "    model.add(Conv2D(128, (3, 3), data_format=\"channels_last\", kernel_initializer=kernel_initializer, \n",
        "                     bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer\n",
        "                    ))\n",
        "    model.add(Activation(activation))\n",
        "    model.add(MaxPooling2D(pool_size=(3, 3)))\n",
        "\n",
        "    model.add(Conv2D(256, (3, 3), data_format=\"channels_last\", kernel_initializer=kernel_initializer, \n",
        "                     bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer\n",
        "                    ))\n",
        "    model.add(Activation(activation))\n",
        "    model.add(Dropout(0.6))\n",
        "\n",
        "    # adding fully connected layers\n",
        "    model.add(Flatten())\n",
        "    model.add(Dense(512, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer))\n",
        "    model.add(Activation(activation))\n",
        "    model.add(Dropout(0.8))\n",
        "    model.add(Dense(256, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer))\n",
        "    model.add(Activation(activation))\n",
        "    model.add(Dropout(0.8))\n",
        "    model.add(Dense(128, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer))\n",
        "    model.add(Activation(activation))\n",
        "    model.add(Dropout(0.7))\n",
        "    model.add(Dense(64, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer))\n",
        "\n",
        "    model.add(Dense(1))\n",
        "    model.add(Activation('sigmoid'))\n",
        "\n",
        "    return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IzkPT2ep7yd2",
        "outputId": "9814b62b-9d84-457e-9c40-0d4444319784"
      },
      "source": [
        "model = get_cnn_model_1(IMAGE_SIZE + (3,))\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 254, 254, 32)      896       \n",
            "_________________________________________________________________\n",
            "activation (Activation)      (None, 254, 254, 32)      0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 127, 127, 32)      0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 125, 125, 64)      18496     \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 125, 125, 64)      0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 41, 41, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 39, 39, 128)       73856     \n",
            "_________________________________________________________________\n",
            "activation_2 (Activation)    (None, 39, 39, 128)       0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 13, 13, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 11, 11, 256)       295168    \n",
            "_________________________________________________________________\n",
            "activation_3 (Activation)    (None, 11, 11, 256)       0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 11, 11, 256)       0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 30976)             0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 512)               15860224  \n",
            "_________________________________________________________________\n",
            "activation_4 (Activation)    (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 256)               131328    \n",
            "_________________________________________________________________\n",
            "activation_5 (Activation)    (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 128)               32896     \n",
            "_________________________________________________________________\n",
            "activation_6 (Activation)    (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 64)                8256      \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 1)                 65        \n",
            "_________________________________________________________________\n",
            "activation_7 (Activation)    (None, 1)                 0         \n",
            "=================================================================\n",
            "Total params: 16,421,185\n",
            "Trainable params: 16,421,185\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BasBqV4Y78f1"
      },
      "source": [
        "model.load_weights(\"w.h5\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OfMumFHnBEvx"
      },
      "source": [
        "adam_opt = tf.keras.optimizers.Adam(learning_rate=0.0001, amsgrad=True)\n",
        "metrics = [\"accuracy\", tf.keras.metrics.Precision(name=\"precision\")]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G1T-6zfZBC0S"
      },
      "source": [
        "# compile our new model\n",
        "model.compile(\n",
        "    optimizer=adam_opt,\n",
        "    loss=tf.keras.losses.BinaryCrossentropy(),\n",
        "    metrics=metrics\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JT5ft0Ek8a5g",
        "outputId": "bf192125-c5dc-4639-a0f9-b5b6de431b43"
      },
      "source": [
        "model_default = model\n",
        "print(model_default.summary())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 254, 254, 32)      896       \n",
            "_________________________________________________________________\n",
            "activation (Activation)      (None, 254, 254, 32)      0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 127, 127, 32)      0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 125, 125, 64)      18496     \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 125, 125, 64)      0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 41, 41, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 39, 39, 128)       73856     \n",
            "_________________________________________________________________\n",
            "activation_2 (Activation)    (None, 39, 39, 128)       0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 13, 13, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 11, 11, 256)       295168    \n",
            "_________________________________________________________________\n",
            "activation_3 (Activation)    (None, 11, 11, 256)       0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 11, 11, 256)       0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 30976)             0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 512)               15860224  \n",
            "_________________________________________________________________\n",
            "activation_4 (Activation)    (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 256)               131328    \n",
            "_________________________________________________________________\n",
            "activation_5 (Activation)    (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 128)               32896     \n",
            "_________________________________________________________________\n",
            "activation_6 (Activation)    (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 64)                8256      \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 1)                 65        \n",
            "_________________________________________________________________\n",
            "activation_7 (Activation)    (None, 1)                 0         \n",
            "=================================================================\n",
            "Total params: 16,421,185\n",
            "Trainable params: 16,421,185\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MEcJ58dn80ZV"
      },
      "source": [
        "# Create Data Splits"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qdEPwTiA-S2J",
        "outputId": "1873c0e6-c235-4725-d73a-343a6ccec45b"
      },
      "source": [
        "train_data = tf.keras.preprocessing.image_dataset_from_directory(\n",
        "    './C-NMC_Leukemia/training_data/fold_0',\n",
        "    labels=\"inferred\",\n",
        "    class_names=[\"all\", \"hem\"],\n",
        "    label_mode=\"int\",\n",
        "    image_size=IMAGE_SIZE,\n",
        "    batch_size=BATCH_SIZE, \n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 8531 files belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tZ4ng-Xb_ozf",
        "outputId": "745f1813-4387-4baa-c698-bffccc64b868"
      },
      "source": [
        "val_data = tf.keras.preprocessing.image_dataset_from_directory(\n",
        "    './C-NMC_Leukemia/training_data/val',\n",
        "    labels=\"inferred\",\n",
        "    class_names=[\"all\", \"hem\"],\n",
        "    label_mode=\"int\",\n",
        "    image_size=IMAGE_SIZE,\n",
        "    batch_size=BATCH_SIZE, \n",
        "    # seed=100,\n",
        "    # validation_split=0.2,\n",
        "    # subset='validation'\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 2130 files belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lUSb-nhAHY7B"
      },
      "source": [
        "test_df = pd.read_csv('./C-NMC_Leukemia/validation_data/C-NMC_test_prelim_phase_data_labels.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FDpW3PHZHuXt",
        "outputId": "55945517-4a66-425c-c445-86b88f9f6811"
      },
      "source": [
        "test_labels = test_df.labels.values\n",
        "print(test_labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1 1 1 ... 0 1 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H6Mrw3TUGhX8",
        "outputId": "12fe9981-6404-46d1-d584-f09936fc185c"
      },
      "source": [
        "test_data = tf.keras.preprocessing.image_dataset_from_directory(\n",
        "    './C-NMC_Leukemia/validation_data/',\n",
        "    labels=test_labels.tolist(),\n",
        "    label_mode=\"int\",\n",
        "    image_size=IMAGE_SIZE,\n",
        "    batch_size=BATCH_SIZE, \n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 1867 files belonging to 1 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u5Tg-fPgD92X"
      },
      "source": [
        "# Transfer Learning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yytlz_TlEI5Z"
      },
      "source": [
        "# Don't run this\n",
        "for l in model_default.layers[:-5]:\n",
        "  l.trainable = False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yMD-uD3bENXO"
      },
      "source": [
        "# Don't run this\n",
        "for l in model_default.layers:\n",
        "  print(l.name, l.trainable)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w8uurO5CEVA0"
      },
      "source": [
        "model_checkpoint_callback1 = tf.keras.callbacks.ModelCheckpoint(\n",
        "    filepath=\"./prec_checkpoints/\",\n",
        "    save_weights_only=True,\n",
        "    monitor='val_precision',\n",
        "    mode='max',\n",
        "    save_best_only=True)\n",
        "\n",
        "model_checkpoint_callback2 = tf.keras.callbacks.ModelCheckpoint(\n",
        "    filepath=\"./\",\n",
        "    save_weights_only=True,\n",
        "    monitor='val_accuracy',\n",
        "    mode='max',\n",
        "    save_best_only=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dBp6Ow5xA5ZF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5fe6f458-e53f-468e-eaf0-fe7c1df381f9"
      },
      "source": [
        "# One epoch takes like 10-15 minutes, so maybe 3 is the max we have time for\n",
        "model_default_history = model_default.fit(train_data, epochs=5, callbacks=[model_checkpoint_callback1], validation_data=val_data)\n",
        "\n",
        "# model_default.fit(train_0, epochs=20, callbacks=[model_checkpoint_callback1], validation_data=val_0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "534/534 [==============================] - 2184s 4s/step - loss: 0.4825 - accuracy: 0.8164 - precision: 0.7789 - val_loss: 0.3605 - val_accuracy: 0.8484 - val_precision: 0.9685\n",
            "Epoch 2/5\n",
            "534/534 [==============================] - 179s 335ms/step - loss: 0.3900 - accuracy: 0.8468 - precision: 0.8338 - val_loss: 0.2692 - val_accuracy: 0.8958 - val_precision: 0.8990\n",
            "Epoch 3/5\n",
            "534/534 [==============================] - 178s 333ms/step - loss: 0.3823 - accuracy: 0.8524 - precision: 0.8472 - val_loss: 0.3039 - val_accuracy: 0.8878 - val_precision: 0.9002\n",
            "Epoch 4/5\n",
            "534/534 [==============================] - 179s 335ms/step - loss: 0.3357 - accuracy: 0.8732 - precision: 0.8727 - val_loss: 0.3790 - val_accuracy: 0.8230 - val_precision: 0.9967\n",
            "Epoch 5/5\n",
            "534/534 [==============================] - 180s 338ms/step - loss: 0.3083 - accuracy: 0.8865 - precision: 0.8867 - val_loss: 0.3602 - val_accuracy: 0.8221 - val_precision: 1.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f1ab45dbd30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tMUohvAqxFR8",
        "outputId": "10013732-7ff9-426e-80ea-56b86fea4141"
      },
      "source": [
        "# save the model and history (just in case we need it later)\n",
        "import pickle\n",
        "\n",
        "# model_retrain_noise.model.save_weights('./retrain_imbalance/noise')\n",
        "model_default_history.model.save('./retrain_inception_resnet/model_default')\n",
        "\n",
        "model_default_history.model.evaluate(test_data, batch_size=128)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: ./retrain_inception_resnet/model_default/assets\n",
            "117/117 [==============================] - 493s 4s/step - loss: 1.6811 - accuracy: 0.3626 - precision: 0.7302\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.6810966730117798, 0.36261382699012756, 0.7301587462425232]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XDX6Nq1IxQ8b"
      },
      "source": [
        "# Save history\n",
        "with open('./retrain_inception_resnet/model_default_history', 'wb') as file_pi:\n",
        "  pickle.dump(model_default_history.history, file_pi) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zlB113BVUfo6"
      },
      "source": [
        "def transfer_learn(model, epochs, train_data, val_data):\n",
        "\n",
        "  # make last few layers the only trainable ones\n",
        "  for l in model.layers[:-5]:\n",
        "    l.trainable = False\n",
        "\n",
        "  return model.fit(train_data, epochs=epochs, callbacks=[model_checkpoint_callback1], validation_data=val_data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MmyV4brU0dDk"
      },
      "source": [
        "def retrain(model, epochs, train_data, val_data):\n",
        "  for l in model.layers:\n",
        "    l.set_trainable = True\n",
        "  \n",
        "  return model.fit(train_data, epochs=epochs, callbacks=[model_checkpoint_callback1], validation_data=val_data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U9hjvlcSU4oG"
      },
      "source": [
        "# Create Augmented Datasets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jAoZ9ERPY1GR"
      },
      "source": [
        "import cv2\n",
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bpacC-4kU6ML"
      },
      "source": [
        "## Code to Combine Generators (didn't work)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "teWO73gTcIW9"
      },
      "source": [
        "def getDataframe(path):\n",
        "  #return df with filenames and labels, chose 30% of data randomly to put in this df\n",
        "\n",
        "  all = path+'all/'\n",
        "  files = os.listdir(all)\n",
        "  # augment 30% of images\n",
        "  nFilesToAugment = int(len(files) * 0.3)\n",
        "  all_filesToAugment = np.random.choice(files, nFilesToAugment, replace=False)\n",
        "  all_filesToAugment = [all+f for f in all_filesToAugment]\n",
        "\n",
        "  all_labels = ['1'] * nFilesToAugment\n",
        "\n",
        "  hem = path+'hem/'\n",
        "  files = os.listdir(hem)\n",
        "  # augment 30% of images\n",
        "  nFilesToAugment = int(len(files) * 0.3)\n",
        "  hem_filesToAugment = np.random.choice(files, nFilesToAugment, replace=False)\n",
        "  hem_filesToAugment = [hem+f for f in hem_filesToAugment]\n",
        "\n",
        "  hem_labels = ['0'] * nFilesToAugment\n",
        "\n",
        "  # create df\n",
        "  filenames = np.concatenate((all_filesToAugment, hem_filesToAugment), axis=0)\n",
        "  labels = all_labels + hem_labels\n",
        "\n",
        "  d = {\"files\": filenames, \"labels\": labels}\n",
        "  df = pd.DataFrame(data=d)\n",
        "  \n",
        "  # shuffle\n",
        "  df = df.sample(frac=1) \n",
        "\n",
        "  return df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a4jnJnhwhV-u"
      },
      "source": [
        "# https://stackoverflow.com/questions/46313525/how-do-i-combine-two-keras-generator-functions\n",
        "def combine_generator(gen1, gen2):\n",
        "    while True:\n",
        "        # yield(next(gen1), next(gen2))\n",
        "        X1i = gen1.next()\n",
        "        X2i = gen2.next()\n",
        "\n",
        "        yield X1i[0], X2i[0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o1ZQx-cbkMbo"
      },
      "source": [
        "seed = 528"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QzdGMdSzVX6m"
      },
      "source": [
        "## Flipped"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-FhSgTa3tPpM"
      },
      "source": [
        "# dummy preprocessing for flip\n",
        "def preprocessing_flip(img):\n",
        "  # plt.imshow(img)\n",
        "  return img"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZYYngzeQV6Cg"
      },
      "source": [
        "datagen = tf.keras.preprocessing.image.ImageDataGenerator(\n",
        "    rescale=1./255, \n",
        "    validation_split=0.2,\n",
        "    horizontal_flip=True,\n",
        "    vertical_flip=True,\n",
        "    zoom_range = 0.2\n",
        "    # preprocessing_function=preprocessing_flip\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6gsvvdpzU8EW",
        "outputId": "36e51f72-b52c-4431-d0d4-638f9ff0bc5d"
      },
      "source": [
        "train_data_flipped = datagen.flow_from_directory(\n",
        "    './C-NMC_Leukemia/training_data/aug',\n",
        "    class_mode='binary',\n",
        "    target_size=IMAGE_SIZE,\n",
        "    batch_size=BATCH_SIZE\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 2527 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-jb-FZG2tjT4",
        "outputId": "7231bcfa-1138-4299-f012-972043984d79"
      },
      "source": [
        "first_item = train_data_flipped.__getitem__(0)\n",
        "print(first_item.count)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<built-in method count of tuple object at 0x7f88076dd608>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7kL07iI0YG0w"
      },
      "source": [
        "## Blur (train_data_blur)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BqgA1EVN729o"
      },
      "source": [
        "np.random.seed(1234)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-QYKN5plYkjC"
      },
      "source": [
        "def preprocessing_blur(img):\n",
        "  number_list = np.random.choice(3, 1, p = [0.33, 0.37, 0.30])\n",
        "  if number_list[0] == 0:\n",
        "    newImg = cv2.GaussianBlur(img, (21, 21),0)\n",
        "    return newImg\n",
        "  return img"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QX8b35kuYFmL"
      },
      "source": [
        "datagen_blur = tf.keras.preprocessing.image.ImageDataGenerator(\n",
        "    # rescale=1./255, \n",
        "    horizontal_flip=True,\n",
        "    vertical_flip=True,\n",
        "    zoom_range = 0.2,\n",
        "    preprocessing_function=preprocessing_blur,\n",
        "    featurewise_center=True,\n",
        "    featurewise_std_normalization=True\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XIKMCkL-lqnY"
      },
      "source": [
        "# df_blur = getDataframe('./C-NMC_Leukemia/training_data/train/')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0JsYnunMYMQA",
        "outputId": "b6ae9652-7ec0-4ae5-98a2-a81f90328301"
      },
      "source": [
        "train_data_blur = datagen_blur.flow_from_directory (\n",
        "    './C-NMC_Leukemia/training_data/fold_0',\n",
        "    class_mode='binary',\n",
        "    target_size=IMAGE_SIZE,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    color_mode='rgb'\n",
        "    # seed=seed\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 8531 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TuiEbFHepDa9"
      },
      "source": [
        "## Noise (train_data_noise)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RS8qeUpOnPZu"
      },
      "source": [
        "from skimage.util import random_noise"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GlJcvZAOpHFi"
      },
      "source": [
        "def preprocessing_noise(img):\n",
        "  number_list = np.random.choice(3, 1, p = [0.33, 0.37, 0.30])\n",
        "  if number_list[0] == 0:\n",
        "    newImg = random_noise(img, mode='gaussian', mean=0, var=0.1, clip=False)\n",
        "    return newImg\n",
        "  return img"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "riHWkDiEpRiL"
      },
      "source": [
        "datagen_noise = tf.keras.preprocessing.image.ImageDataGenerator(\n",
        "    # rescale=1./255, \n",
        "    horizontal_flip=True,\n",
        "    vertical_flip=True,\n",
        "    zoom_range = 0.2,\n",
        "    preprocessing_function=preprocessing_noise,\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lzuPzbskpadH",
        "outputId": "c48048c3-1c47-4545-c9b0-7b4803404bfa"
      },
      "source": [
        "train_data_noise = datagen_noise.flow_from_directory (\n",
        "    './C-NMC_Leukemia/training_data/fold_0',\n",
        "    class_mode='binary',\n",
        "    target_size=IMAGE_SIZE,\n",
        "    batch_size=BATCH_SIZE, \n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 8531 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AklIMiT3riLv"
      },
      "source": [
        "## Greyscale (train_data_grey)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lz1qQjA_rhCY"
      },
      "source": [
        "def preprocessing_grey(img):\n",
        "  return random_noise(img, mode='gaussian', mean=0, var=0.01, clip=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HQVhLrRmr13D"
      },
      "source": [
        "datagen_grey = tf.keras.preprocessing.image.ImageDataGenerator(\n",
        "    rescale=1./255, \n",
        "    horizontal_flip=True,\n",
        "    vertical_flip=True,\n",
        "    zoom_range = 0.2,\n",
        "    preprocessing_function=preprocessing_grey,\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PhldDDBAr1qX"
      },
      "source": [
        "# df_grey = getDataframe('./C-NMC_Leukemia/training_data/train/')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NB4WEaDUr0_a",
        "outputId": "5302b6a6-d202-484e-f386-2d4e711c88da"
      },
      "source": [
        "train_data_grey = datagen_noise.flow_from_directory (\n",
        "    './C-NMC_Leukemia/training_data/aug',\n",
        "    class_mode='binary',\n",
        "    target_size=IMAGE_SIZE,\n",
        "    batch_size=BATCH_SIZE, \n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 2527 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T01fo9xVr0tZ"
      },
      "source": [
        "# train_data_grey = zip(train_data, train_data_grey)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QnoQfJMRKTdr"
      },
      "source": [
        "# Evaluate Default Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nAzCnN-SEdN5",
        "outputId": "5447b5de-d8b0-43e1-f314-cde66aa7f00b"
      },
      "source": [
        "results = model_default.evaluate(test_images, batch_size=128)\n",
        "print(\"test loss, test acc:\", results)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "117/117 [==============================] - 477s 4s/step - loss: 1.5249 - accuracy: 0.3739 - precision: 0.6786\n",
            "test loss, test acc: [1.5248767137527466, 0.37386181950569153, 0.6785714030265808]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x9M8Qr2eXSdL"
      },
      "source": [
        "# Full Workflow"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lqqPa6mOp6k7"
      },
      "source": [
        "## Retrain Flipped Transfer Learning Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WnXcs5CqXRti",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7d665899-6b66-46b4-eb4a-bbf09182b9be"
      },
      "source": [
        "# re-train flipped transfer learning model\n",
        "\n",
        "model_flipped = transfer_learn(model_default, 5, train_data_flipped, val_data)\n",
        "# model_flipped.model.save_weights(\"./w_flipped.hdf5\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "158/158 [==============================] - 1344s 9s/step - loss: 0.6446 - accuracy: 0.6791 - precision: 0.5015 - val_loss: 0.6322 - val_accuracy: 0.8178 - val_precision: 0.7500\n",
            "Epoch 2/5\n",
            "158/158 [==============================] - 57s 359ms/step - loss: 0.5552 - accuracy: 0.7372 - precision: 0.6462 - val_loss: 0.7729 - val_accuracy: 0.8056 - val_precision: 0.6962\n",
            "Epoch 3/5\n",
            "158/158 [==============================] - 48s 303ms/step - loss: 0.5470 - accuracy: 0.7511 - precision: 0.6770 - val_loss: 1.5111 - val_accuracy: 0.7714 - val_precision: 0.6059\n",
            "Epoch 4/5\n",
            "158/158 [==============================] - 48s 304ms/step - loss: 0.5090 - accuracy: 0.7677 - precision: 0.7135 - val_loss: 2.7006 - val_accuracy: 0.7352 - val_precision: 0.5561\n",
            "Epoch 5/5\n",
            "158/158 [==============================] - 47s 299ms/step - loss: 0.5007 - accuracy: 0.7780 - precision: 0.7278 - val_loss: 3.2969 - val_accuracy: 0.7042 - val_precision: 0.5222\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gHG9XVVt_qy7"
      },
      "source": [
        "import pickle"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rqKirU4BqYNd"
      },
      "source": [
        "# model_retrain_noise.model.save_weights('./retrain_imbalance/noise')\n",
        "model_flipped.model.save('./retrain_inception_resnet/flipped')\n",
        "\n",
        "model_flipped.model.evaluate(test_data, batch_size=128)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nwn4D7Cd_i27"
      },
      "source": [
        "# Save history\n",
        "with open('./retrain_inception_resnet/flipped_history', 'wb') as file_pi:\n",
        "  pickle.dump(model_flipped.history, file_pi) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6kzNnGUVXhGT"
      },
      "source": [
        "# evaluate results\n",
        "# results_flipped = model_default.evaluate(test_images, batch_size=128)\n",
        "# print(\"test loss, test acc:\", results_flipped)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YWVFnTB1sIuB"
      },
      "source": [
        "## Retrain Noise Transfer Learning Model\n",
        "* @Marlin, just replicate the 3 cells below for each augmentation dataset I created\n",
        "  * 3 epochs take me like an hour, but you can try more if you want\n",
        "* If you get through those, try combining augmentations by looking at the code in section 'Created Augmented Datasets'"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dGkGFiS5qgJ9",
        "outputId": "e9f06c5b-7441-4033-ef9c-25c2714fc280"
      },
      "source": [
        "# Noise\n",
        "model_noise = transfer_learn(model_default, 5, train_data_flipped, val_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "158/158 [==============================] - 48s 302ms/step - loss: 0.4636 - accuracy: 0.7998 - precision: 0.7567 - val_loss: 11.2080 - val_accuracy: 0.4901 - val_precision: 0.3803\n",
            "Epoch 2/5\n",
            "158/158 [==============================] - 48s 303ms/step - loss: 0.4360 - accuracy: 0.8077 - precision: 0.7717 - val_loss: 6.2677 - val_accuracy: 0.5169 - val_precision: 0.3927\n",
            "Epoch 3/5\n",
            "158/158 [==============================] - 48s 302ms/step - loss: 0.4282 - accuracy: 0.8223 - precision: 0.7923 - val_loss: 7.2848 - val_accuracy: 0.5089 - val_precision: 0.3895\n",
            "Epoch 4/5\n",
            "158/158 [==============================] - 48s 304ms/step - loss: 0.4149 - accuracy: 0.8294 - precision: 0.8000 - val_loss: 12.2140 - val_accuracy: 0.4202 - val_precision: 0.3520\n",
            "Epoch 5/5\n",
            "158/158 [==============================] - 48s 303ms/step - loss: 0.3916 - accuracy: 0.8350 - precision: 0.8006 - val_loss: 8.3884 - val_accuracy: 0.4648 - val_precision: 0.3705\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Op7OGpuhrQw1"
      },
      "source": [
        "# model_noise.model.save_weights(\"./w_noise.hdf5\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DWUTfAGRwLly",
        "outputId": "e29d8a7a-fb00-466c-ae90-8fe379fe41d3"
      },
      "source": [
        "# model_retrain_noise.model.save_weights('./retrain_imbalance/noise')\n",
        "model_noise.model.save('./retrain_inception_resnet/noise')\n",
        "\n",
        "model_noise.model.evaluate(test_data, batch_size=128)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: ./retrain_inception_resnet/noise/assets\n",
            "117/117 [==============================] - 9s 76ms/step - loss: 1.3184 - accuracy: 0.3685 - precision: 0.6099\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.3183867931365967, 0.3685056269168854, 0.6098901033401489]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "soc2UeQK_92m"
      },
      "source": [
        "# Save history\n",
        "with open('./retrain_inception_resnet/noise_history', 'wb') as file_pi:\n",
        "  pickle.dump(model_noise.history, file_pi) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Iil828HDqkNI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a27e0710-ac2b-412c-ea87-dac4729f51ff"
      },
      "source": [
        "# results_noise = model_noise.model.evaluate(test_data, batch_size=128)\n",
        "# print(\"test loss, test acc:\", results_noise)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "117/117 [==============================] - 29s 251ms/step - loss: 21.8192 - accuracy: 0.6508 - precision: 0.6528\n",
            "test loss, test acc: [21.819196701049805, 0.6507766246795654, 0.652830183506012]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SR8rb7ljrPca"
      },
      "source": [
        "## Retrain Blur Transfer Learning Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OUxofydfrQ1Y",
        "outputId": "908cb1a8-d04c-461a-d0e4-dd74f9e71e14"
      },
      "source": [
        "# Noise\n",
        "model_blur = transfer_learn(model_default, 5, train_data_blur, val_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:720: UserWarning: This ImageDataGenerator specifies `featurewise_center`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n",
            "  warnings.warn('This ImageDataGenerator specifies '\n",
            "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:728: UserWarning: This ImageDataGenerator specifies `featurewise_std_normalization`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n",
            "  warnings.warn('This ImageDataGenerator specifies '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "534/534 [==============================] - 2289s 4s/step - loss: 0.7245 - accuracy: 0.7209 - precision: 0.5989 - val_loss: 0.4399 - val_accuracy: 0.7920 - val_precision: 0.9958\n",
            "Epoch 2/5\n",
            "534/534 [==============================] - 196s 367ms/step - loss: 0.4956 - accuracy: 0.7861 - precision: 0.7509 - val_loss: 0.3538 - val_accuracy: 0.8498 - val_precision: 0.9545\n",
            "Epoch 3/5\n",
            "534/534 [==============================] - 197s 369ms/step - loss: 0.4655 - accuracy: 0.8020 - precision: 0.7881 - val_loss: 0.4038 - val_accuracy: 0.8122 - val_precision: 0.9965\n",
            "Epoch 4/5\n",
            "534/534 [==============================] - 193s 362ms/step - loss: 0.4458 - accuracy: 0.8157 - precision: 0.8068 - val_loss: 0.3361 - val_accuracy: 0.8441 - val_precision: 0.9807\n",
            "Epoch 5/5\n",
            "534/534 [==============================] - 192s 360ms/step - loss: 0.4332 - accuracy: 0.8212 - precision: 0.8071 - val_loss: 0.2963 - val_accuracy: 0.8812 - val_precision: 0.9211\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "20iVpkuUrS4x"
      },
      "source": [
        "# model_blur.model.save_weights(\"./w_noise.hdf5\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_AYTwebowNQf",
        "outputId": "dce2cce1-274a-4524-ecc5-a2a4ddbb32bf"
      },
      "source": [
        "# model_retrain_noise.model.save_weights('./retrain_imbalance/noise')\n",
        "model_blur.model.save('./retrain_inception_resnet/blur')\n",
        "\n",
        "model_blur.model.evaluate(test_data, batch_size=128)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: ./retrain_inception_resnet/blur/assets\n",
            "117/117 [==============================] - 9s 73ms/step - loss: 1.3184 - accuracy: 0.3685 - precision: 0.6099\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.3183866739273071, 0.3685056269168854, 0.6098901033401489]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HaXYa4shAOrr"
      },
      "source": [
        "# Save history\n",
        "with open('./retrain_inception_resnet/blur_history', 'wb') as file_pi:\n",
        "  pickle.dump(model_blur.history, file_pi) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7kiuQdYKrU4z"
      },
      "source": [
        "# results_noise = model_noise.model.evaluate(test_data, batch_size=128)\n",
        "# print(\"test loss, test acc:\", results_noise)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2aQPKB2HnQiL"
      },
      "source": [
        "# Model replication for ther datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "svCjh09Ypgcy"
      },
      "source": [
        "## Blur"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "egga0Kw6ptVj"
      },
      "source": [
        "# Blur\n",
        "model_blur = transfer_learn(model_default, 5, test, val_data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ScvmHwtaTJCw",
        "outputId": "c968e9d8-19ce-440e-e085-1885f0149ce6"
      },
      "source": [
        "print(model_blur)\n",
        "print(model_blur.model.get_weights)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<tensorflow.python.keras.callbacks.History object at 0x7fd492c8d668>\n",
            "<bound method Model.get_weights of <tensorflow.python.keras.engine.sequential.Sequential object at 0x7fd4f5cbe978>>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bkJoY3FYp4Vr"
      },
      "source": [
        "# model_blur.save_weights(\"./w_blur.hdf5\")\n",
        "\n",
        "model_blur.model.save_weights('./w_blur.hdf5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tqofCngkp8yx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "222bf09c-b888-4c94-9261-b21ca72c68df"
      },
      "source": [
        "# replacing test_images for test_data\n",
        "\n",
        "results_blur = model_blur.model.evaluate(test_data, batch_size=128)\n",
        "print(\"test loss, test acc:\", results_blur)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "117/117 [==============================] - 485s 4s/step - loss: 3.7525 - accuracy: 0.5983 - precision: 0.6548\n",
            "test loss, test acc: [3.752495527267456, 0.5982860326766968, 0.6547854542732239]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uDo6dS83pihB"
      },
      "source": [
        "## Grayscale"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ATdzmW76qFQE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d61e211c-1f9d-4c2b-ac10-ea2d8883e3fe"
      },
      "source": [
        "# Grayscale\n",
        "model_grayscale = transfer_learn(model_default, 3, train_data_flipped, val_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/3\n",
            "427/427 [==============================] - 163s 382ms/step - loss: 0.4542 - accuracy: 0.8072 - precision: 0.6468 - val_loss: 7.4846 - val_accuracy: 0.6906 - val_precision: 0.6934\n",
            "Epoch 2/3\n",
            "427/427 [==============================] - 161s 376ms/step - loss: 0.4300 - accuracy: 0.8244 - precision: 0.6923 - val_loss: 11.0075 - val_accuracy: 0.6826 - val_precision: 0.6825\n",
            "Epoch 3/3\n",
            "427/427 [==============================] - 160s 376ms/step - loss: 0.4058 - accuracy: 0.8332 - precision: 0.7258 - val_loss: 19.0789 - val_accuracy: 0.6821 - val_precision: 0.6823\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w07sTumCqKmq"
      },
      "source": [
        "model_grayscale.model.save_weights(\"./w_grayscale.hdf5\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aUE0NVTgqPNK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "88f3472c-0688-4619-84cb-efe1233b8fac"
      },
      "source": [
        "results_grayscale = model_grayscale.model.evaluate(test_data, batch_size=128)\n",
        "print(\"test loss, test acc:\", results_grayscale)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "117/117 [==============================] - 29s 251ms/step - loss: 24.5485 - accuracy: 0.6513 - precision: 0.6527\n",
            "test loss, test acc: [24.548465728759766, 0.6513122916221619, 0.6526881456375122]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ORWIDGqHfeGx"
      },
      "source": [
        "train_data_grey_zip = zip(train_data, train_data_grey)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QWQgXxiJfhEa"
      },
      "source": [
        "# Grayscale\n",
        "model_grayscale_new = transfer_learn(model_default, 3, train_data_grey_zip, val_data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RoUHg56UalBs"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P_Q5cx6-aliF"
      },
      "source": [
        "# Implementation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_rgzUIxp0OOI"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oBepIE8_awsK",
        "outputId": "6948d041-1983-4136-dffd-e21f225cbd45"
      },
      "source": [
        "# transfer learning on the noise dataset\n",
        "model_train_noise = model_default\n",
        "\n",
        "model_train_noise = transfer_learn(model_train_noise, 3, train_data_noise, val_data)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/3\n",
            "158/158 [==============================] - 107s 677ms/step - loss: 0.5563 - accuracy: 0.7471 - precision: 0.6784 - val_loss: 0.4034 - val_accuracy: 0.8225 - val_precision: 0.9809\n",
            "Epoch 2/3\n",
            "158/158 [==============================] - 72s 454ms/step - loss: 0.5123 - accuracy: 0.7824 - precision: 0.7391 - val_loss: 0.4313 - val_accuracy: 0.7962 - val_precision: 0.9920\n",
            "Epoch 3/3\n",
            "158/158 [==============================] - 72s 455ms/step - loss: 0.4899 - accuracy: 0.7915 - precision: 0.7577 - val_loss: 0.5744 - val_accuracy: 0.7446 - val_precision: 1.0000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6f1uIbgYayyS",
        "outputId": "cca9bb73-4103-4f34-ba2a-f1620fc56d26"
      },
      "source": [
        "# evaluate the noise model\n",
        "model_noise_eval = model_train_noise.model.evaluate(test_data, batch_size=128)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "117/117 [==============================] - 29s 248ms/step - loss: 2.0918 - accuracy: 0.3519 - precision: 0.7647\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PlyjtUdE1MEa"
      },
      "source": [
        "Retrain Noise Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oq-q3XRB1N2p"
      },
      "source": [
        "# model_retrain_noise = \n",
        "\n",
        "# model = get_cnn_model_1(IMAGE_SIZE + (3,))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZXYPtxgUkhEW"
      },
      "source": [
        "first_item = train_data_noise.__getitem__(0)\n",
        "print(first_item.count)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MDYRj7uCao6s",
        "outputId": "2416fb17-9e09-419c-cbe4-0e320720be65"
      },
      "source": [
        "# leave this last\n",
        "# transfer learning on the flipped dataset\n",
        "\n",
        "model_train_flipped = model_default\n",
        "\n",
        "model_train_flipped = transfer_learn(model_train_flipped, 1, train_data_flipped, val_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "158/158 [==============================] - 1365s 9s/step - loss: 0.6499 - accuracy: 0.6664 - precision: 0.4448 - val_loss: 0.8422 - val_accuracy: 0.8047 - val_precision: 0.7050\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x6uXkD7zau2l",
        "outputId": "17577e19-32de-42c9-9df7-3cba7ac64cbf"
      },
      "source": [
        "# evaluate the flipped model\n",
        "\n",
        "model_flipped_eval = model_train_flipped.model.evaluate(test_data, batch_size=128)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "117/117 [==============================] - 438s 4s/step - loss: 2.1606 - accuracy: 0.3996 - precision: 0.6303\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NNoCAC6ma03O"
      },
      "source": [
        "# transfer learning on the blur dataset\n",
        "model_train_blur = model_default\n",
        "\n",
        "model_train_blur = transfer_learn(model_train_blur, 3, train_data_blur, val_data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DQrF3NcCa3De",
        "outputId": "5e2e3545-57e8-4ffd-a6ac-e53cb9a9e990"
      },
      "source": [
        "# evaluate the blur model\n",
        "model_blur_eval = model_train_blur.model.evaluate(test_data, batch_size=128)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "117/117 [==============================] - 29s 248ms/step - loss: 1.4774 - accuracy: 0.3621 - precision: 0.6707\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WTX7C_NF1XQO"
      },
      "source": [
        "Retrain Model Blur"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hap8W4Zm1WsC",
        "outputId": "1c7d736b-ce0b-4c45-d340-4aaf1de868da"
      },
      "source": [
        "# TODO instead of train_data_blur try to concatenate both training and \n",
        "\n",
        "model_retrain_blur = model\n",
        "\n",
        "model_retrain_blur = retrain(model_retrain_blur, 5, train_data_blur, val_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:720: UserWarning: This ImageDataGenerator specifies `featurewise_center`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n",
            "  warnings.warn('This ImageDataGenerator specifies '\n",
            "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:728: UserWarning: This ImageDataGenerator specifies `featurewise_std_normalization`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n",
            "  warnings.warn('This ImageDataGenerator specifies '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "534/534 [==============================] - 2427s 5s/step - loss: 0.4299 - accuracy: 0.8237 - precision: 0.8186 - val_loss: 0.3269 - val_accuracy: 0.8789 - val_precision: 0.9607\n",
            "Epoch 2/5\n",
            "534/534 [==============================] - 208s 390ms/step - loss: 0.4037 - accuracy: 0.8311 - precision: 0.8401 - val_loss: 0.3423 - val_accuracy: 0.8493 - val_precision: 0.9972\n",
            "Epoch 3/5\n",
            "534/534 [==============================] - 203s 380ms/step - loss: 0.3797 - accuracy: 0.8475 - precision: 0.8574 - val_loss: 0.2819 - val_accuracy: 0.8822 - val_precision: 0.9694\n",
            "Epoch 4/5\n",
            "534/534 [==============================] - 203s 379ms/step - loss: 0.3835 - accuracy: 0.8467 - precision: 0.8533 - val_loss: 0.5372 - val_accuracy: 0.7653 - val_precision: 1.0000\n",
            "Epoch 5/5\n",
            "534/534 [==============================] - 201s 376ms/step - loss: 0.3915 - accuracy: 0.8421 - precision: 0.8464 - val_loss: 0.3792 - val_accuracy: 0.8038 - val_precision: 1.0000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CqccVQgs9IQA"
      },
      "source": [
        "# save the model weights\n",
        "model_retrain_blur.model.save_weights('./retrain_blur.hdf5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "03Ohtmv41xcJ",
        "outputId": "5267f379-86e5-4474-b597-fcefbe77d061"
      },
      "source": [
        "# evaluate the retrained model\n",
        "model_retrain_blur.model.evaluate(test_data, batch_size=128)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "117/117 [==============================] - 29s 252ms/step - loss: 1.5392 - accuracy: 0.3498 - precision: 0.6923\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.5391560792922974, 0.34975898265838623, 0.692307710647583]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 208
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8HJt0Vw7NQp4"
      },
      "source": [
        "Apply SGD optimization to the blur data augmentation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qg7_dYW1LPLd"
      },
      "source": [
        "# try again, but comparing SGD versus Adam\n",
        "# assuming same learning rate as the Adam implementation\n",
        "\n",
        "model_retrain_blur_sgd = model\n",
        "\n",
        "# new optimizations\n",
        "sgd_opt = tf.keras.optimizers.SGD(learning_rate=0.0001)\n",
        "metrics = [\"accuracy\", tf.keras.metrics.Precision(name=\"precision\")]\n",
        "\n",
        "# compile our new model\n",
        "model_retrain_blur_sgd.compile(\n",
        "    optimizer=sgd_opt,\n",
        "    loss=tf.keras.losses.BinaryCrossentropy(),\n",
        "    metrics=metrics\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T1LeKyZeMjMN",
        "outputId": "a9d0fbbb-7783-4cfe-9997-18a5fc449731"
      },
      "source": [
        "# retrain the model\n",
        "\n",
        "model_retrain_blur_sgd = retrain(model_retrain_blur_sgd, 10, train_data_blur, val_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:720: UserWarning: This ImageDataGenerator specifies `featurewise_center`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n",
            "  warnings.warn('This ImageDataGenerator specifies '\n",
            "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:728: UserWarning: This ImageDataGenerator specifies `featurewise_std_normalization`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n",
            "  warnings.warn('This ImageDataGenerator specifies '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "534/534 [==============================] - 190s 355ms/step - loss: 0.3786 - accuracy: 0.8371 - precision: 0.8985 - val_loss: 0.3642 - val_accuracy: 0.8324 - val_precision: 0.9969\n",
            "Epoch 2/10\n",
            "534/534 [==============================] - 193s 361ms/step - loss: 0.3849 - accuracy: 0.8364 - precision: 0.9005 - val_loss: 0.3595 - val_accuracy: 0.8347 - val_precision: 0.9970\n",
            "Epoch 3/10\n",
            "534/534 [==============================] - 193s 361ms/step - loss: 0.3749 - accuracy: 0.8425 - precision: 0.9070 - val_loss: 0.3535 - val_accuracy: 0.8362 - val_precision: 0.9970\n",
            "Epoch 4/10\n",
            "534/534 [==============================] - 194s 364ms/step - loss: 0.3721 - accuracy: 0.8422 - precision: 0.9006 - val_loss: 0.3516 - val_accuracy: 0.8371 - val_precision: 0.9970\n",
            "Epoch 5/10\n",
            "534/534 [==============================] - 195s 366ms/step - loss: 0.3749 - accuracy: 0.8405 - precision: 0.8988 - val_loss: 0.3568 - val_accuracy: 0.8362 - val_precision: 0.9970\n",
            "Epoch 6/10\n",
            "534/534 [==============================] - 194s 364ms/step - loss: 0.3804 - accuracy: 0.8385 - precision: 0.8945 - val_loss: 0.3530 - val_accuracy: 0.8362 - val_precision: 0.9970\n",
            "Epoch 7/10\n",
            "534/534 [==============================] - 195s 365ms/step - loss: 0.3771 - accuracy: 0.8374 - precision: 0.8930 - val_loss: 0.3497 - val_accuracy: 0.8357 - val_precision: 0.9970\n",
            "Epoch 8/10\n",
            "534/534 [==============================] - 193s 361ms/step - loss: 0.3836 - accuracy: 0.8344 - precision: 0.8951 - val_loss: 0.3497 - val_accuracy: 0.8347 - val_precision: 0.9970\n",
            "Epoch 9/10\n",
            "534/534 [==============================] - 192s 360ms/step - loss: 0.3767 - accuracy: 0.8399 - precision: 0.9028 - val_loss: 0.3499 - val_accuracy: 0.8366 - val_precision: 0.9970\n",
            "Epoch 10/10\n",
            "534/534 [==============================] - 194s 364ms/step - loss: 0.3800 - accuracy: 0.8387 - precision: 0.8993 - val_loss: 0.3499 - val_accuracy: 0.8366 - val_precision: 0.9970\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "byXPOcNlMn9Y"
      },
      "source": [
        "# save the model weights\n",
        "model_retrain_blur_sgd.model.save_weights('./retrain_blur_sgd.hdf5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "emqkouWEMqG5",
        "outputId": "8811145a-c475-45c9-f2fc-45ea39f78794"
      },
      "source": [
        "# evaluate the retrained model\n",
        "model_retrain_blur_sgd.model.evaluate(test_data, batch_size=128)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "117/117 [==============================] - 30s 253ms/step - loss: 1.9457 - accuracy: 0.3557 - precision: 0.6667\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.945739507675171, 0.35565078258514404, 0.6666666865348816]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 213
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c1rC2FfC85Lb"
      },
      "source": [
        "Retrain the noise model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z_49YccH87de"
      },
      "source": [
        "# TODO instead of train_data_blur try to concatenate both training and \n",
        "\n",
        "model_retrain_noise = model\n",
        "\n",
        "model_retrain_noise = retrain(model_retrain_noise, 5, train_data_noise, val_data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TK0PkBeC9VPh"
      },
      "source": [
        "# save the model weights\n",
        "model_retrain_noise.model.save_weights('./retrain_noise.hdf5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cg_J2AQ38-UC"
      },
      "source": [
        "# evaluate the retrained model\n",
        "model_retrain_noise.model.evaluate(test_data, batch_size=128)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j0twVcbPa5R0"
      },
      "source": [
        "# transfer learning on the greyscale dataset\n",
        "model_train_grey = model_default\n",
        "\n",
        "model_train_grey = transfer_learn(model_train_grey, 1, train_data_grey, val_data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OgBZfdTQa7TS"
      },
      "source": [
        "# evaluate the greyscale model\n",
        "model_grey_eval = model_train_grey.model.evaluate(test_data, batch_size=128)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4K2onbircwUh"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wQSwJ6qccw4G"
      },
      "source": [
        "# Implement InceptionV3 and Resnet"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YjSk5CmYdP2X"
      },
      "source": [
        "from tensorflow.keras.applications.inception_v3 import InceptionV3\n",
        "from tensorflow.keras.models import Model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Su3NmHEldG0m"
      },
      "source": [
        "# Creating model with pre trained imagenet weights\n",
        "\n",
        "incep_v3 = InceptionV3(include_top=False, weights='imagenet', input_shape=(128,128,3))\n",
        "\n",
        "for layers in incep_v3.layers:\n",
        "    layers.trainable = False\n",
        "\n",
        "\n",
        "x = Flatten()(incep_v3.output)\n",
        "\n",
        "fcc_layer_1 = Dense(units = 1024, activation = 'relu')(x)\n",
        "dropout_1   = Dropout(0.3)(fcc_layer_1)\n",
        "\n",
        "fcc_layer_2 = Dense(units = 512, activation = 'relu')(dropout_1)\n",
        "dropout_2   = Dropout(0.3)(fcc_layer_2)\n",
        "\n",
        "final_layer = Dense(units = 1, activation = 'sigmoid')(dropout_2)\n",
        "\n",
        "model_incepV3 = Model(inputs = incep_v3.input, outputs = final_layer)\n",
        "\n",
        "model_incepV3.compile(optimizer = 'adam', \n",
        "              loss = 'binary_crossentropy',\n",
        "              metrics = ['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tRshX8zOc08a"
      },
      "source": [
        "model_incepV3 = tf.keras.models.load_model('best_weights.hdf5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hpAc5IJyeOlH",
        "outputId": "58c48c6b-7c42-42ba-d267-da501d67cf99"
      },
      "source": [
        "test_data_incep = tf.keras.preprocessing.image_dataset_from_directory(\n",
        "    './C-NMC_Leukemia/validation_data/',\n",
        "    labels=test_labels.tolist(),\n",
        "    label_mode=\"int\",\n",
        "    image_size=(128, 128),\n",
        "    batch_size=BATCH_SIZE, \n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 1867 files belonging to 1 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u25gIqq7dtVy",
        "outputId": "891d0ab3-c343-48d6-ca2e-ad7c51c96c57"
      },
      "source": [
        "model_incepV3.evaluate(test_data_incep, callbacks=[model_checkpoint_callback1], batch_size=128)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "117/117 [==============================] - 8s 72ms/step - loss: 6.9853 - accuracy: 0.6529\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[6.9853410720825195, 0.6529191136360168]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 224
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0MjzmplGdZfS"
      },
      "source": [
        "ResNet"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B2NYDbTDfMHI"
      },
      "source": [
        "from tensorflow.keras.applications import ResNet50V2"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VUMBpO8YfMow",
        "outputId": "5d146eb9-9edd-49e4-f640-51de3d2e4325"
      },
      "source": [
        "model_resnet = ResNet50V2(\n",
        "    include_top=False,\n",
        "    weights=\"imagenet\",\n",
        "    input_tensor=None,\n",
        "    input_shape=(128,128, 3),\n",
        "    pooling=None,\n",
        "    classes=1,\n",
        "    classifier_activation=\"softmax\",\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50v2_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "94674944/94668760 [==============================] - 1s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v_1GZZb8fO4w"
      },
      "source": [
        "for layers in model_resnet.layers:\n",
        "  layers.trainable = False\n",
        "\n",
        "x = Flatten()(model_resnet.output)\n",
        "\n",
        "fcc_layer_1 = Dense(units = 1024, activation = 'relu')(x)\n",
        "dropout_1   = Dropout(0.3)(fcc_layer_1)\n",
        "\n",
        "fcc_layer_2 = Dense(units = 512, activation = 'relu')(dropout_1)\n",
        "dropout_2   = Dropout(0.3)(fcc_layer_2)\n",
        "\n",
        "final_layer = Dense(units = 1, activation = 'sigmoid')(dropout_2)\n",
        "\n",
        "model_resnet = Model(inputs = model_resnet.input, outputs = final_layer)\n",
        "\n",
        "model_resnet.compile(optimizer = 'adam', \n",
        "              loss = 'binary_crossentropy',\n",
        "              metrics = ['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Ee85NXqfYZ6"
      },
      "source": [
        "model_resnet = tf.keras.models.load_model('best_weights_resnet.hdf5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nydgz73jfiJD",
        "outputId": "a9c26377-f228-4889-f80e-50a4b4f160be"
      },
      "source": [
        "test_data_resnet = tf.keras.preprocessing.image_dataset_from_directory(\n",
        "    './C-NMC_Leukemia/validation_data/',\n",
        "    labels=test_labels.tolist(),\n",
        "    label_mode=\"int\",\n",
        "    image_size=(128, 128),\n",
        "    batch_size=BATCH_SIZE, \n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 1867 files belonging to 1 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jDYPAgZwfkgz",
        "outputId": "6f85531c-f46e-4ca0-c73a-9dbc407eb03f"
      },
      "source": [
        "model_resnet.evaluate(test_data_resnet, callbacks=[model_checkpoint_callback1], batch_size=128)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "117/117 [==============================] - 8s 69ms/step - loss: 2.9765 - accuracy: 0.6535\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2.9765355587005615, 0.6534547209739685]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 230
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z195zGqPfRyG"
      },
      "source": [
        "# Graphical Representations for Information\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2-6_YVR1fgRC"
      },
      "source": [
        "## InceptionV3 Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2tIOKKsDhmR8"
      },
      "source": [
        "from tensorflow.keras.applications.inception_v3 import InceptionV3\n",
        "from tensorflow.keras.models import Model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TTBteA0ThqjN",
        "outputId": "d38540ad-12d2-48f2-bd26-d6eba0b2ec9a"
      },
      "source": [
        "# Creating model with pre trained imagenet weights\n",
        "\n",
        "incep_v3 = InceptionV3(include_top=False, weights='imagenet', input_shape=(128,128,3))\n",
        "\n",
        "for layers in incep_v3.layers:\n",
        "    layers.trainable = False\n",
        "\n",
        "\n",
        "x = Flatten()(incep_v3.output)\n",
        "\n",
        "fcc_layer_1 = Dense(units = 1024, activation = 'relu')(x)\n",
        "dropout_1   = Dropout(0.3)(fcc_layer_1)\n",
        "\n",
        "fcc_layer_2 = Dense(units = 512, activation = 'relu')(dropout_1)\n",
        "dropout_2   = Dropout(0.3)(fcc_layer_2)\n",
        "\n",
        "final_layer = Dense(units = 1, activation = 'sigmoid')(dropout_2)\n",
        "\n",
        "model_incepV3 = Model(inputs = incep_v3.input, outputs = final_layer)\n",
        "\n",
        "model_incepV3.compile(optimizer = 'adam', \n",
        "              loss = 'binary_crossentropy',\n",
        "              metrics = ['accuracy'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "87916544/87910968 [==============================] - 1s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NbRDjxUwhuOR"
      },
      "source": [
        "# load existing model weights\n",
        "\n",
        "model_incepV3 = tf.keras.models.load_model('best_weights.hdf5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f9VNptaBhxGM",
        "outputId": "5c120b89-0a18-465e-f685-06bbbff1b34b"
      },
      "source": [
        "# define (128, 128) image data for the training\n",
        "\n",
        "test_data_128 = tf.keras.preprocessing.image_dataset_from_directory(\n",
        "    './C-NMC_Leukemia/validation_data/',\n",
        "    labels=test_labels.tolist(),\n",
        "    label_mode=\"int\",\n",
        "    image_size=(128, 128),\n",
        "    batch_size=BATCH_SIZE, \n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 1867 files belonging to 1 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RatJtQ8Fh0Vz",
        "outputId": "d4372048-be72-44bb-a491-161b2e1c7bf8"
      },
      "source": [
        "# evaluate the model\n",
        "model_incepV3_eval = model_incepV3.evaluate(test_data_128, batch_size=128)\n",
        "\n",
        "# obtain the model loss and accuracy\n",
        "\n",
        "loss_incepv3 = model_incepV3_eval[0]\n",
        "accuracy_incepv3 = model_incepV3_eval[1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "117/117 [==============================] - 8s 70ms/step - loss: 6.9853 - accuracy: 0.6529\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k1TUALLatvqU"
      },
      "source": [
        "predict_incepV3 = model_incepV3.predict(test_data_128, batch_size=128)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hVPEgNHFfwTi"
      },
      "source": [
        "For now, is assumes a Gaussian Distribution for Prediction Accuracy\n",
        "\n",
        "Assumes values of Z:\n",
        "\n",
        "1.64 (90%)\n",
        "\n",
        "1.96 (95%)\n",
        "\n",
        "2.33 (98%)\n",
        "\n",
        "2.58 (99%)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6IHAT9TxTibE"
      },
      "source": [
        "# Computes confidence interval without changing the network architecture\n",
        "# assuming Gaussian distribution of prediction accuracy\n",
        "\n",
        "# size of our test dataset\n",
        "n = predict_incepV3.shape[0]\n",
        "\n",
        "# assuming 95% confidence interval\n",
        "Z = 1.96\n",
        "\n",
        "conf_radius_incepV3 = Z * np.sqrt((accuracy_incepv3 * (1 - accuracy_incepv3)) / n)\n",
        "\n",
        "conf_incepV3_lower = accuracy_incepv3 - conf_radius_incepV3\n",
        "conf_incepV3_upper = accuracy_incepv3 + conf_radius_incepV3"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UXMpx2xcJ2ZL",
        "outputId": "a1b26054-3b0c-4871-e7bd-ced9700fdeed"
      },
      "source": [
        "print('radius: ', conf_radius_incepV3)\n",
        "print('lower bound: ', conf_incepV3_lower)\n",
        "print('upper bound: ', conf_incepV3_upper)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "radius:  0.021593793926591463\n",
            "lower bound:  0.6313253197094254\n",
            "upper bound:  0.6745129075626083\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wA6CkOTWuElY"
      },
      "source": [
        "# reshaping test_labels so it's same shape as predictions\n",
        "\n",
        "test_labels_reshape = test_labels.reshape((test_labels.shape[0], 1))\n",
        "\n",
        "# convert numpy arrays to tensors before calling tf confusion matrix function\n",
        "\n",
        "test_labels_reshape_tensor = tf.constant(test_labels)\n",
        "predict_incepV3_tensor = tf.constant(predict_incepV3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SoPtohd41zSU"
      },
      "source": [
        "The segment below creates a confusion matrix and plots it"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hlpA5zabtmRJ"
      },
      "source": [
        "# use the tensorflow confusion matrix function\n",
        "\n",
        "confusion_mat_incepV3 = tf.math.confusion_matrix(labels=test_labels_reshape_tensor,\n",
        "                         predictions=predict_incepV3_tensor,\n",
        "                         num_classes=2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aKTh4LqCzPzj"
      },
      "source": [
        "# for plotting the matrix \n",
        "import seaborn as sn"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "dAIczOpIxcV3",
        "outputId": "b77b903e-6273-4cca-aa6d-e1ff87b936e8"
      },
      "source": [
        "# plot the confusion matrix\n",
        "\n",
        "df_cm_incepV3 = pd.DataFrame(confusion_mat_incepV3.numpy())\n",
        "\n",
        "# for label size\n",
        "sn.set(font_scale=1.4)\n",
        "sn.heatmap(df_cm_incepV3, annot=True, fmt='g', annot_kws={'size': 16}) # font size\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gi5gFcrAfltm"
      },
      "source": [
        "## ResNet Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sfbFL8jcf4EP"
      },
      "source": [
        "from tensorflow.keras.applications import ResNet50V2\n",
        "\n",
        "model_resnet = ResNet50V2(\n",
        "    include_top=False,\n",
        "    weights=\"imagenet\",\n",
        "    input_tensor=None,\n",
        "    input_shape=(128,128, 3),\n",
        "    pooling=None,\n",
        "    classes=1,\n",
        "    classifier_activation=\"softmax\",\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FPfVfMpjgEPm"
      },
      "source": [
        "for layers in model_resnet.layers:\n",
        "  layers.trainable = False\n",
        "\n",
        "x = Flatten()(model_resnet.output)\n",
        "\n",
        "fcc_layer_1 = Dense(units = 1024, activation = 'relu')(x)\n",
        "dropout_1   = Dropout(0.3)(fcc_layer_1)\n",
        "\n",
        "fcc_layer_2 = Dense(units = 512, activation = 'relu')(dropout_1)\n",
        "dropout_2   = Dropout(0.3)(fcc_layer_2)\n",
        "\n",
        "final_layer = Dense(units = 1, activation = 'sigmoid')(dropout_2)\n",
        "\n",
        "model_resnet = Model(inputs = model_resnet.input, outputs = final_layer)\n",
        "\n",
        "model_resnet.compile(optimizer = 'adam', \n",
        "              loss = 'binary_crossentropy',\n",
        "              metrics = ['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wM9RV60ogGM3"
      },
      "source": [
        "model_resnet = tf.keras.models.load_model('best_weights_resnet.hdf5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3maHNYk3gMig",
        "outputId": "421ec6d7-b511-4af9-c1c4-cd9ebe00bb63"
      },
      "source": [
        "model_resnet_eval = model_resnet.evaluate(test_data_128, batch_size=128)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "117/117 [==============================] - 9s 74ms/step - loss: 2.9765 - accuracy: 0.6535\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ZIOOF4Ygr9M"
      },
      "source": [
        "# obtain the model loss and accuracy\n",
        "loss_resnet = model_resnet_eval[0]\n",
        "accuracy_resnet = model_resnet_eval[1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tc61x2EM7Zu-"
      },
      "source": [
        "predict_resnet = model_resnet.predict(test_data_128, batch_size=128)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LF0n76UAhMYH"
      },
      "source": [
        "# Computes confidence interval without changing the network architecture\n",
        "# assuming Gaussian distribution of prediction accuracy\n",
        "\n",
        "# size of our test dataset\n",
        "n = predict_resnet.shape[0]\n",
        "\n",
        "# assuming 95% confidence interval\n",
        "Z = 1.96\n",
        "\n",
        "conf_radius_resnet = Z * np.sqrt((accuracy_resnet * (1 - accuracy_resnet)) / n)\n",
        "\n",
        "conf_resnet_lower = accuracy_resnet - conf_radius_resnet\n",
        "conf_resnet_upper = accuracy_resnet + conf_radius_resnet"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XrWvaAIaKbBD",
        "outputId": "bbf5e112-4736-4d2e-b624-6376ef050643"
      },
      "source": [
        "print('radius: ', conf_radius_resnet)\n",
        "print('lower bound: ', conf_resnet_lower)\n",
        "print('upper bound: ', conf_resnet_upper)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "radius:  0.021585974305516096\n",
            "lower bound:  0.6318687466684524\n",
            "upper bound:  0.6750406952794846\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6r7mfOYX72Or"
      },
      "source": [
        "# reshaping test_labels so it's same shape as predictions\n",
        "\n",
        "test_labels_reshape = test_labels.reshape((test_labels.shape[0], 1))\n",
        "\n",
        "# convert numpy arrays to tensors before calling tf confusion matrix function\n",
        "\n",
        "test_labels_reshape_tensor = tf.constant(test_labels)\n",
        "predict_resnet_tensor = tf.constant(predict_resnet)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B6CS92Is788I"
      },
      "source": [
        "# use the tensorflow confusion matrix function\n",
        "\n",
        "confusion_mat_resnet = tf.math.confusion_matrix(labels=test_labels_reshape_tensor,\n",
        "                         predictions=predict_resnet_tensor,\n",
        "                         num_classes=2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "sx2OXIMP8E5U",
        "outputId": "c4e9410a-a347-4644-e77a-4b0719d7ae78"
      },
      "source": [
        "# for plotting the matrix \n",
        "import seaborn as sn\n",
        "\n",
        "# plot the confusion matrix\n",
        "\n",
        "df_cm_resnet = pd.DataFrame(confusion_mat_resnet.numpy())\n",
        "\n",
        "# for label size\n",
        "sn.set(font_scale=1.4)\n",
        "sn.heatmap(df_cm_resnet, annot=True, fmt='g', annot_kws={'size': 16}) # font size\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ygQ6ySfHfpzi"
      },
      "source": [
        "## Flipped Data Augmentation Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UVBNOF6CATuT"
      },
      "source": [
        "model_train_flipped_data = model_default\n",
        "\n",
        "model_train_flipped_data.load_weights('w_flipped.hdf5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HMOB8TnpAotG",
        "outputId": "b1f4396a-d6c2-4ed9-f209-2ab30d026777"
      },
      "source": [
        "model_train_flipped_eval = model_train_flipped_data.evaluate(test_data, batch_size=128)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "117/117 [==============================] - 9s 73ms/step - loss: 8.6205 - accuracy: 0.6031 - precision: 0.6511\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MOfICEGAAtUL"
      },
      "source": [
        "# obtain the model loss and accuracy\n",
        "loss_flipped = model_train_flipped_eval[0]\n",
        "accuracy_flipped = model_train_flipped_eval[1]\n",
        "precision_flipped = model_train_flipped_eval[2]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VcvVl67mA2xT"
      },
      "source": [
        "predict_train_flipped = model_train_flipped_data.predict(test_data, batch_size=128)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zbZKvEDiA8lK"
      },
      "source": [
        "# Computes confidence interval without changing the network architecture\n",
        "# assuming Gaussian distribution of prediction accuracy\n",
        "\n",
        "# size of our test dataset\n",
        "n = predict_train_flipped.shape[0]\n",
        "\n",
        "# assuming 95% confidence interval\n",
        "Z = 1.96\n",
        "\n",
        "conf_radius_flipped = Z * np.sqrt((accuracy_flipped * (1 - accuracy_flipped)) / n)\n",
        "\n",
        "conf_flipped_lower = accuracy_flipped - conf_radius_flipped\n",
        "conf_flipped_upper = accuracy_flipped + conf_radius_flipped"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UwvvPuqKKk8i",
        "outputId": "66c8517e-0b9d-4714-f522-ff6070ffa0ee"
      },
      "source": [
        "print('radius: ', conf_radius_flipped)\n",
        "print('lower bound: ', conf_flipped_lower)\n",
        "print('upper bound: ', conf_flipped_upper)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "radius:  0.022193096382178312\n",
            "lower bound:  0.5809134619407281\n",
            "upper bound:  0.6252996547050849\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ItmnUTqBMig"
      },
      "source": [
        "# reshaping test_labels so it's same shape as predictions\n",
        "\n",
        "test_labels_reshape = test_labels.reshape((test_labels.shape[0], 1))\n",
        "\n",
        "# convert numpy arrays to tensors before calling tf confusion matrix function\n",
        "\n",
        "test_labels_reshape_tensor = tf.constant(test_labels)\n",
        "predict_flipped_tensor = tf.constant(predict_train_flipped)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SAXqRPZbBRwV"
      },
      "source": [
        "# use the tensorflow confusion matrix function\n",
        "\n",
        "confusion_mat_flipped = tf.math.confusion_matrix(labels=test_labels_reshape_tensor,\n",
        "                         predictions=predict_flipped_tensor,\n",
        "                         num_classes=2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "CvHyK2IKBXCn",
        "outputId": "2e651e39-2656-4fdd-c864-524ec66c5607"
      },
      "source": [
        "# for plotting the matrix \n",
        "import seaborn as sn\n",
        "\n",
        "# plot the confusion matrix\n",
        "\n",
        "df_cm_flipped = pd.DataFrame(confusion_mat_flipped.numpy())\n",
        "\n",
        "# for label size\n",
        "sn.set(font_scale=1.4)\n",
        "sn.heatmap(df_cm_flipped, annot=True, fmt='g', annot_kws={'size': 16}) # font size\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xhA-fkjZfvQY"
      },
      "source": [
        "## Noise Data Augmentation Data\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rmVwz_Su-rMS"
      },
      "source": [
        "model_train_noise_data = model_default\n",
        "\n",
        "model_train_noise_data.load_weights('retrain_noise.hdf5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1u9u1QbC-3F-",
        "outputId": "da7668c4-e70e-43f1-d08f-dcd24e4c71b9"
      },
      "source": [
        "model_train_noise_eval = model_train_noise_data.evaluate(test_data, batch_size=128)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "117/117 [==============================] - 8s 71ms/step - loss: 1.9245 - accuracy: 0.3594 - precision: 0.6949\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b3ASI9bc_Gq5"
      },
      "source": [
        "# obtain the model loss and accuracy\n",
        "loss_noise = model_train_noise_eval[0]\n",
        "accuracy_noise = model_train_noise_eval[1]\n",
        "precision_noise = model_train_noise_eval[2]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kysXow0c_Rjj"
      },
      "source": [
        "predict_train_noise = model_train_noise_data.predict(test_data, batch_size=128)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OW5NIM9O_Xki"
      },
      "source": [
        "# Computes confidence interval without changing the network architecture\n",
        "# assuming Gaussian distribution of prediction accuracy\n",
        "\n",
        "# size of our test dataset\n",
        "n = predict_train_noise.shape[0]\n",
        "\n",
        "# assuming 95% confidence interval\n",
        "Z = 1.96\n",
        "\n",
        "conf_radius_noise = Z * np.sqrt((accuracy_noise * (1 - accuracy_noise)) / n)\n",
        "\n",
        "conf_noise_lower = accuracy_noise - conf_radius_noise\n",
        "conf_noise_upper = accuracy_noise + conf_radius_noise"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FtN3HPLKKr9b",
        "outputId": "f2737ac7-dc80-46a9-8449-a01c21074b24"
      },
      "source": [
        "print('radius: ', conf_radius_noise)\n",
        "print('lower bound: ', conf_noise_lower)\n",
        "print('upper bound: ', conf_noise_upper)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "radius:  0.021765389365465224\n",
            "lower bound:  0.3376347041899852\n",
            "upper bound:  0.3811654829209157\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PJk0SFfB_qqW"
      },
      "source": [
        "# reshaping test_labels so it's same shape as predictions\n",
        "\n",
        "test_labels_reshape = test_labels.reshape((test_labels.shape[0], 1))\n",
        "\n",
        "# convert numpy arrays to tensors before calling tf confusion matrix function\n",
        "\n",
        "test_labels_reshape_tensor = tf.constant(test_labels)\n",
        "predict_noise_tensor = tf.constant(predict_train_noise)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q3IhcW_x_57K"
      },
      "source": [
        "# use the tensorflow confusion matrix function\n",
        "\n",
        "confusion_mat_noise = tf.math.confusion_matrix(labels=test_labels_reshape_tensor,\n",
        "                         predictions=predict_noise_tensor,\n",
        "                         num_classes=2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        },
        "id": "Wp0B4-S8_9RT",
        "outputId": "c2b77137-91c5-4ce9-d9d1-4be4d42ed2c4"
      },
      "source": [
        "# for plotting the matrix \n",
        "import seaborn as sn\n",
        "\n",
        "# plot the confusion matrix\n",
        "\n",
        "df_cm_noise = pd.DataFrame(confusion_mat_noise.numpy())\n",
        "\n",
        "# for label size\n",
        "sn.set(font_scale=1.4)\n",
        "sn.heatmap(df_cm_noise, annot=True, fmt='g', annot_kws={'size': 16}) # font size\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1LwoP0w4f0RH"
      },
      "source": [
        "## Blur Data Augmentation Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vdihhRU2uHl9"
      },
      "source": [
        "model_train_blur_data = model_default"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "whZ-FdJvuRpZ"
      },
      "source": [
        "# save the model weights\n",
        "model_train_blur_data.load_weights('retrain_blur.hdf5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a3kLASAzuWA5",
        "outputId": "841ba91a-61ef-4e6f-c830-fca2f1fe51f8"
      },
      "source": [
        "# evaluate the retrained model\n",
        "model_train_blur_data_eval = model_train_blur_data.evaluate(test_data, batch_size=128)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "117/117 [==============================] - 9s 74ms/step - loss: 1.5392 - accuracy: 0.3498 - precision: 0.6923\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XsOMU8H79Evw"
      },
      "source": [
        "# obtain the model loss and accuracy\n",
        "loss_blur = model_train_blur_data_eval[0]\n",
        "accuracy_blur = model_train_blur_data_eval[1]\n",
        "precision_blur = model_train_blur_data_eval[2]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0I3b9QLU9qg7"
      },
      "source": [
        "predict_train_blur = model_train_blur_data.predict(test_data, batch_size=128)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8-OMnhQ39zKh"
      },
      "source": [
        "# Computes confidence interval without changing the network architecture\n",
        "# assuming Gaussian distribution of prediction accuracy\n",
        "\n",
        "# size of our test dataset\n",
        "n = predict_train_blur.shape[0]\n",
        "\n",
        "# assuming 95% confidence interval\n",
        "Z = 1.96\n",
        "\n",
        "conf_radius_blur = Z * np.sqrt((accuracy_blur * (1 - accuracy_blur)) / n)\n",
        "\n",
        "conf_blur_lower = accuracy_blur - conf_radius_blur\n",
        "conf_blur_upper = accuracy_blur + conf_radius_blur"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RGPAlOrjK3HT",
        "outputId": "eccc63a8-9cf4-4c10-8ac6-724d69278854"
      },
      "source": [
        "print('radius: ', conf_radius_blur)\n",
        "print('lower bound: ', conf_blur_lower)\n",
        "print('upper bound: ', conf_blur_upper)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "radius:  0.021632441184738505\n",
            "lower bound:  0.3281265414736477\n",
            "upper bound:  0.37139142384312473\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RHMhRcoa-CII"
      },
      "source": [
        "# reshaping test_labels so it's same shape as predictions\n",
        "\n",
        "test_labels_reshape = test_labels.reshape((test_labels.shape[0], 1))\n",
        "\n",
        "# convert numpy arrays to tensors before calling tf confusion matrix function\n",
        "\n",
        "test_labels_reshape_tensor = tf.constant(test_labels)\n",
        "predict_blur_tensor = tf.constant(predict_train_blur)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WCSRjEKS-Jzq"
      },
      "source": [
        "# use the tensorflow confusion matrix function\n",
        "\n",
        "confusion_mat_blur = tf.math.confusion_matrix(labels=test_labels_reshape_tensor,\n",
        "                         predictions=predict_blur_tensor,\n",
        "                         num_classes=2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        },
        "id": "-lD7eWic-SmR",
        "outputId": "4cd7bb67-7af0-44ab-c761-6080f87a80cc"
      },
      "source": [
        "# for plotting the matrix \n",
        "import seaborn as sn\n",
        "\n",
        "# plot the confusion matrix\n",
        "\n",
        "df_cm_blur = pd.DataFrame(confusion_mat_blur.numpy())\n",
        "\n",
        "# for label size\n",
        "sn.set(font_scale=1.4)\n",
        "sn.heatmap(df_cm_blur, annot=True, fmt='g', annot_kws={'size': 16}) # font size\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6YxZ8CAMXx7A"
      },
      "source": [
        "Function to implement Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bJpfR2JcTQ9V"
      },
      "source": [
        "import seaborn as sn\n",
        "\n",
        "# create a function that generates and plots confusion matrices (for convenience)\n",
        "def createPlotConfusionMatrix(real_labels, predict_labels):\n",
        "  # try converting to classification prediction instead of using full prediction\n",
        "  pred = []\n",
        "  count = 0\n",
        "  for g in predict_labels.ravel():\n",
        "   if g < 0.5:\n",
        "     pred.append(0)\n",
        "   else:\n",
        "     pred.append(1)\n",
        "  \n",
        "  # reshaping test_labels so it's same shape as predictions\n",
        "  real_labels_reshape = real_labels.reshape((real_labels.shape[0], 1))\n",
        "\n",
        "  # convert numpy arrays to tensors before calling tf confusion matrix function\n",
        "  real_labels_reshape_tensor = tf.constant(real_labels)\n",
        "\n",
        "  # predict_blur_tensor = tf.constant(predict_train_blur)\n",
        "  predict_tensor = tf.constant(pred)\n",
        "\n",
        "  # use the tensorflow confusion matrix function\n",
        "  confusion_mat = tf.math.confusion_matrix(labels=real_labels_reshape_tensor,\n",
        "                         predictions=predict_tensor,\n",
        "                         num_classes=2)\n",
        "\n",
        "  # plot the confusion matrix\n",
        "  df_cm = pd.DataFrame(confusion_mat.numpy())\n",
        "\n",
        "  # for label size\n",
        "  sn.set(font_scale=1.4)\n",
        "  sn.heatmap(df_cm, annot=True, fmt='g', annot_kws={'size': 16}) # font size\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        },
        "id": "N3wD31qPUrnG",
        "outputId": "685401c3-deb5-4fb0-cd1c-26ff1ca295b2"
      },
      "source": [
        "# plot for InceptionV3\n",
        "createPlotConfusionMatrix(test_labels, predict_incepV3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        },
        "id": "XHq14rbdU7sz",
        "outputId": "bdeac9eb-280c-4328-fb7a-21b9a3fdf7ce"
      },
      "source": [
        "# plot for ResNet\n",
        "createPlotConfusionMatrix(test_labels, predict_resnet)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "wJmaKORLU9lW",
        "outputId": "59a87ee2-7225-4e71-e062-671d7dbdf2eb"
      },
      "source": [
        "# plot for Flipped Transfer\n",
        "createPlotConfusionMatrix(test_labels, predict_train_flipped)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "__G7mblJVASx",
        "outputId": "e805d1bf-4963-470b-feca-7f5ed051df8d"
      },
      "source": [
        "# plot for Noise Transfer\n",
        "createPlotConfusionMatrix(test_labels, predict_train_noise)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        },
        "id": "9_vLghh1VCCJ",
        "outputId": "cccf4224-73e7-4010-b0af-ac0ac9334b98"
      },
      "source": [
        "# plot for Blur Transfer\n",
        "createPlotConfusionMatrix(test_labels, predict_train_blur)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c5H6f-UHj88Z"
      },
      "source": [
        "# Re-training Models for ROC plots"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "duqPzQSl20Uy",
        "outputId": "9f1572dd-87c4-4ddd-f583-b91b16e77aa4"
      },
      "source": [
        "train_data_128 = tf.keras.preprocessing.image_dataset_from_directory(\n",
        "    './C-NMC_Leukemia/training_data/fold_0',\n",
        "    labels=\"inferred\",\n",
        "    class_names=[\"all\", \"hem\"],\n",
        "    label_mode=\"int\",\n",
        "    image_size=(128, 128),\n",
        "    batch_size=BATCH_SIZE, \n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 8531 files belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9iiJkD3l1JKA",
        "outputId": "2df23910-a704-4a91-bf0b-f065056f0c67"
      },
      "source": [
        "# define (128, 128) image data for the training\n",
        "\n",
        "test_data_128 = tf.keras.preprocessing.image_dataset_from_directory(\n",
        "    './C-NMC_Leukemia/validation_data/',\n",
        "    labels=test_labels.tolist(),\n",
        "    label_mode=\"int\",\n",
        "    image_size=(128, 128),\n",
        "    batch_size=BATCH_SIZE, \n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 1867 files belonging to 1 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mYKaSFr321T-",
        "outputId": "045bd32d-dbf6-4091-a3b9-e1e1d352a8ba"
      },
      "source": [
        "val_data_128 = tf.keras.preprocessing.image_dataset_from_directory(\n",
        "    './C-NMC_Leukemia/training_data/val',\n",
        "    labels=\"inferred\",\n",
        "    class_names=[\"all\", \"hem\"],\n",
        "    label_mode=\"int\",\n",
        "    image_size=(128, 128),\n",
        "    batch_size=BATCH_SIZE,\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 2130 files belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gDnGjoHhkESZ"
      },
      "source": [
        "## Retrain InceptionV3 Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_eABtyJp0Bnz"
      },
      "source": [
        "from tensorflow.keras.models import Sequential, Model\n",
        "from tensorflow.keras.applications.inception_v3 import InceptionV3, preprocess_input\n",
        "from tensorflow.keras.layers import InputLayer, Conv2D, BatchNormalization, MaxPool2D, Dropout, Flatten, Dense\n",
        "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WnYO5fgo0D3r",
        "outputId": "aea007c1-1e83-46a9-e319-5c652d9d6515"
      },
      "source": [
        "# Creating model with pre trained imagenet weights\n",
        "\n",
        "incep_v3 = InceptionV3(include_top=False, weights='imagenet', input_shape=(128,128,3))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "87916544/87910968 [==============================] - 1s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A1Ln8pKs0ISV"
      },
      "source": [
        "# We dont want to train all layers so, we do following step because we are using same weights given in VGG16\n",
        "\n",
        "for layers in incep_v3.layers:\n",
        "    layers.trainable = False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ELD1O5ZU0Mw-"
      },
      "source": [
        "# Introducing Flatten Layer\n",
        "\n",
        "x = Flatten()(incep_v3.output)\n",
        "\n",
        "# Introducing FCC & Output Layer\n",
        "\n",
        "fcc_layer_1 = Dense(units = 1024, activation = 'relu')(x)\n",
        "dropout_1   = Dropout(0.3)(fcc_layer_1)\n",
        "\n",
        "fcc_layer_2 = Dense(units = 512, activation = 'relu')(dropout_1)\n",
        "dropout_2   = Dropout(0.3)(fcc_layer_2)\n",
        "\n",
        "final_layer = Dense(units = 1, activation = 'sigmoid')(dropout_2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "th17akL80O0p"
      },
      "source": [
        "# Creating Final Model\n",
        "\n",
        "model = Model(inputs = incep_v3.input, outputs = final_layer)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mo1V9_ji0Q0i"
      },
      "source": [
        "# Model Compile \n",
        "\n",
        "model.compile(optimizer = 'adam', \n",
        "              loss = 'binary_crossentropy',\n",
        "              metrics = ['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FFkk8-v-0S2F"
      },
      "source": [
        "# Defining Callbacks\n",
        "\n",
        "filepath = './retrain_best_weights_inception.hdf5'\n",
        "\n",
        "earlystopping = EarlyStopping(monitor = 'val_accuracy', \n",
        "                              mode = 'max' , \n",
        "                              patience = 15,\n",
        "                              verbose = 1)\n",
        "\n",
        "checkpoint    = ModelCheckpoint(filepath, \n",
        "                                monitor = 'val_accuracy', \n",
        "                                mode='max', \n",
        "                                save_best_only=True, \n",
        "                                verbose = 1)\n",
        "\n",
        "learning_rate = ReduceLROnPlateau(monitor = 'val_accuracy',\n",
        "                                  mode = 'max',\n",
        "                                  patience = 5,\n",
        "                                  factor = 0.3,\n",
        "                                  min_delta = 0.00001)\n",
        "\n",
        "\n",
        "callback_list = [earlystopping, checkpoint, learning_rate]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p-5amoAn0Yzz",
        "outputId": "40398b1f-807b-40d4-c38f-3c6b95381667"
      },
      "source": [
        "# Fitting Model\n",
        "\n",
        "model_inception_history = model.fit(train_data_128, \n",
        "                          validation_data=val_data_128,\n",
        "                          epochs=500,\n",
        "                          verbose=1,\n",
        "                          callbacks=callback_list)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/500\n",
            "534/534 [==============================] - ETA: 0s - loss: 3.3350 - accuracy: 0.7298\n",
            "Epoch 00001: val_accuracy improved from -inf to 0.78967, saving model to ./retrain_best_weights_inception.hdf5\n",
            "534/534 [==============================] - 1236s 2s/step - loss: 3.3350 - accuracy: 0.7298 - val_loss: 0.5185 - val_accuracy: 0.7897\n",
            "Epoch 2/500\n",
            "532/534 [============================>.] - ETA: 0s - loss: 0.5910 - accuracy: 0.7361\n",
            "Epoch 00002: val_accuracy did not improve from 0.78967\n",
            "534/534 [==============================] - 178s 333ms/step - loss: 0.5908 - accuracy: 0.7363 - val_loss: 0.5203 - val_accuracy: 0.7765\n",
            "Epoch 3/500\n",
            "532/534 [============================>.] - ETA: 0s - loss: 0.5859 - accuracy: 0.7418\n",
            "Epoch 00003: val_accuracy did not improve from 0.78967\n",
            "534/534 [==============================] - 178s 333ms/step - loss: 0.5859 - accuracy: 0.7421 - val_loss: 0.5650 - val_accuracy: 0.7887\n",
            "Epoch 4/500\n",
            "532/534 [============================>.] - ETA: 0s - loss: 0.5943 - accuracy: 0.7236\n",
            "Epoch 00004: val_accuracy did not improve from 0.78967\n",
            "534/534 [==============================] - 178s 333ms/step - loss: 0.5946 - accuracy: 0.7232 - val_loss: 0.6267 - val_accuracy: 0.6808\n",
            "Epoch 5/500\n",
            "532/534 [============================>.] - ETA: 0s - loss: 0.6250 - accuracy: 0.6836\n",
            "Epoch 00005: val_accuracy did not improve from 0.78967\n",
            "534/534 [==============================] - 178s 333ms/step - loss: 0.6251 - accuracy: 0.6835 - val_loss: 0.6264 - val_accuracy: 0.6808\n",
            "Epoch 6/500\n",
            "532/534 [============================>.] - ETA: 0s - loss: 0.6252 - accuracy: 0.6823\n",
            "Epoch 00006: val_accuracy did not improve from 0.78967\n",
            "534/534 [==============================] - 178s 333ms/step - loss: 0.6251 - accuracy: 0.6825 - val_loss: 0.6265 - val_accuracy: 0.6808\n",
            "Epoch 7/500\n",
            "533/534 [============================>.] - ETA: 0s - loss: 0.6250 - accuracy: 0.6826\n",
            "Epoch 00007: val_accuracy did not improve from 0.78967\n",
            "534/534 [==============================] - 178s 333ms/step - loss: 0.6251 - accuracy: 0.6825 - val_loss: 0.6263 - val_accuracy: 0.6808\n",
            "Epoch 8/500\n",
            "532/534 [============================>.] - ETA: 0s - loss: 0.6252 - accuracy: 0.6823\n",
            "Epoch 00008: val_accuracy did not improve from 0.78967\n",
            "534/534 [==============================] - 178s 333ms/step - loss: 0.6251 - accuracy: 0.6825 - val_loss: 0.6263 - val_accuracy: 0.6808\n",
            "Epoch 9/500\n",
            "532/534 [============================>.] - ETA: 0s - loss: 0.6246 - accuracy: 0.6824\n",
            "Epoch 00009: val_accuracy did not improve from 0.78967\n",
            "534/534 [==============================] - 178s 333ms/step - loss: 0.6247 - accuracy: 0.6825 - val_loss: 0.6263 - val_accuracy: 0.6808\n",
            "Epoch 10/500\n",
            "532/534 [============================>.] - ETA: 0s - loss: 0.6250 - accuracy: 0.6823\n",
            "Epoch 00010: val_accuracy did not improve from 0.78967\n",
            "534/534 [==============================] - 178s 333ms/step - loss: 0.6249 - accuracy: 0.6825 - val_loss: 0.6263 - val_accuracy: 0.6808\n",
            "Epoch 11/500\n",
            "534/534 [==============================] - ETA: 0s - loss: 0.6256 - accuracy: 0.6825\n",
            "Epoch 00011: val_accuracy did not improve from 0.78967\n",
            "534/534 [==============================] - 178s 333ms/step - loss: 0.6256 - accuracy: 0.6825 - val_loss: 0.6263 - val_accuracy: 0.6808\n",
            "Epoch 12/500\n",
            "532/534 [============================>.] - ETA: 0s - loss: 0.6252 - accuracy: 0.6826\n",
            "Epoch 00012: val_accuracy did not improve from 0.78967\n",
            "534/534 [==============================] - 178s 333ms/step - loss: 0.6252 - accuracy: 0.6825 - val_loss: 0.6263 - val_accuracy: 0.6808\n",
            "Epoch 13/500\n",
            "532/534 [============================>.] - ETA: 0s - loss: 0.6253 - accuracy: 0.6822\n",
            "Epoch 00013: val_accuracy did not improve from 0.78967\n",
            "534/534 [==============================] - 178s 333ms/step - loss: 0.6251 - accuracy: 0.6825 - val_loss: 0.6263 - val_accuracy: 0.6808\n",
            "Epoch 14/500\n",
            "534/534 [==============================] - ETA: 0s - loss: 0.6250 - accuracy: 0.6825\n",
            "Epoch 00014: val_accuracy did not improve from 0.78967\n",
            "534/534 [==============================] - 178s 333ms/step - loss: 0.6250 - accuracy: 0.6825 - val_loss: 0.6263 - val_accuracy: 0.6808\n",
            "Epoch 15/500\n",
            "532/534 [============================>.] - ETA: 0s - loss: 0.6254 - accuracy: 0.6822\n",
            "Epoch 00015: val_accuracy did not improve from 0.78967\n",
            "534/534 [==============================] - 178s 333ms/step - loss: 0.6252 - accuracy: 0.6825 - val_loss: 0.6263 - val_accuracy: 0.6808\n",
            "Epoch 16/500\n",
            "532/534 [============================>.] - ETA: 0s - loss: 0.6252 - accuracy: 0.6823\n",
            "Epoch 00016: val_accuracy did not improve from 0.78967\n",
            "534/534 [==============================] - 178s 333ms/step - loss: 0.6251 - accuracy: 0.6825 - val_loss: 0.6263 - val_accuracy: 0.6808\n",
            "Epoch 00016: early stopping\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GGDgcPvi35z-",
        "outputId": "01a8f070-e896-4d23-f87f-88415c49bf72"
      },
      "source": [
        "# model_retrain_noise.model.save_weights('./retrain_imbalance/noise')\n",
        "model_inception_history.model.save('./retrain_inception_resnet/inception')\n",
        "\n",
        "model_inception_history.model.evaluate(test_data_128, batch_size=128)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: ./retrain_inception_resnet/inception/assets\n",
            "117/117 [==============================] - 282s 2s/step - loss: 0.8816 - accuracy: 0.3471\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.8815616965293884, 0.34708088636398315]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pRRTjisWCCBk"
      },
      "source": [
        "# Save history\n",
        "with open('./retrain_inception_resnet/inception_history', 'wb') as file_pi:\n",
        "  pickle.dump(model_inception_history.history, file_pi)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lLHqZDnBkGi4"
      },
      "source": [
        "## Retrain ResNet Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dwtj8MTT4Y84"
      },
      "source": [
        "from tensorflow.keras.applications import ResNet50V2"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aVdsTFLR4ZpQ"
      },
      "source": [
        "model_resnet = ResNet50V2(\n",
        "    include_top=False,\n",
        "    weights=\"imagenet\",\n",
        "    input_tensor=None,\n",
        "    input_shape=(128,128, 3),\n",
        "    pooling=None,\n",
        "    classes=1,\n",
        "    classifier_activation=\"softmax\",\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bq7zhahy4cUh"
      },
      "source": [
        "for layers in model_resnet.layers:\n",
        "  layers.trainable = False\n",
        "\n",
        "x = Flatten()(model_resnet.output)\n",
        "\n",
        "fcc_layer_1 = Dense(units = 1024, activation = 'relu')(x)\n",
        "dropout_1   = Dropout(0.3)(fcc_layer_1)\n",
        "\n",
        "fcc_layer_2 = Dense(units = 512, activation = 'relu')(dropout_1)\n",
        "dropout_2   = Dropout(0.3)(fcc_layer_2)\n",
        "\n",
        "final_layer = Dense(units = 1, activation = 'sigmoid')(dropout_2)\n",
        "\n",
        "model_resnet = Model(inputs = model_resnet.input, outputs = final_layer)\n",
        "\n",
        "model_resnet.compile(optimizer = 'adam', \n",
        "              loss = 'binary_crossentropy',\n",
        "              metrics = ['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pb6Dn7JK4gqy"
      },
      "source": [
        "filepath = './retrain_best_weights_resnet.hdf5'\n",
        "\n",
        "earlystopping = EarlyStopping(monitor = 'val_accuracy', \n",
        "                              mode = 'max' , \n",
        "                              patience = 15,\n",
        "                              verbose = 1)\n",
        "\n",
        "checkpoint    = ModelCheckpoint(filepath, \n",
        "                                monitor = 'val_accuracy', \n",
        "                                mode='max', \n",
        "                                save_best_only=True, \n",
        "                                verbose = 1)\n",
        "\n",
        "learning_rate = ReduceLROnPlateau(monitor = 'val_accuracy',\n",
        "                                  mode = 'max',\n",
        "                                  patience = 5,\n",
        "                                  factor = 0.3,\n",
        "                                  min_delta = 0.00001)\n",
        "\n",
        "\n",
        "callback_list = [earlystopping, checkpoint, learning_rate]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I0fgNitM4odP",
        "outputId": "ff186026-8d09-45c7-9732-6ccc52b26ac6"
      },
      "source": [
        "model_resnet_history = model_resnet.fit(train_data_128, \n",
        "                          validation_data=val_data_128,\n",
        "                          epochs=500,\n",
        "                          verbose=1,\n",
        "                          callbacks=callback_list)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/500\n",
            "534/534 [==============================] - ETA: 0s - loss: 10.6378 - accuracy: 0.7202\n",
            "Epoch 00001: val_accuracy improved from -inf to 0.78404, saving model to ./retrain_best_weights_resnet.hdf5\n",
            "534/534 [==============================] - 3306s 6s/step - loss: 10.6378 - accuracy: 0.7202 - val_loss: 0.5602 - val_accuracy: 0.7840\n",
            "Epoch 2/500\n",
            "532/534 [============================>.] - ETA: 0s - loss: 0.6140 - accuracy: 0.7355\n",
            "Epoch 00002: val_accuracy did not improve from 0.78404\n",
            "534/534 [==============================] - 178s 333ms/step - loss: 0.6137 - accuracy: 0.7358 - val_loss: 0.6268 - val_accuracy: 0.6812\n",
            "Epoch 3/500\n",
            "532/534 [============================>.] - ETA: 0s - loss: 0.6526 - accuracy: 0.6816\n",
            "Epoch 00003: val_accuracy did not improve from 0.78404\n",
            "534/534 [==============================] - 178s 333ms/step - loss: 0.6524 - accuracy: 0.6817 - val_loss: 0.6263 - val_accuracy: 0.6808\n",
            "Epoch 4/500\n",
            "532/534 [============================>.] - ETA: 0s - loss: 0.6333 - accuracy: 0.6823\n",
            "Epoch 00004: val_accuracy did not improve from 0.78404\n",
            "534/534 [==============================] - 178s 333ms/step - loss: 0.6332 - accuracy: 0.6825 - val_loss: 0.6269 - val_accuracy: 0.6808\n",
            "Epoch 5/500\n",
            "532/534 [============================>.] - ETA: 0s - loss: 0.6332 - accuracy: 0.6826\n",
            "Epoch 00005: val_accuracy did not improve from 0.78404\n",
            "534/534 [==============================] - 178s 333ms/step - loss: 0.6333 - accuracy: 0.6825 - val_loss: 0.6270 - val_accuracy: 0.6812\n",
            "Epoch 6/500\n",
            "532/534 [============================>.] - ETA: 0s - loss: 0.6254 - accuracy: 0.6828\n",
            "Epoch 00006: val_accuracy did not improve from 0.78404\n",
            "534/534 [==============================] - 178s 333ms/step - loss: 0.6255 - accuracy: 0.6827 - val_loss: 0.6262 - val_accuracy: 0.6812\n",
            "Epoch 7/500\n",
            "532/534 [============================>.] - ETA: 0s - loss: 0.6249 - accuracy: 0.6828\n",
            "Epoch 00007: val_accuracy did not improve from 0.78404\n",
            "534/534 [==============================] - 178s 333ms/step - loss: 0.6250 - accuracy: 0.6827 - val_loss: 0.6262 - val_accuracy: 0.6812\n",
            "Epoch 8/500\n",
            "534/534 [==============================] - ETA: 0s - loss: 0.6250 - accuracy: 0.6827\n",
            "Epoch 00008: val_accuracy did not improve from 0.78404\n",
            "534/534 [==============================] - 178s 333ms/step - loss: 0.6250 - accuracy: 0.6827 - val_loss: 0.6262 - val_accuracy: 0.6812\n",
            "Epoch 9/500\n",
            "532/534 [============================>.] - ETA: 0s - loss: 0.6249 - accuracy: 0.6826\n",
            "Epoch 00009: val_accuracy did not improve from 0.78404\n",
            "534/534 [==============================] - 178s 333ms/step - loss: 0.6248 - accuracy: 0.6827 - val_loss: 0.6262 - val_accuracy: 0.6812\n",
            "Epoch 10/500\n",
            "534/534 [==============================] - ETA: 0s - loss: 0.6248 - accuracy: 0.6839\n",
            "Epoch 00010: val_accuracy did not improve from 0.78404\n",
            "534/534 [==============================] - 178s 333ms/step - loss: 0.6248 - accuracy: 0.6839 - val_loss: 0.6264 - val_accuracy: 0.6808\n",
            "Epoch 11/500\n",
            "532/534 [============================>.] - ETA: 0s - loss: 0.6253 - accuracy: 0.6826\n",
            "Epoch 00011: val_accuracy did not improve from 0.78404\n",
            "534/534 [==============================] - 178s 333ms/step - loss: 0.6252 - accuracy: 0.6827 - val_loss: 0.6264 - val_accuracy: 0.6808\n",
            "Epoch 12/500\n",
            "532/534 [============================>.] - ETA: 0s - loss: 0.6248 - accuracy: 0.6834\n",
            "Epoch 00012: val_accuracy did not improve from 0.78404\n",
            "534/534 [==============================] - 178s 333ms/step - loss: 0.6253 - accuracy: 0.6827 - val_loss: 0.6264 - val_accuracy: 0.6808\n",
            "Epoch 13/500\n",
            "532/534 [============================>.] - ETA: 0s - loss: 0.6251 - accuracy: 0.6826\n",
            "Epoch 00013: val_accuracy did not improve from 0.78404\n",
            "534/534 [==============================] - 178s 333ms/step - loss: 0.6251 - accuracy: 0.6827 - val_loss: 0.6264 - val_accuracy: 0.6808\n",
            "Epoch 14/500\n",
            "532/534 [============================>.] - ETA: 0s - loss: 0.6255 - accuracy: 0.6826\n",
            "Epoch 00014: val_accuracy did not improve from 0.78404\n",
            "534/534 [==============================] - 178s 333ms/step - loss: 0.6254 - accuracy: 0.6827 - val_loss: 0.6264 - val_accuracy: 0.6808\n",
            "Epoch 15/500\n",
            "534/534 [==============================] - ETA: 0s - loss: 0.6248 - accuracy: 0.6827\n",
            "Epoch 00015: val_accuracy did not improve from 0.78404\n",
            "534/534 [==============================] - 178s 333ms/step - loss: 0.6248 - accuracy: 0.6827 - val_loss: 0.6264 - val_accuracy: 0.6808\n",
            "Epoch 16/500\n",
            "532/534 [============================>.] - ETA: 0s - loss: 0.6254 - accuracy: 0.6826\n",
            "Epoch 00016: val_accuracy did not improve from 0.78404\n",
            "534/534 [==============================] - 178s 333ms/step - loss: 0.6253 - accuracy: 0.6827 - val_loss: 0.6264 - val_accuracy: 0.6808\n",
            "Epoch 00016: early stopping\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OcA2NjuG42Q-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cd03f417-d00e-424e-d3fb-4c1ac322dd0c"
      },
      "source": [
        "# model_retrain_noise.model.save_weights('./retrain_imbalance/noise')\n",
        "model_resnet_history.model.save('./retrain_inception_resnet/resnet')\n",
        "\n",
        "model_resnet_history.model.evaluate(test_data_128, batch_size=128)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/tracking.py:111: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/tracking.py:111: Layer.updates (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
            "INFO:tensorflow:Assets written to: ./retrain_inception_resnet/resnet/assets\n",
            "117/117 [==============================] - 495s 4s/step - loss: 0.8766 - accuracy: 0.3465\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.8766463994979858, 0.3465452492237091]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KL8XwjH1CEzg"
      },
      "source": [
        "# Save history\n",
        "with open('./retrain_inception_resnet/resnet_history', 'wb') as file_pi:\n",
        "  pickle.dump(model_resnet_history.history, file_pi)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GOYXxpgq2QBy"
      },
      "source": [
        "# Confidence Interval Bar Plots"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mAp5qr7L2YBk"
      },
      "source": [
        "# width of the bars\n",
        "barWidth = 0.3"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GeFJfH7w2grM"
      },
      "source": [
        "# choose the weight of the bars (for Inception, Resnet, and 3 transfer learning)\n",
        "\n",
        "acc_incepV3 = accuracy_incepv3\n",
        "acc_resnet = accuracy_resnet\n",
        "acc_flip = accuracy_flipped\n",
        "acc_noise = accuracy_noise\n",
        "acc_blur = accuracy_blur\n",
        "\n",
        "conf_int_incepV3 = conf_radius_incepV3\n",
        "conf_int_resnet = conf_radius_resnet\n",
        "conf_int_flip = conf_radius_flipped\n",
        "conf_int_noise = conf_radius_noise\n",
        "conf_int_blur = conf_radius_blur"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iU4oPy2t5q5g"
      },
      "source": [
        "## Confidence Interval for InceptionV3, ResNet and 3 Transfer Learnings (combined)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uuAoc3xp57WD"
      },
      "source": [
        "def confIntervalBarChart(bars, yer, labels):\n",
        "  # x position of the bars\n",
        "  r1 = np.arange(len(bars))\n",
        "  r2 = [x + barWidth for x in r1]\n",
        "\n",
        "  # create blur bars\n",
        "  plt.bar(r1, bars, width=barWidth, color='cyan', edgecolor='black', yerr=yer, capsize=7)\n",
        "\n",
        "  # general layout\n",
        "  plt.xticks([r + barWidth for r in range(len(bars))], labels)\n",
        "  plt.ylabel('accuracy')\n",
        "\n",
        "  # Show graphic\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7IBzLZs-6t9V"
      },
      "source": [
        "Displaying confidence intervals for all 5 at once. If I want to create charts for only a single model, we can run the function on smaller lists."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "P7wKRzs86a0r",
        "outputId": "b0a1f7c0-d0fd-47f6-aa4b-93c8eaf201aa"
      },
      "source": [
        "allBars = [acc_incepV3, acc_resnet, acc_flip, acc_noise, acc_blur]\n",
        "allYers = [conf_int_incepV3, conf_int_resnet, conf_int_flip, conf_int_noise, conf_int_blur]\n",
        "allLabels = ['InceptionV3', 'ResNet', 'Flip', 'Noise', 'Blur']\n",
        "\n",
        "# display confidence intervals for all 5 models at once\n",
        "confIntervalBarChart(allBars, allYers, allLabels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "UFJsA-7A64eR",
        "outputId": "72bc973a-c037-499a-d212-42f743d5ef55"
      },
      "source": [
        "# confidence interval for only InceptionV3\n",
        "incepV3Bars = [acc_incepV3]\n",
        "incepV3Yers = [conf_int_incepV3]\n",
        "incepV3Labels = ['InceptionV3']\n",
        "\n",
        "confIntervalBarChart(incepV3Bars, incepV3Yers, incepV3Labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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x3rDXAg9X1c8C7wXe1VU9kqThujwj2ARMV9WJqjoD7Ae2zhuzFfhw//3dwE1J0mFNkqR5ugyCtcCpgfZMv2/omKo6C3wbeGqHNUmS5lm90gWMIskOYEe/+Z0kxy/jYEtSkySthMuYNLlhoQ1dBsFpYP1Ae12/b9iYmSSrgScBD80/UFXtA/Z1VKckNa3LqaFDwESSDUnWANuAyXljJoFX9d+/HPhsVVWHNUmS5unsjKCqzibZCRwEVgEfrKojSfYAU1U1CfwF8JEk08C3mAsLSdIyin+AS1LbvLNYkhpnEEhS4wwCSWqcQSBJjTMIJKlxBoEkNc4gkKTGGQSS1Lj/B2VF4YI4t3UeAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "1y8-daFu64xG",
        "outputId": "b074014a-985a-4344-c7c7-9987ce3553ab"
      },
      "source": [
        "# confidence interval for only ResNet\n",
        "resnetBars = [acc_resnet]\n",
        "resnetYers = [conf_int_resnet]\n",
        "resnetLabels = ['ResNet']\n",
        "\n",
        "confIntervalBarChart(resnetBars, resnetYers, resnetLabels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "id": "k_19kCza65Eg",
        "outputId": "b0c49974-0fd9-4f16-c590-9a6640653b74"
      },
      "source": [
        "# confidence interval for only Flipped Transfer Learn\n",
        "flipBars = [acc_flip]\n",
        "flipYers = [conf_int_flip]\n",
        "flipLabels = ['Flip']\n",
        "\n",
        "confIntervalBarChart(flipBars, flipYers, flipLabels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 257
        },
        "id": "_TIgicG165bB",
        "outputId": "1b9417fe-b5ab-46d0-ad38-fea07e14b7f4"
      },
      "source": [
        "# confidence interval for only Noise Transfer Learn\n",
        "noiseBars = [acc_noise]\n",
        "noiseYers = [conf_int_noise]\n",
        "noiseLabels = ['Noise']\n",
        "\n",
        "confIntervalBarChart(noiseBars, noiseYers, noiseLabels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "id": "r3H7rPdF657j",
        "outputId": "354bb3ed-1dc6-4e55-e01f-d6404507505b"
      },
      "source": [
        "# confidence interval for only Blur Transfer Learn\n",
        "blurBars = [acc_blur]\n",
        "blurYers = [conf_int_blur]\n",
        "blurLabels = ['Blur']\n",
        "\n",
        "confIntervalBarChart(blurBars, blurYers, blurLabels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Na3iDMa6WbI"
      },
      "source": [
        "Commenting below (implemented as function)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3KcCpVrD4Wky"
      },
      "source": [
        "# x position of the bars\n",
        "# r1 = np.arange(len(bars))\n",
        "# r2 = [x + barWidth for x in r1]\n",
        "\n",
        "# create blur bars\n",
        "# plt.bar(r1, bars, width=barWidth, color='blue', edgecolor='black', yerr=yer, capsize=7)\n",
        "\n",
        "# general layout\n",
        "# plt.xticks([r + barWidth for r in range(len(bars))], ['InceptionV3', 'ResNet', 'Flip', 'Noise', 'Blur'])\n",
        "# plt.ylabel('accuracy')\n",
        "\n",
        "# Show graphic\n",
        "# plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SWt_M9nkI0gQ"
      },
      "source": [
        "# ROC Curves (TODO plot for all of them)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RVOF4NEcJmtm"
      },
      "source": [
        "import pickle"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m5qvl3PcI4Hc"
      },
      "source": [
        "## Load Files"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wam3kTSOI6A3"
      },
      "source": [
        "# model_retrain_5     = tf.keras.models.load_model('./retrain_inception_resnet/5.hdf5')\n",
        "model_retrain_flipped  = tf.keras.models.load_model('./retrain_inception_resnet/flipped')\n",
        "model_retrain_noise = tf.keras.models.load_model('./retrain_inception_resnet/noise')\n",
        "model_retrain_blur  = tf.keras.models.load_model('./retrain_inception_resnet/blur')\n",
        "model_retrain_inception = tf.keras.models.load_model('./retrain_inception_resnet/inception')\n",
        "model_retrain_resnet = tf.keras.models.load_model('./retrain_inception_resnet/resnet')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bYuAXWslJg04"
      },
      "source": [
        "## Load Histories"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Itto07N_JB5c"
      },
      "source": [
        "# history_5 = pickle.load(open('./retrain_imbalance/5_history', \"rb\"))\n",
        "history_flipped = pickle.load(open('./retrain_inception_resnet/flipped_history', \"rb\"))\n",
        "history_noise = pickle.load(open('./retrain_inception_resnet/noise_history', \"rb\"))\n",
        "history_blur = pickle.load(open('./retrain_inception_resnet/blur_history', \"rb\"))\n",
        "history_inception = pickle.load(open('./retrain_inception_resnet/inception_history', \"rb\"))\n",
        "history_resnet = pickle.load(open('./retrain_inception_resnet/resnet_history', \"rb\"))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YXB6twNOJCem"
      },
      "source": [
        "# print(history_5.keys())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aIXLL3K7JsOV"
      },
      "source": [
        "## ROC"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YBQHJ93AJtoY"
      },
      "source": [
        "from sklearn.metrics import roc_curve\n",
        "from sklearn.metrics import auc"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bI0juX8SaGta"
      },
      "source": [
        "# predict the model\n",
        "# TODO do we make batch size 128?\n",
        "hi = model_retrain_flipped.predict(test_data, batch_size=128)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p6Zlis8pa6Ki",
        "outputId": "814e075c-1b24-47e1-d698-116b147c4091"
      },
      "source": [
        "print(len(hi))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1867\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rb2B62dPJ-xR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3045ab28-b543-43e2-b348-b430d2efd914"
      },
      "source": [
        "# uses our default comparison (model flipped) as our comparison\n",
        "pred = []\n",
        "count = 0\n",
        "for g in hi.ravel():\n",
        "  if g < 0.5:\n",
        "    count +=1\n",
        "    pred.append(0)\n",
        "  else:\n",
        "    pred.append(1)\n",
        "print(count)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1685\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cJ5PE2F6Jwhr"
      },
      "source": [
        "fpr_keras, tpr_keras, thresholds_keras = roc_curve(test_labels, pred)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A9mmIbcOJ_SG"
      },
      "source": [
        "auc_keras = auc(fpr_keras, tpr_keras)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kvybw9odKKkH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "a1ec2147-64aa-4758-b767-b7a1b14003ea"
      },
      "source": [
        "plt.figure(1)\n",
        "plt.plot([0, 1], [0, 1], 'k--')\n",
        "plt.plot(fpr_keras, tpr_keras, label='Default (area = {:.3f})'.format(auc_keras))\n",
        "# plt.plot(fpr_rf, tpr_rf, label='RF (area = {:.3f})'.format(auc_rf))\n",
        "plt.xlabel('False positive rate')\n",
        "plt.ylabel('True positive rate')\n",
        "plt.title('ROC curve')\n",
        "plt.legend(loc='best')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UjFNvU9kHMt9"
      },
      "source": [
        "# TODO is something wrong with displaying the information???\n",
        "# implement ROC as a function\n",
        "def createROCPlot(model, test_data):\n",
        "  # predict the model\n",
        "  hi = model.predict(test_data, batch_size=128)\n",
        "\n",
        "  pred = hi.ravel()\n",
        "  # TODO implement this\n",
        "  #pred = []\n",
        "  #count = 0\n",
        "  #for g in hi.ravel():\n",
        "  #  if g < 0.5:\n",
        "  #    count +=1\n",
        "  #    pred.append(0)\n",
        "  #  else:\n",
        "  #    pred.append(1)\n",
        "\n",
        "\n",
        "  # y_true: true binary labels\n",
        "  # y_score: target scores, can be probability estimates of a positive class\n",
        "  #   or non-threshold measure of decisions\n",
        "  fpr_keras, tpr_keras, thresholds_keras = roc_curve(y_true=test_labels,\n",
        "                                                     y_score=pred,\n",
        "                                                     pos_label=1)\n",
        "\n",
        "  # compute the area under the curve\n",
        "  auc_keras = auc(fpr_keras, tpr_keras)\n",
        "\n",
        "  plt.figure(1)\n",
        "  plt.plot([0, 1], [0, 1], 'k--')\n",
        "  plt.plot(fpr_keras, tpr_keras, label='Default (area = {:.3f})'.format(auc_keras))\n",
        "  # plt.plot(fpr_rf, tpr_rf, label='RF (area = {:.3f})'.format(auc_rf))\n",
        "  plt.xlabel('False positive rate')\n",
        "  plt.ylabel('True positive rate')\n",
        "  plt.title('ROC curve')\n",
        "  plt.legend(loc='best')\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "We26FtLZIXcf"
      },
      "source": [
        "Plot the ROC plots for InceptionV3, ResNet, and 3 Transfer Learning Models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "id": "7y-dbxz3I2kE",
        "outputId": "82258edb-8730-40e8-b202-c8cadf6074d9"
      },
      "source": [
        "# flipped\n",
        "createROCPlot(model_retrain_flipped, test_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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atWuBr+9gLl26qHHt2bOn0adPt3TxNG1aj61bN2UY6/79e5gzZzoAHTq8T9Om9fj44w+ybN+BA3vx9Gytse3u3dtMnTqBzp298fRsgo/Px2za9CMvX/6vYsLDhw9o2rQeBw7sZf78OXh7t6Jv3x4AJCUl8cMPK+na9UNatmxEjx6d2bVrp8Y9rl27ytdfj6Jjx3a8/35T+vXrwe7dv2YZqy6dPn2SunXrqxMKQIsWnhgZGXH69Emd3CM3JZaePYvjxIk/aNmyNQYGmp9nW7Z8n5Mn/yQ6Olon8RVFxy7eZ/6W82wM/Jd/w6IpY64i8sZu/jm8kOa17DE3N8/3hALypJJjJ6485M/LD1EoQMuiyjrVtGY5mtQop7PrVazogq2tHVevXlZv+/nn7SxbtpiPP+6Or+8IHjy4x/ffryAh4QVjxkygUaOmLFq0lNGjh9Ov3yAaNWpKqVKvVuY+fPiANm3a4+DgyMuXL/n99yOMGDGUNWs24eZWOddxNmrUlH79BrFhw1oWLVqKqakZRkaZd7nduxfGw4cPqFGjlsb2p0+f4ujoROvWbSlZ0pTQ0P9Yu3Y1SqWSzz/X7PJZtWoZDRs2YsqUWepxvalTJ3D+/DkGDBiMi0slzp8/x6JF8yhZsiStW7cD4NGjh1SrVpMPP+xMiRIluHbtCkuWLCApKYkuXdIn07TSJrfM6OnpZflH/fbtW3h7f6ixzcjICHt7B+7evZ3t9S9cOE/r1u+RnJyMu3sVBg0aSr1672Z7XnaOHTtKYmICbdu2T7evZs1aJCcnc+HCOVq2fP+N71XUpJ0i7FLOlPD/TnJg+/dUruzB5GmzCtR0a0kqAju7MkRGRgDw/PkzfvhhJd27++DrO0J9jJmZOTNnTsHHpz/lytnj7l4FeNXFkbarpH//wer/TklJoV69dwkOvsm+fbv48suxuY6xVKlS6q4Ud/cqGp/CM3LjxjUAXF3dNLbXqVNP/WlOpVJRs+Y7pKSoWLfue3x9R2j8crq4uDJ58nT1QsDz588RFPQ7Cxb4qccE6tdvgFIZw5o1q9RJpVWr/z0dpd4jOjqK3bt3ZplUHj58QNeuH2a6P9WAAZ8waNDQTPfHxioxM0u/Utrc3BylUpnltWvXrouXVwfs7R2IjIzk55+3MmrUMBYvXk7duvWzjS0rhw4dwN6+fLpED6+6wMqUKcv169ckqbwmbUJpV9uK5XM+4/nz5wwb9iX9+g3C0DDvxzNzQpJKDjWp8epJoaCuOs4NlUql/mN69eoVnj9/RqtWbTQ+Ndet+y7JycncvPkP5crZZ3qt27dvsXr1Cq5evaxOVABWVrqvMZSViIin6OnppauwkJCQwObN6zl06ADh4Y9JSkpS74uMjMDGprT669dnzpw9ewZzcwvq12+g8b2pV+9ddu3aiVIZg4WFJUqlknXrVvPnn8d58iRc/ZRjZGSUZcylS9uyZs3GbNtWurRttsfk1qBBQzX+bTdt2oz+/Xuybt3qN0oqT58+4cKFv+nbd2Cmx1haWhER8TTX9yiK0iaUvu3caVLNjhunW9Onz4Bsx8byiyQVwZMn4Tg6OgEQHR0FwKBBvTM89vHjR5le5/nzZ4waNQwLC0s+//wLypYth7GxMUuWLCQxMUH3gWchMTERAwODdN0CK1cuZffuXxkwYDAeHlUwMzPn7NkzrF69Qj1mlKpUKRuNr6Ojo4iNVdKiRcMM7/n48SMsLCyZM2caV65con//T3BxccXU1JQDB/bi778jy5gNDQ2pVCn7LsLsxjPMzS2Ii0tffiM2NhZn5wrZXj8tIyMjmjZtzs6dWceenUOHAklJSaFNm/RdX6mMjY1ISHjxRvcpak5fe/X79iz0EHVc6mFoaMjUqbPyOaqsSVIp5kJDQ3jyJJz27V+9ytnc/FX159mzv6FMGc3Cffr6etjYZP4p+erVK4SHP2b+/G9xc3NXb3/+/BkWFv97YjAyMiIpSXPsQKnUXXVpeFXFOjExkYSEBIyNjdXbf//9CB07dqZPnwHqbRcvXsjwGq93U5ubW2BlZcXChd9leLyjozMJCQmcPPknw4Z9SdeuPdT7MluvkZauur8qVKjInTu3NLYlJiby4ME9vLyyntyQVw4dOkCVKlVxcnLO9JjY2FgqVCiYn77zw87fLnEzLIbY8JvY6IXz4kV8uursBZEklWIsISGBJUsWYGRkxAcffARAjRq1MDEx4fHjxzRv7qlxfHZdfqmfMg0N/9fNExx8k1u3QqlVq7Z6m51dGcLDH/P8+TNKlnxVxvzMmVPZxpu6Fkabpx4npwrAqz/UaRd3JiQkaPRBq1QqDh8OzPZ68Gr8ZOvWjejr62skzbTi4uJISUnR+B4kJCRkOu02LV11fzVs2JgNG9YSExONpeWrsaegoN9JTEzUWB+ijcTERP7441i6hY05ERoawn//3eSLL8ZkekxycjKPHz/C2zvzpFMcpJanf/zoETFJr1bBN63pwIh+QwvUYHxWJKkUEykpKVy9egWA+PjnhIa+Wvz44MF9JkyYqh4nMTMz45NPPmPVqqU8eRJOnTp1MTAw5OHDB5w69ScjR36VaUG6atVqYGJSkoUL59KnzwAiIyNYu/Z7bG3tNI5r0aIVa9d+z5w50+nYsTN3795lz56AbNtQoUIFAH755SeaN/ekRIkSuLpWyvDYKlWqoa+vzz//XNdIKvXrN2D37p04O1fA2tqa3bsDiI3VrlJr/foNeO+9FowZM4KePfvg5uZOQkICd+7c5saNa8yYMRczMzOqVKnKpk0/YmFhiZGRIdu3b8HIyDjb6xsaGr7RH+9UHTt2wd9/B19/PZr+/QerFz+2atWaihX/V3B17twZBAbu4/jxMwBcunSBrVs30rKlJ3Z25YiIeMrPP2/nwYP76tXvqS5c+Jvo6Chu3Xr10ry//z7Lw4cPsLIqRe3adTWOPXToAPr6+rz/fptMY75z5xbx8fHUqvXOG7e/sEo7fqL3QomBXhwftqxFh/c88jmynJGkUkwkJCTw6acDUCgUmJiUpGzZstStW585cxam62fv1q0XtrZ2bN++hZ07d6Cvb0C5cuVo1KiJunssI9bWNsyaNZ/ly5cwfvwYypcvz/Dho9i3bxfPnz9XH+fsXIHJk2ewbt1qvv56NFWqVGPGjDn06vVxlm2oXNmDgQOHsHfvLrZv34ydXRl++WVPhseamJjQqFETTp06Qbt2/3v19KhRX7FgwVy++24RhoZGtGnTjpYtWzFjxmQtvoswc+Y8tm7dqE7IpqamODlV0BgrmDp1NgsWzGXevBmYmZnTsWNnjIyMWLEi424zXTM3N8fPbyVLlixk4sSx6jItr6+Sf70Eko1NaZKSXrJy5XJiYqIpUaIEVavWYOnS76lZU/OP/dq132uUc1m5cikA77xTh2XLVqu3pz4J1q/fgFKlMi/2eurUCcqWLUeVKtXeqO2FSeoiRoAUVQrB917NzOvbzp2GHo21LkVU0ChUqvxYbVFwZPU+lUeP7lC2bMaP40Vp9pe2ClubT5z4g2nTJrBr10FKliyZq2sUtjbrQn60ecCAXjRr1pIBAz7J8risfiffxNt6n0raRJK6Ir58KX1u37nNixfx1KtcmnGfZv3hSldy2+bs3qciK+pFkdWkyXs4O1ckIMA/v0MRWfj777M8evSIjz/ukf3BhdyZa4+5Gx4HQKXy5pQhhL2rPuXphQ2M/LjaW0soeUmSiijSxowZT8mSb6/st8i5+PjnTJo0HXPzovs639QSK3fD43CyM2OcTx1MI4MI/OlbevTw4ZdfdtO48Xv5HaZOyJiKKNI8PKrg4VElv8MQWWjatGi+siKjri5XezPcyuoDMHDgEJo3b1lgC7LmljypCCFEHkjb1eXuaMW7zi85umEUO36YjkqlwtzcvMglFJCkkq1iPo9BiAKjsPwuvt7VNaitI08urGfV/BHY2toxbdrsQrPmJDek+ysL+voGJCUlarXGQAiRt5KSEtHXL5h/sjLq6nJ3tKKCjYrOnTvw4kU8I0aMok+fAQWuAKSuFcyfUAFhZmZFdPQTrKxsMTQ0KtKfLoQoqFQqFUlJiURHP8Hc/O0WJtVG2kWL7o5WuDtaUd/DFs+6jiQlJfHvX23o23cAFSq4ZHOlokGSShZMTF6VEImJeUpysmatKj09PVJSitf6BWlz8VAQ26yvb4C5eSn172RBkvqE0redO81qluOnn7awZPpm6m3+GQsLC6ZMmZnPEb5dklSyYWJimuE/5Le1WKogkTYXD8Wxzbl17OJ9/g2Lxt3RCmfLBAYO7M3Fi+dp3Ljp/9fCK/gFIHUtx0klLi6OS5cuERERQePGjSldunT2JwkhRBGTttsrKepfunWbg4lJSWbOnEeHDh2LbXd5jmZ/rVq1ivfee49BgwYxbtw4goODAYiMjKRWrVps27YtT4IUQoiCROPlWW3deXjjKM2be/Lrr/v44IOPim1CgRwklW3btrFkyRI6dOjA4sWLNab3WVtb06pVKwIDtSshLoQQhdmpKw8B+LCBHS1ql2fRoqUsXOin8ebQ4krrpLJp0ybatWvHzJkzadgw/ZvvqlSpQkhIiE6DE0KIgmbT7lME31cSG36T5w9fVWourBWF84LWYyphYWH069cv0/2WlpbExOj27X1CCFFQHDoTyq6jl4hXvHqLqVfTqvTq3Cyfoyp4tE4qlpaWREREZLo/ODgYW9us30gnhBCFRdoFjfD/ixoVlpRESUfPWrR+t2IWZxdfWieV5s2bs2PHDnr16pVu3z///MPPP/9M165ddRqcEEK8TRmtjHe2NaZECRMq2ZvjbJ2MTwfPrC5R7GmdVL788ktOnjxJhw4daNGiBQqFAn9/f3bs2MGRI0coW7Ysvr6+Obr57du3mTlzJufPn8fY2Bhvb2/GjBmDiUnWpcqfP3/OihUrCAwM5MmTJ5QpU4YPP/yQIUOGYGRklOW5QgjxusBTtzly5o5GiRU7sxSun9nD86Qwtm3zL9YzunJC66Ria2uLv78/ixcv5tChQ6hUKvbu3YupqSkffPABY8aMoVQp7UsoKJVK+vbti729PX5+fkRGRjJ37lwiIyNZvHhxludOmzaNI0eOMHLkSNzc3Lh8+TLfffcdSqWSCRMmZHmuEEKk9XqZlWpOJTn663KOHj1M1arVmDp9jiSUHMjR4kdra2tmzpzJzJkziYyMJCUlBWtra/T0cl7sePv27SiVSgICArC2fvXuan19fcaMGYOvry9ubm4Znvfy5UsCAwMZPHgwffr0AaBhw4Y8ePCAvXv3SlIRQmgltasr9emkbzt3ypnEMnhwXxITE/jiizH06dMfAwMpPJITWmeD8ePHc+nSJfXX1tbWlC5dWp1QLl++zPjx47W+cVBQEA0bNlQnFIC2bdtiZGREUFBQpuepVCqSk5PTvSXOwsKi0JTGFkLkr9Snk9QSK592qk6Ld8rj4uJK27bt2bEjgAEDBktCyQWtk8qvv/7K3bt3M91/7949AgICtL5xSEgIlSpV0thmZGSEk5MToaGhmZ5naGhIx44d2bRpE5cuXeLZs2ecPn2aHTt24OPjo/X9hRDFT+q7TlK7u/q0ccOBq8wZ1xelMgZDQ0MmTZqOs7PM7MotnaXhqKioHA2SK5VKLCzSF1uzsLDIdr3LjBkzmDp1Kt26dVNv69+/P8OGDdM+4P9nY2OW43NS2doW3XdqZ0baXDwUxTYHnrqtTibVXW3wsDdk07JxnD9/Hk9PT8zNjYpku7OSF+3NMqmcPXuWM2fOqL8+fPgwd+7cSXecUqlk//79eHh46DzAjCxatIjjx48za9YsKlSowMWLF1m+fDmlS5fmk08+ydG1IiLiSEnJebdZcazkKm0uHopam18fO+nTxo3gc3uY+s1KTE1NmT17Af369eTp07gi1e7s5PbnrKenyPLDeJZJ5cyZMyxbtgwAhULBoUOHOHToUIbHurm5MXHiRK0Ds7CwQKlUptuuVCpxccn8ZTY3b95k3bp1rFixglatWgFQv359Xr58yXfffUfPnj0xM8v904cQovDL7E2MDaqVoXkte/zXXaJVq9Z89dVErK1tZHaXDmWZVAYPHoyPjw8qlYrGjRszffp02prNecYAACAASURBVLRpo3GMQqHAxMQEY+OcvXLX1dU1Xa2wxMRE7t69S+fOnTM977///gNe1RpLq2rVqiQmJvL48WNJKkIUYxm9ibFuZWuun9qJm203FAoFixZ9l+O/WUI7WSaVEiVKqAul/fbbb1hbW2e7MFFbzZo1Y+XKlURFRanXtxw+fJjExESaN2+e6Xnly5cH4Nq1a9jb26u3X716FYVCobFNCFH8pH0TY4t3ynPu3F9Mnz6UsLA72Nra0rNnH0koeUjrgfrUP+a60qNHDzZv3oyvry++vr5EREQwb948vLy8NGaFTZgwgYCAAK5fvw5A9erVqVmzJlOnTiUiIgJnZ2cuX77M6tWr6dKli86SnhCi8En7JsZ6lSyZPXsaP/+8HQcHR1avXs+776avsC50K0ezv27evMmmTZu4du0asbGx6d5jrVAoOHLkiFbXsrCwYMOGDcyaNYvhw4ery7SMHTtW47iUlBSSk5PVX+vr67Nq1Sr8/PxYvXo1T58+pVy5cgwcOJChQ4fmpDlCiCIkbbdXg2plWLv2e/z9d9CnT398fUdgYlIynyMsHhQqLVcMnjt3joEDB2Jubk6NGjU4duwYDRs2JCEhgYsXL1KpUiWqV6/O3Llz8zpmnZLZX9qTNhcPhbHNaROKV11rPm79DnFxcdy6FUKNGrWyPb8wtvlN5cvsr7T8/Pywt7fn559/JikpicaNGzN06FAaNWrE+fPnGTp0KOPGjctxgEIIkVuvTxd+ei2AXy7eo8v7/piZmWmVUIRuab2i/urVq3z88ceYm5urS7Okdn/VqVOHrl274ufnlzdRCiHEa9KWWtFPfMKds1uwUD1i1qx5MkU4H2n9pKJQKNQr4EuWfNU3GR0drd5fsWJFtm7dquPwhBDifzJaf/L4sj8RoSf4/PMv6NWrL/r6+vkZYrGndVJxcHAgLCwMeFWjy8HBgRMnTuDt7Q28GnOxsrLKmyiFEMXa691clR0sX83w8ijN6WQ7+s7fjaOjUz5HKSAHSaVJkyYcOHCA0aNHA9CtWze+/fZb7t+/j0ql4q+//spxiRQhhMhKumTiaIkiNpQ/ts9l69ZfsLCwpFXdqfkcpUhL66Ty6aef4u3tTVJSEoaGhnzyySckJydz8OBB9PT0GDZsmEzpFULo1Jlrj7kbHoe7oxUutrBr4zyuXr1Ms2YtSEpKyu/wRAa0TiqWlpZYWlqqv1YoFHz22Wd89tlneRKYEKL4Sn1CuRseh6OdKZaxJ5m/+HvMzc2YN28Rbdt6yWB8AaXV7K/4+HiqVKnCypUr8zoeIUQxl3ZWl5OdGQ2rluXatSu0bt0Wf/99tGvnLQmlANPqScXExARra+t0b1sUQghdSruIsbzBHXyau+HgUJ7Gi5bm6H1NIv9ovU6lffv2HDhwIF1pFiGE0IW0CUUZfIA9m+fz55+vXi0uCaXw0HpM5f333+f06dP06tWLrl274ujoqK5gnFbNmjV1GqAQouhLm1DunN1CycR7rFmzkXr13s3nyEROaZ1U+vfvr/7vixcvpuvTVKlUKBQKbty4obPghBBFV0YLGcPObaNDUw8+/XSZVBwvpLROKoWtUKQQouBK+2TiZGuMu6MV77ha4fDBCKpVq5HP0Yk3oXVS6dSpU17GIYQoJtImlCdXf+X5y/ts2+YvM7qKiBy9T0UIIXLr9dXxd85uwb5kHFNnzZeEUoRIUhFC5Lm0TyfPn4YQHXaeAZ2a0bNnbykAWcRIUhFC5Km0CcWndSX+OvQnfb6cgIODYz5HJvKCJBUhhE6lndWlUqm4eS8GgK7NnWhV14lWdafkZ3gij0lSEULoTNqnEsfSRty5fYtnz59hVyKOBu7yFsbiQJKKEEIn0iYU5xL32bNqPhYWlnz99WRat24rg/HFhNZlWgCioqJYvHgxPXr0oE2bNly4cEG9fdmyZYSEhORJkEKIgi1tQunb1p2I0BO0a+fNzp17adOmnSSUYkTrJ5V79+7Rq1cvoqOjqVy5MmFhYbx48QKAUqVKsX//fiIjI5kyRfpLhSjq0o6bwP9WxH/YwI4WtcvTpPp3GBpKva7iSOuksmDBAlQqFfv27cPU1JTGjRtr7G/VqhVHjhzReYBCiILj9bUm7o5WKJUxJEbf4WHwnyRUbAVUl4RSjGmdVE6dOsWgQYNwdHQkKioq3X4HBwcePXqk0+CEEAVDRsmkloslpw+uY1+AP05OziyYNps6derlc6Qiv2mdVBISErCwsMh0v1KpRE8vR0M0QohCIu1rfRtUK0OLd8rj57eIPXsCGDjwE4YM+TzDquWi+NE6qbi5uXH27Fl69uyZ4f7ffvuNqlWr6iwwIUT+S/taXyc7Mwa3c1L3VAwaNJQ2bdpRpUq1fI5SFCRaP1r069ePAwcOsHLlSmJiXi1mSklJISQkhNGjR3Pp0iUGDBiQo5vfvn2bQYMGUbt2bRo2bMjMmTOJj4/X6tzY2Fhmz55Ns2bNqF69Op6envj5+eXo/kKIrKVNKCYvH9KpkzeTJo1DpVJhZmYmCUWko/WTygcffMDDhw/57rvv+O677wAYPHgwAHp6eowdOxZPT0+tb6xUKunbty/29vb4+fkRGRnJ3LlziYyMZPHixVme+/z5c3r37o1CoWDs2LHY2dkRFhYmYzpC6EjaJ5SyVobcPbmSEyf+oFat2kybNlumCItM5Wjx45AhQ/jggw84ePAgd+7cISUlBScnJ9q0aYOjY87q+Gzfvh2lUklAQADW1tYA6OvrM2bMGHx9fXFzc8v03NWrVxMbG8uePXswNTUFoEGDBjm6vxAivWMX73M++ClXQyIAcLQx5OShjUTf/ptx4ybSvbuPjJ2KLOV4RX25cuU03gKZW0FBQTRs2FCdUADatm3LhAkTCAoKyjKp/PLLL/Tq1UudUIQQby7tAkY3BwsaVS9H0+plWBQZRO+FEylf3iGfIxSFQY7eUe/t7U379u3x8PB44xuHhITQpUsXjW1GRkY4OTkRGhqa6Xn37t3jyZMnlCpVik8//ZQTJ05gbGyMp6cnEydOxNLS8o1jE6I4yGwBY1WbSH7bMgvf7TsxMDBg3LiJ+RWiKIS0TioODg6sWbOG1atX4+LigpeXF15eXlSsWDFXN1YqlRlOUbawsFBPBMjI06dPAfjmm2/w9PTk+++/5/79+yxatIiIiAjWrl2bq3iEKA4yei+8u6MVAI42RoRcPMjm7T/TqlVrkpNT8i1OUXhpnVTWr19PZGQkBw4cIDAwkOXLl7Ns2TI8PDzw9vbGy8sLe3v7vIwVeDXjDMDZ2ZmFCxeqBwzNzc354osvuHz5MjVr1tT6ejY2ZrmOxdbWPNfnFlbS5sIr8NRtdfdWdVcbqrva0Ly2A63fdWThwoWs+H4FpUqVYvXq1Xh7e+dvsPmgqPyccyIv2pyjMRVra2t8fHzw8fEhPDyc/fv3c+DAARYuXMiiRYuoVasW27dv1+paFhYWKJXKdNuVSiUuLi6ZnpfavdWoUSONGSiNGjUCIDg4OEdJJSIijpQUldbHp7K1NefJk9gcn1eYSZsLn4yeTPq2c6fFO+XVx0REPOPSpSu0b9+BMWO+plIlx0Ld5two7D/n3Mhtm/X0FFl+GM916Xs7Ozv69+9P37598ff3Z968eVy6dEnr811dXdNVNU5MTOTu3bt07tw50/McHR0xMsq8rlBCQoLWMQhRFGXWxZV2Nfzz589Yvvw7evTwwdHRiYULv8PQ0DA/wxZFRK6TytmzZ9m/fz+HDh0iMjISCwuLdAPvWWnWrBkrV64kKiqKUqVKAXD48GESExNp3rx5pucZGRnRpEkTTp48iUqlUj+tnDhxAoDq1avntklCFGoZ1edKm0hSnTz5BzNnTuXRo4dUqFARR0cnSShCZ3KUVC5evMj+/fsJDAzkyZMnlCxZEk9PT7y9vWnatCkGBtpfrkePHmzevBlfX198fX2JiIhg3rx5eHl5UalSJfVxEyZMICAggOvXr6u3DRs2jB49ejBq1Cg6d+7MgwcP+Pbbb2natGmOur6EKMwym72VUSIBiImJZuHCeezZE0DFii6sW7eF2rXrvNWYRdGndRZo2bIljx49wtjYmGbNmuHt7U2LFi0wNjbO1Y0tLCzYsGEDs2bNYvjw4RgbG+Pt7c3YsWM1jktJSSE5OVljW/Xq1VmzZg2LFi3C19cXMzMzvLy8GDNmTK5iEaKwSbumJHX2VmbJJNX69WvZv38Pgwd/yieffJbr310hsqJQqVRajVJ/+umneHl50apVqyK16FAG6rUnbS4YNN6y+Nqg++uePn1CdHQUlSpV5tmzOMLCwvDwqJLl9Qtim/OatFl7OhuoX7VqVY5vLoTQDW1mcaWlUqnYvftXFi2aj719ebZt88fU1CzbhCLEm8r1QL0QIu9pO/ie1v3795g1ayqnTp2gdu26TJ06UwpAircm06Ti4eGBnp4eFy9exMjICA8Pj2z/YSoUCo0BdSFEzmU2JTirRJLqxo1rDBzYB4UCxo+fQteuPaQApHirMk0qn3/+OQqFQj2jK/VrIUTeeX0AXttkkpCQgLGxMW5u7nTu3BUfn77Y22d9jhB5QeuB+qJKBuq1J23OGzkdL0krKSmJ9evX4u//E9u378TKqtQbxyM/5+IhrwbqtX4uXrZsGTdv3sx0f3BwMMuWLctZdEIUc6lPJmm7ubRNKDduXMPHpyvLly+hZs13KN4fD0VBofVA/bJly3B2dqZy5coZ7g8ODmb58uUMGzZMZ8EJUdSlPqFom0gAkpOTWbZsCRs3rsPa2ppvv12Gp+f7eRmmEFrT2eyvZ8+e5WhFvRDiFXdHK60TCrx6ffetWyF8+GEnRo78KsNXSAiRX7LMAv/88w///POP+utz586lW90OEBMTw7Zt27KsLiyE0HTs4n3+DYtWr4jPyrNncSxb5kfPnr1xcnJmwQI/qdclCqQsk8qRI0fU4yQKhYKffvqJn376KcNjLS0t+eabb3QfoRBFVGrXV4NqZbI87s8/g5g1ayqPHz/C1dUVJydnSSiiwMoyqXTr1o0WLVqgUqno2rUrI0aMoFmzZhrHKBQKTExMcHJyku4vIbKRdqbX3fC4LLu+oqOjWLhwHnv37sLFpRLr12+lVq3abzNcIXIsyyxgZ2eHnZ0dABs3bsTV1RUbG5u3EpgQRc3ra1Cc7MyyfErZsGEdgYH7GDLEl8GDP83yPUJCFBRaP1q8++67eRmHEEXW66VWsprpFR7+mJiYaNzc3Bk8eCjt23egcmX3txmuEG8k06Qyfvx4FAoFM2fORF9fn/Hjx2d7MYVCwZw5c3QaoBCF2etPJ5mtjlepVAQE+LNo0XwcHBzVBSAloYjCJtOkcubMGRQKBSkpKejr63PmzJlsLyZlXIT4H21L1N+7F8bMmVM4c+YUdevWZ+rUWfK7JAqtTJPK0aNHs/xaCJE5bRPKjRvXGDCgN/r6ekyaNI3OnbtJAUhRqMl0LSHyQHYr5dMWgOzWrQe9evWlbNlybztMIXRO66Ty9OlTwsPDqVq1qnpbSEgIGzZsQKlU4u3tTevWrfMkSCEKA22mCyclJfLjj2vw99/BTz/9ipVVKUaNGpcf4QqRJ7ROKjNmzCAiIoItW7YAEB0dTe/evYmNjcXY2JiDBw+yYsUKWrZsmWfBClFQaTNd+OrVK0yfPpHg4Ju0a+eVH2EKkee0TioXLlzAx8dH/fXu3btRKpX8+uuvVKxYkf79+7N27VpJKqLY0LZkfXJyMt999y2bNv2IjU1plixZQYsWnm89XiHeBq1HBKOjo7G1tVV//fvvv1OvXj0qV66MoaEhXl5eBAcH50mQQhREZ6495m54HJB1yXo9PT3Cwu7y0Udd2LlznyQUUaRp/aRiaWnJkydPAIiPj+f8+fN8/vnn6v0KhYLExETdRyhEAZS2GOQ4nzrp9sfGxrJs2WJ69eqLs3MFvvlmsZQxEsWC1v/K69aty9atW3F1deWPP/4gMTGRVq1aqfffunVLXdJFiKIs7fhJRmVWgoKOMWvWVJ4+fYKbmzvOzhUkoYhiQ+t/6aNHj2bgwIEMHz4cgAEDBuDq6gq86jM+ePAgzZs3z5sohSggslp/EhkZyYIFczhwYC+VKrmxaNFSatSomV+hCpEvtE4qTk5OBAYG8t9//2FmZoaDg4N6X3x8PFOmTMHDwyNPghQiv2lTv2vTph85fPggn346jEGDhmBoKAUgRfGjUKmK95utIyLiSEnJ+bfA1tacJ09i8yCigqs4tTk1iRga6ZOUmKzxDvm09bseP35VALJyZXeeP3/Ggwf3qVQp41duFxbF6eecStqsPT09BTY2Zpnuz1FHb1JSEjt27OD48ePcv38fgPLly9OyZUs+/vhjeXGQKNQymiJc3fXVqx5eTyYqlYqdO39m8eJvcHBwYts2f0qWNC30CUWIN6V1UomJiaF///7cuHGD0qVL4+zsDMD169cJCgri559/5scff8TS0lLrm9++fZuZM2dy/vx5jI2N8fb2ZsyYMZiYmGh9jcOHDzNs2DDc3NzYu3ev1ucJ8brUKcJOdmbqJNK1tUe6T3NhYXeZMWMyZ8+eoX79BkyZMlMKQArx/7ROKosWLSI4OJi5c+fSsWNHddE7lUrFrl27mDRpEt9++y3Tp0/X6npKpZK+fftib2+Pn58fkZGRzJ07l8jISBYvXqzVNeLj45kzZw6lS5fWthlCpJP6hJKaUDKaIpzq+vWrDBzYGwMDAyZPnkHnzl0loQiRhtZJ5ejRo/j4+NCpUyeN7QqFgo8++ogbN26wb98+rZPK9u3bUSqVBAQEYG1tDYC+vj5jxozB19cXNze3bK+xYsUKHBwcKF++PFevXtW2KUKoZfS+k4y8ePGCEiVKULmyB927+9CrV1/KlMn63fJCFEdar6hXKpU4OTllut/JyQmlUqn1jYOCgmjYsKE6oQC0bdsWIyMjgoKCsj0/JCSETZs2MXnyZK3vKURar08PHudTJ92MroSEBFauXMqHH7YlKioKAwMDRo4cKwlFiExonVScnZ357bffyGiymEql4siRI+pxFm2EhIRQqVIljW1GRkY4OTkRGhqa7fkzZszg448/pnJlGRgVOXPs4n3mbzmf7ftOrly5RPv27fn+++XUr98APT3p5hIiO1p3f/Xq1Yvp06czePBg+vbtS4UKFYBXK+k3bdrE6dOnmTZtmtY3ViqVWFhYpNtuYWFBTExMlufu27ePmzdvsnTpUq3vl5mspsZlx9bW/I3vX9gUxjYHnrrN8Qv31F9fDYkAXs3sal7bgXaNKmgc//LlS2bNmsWaNWsoW7YsGzZs4P3333+LEee/wvhzflPSZt3QOqn07NmTqKgoVq5cycmTJ9XbVSoVhoaGjBgxgu7du+s8wNfFxcUxb948Ro0alWFSyilZp6K9wtbm1xcsujtaqf8/7fTg19ukUqkIDb3Nxx/3YObMqbx4kf6Yoqyw/Zx1QdqsPZ2uU/H19aVHjx6cPHmSBw8eAK/WqTRu3JhSpUrlKDALC4sMx2CUSiUuLi6Znrdq1SqsrKxo3bq1+vykpCRSUlJQKpWUKFECIyNZyVycZZRM0iaRjCiVSpYuXUzv3v3+vwDkEvT19TE3N+fFi+L1x0aIN5HjKnfW1tZ06NDhjW/s6upKSEiIxrbExETu3r1L586dMz0vNDSUmzdv0qBBg3T76tevz/jx4+nfv/8bxycKp4xmc2WVTACOHTvK7NnTiIh4SpUqVXF2roC+vv7bCFeIIifHSeXUqVMcO3ZMY0V9ixYtaNSoUY6u06xZM1auXElUVJT6Kefw4cMkJiZmWZjyyy+/pF+/fhrbVq9eza1bt5g7d26OJguIoie7d8OnFRkZwfz5szl4cD9ubpVZsmQ51arVeBthClFkaZ1Unj9/zsiRIwkKCkKlUqlXzh85coSNGzfStGlTlixZgqmpqVbX69GjB5s3b8bX1xdfX18iIiKYN28eXl5eGrPCJkyYQEBAANevXwfIcLbXr7/+yuPHjzN8ehHFR9p3nGSXUAA2bVrP0aOH+fzzL+jff5AUgBRCB7ROKvPmzeP48eP4+vrSp08f9dNFVFQUGzduZOXKlcyfP58ZM2ZodT0LCws2bNjArFmzGD58uLpMy9ixYzWOS0lJITk5OQdNEsXN62MomS1gBHj06CHR0dF4eFRhyJDP6NChI66ulTI9XgiRM1pXKX733Xdp165dpklj8uTJHDx4kL/++kunAeY1mf2lvYLW5pwMyKekpPDLLz/h57cQJydntm7116q8SkFr89sgbS4e8n32V0pKSpbvS/Hw8CAwMDBn0QmRCzmd3XXnzm1mzJjE33+fo0GDxkyZMkPqdQmRR7ROKs2bN+fYsWP06tUrw/3Hjh2jWbNmOgtMiFRpS9IDOZoqfO3aFQYO7I2hoRHTps2mY8fOklCEyENaJxVfX19GjhzJ0KFD8fHxUc+yun37Nlu2bCE8PJyvv/6aiIgIjfNsbGx0G7EodtJWEAbtkkl8fDwmJiZ4eFTFx6cvPXr0xs5O6nUJkde0Tire3t4A3Lx5M13Bx9RhmYzWr9y4ceNN4hMCINuS9KkSExP54YeVBAT4s2PHLkqVKsWIEaPfQoRCCMhBUvn888+l20C8Va+/5yQ7ly5dYPr0SYSGhtChQ0cpAClEPtA6qQwfPjwv4xBCg7bvOYFXBSC//fYbtm3bRNmy5Vi+/AeaNHnvbYUqhEgjxyvqhXgbcrIy3sDAgPDwR3Tr1pMRI0Zhapr7ytNCiDcjSUUUONqsjFcqY/DzW0SfPv2pUMGF+fMXS70uIQoASSqiwNB2ZfzRo4eZM2cGUVGRVKtWgwoVXCShCFFASFIRBYI21YWfPn3CvHmzOHLkIO7uVVi6dBVVqlTLj3CFEJmQpCLy1etPJ1mNoWzZspGgoN8ZPnwkffsOxNDQ8G2GKoTQgiQVkW+0eTp5+PABMTHReHhUZciQz/jww05UrJj5S9yEEPkrR0klMTGRXbt2cfr0aSIjIxk7dixVq1ZFqVRy9OhRGjZsSNmyZfMqVlGEpE0oGT2dpKSksGPHNr77bhHOzhXYutUfE5OSklCEKOC0TipRUVH069ePmzdvUrp0aSIiIoiJiQHAzMwMPz8/goOD05WuFyItbbq7bt8OZfr0yVy48DeNGjVh0qTpsvBWiEJC66SycOFCHjx4wNatW6lQoQKNGzdW79PT06NNmzYEBQVJUhEZ0ray8NWrVxg40IcSJUyYMWMuH3zwkSQUIQoRrZPK77//Tp8+fahTpw5RUVHp9js7O+Pv76/T4ETRkVpuJbNkEh//HBOTklSpUpU+fQbQs2dvSpe2zadohRC5pXVSiYuLo1y5cpnuT0xMlDc0inRer9/1elHIhIQEVq9ewe7dO/npp11YW1szfPjIfIpWCPGm9LQ90NnZmatXr2a6/88//8TNzU0nQYmiIXUw/t+waJzszNItZrxw4Tzdu3/E2rXf06hRUwwMZAGjEIWd1k8q3bp1Y/78+dSrV4+mTZsCoFAoiI+PZ/ny5Zw4cYLZs2fnWaCi8MmsftfLly9ZuHAuP/20lbJly7FixRoaN26aX2EKIXRI66TSp08fgoOD+eqrrzA1NQVg5MiRKJVKkpOT8fHxoXPnznkWqCicMqrfZWBgQEREBD169Gb48C8pWdI0n6ITQuhajtapzJgxg48++ogDBw5w584dUlJScHJywsvLi3r16uVVjKIQSlsUEiAmJpolSxbRr9+A/y8A+S16elr3vgohCokcr6ivU6cOdepk/wY+UXylXdjYoFoZjhw5yNy5M4mJiaZWrXeoUMFFEooQRZSUaRE68/palM5N7NmzaR6//XaYKlWqsnz5D3h4VMnnKIUQeUnrpOLp6ZntIjSFQsGRI0feOChRuGS2sPHS8e388cdxvvhiNH36DMDAQD7DCFHUaf1b/u6776ZLKsnJyTx48IDz58/j5uZG1apVdR6gKNheLwrpbm+ERzk9PDzK08D9Mz76qDPOzhXzOUohxNuidVKZN29epvv++ecfBg0axAcffKCToEThkTptuE8bNx7+8zvzJyyhYsWKbNnyCyYmJSWhCFHM6KQ/wsPDg+7du7Nw4UJ27typ9Xm3b99m5syZnD9/HmNjY7y9vRkzZgwmJiaZnhMXF8ePP/5IUFAQt27dwsDAgGrVqjFq1CiqVZMXNr0tGivlbY1Z7/cVly5doEmT96QApBDFmM46uW1sbPjvv/+0Pl6pVNK3b1/s7e3x8/MjMjKSuXPnEhkZyeLFizM978GDB/z000906dKFESNG8PLlSzZu3EiPHj3Yvn27JJa3JDWh2JgqOHlwAwmPQ5k9+xu8vD6QhCJEMaaTpBIVFYW/v3+O3qWyfft2lEolAQEBWFtbA6Cvr8+YMWPw9fXNtOSLg4MDhw8f1niaady4Ma1atWLz5s3MnTv3zRojMhV46jZHztwB4O7jWJzKmDOmRy2+f3mBHj3mYm1tk78BCiHyndZJpW/fvhluj42NJTQ0lKSkJL755hutbxwUFETDhg3VCQWgbdu2TJgwgaCgoEyTSsmSJdNtMzY2xtXVlfDwcK3vL7ST2s0FqGd3merFEvX0Kd4N3kNfXx9f3xH5GaIQogDROqmoVKp02xQKBQ4ODjRq1IguXbrg6uqq9Y1DQkLo0qWLxjYjIyOcnJwIDQ3V+joAz58/58aNG3Ts2DFH54nMZTRN2MnWkGun93D+7G46dfqYpjW65nOUQoiCRuuksmnTJp3eWKlUYmFhkW67hYWF+o2S2lqyZAnx8fH07t1bV+EVe2nff1Lfw5bTB38kYMdWypd34Pvvf6RBg0b5HaIQogDSKqnEx8czdOhQOnbsmO7pIr/t2bOHDRs2MGXKFJydnXN8vo2NWa7vbWtrnutzC7LAU7f5Nyya6q42zPV9VT3494BYyg0KKAAAHRRJREFUBg8ezLhx4zLsgizKiurPOSvS5uIhL9qsVVIxMTHh2rVrdOjQQWc3trCwQKlUptuuVCpxcXHR6honTpxg/PjxDBo0CB8fn1zFERERR0pK+q697NjamvPkSWyu7lmQpV3MeP/Gn/z1lzkVK7owffp8ypSx5MmTWJ49K3rtzkxR/TlnRdpcPOS2zXp6iiw/jGtd1a9+/fqcO3cuxwFkxtXVlZCQEI1tiYmJ3L17V6ukcvnyZYYNG0b79u0ZO3aszuIq7lIH5Z9eC+D47pVcvnwRQApACiG0ovVfismTJ3Pp0iXmz59PWFgYKSkpb3TjZs2acfr0aY333R8+fJjExESaN2+e5bkhISF88skn1KlThzlz5si6CB04dvE+M9ef5t874cSG38Qs+QFbt/rTsaO8I0cIoT2FKqNpXf8vICCAevXq4eDgQM2aNVGpVLx8+RJ49cn19QKBCoWCixcvanVjpVJJhw4dKF++PL6+vkRERDBv3jwaNWqksfhxwoQJBAQEcP36dQAiIiLo0qULL1++ZMGCBRrrVYyMjHJcf0y6vzS7vJ49+Y93Klkx9pPO6X6+RanN2pI2Fw/SZu1l1/2V5ZjK+PHj+eabb3BwcMDLy0unTwQWFhZs2LCBWbNmMXz4cHWZlte7slJSUkhOTlZ//d9///Hw4UMA+vfvr3Fs+fLlOXr0qM5iLOqOXbxP0Pm73A6PB6CnpwvuZV1xcsr5hAchhIBsnlQ8PDxYsGBBkS4UWVyeVNIuYgRQoeJm2Kup2ylx9+nfxZMWtctndjpQ+NqsC9Lm4kHarL03elIRRcPr5enj4+O5fecWz57FYWMYw/RR/ShTRvsSO0IIkZlsk4oMghduaRNK33bu2OhHMGDAp5ibm/HVVxNp126o/IyFEDqTbVIZP348EydO1OpiORmoF3kvbULp0bICLd4pT3JyWQYNGkL37j4addeEEEIXsk0qtWrVwtHR8W3EInTk9bpd9vq3mTd+MvV+2Y21tQ2ffTY8nyMUQhRV2SaV7t27F+mB+qIkXTKx0uPfc/vYe24PnTt3xdDQKJ8jFEIUdTJQXwS8nkwqO1gScfsv9q7yw8HBkdWr1/Puuw3zOUohRHEgSaUQy6g8fYNqZWjxTnm+/noLffr0x9f3iyxfzyyEELokSaUQS1uevkYFM04eWItz46EAzJmzUOp1CSHeuiyTyj///PO24hA5dOziff4Ni8bd0Yqa1g/4ZtJsYmPjaNCgERUrukhCEULkC3lSKYTSThW+feUI2wLXUb16TaZNm0WlSpXzOTohRHEmSaUQSi234mAUxqFjWxk9+mt69eqDvr5+PkcmhCjuJKkUEqmD8i8SXvAoMgF3RytGdG7EJ11b4ujolN/hCSEEIEmlUNAoTf/0P0xKmNCgVWVMTEwkoQghChRJKgXQ6xWFU6cM3zm7heoORkwYO40yZcrkV3hCCJEpSSoFzOsVhZ89iyPuSTAvwq/x1ZAutGnTXgpACiEKLEkqBUjahNK9hTNtG7qSkpLCDz9coduk6ZQqVSqfIxRCiKzJYoYCIm1CKacXyrzxfYiIeIqenh5Dh34uCUUIUSjIk0oBkDahKIP3c/7vvXTt2hNj4xL5HJkQQuSMJJUC4PTVR8CrgXjTpHusWbORevXezeeohBAi5ySpFAAKhQLDlxF88F4VPv10OSVKyBOKEKJwkqSSTyIjI5j53RYsHevyRJmMS0VXvvTpmt9hCSHEG5GB+rdMpVKxb98eOnf25n6sMQ8jXuBkZ0aDarLuRAhR+MmTylv06NFDZs2axp9/HqdmzVq4VK2OSQkTxvnUye/QhBBCJySpvEU7dmzl3Lm/6PXZbFLMKhAW/gwnGT4RQhQhklTy2J07t1AqY6lRoyZVGnbBy7wx/zyOh6gY9Zsaxf+1d+9RTZ3Z38C/gIKARrHomoIigpKKCegMESyghZnKJVgYxZYCWpABHS/oiI5KO95AnbZSBpG+2A61ihfaimJlAIv1gkqpddoC46hYwIKMrUqQEAMEyPn94cp5ieEackHZn7WyljznPCd7J/Hs5HnOhRDyvKCioiXt7e3IzPwU6empsLefgiNHjuP72yL8+qhN6ba/hBDyPKGiogW3bt3Etm3xuC+zgJMwHpNsJuG9oz+g5r4ENuNH0hwKIeS5pdeicufOHSQkJOD777+HiYkJhEIh1q9fD1NT01775uTkID09HXV1dbCxscHKlSvh7++vg6i7d+HHOpy7Wombt25g2EQhJo2djA4Aw4cbAwAd5UUIee7praiIxWIsWbIEVlZWSElJgUgkwu7duyESiZCcnNxj34KCAmzcuBExMTFwd3fH2bNnsW7dOpibm2Pu3Lk6yuCpmL6pxOcXfwYAWL1ojXHjx2OY0TAa5iKEDCl6KypZWVkQi8XIycnB2LFjAQBGRkZYv349VqxYgalTp3bbNyUlBb6+voiLiwMAuLm5oaqqCqmpqTovKl9drcapc6VoBgcAsMSXS0WEEDJk6e3kx6KiIri5ubEFBQB8fHxgbGyMoqKibvvV1taiqqoKQqFQqT0gIADl5eUQiURai7mzgm/u4O3/dx5Z56rRDA5MmUaEeNlSQSGEDGl6+6VSWVmJhQsXKrUZGxvDxsYGVVVV3fZTLLO3t1dqnzJlCru8c6HShkuldTiQ/+Sqwm2NNXh1tgOWvOat1eckhJBngV7nVDgcjko7h8NBY2Njt/0Uy57uO3r0aKXlffXCCyP7tT4AjBljBhN5A6w5zXh3Z/SQugDkuHGj9B2CzlHOQwPlrBlD/pDi+noJ5HKmX334kyzwxQdv4eFDCZqa2tDU1Kal6AaXceNG4cGDJn2HoVOU89BAOfedoaFBj1/G9TanwuFwIBaLVdrFYjH7q6MrimVP91X8QumprybRfeIJIUSV3oqKvb09KisrldpkMhlqampgZ2fXbT/FsqfnXRTb6qkvIYQQ7dJbUZkzZw5KSkrQ0NDAthUWFkImk/V4WPDEiRNhZ2eHvLw8pfbc3Fzw+XytT9ITQgjpnt6KSkhICEaNGoUVK1bg0qVLyMnJQUJCAvz9/dkjuQAgPj4ejo6OSn1jY2ORn5+P5ORkfPvtt9i1axeuXLmC1atX6zoNQgghnehtop7D4eDgwYNITEzE6tWr2cu0bNiwQWk9uVyOjo4OpTY/Pz+0tLQgPT0dGRkZsLGxQVJSkt7OpieEEPKEAcMw/Tv06TmjztFfAB0tMlRQzkMD5dx3g/boL0IIIc+fIX+eiqGh+ocGD6Tvs4pyHhoo56FBnZx76zPkh78IIYRoDg1/EUII0RgqKoQQQjSGigohhBCNoaJCCCFEY6ioEEII0RgqKoQQQjSGigohhBCNoaJCCCFEY6ioEEII0RgqKk+5c+cOoqKiMHPmTLi5uSEhIQHNzc196puTkwNfX1/w+XwIhUKVe74MVurkLJFIkJqaikWLFsHFxQVubm6IiorC9evXdRT1wAzkfVYoLCwEl8tFQECAlqLUrIHk3NTUhJ07d2LOnDng8Xjw9vZGSkqKliMeOHVzlkql2LNnD/7whz/A2dkZ8+bNw759+yCTyXQQtfp+/vlnbNmyBYGBgXB0dOzXZ1NT+68hf+2vzsRiMZYsWQIrKyukpKRAJBJh9+7dEIlESE5O7rFvQUEBNm7ciJiYGLi7u+Ps2bNYt24dzM3NB/Ul+dXN+X//+x8+++wzLFy4ELGxsWhvb8ehQ4cQEhKCrKwsTJ8+XYdZ9M9A3meF5uZm7Nq1C5aWllqOVjMGkrNUKkV4eDgMDAywYcMGjB8/HrW1tfjll190FL16BpLztm3bcPbsWfzlL3/B1KlTUVZWhr1790IsFiM+Pl5HGfTf7du3cfHiRTg7O0Mul6OvV+HS6P6LIaz9+/czzs7OTH19Pdv25ZdfMg4ODkxFRUWPfX19fZnY2FiltsjISGbhwoVaiVVT1M358ePHjFQqVWpraWlh3N3dmU2bNmktXk0YyPussGfPHiY8PJzZuHEjIxQKtRWqxgwk5+TkZMbLy4uRSCTaDlOj1M25ra2N4fP5TEpKilL71q1bmdmzZ2stXk3o6Ohg/92fz6Ym9180/NVJUVER3NzclG5J7OPjA2NjYxQVFXXbr7a2FlVVVRAKhUrtAQEBKC8vh0gk0lrMA6VuzmZmZjA1NVVqMzExgb29Pe7fv6+1eDVB3ZwVKisrkZmZib/97W/aDFOjBpLz8ePHERwcDHNzc22HqVHq5swwDDo6OjBq1Cildg6H0+dv/vpiaNj/Xbqm919UVDqprKxUupUxABgbG8PGxgZVVVXd9lMss7e3V2pXbKunvvqmbs5dkUqluHHjBuzs7DQZosYNNOcdO3YgODgYDg4O2gpR49TN+e7du3jw4AEsLCywfPly8Pl8uLi44K9//SsaGxu1HfaAqJvz8OHDERgYiMzMTJSWluLx48coKSnB559/jrCwMG2HrXOa3n/RnEonYrEYHA5HpZ3D4fT4H0ix7Om+o0ePVlo+GKmbc1f+8Y9/oLm5GeHh4ZoKTysGkvO//vUvVFRUIDU1VVvhaYW6OT98+BAA8N5778Hb2xv79+9HXV0dkpKSUF9fj4yMDK3FPFADeZ937NiBrVu34vXXX2fbIiIisGrVKo3HqW+a3n9RUSEacfr0aRw8eBBbtmzBpEmT9B2OVkgkEvz973/HunXrutxZPY/kcjkAYNKkSdizZw8MDJ7coGnUqFFYs2YNysrK4OTkpM8QtSIpKQkXL15EYmIibG1t8eOPPyItLQ2WlpaIjo7Wd3iDGg1/dcLhcCAWi1XaxWIxW7W7olj2dF9Fhe+pr76pm3NnV65cwebNmxEVFfVMDA+om3N6ejrGjBmDV199FWKxGGKxGG1tbZDL5RCLxYP6cNOBfrZnz57NFhTF38CTo40GK3VzrqiowCeffILt27dj0aJFEAgEiI6OxrJly7B3715IJBJthq1zmt5/UVHpxN7eHpWVlUptMpkMNTU1Pc4TKJY9Pfao2NZgnmNQN2eFsrIyrFq1Cn5+ftiwYYO2wtQodXOuqqpCRUUFXF1dIRAIIBAIkJubi8rKSggEAhw9elTboatN3ZwnTpwIY2Pjbpe3trZqLEZNUzfnn376CQAwbdo0pXZHR0fIZDL8+uuvmg9WjzS9/6Ki0smcOXNQUlKChoYGtq2wsBAymazHY7UnTpwIOzs7lZOFcnNzwefzlY4+GWzUzRl48qGLjo7Gb3/7W+zatUvpm+xgpm7Oa9euxaFDh5QeHh4esLa2xqFDh+Dr66uL8NWibs7GxsZwd3dHcXGx0pFPV65cAQDweDztBT1A6uZsbW0NACon8v7nP/+BgYEBrKystBOwnmh8/9Xvg5CfY42NjYynpycTEhLCFBUVMSdPnmRcXV2ZtWvXKq23efNmZtq0aUpteXl5DJfLZT744AOmpKSE2blzJ8PlcpkLFy7oMoV+Uzfnhw8fMnPnzmXc3d2Z4uJi5ocffmAf169f13Ua/TKQ9/lpz8p5KgPJuby8nJk+fTqzdu1apqioiMnKymJmzZrFLF26VJcp9Ju6Obe3tzPBwcHM7NmzmWPHjjHFxcVMeno64+TkxMTHx+s6jX6RSqVMfn4+k5+fz4SHhzNz585l/7579y7DMNrff9FEfSccDgcHDx5EYmIiVq9eDRMTEwiFQpVhHblcjo6ODqU2Pz8/tLS0ID09HRkZGbCxsUFSUtKgPpseUD/nn376Cffu3QPw5KiYzqytrXHu3Dmtx66ugbzPz6qB5Mzj8fDPf/4TSUlJWLFiBUaOHAl/f3+sX79elyn0m7o5GxkZIT09HSkpKfjoo4/w8OFDvPjii1i6dCmWLVum6zT6pb6+HmvWrFFqU/y9e/duLFiwQOv7LwOGGeRn8xBCCHlm0JwKIYQQjaGiQgghRGOoqBBCCNEYKiqEEEI0hooKIYQQjaGiQgghRGOoqJBnzt27d8HlcnHixAl9h6J1qamp4HK5fVp3KL0uZPCiokJ05sSJE+ByuV0+duzYoe/wnhlHjhx5LgvH6dOn8emnn+o7DDJAdEY90bnVq1dj4sSJSm2TJ0/WUzSD25///GfExMQotR07dgwWFhZYsGCBUru1tTXKysowbNiz+d86NzcXt2/fVrlCA3m2PJufPvJM8/DwwIwZM/QdxjNh2LBhfS4SBgYGMDEx0XJEfcMwDFpbWzFixAh9h0J0jIa/yKBRV1eH7du3w8/PD87OznBxccGyZctw69atXvs+fvwY7777Lry9vcHj8eDm5obFixfju+++U1qvrKwM0dHR+N3vfgcnJye8+eabKCkp6XX7ivmKjz76CJmZmfD29oaTkxNCQkJQVlamsv7NmzfZKzjPmDEDixcvxrVr15TWaW9vx4cffggfHx84OTlh1qxZWLRoEb766it2nafnVLy9vXH79m1cvXqVHTr09vZWilExNFZQUAAul4tvvvlGJb5Tp06By+Xi22+/Zduqq6uxZs0auLq6gs/nIygoCAUFBb2+NgDA5XKxZcsW5OXlYf78+eDz+exVb0+cOIGIiAi4u7uDx+Nh3rx52L9/P3sDMABYvHgxLly4gLq6OqVhUQWGYZCZmclue/bs2YiPj+/3/dOJ9tEvFaJzTU1NKjuDsWPHory8HNeuXYOPjw+srKxw//59ZGVlYfHixcjNzcX48eO73ea2bduQn5+PsLAwTJkyBWKxGKWlpbh58yYEAgEA4OrVq4iKisK0adOwcuVKDBs2DKdOnUJUVBQ++eQTuLq69hr76dOnIRaLERoaCrlcjiNHjiAiIgInT55k73hZWVmJ0NBQmJmZISoqCiYmJvjiiy8QERGBAwcOsPHs27cP6enpCA4OhpOTE6RSKW7cuIGysjLMmzevy+ePj49HQkICzMzMsHz5cgCAubl5l+u+8sorMDc3R15eHntTLYX8/HyMHz+ejaWyshIhISGwtLREVFQUzM3NUVhYiDVr1uC9995DYGBgr6/NtWvXcObMGYSHh8PS0pK9D8eRI0dgb2+PuXPnwtjYGCUlJfjggw/Q1NTEXpRy+fLlaGpqwi+//ILNmzerbHvr1q3Izs5GUFAQwsLCcO/ePRw+fBjl5eU4fvz4oPmFRkCXvie6k52dzTg4OHT5kEgkTHNzs0qfmpoahsfjMWlpaWxbbW0t4+DgwGRnZ7NtLi4uzPbt27t9brlczvj4+DBvvfUWI5fL2fbW1lbG39+feeONN3qMXfGcPB6Pqa2tZdurqqoYR0dHJi4ujm1buXIlM336dKa6upptq6+vZ2bNmsX88Y9/ZNsCAwOZmJiYHp937969jIODg1KbUChkwsPDu42x8+sSFxfHzJo1i2lra2PbGhsbmenTpzOJiYlsW2RkJOPv76/yHkRGRjKenp5Kr1lXHBwcGC6Xy/z3v/9VWSaVSlXa3nnnHWbGjBlMa2sr2xYTE8N4eXmprPvvf/+bcXBwYE6ePKnU/t133zEODg5MVlZWj7ER3aLhL6Jz77zzDg4cOKD0GDFihNL4e3NzMxoaGmBubo7Jkyer3DDpaaNGjUJpaWm3d+W7efMmqqurERAQgIaGBohEIohEIkgkErz88ssoLS1Fc3Nzr7F7eXlhwoQJ7N+TJ0+Gh4cHLl68CADo6OjA5cuX4eXlBVtbW3a9sWPHYsGCBbh+/ToePnzIxnz79m1UV1f3+rzqEgqFePToEYqLi9m2wsJCtLW1QSgUAgC73M/PD1KplH1tRCIRPD098euvv/YpxpkzZ6rcLREATE1NATx5bRobGyESiSAQCCCVSlXuNtiV/Px8mJmZwdPTUyk2Ozs7WFpaKg3hEf2j4S+ic3w+v8uJ+tbWVqSkpODLL7/EgwcPlJaNGTOmx21u2LABmzZtwiuvvIJp06bB09MTgYGB7BCMYqf49ttvd7uNR48esTvA7nQuFJ3bLly4ALFYjNbWVjQ3N3d5NJsilrq6OlhaWiI2NhYrV66Er68vpkyZAg8PDwQEBIDP5/cYQ394eHhg9OjRyMvLw5w5cwAAeXl5sLa2Zt+DmpoaMAyD1NRUpKamdrkdxU68JzY2Nl22X7t2DcnJySgtLUVbW5vSsqampl5zuHPnDqRSKV5++eUul9fX1/e6DaI7VFTIoJGQkIDs7GyEh4dj5syZ4HA4MDQ0xK5du5RuZdsVPz8/uLi44Ouvv8aVK1eQmZmJjIwM7N69G/Pnz2f7x8XFdXsLXF3f9lkgEKCwsBDnz5/H5cuXkZOTg4MHDyIuLg7R0dEaeY7hw4fj1VdfxZkzZyCTySCRSFBSUoLIyEh2HcWEeURERLc3ZZo6dWqvz9XVvEZtbS0iIyNha2uLzZs3w8rKCiYmJrh+/Tr27NmjNFnfHblcjjFjxiA5ObnL5RwOp9dtEN2hokIGjYKCAgQFBan8mmhsbISFhUWv/ceNG4eQkBCEhIRALBbj9ddfR2pqKubPn8+eF2Nubt7tN96+uHPnTpdtHA4HHA4HHR0dMDU17XK4SDHUo7gHOgCMHj0aQUFBCAoKQktLC6Kjo5GamoqlS5fCyMioyxgMDAz6FbNQKMTx48dx6dIlPHjwAO3t7ezQFwD2tTEyMhrQa9OVr7/+GjKZDOnp6Up53717V2Xd7vKysbFBcXExnJ2duz0ogQweNKdCBg0jIyOVXyS5ubm4f/9+j/06OjpUhlE4HA4mTJgAsVgM4MktcSdNmoRPP/0UEolEZRt9PTT1/PnzSjvE6upqXL58mR1aMjIygqenJ86fP4+amhp2vUePHiEnJwc8Hg+WlpYAgIaGBqVtjxgxAnZ2dmhtbUVLS0u3MZiamqKxsbFP8QKAq6srLC0tkZeXh7y8PNjZ2SnNfbzwwgtwdXXFF1980eWc1EAO21UUxs7vq0wmw+HDh1XWNTU1hVgsVvkM+Pv7Qy6XIy0tTaWPYp6GDB70S4UMGt7e3sjJycHIkSMxdepU3LhxA/n5+Spn3z/t8ePHmDNnDubNm4eXXnoJI0eOxPfff49Lly4hPDwcAGBoaIidO3fiT3/6E4RCIRYuXIjf/OY3uH//Pq5evcqeB9EbW1tbhIWFISwsDHK5HIcPH4axsTFWrVrFrrN27VpcvnwZoaGhCA0NhYmJCT7//HM0NTVh06ZN7Hr+/v4QCATg8XiwsLDArVu3cPz4cXh5efX4jZzH4+Ho0aPYt28fbG1tYWZmxp6r0hUjIyP4+PjgxIkTaG1txYoVK1TW2bZtG95880289tprWLRoEWxsbFBfX4/S0lJUVlaisLCw19emKx4eHhg+fDiWL1+ON954AzKZDKdOnYKhoer3WR6Ph7y8POzcuRPOzs4wNDSEUCiEQCBAWFgYMjIycOvWLXh6emL48OGoqanBmTNnEBsbq3J1AaI/VFTIoPH2229j2LBhyMvLg1QqBY/Hw8cff4z333+/x34jRoxAaGgoiouLce7cObS3t2PChAnYuHEjlixZwq4nEAjw2Wef4cMPP8TRo0chkUgwbtw48Pl8BAcH9ynG+fPnw9TUFAcOHMCDBw/g6OiI+Ph4pYl5e3t7HDt2DElJSfj444/BMAx4PB4SExPZ80IA4K233sK5c+dQUlKClpYWvPjii4iOju51PmXlypW4d+8eDhw4AIlEAmtr6x6LCvCkgB05coT999Ps7OyQnZ2NtLQ05OTk4NGjR7CwsMBLL72ENWvW9Om16crkyZORlpaG5ORkvP/++7CwsEBQUBBmzZqFpUuXKq0bGhqKiooKnD59GocPHwbDMOww3ZYtW+Do6IisrCwkJyfDyMgIVlZW8PPzg5ubm9rxEc0zYHqbASWE4O7du/j973+PuLg4lWtxEUL+P5pTIYQQojFUVAghhGgMFRVCCCEaQ3MqhBBCNIZ+qRBCCNEYKiqEEEI0hooKIYQQjaGiQgghRGOoqBBCCNEYKiqEEEI05v8ABMO8s/QsL7AAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "id": "qIs1xEOaJBh7",
        "outputId": "63b9ca74-edd4-49f2-ec81-8d1325f2f3e6"
      },
      "source": [
        "# InceptionV3\n",
        "createROCPlot(model_retrain_inception, test_data_128)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "id": "p3LjCA-xJnop",
        "outputId": "58f08739-674f-467d-fd01-3f2ccf77d8fa"
      },
      "source": [
        "# ResNet\n",
        "createROCPlot(model_retrain_resnet, test_data_128)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "id": "Or_HRCl_Jsrb",
        "outputId": "3cdad7c6-714b-495f-e868-57430d849aaa"
      },
      "source": [
        "# noise\n",
        "createROCPlot(model_retrain_noise, test_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "id": "DbcwCXFPJwTn",
        "outputId": "2e571661-5271-4f8b-ed48-a63b980271a0"
      },
      "source": [
        "# blur\n",
        "createROCPlot(model_retrain_blur, test_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N_e6-83ZKpK8"
      },
      "source": [
        "# Plot of Model Training Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UEWqUu3TPgGV"
      },
      "source": [
        "# function that plots the val loss and val accuracy\n",
        "def summarizeLossAccuracy(history):\n",
        "  # list the names of the keys\n",
        "  print(history.keys())\n",
        "\n",
        "  # plot the accuracy and val accuracy\n",
        "  plt.plot(history['accuracy'])\n",
        "  plt.plot(history['val_accuracy'])\n",
        "  plt.title('model accuracy')\n",
        "  plt.ylabel('accuracy')\n",
        "  plt.xlabel('epoch')\n",
        "  plt.legend(['train', 'test'], loc='upper left')\n",
        "  plt.show()\n",
        "\n",
        "  # summarize history for loss\n",
        "  plt.plot(history['loss'])\n",
        "  plt.plot(history['val_loss'])\n",
        "  plt.title('model loss')\n",
        "  plt.ylabel('loss')\n",
        "  plt.xlabel('epoch')\n",
        "  plt.legend(['train', 'test'], loc='upper left')\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 588
        },
        "id": "Kd4qypCBQJTc",
        "outputId": "ddee6912-2adb-4da7-cdd9-edf58bd9d1cb"
      },
      "source": [
        "# summarize val loss and accuracy for InceptionV3\n",
        "summarizeLossAccuracy(history_inception)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy', 'lr'])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZKKYWhIhETwGRjWLDoGkDdLSnuxIRyWEKiGxUWRvMTb0rhwYhFJGMo4DIRp23uqofQkQipIDIRl3PQqgfQkSio4DIRl0BoRaEiERHAZGNKoaAFagFISKRUkBko4JCqBiq8ZhEJFIKiGwVq9VwGyISKQVEtlJAiEjEFBDZqlIBISLRUkBkq9gw2LMNWvekuxIRyVGRBoSZTTOzpWa2wsyuT7D9djNbGP4sM7PGuG2jzOxJM1tiZovNbEyUtWadWOfMcrqTSUSiURTVic2sELgL+BDQAMwzs5nuvrhzH3e/Nm7/LwEnxJ3il8C/uvtsM6sAOqKqNSvFz009uC69tYhIToqyBTEFWOHuq9x9L/AAcEkv+18O3A9gZpOAInefDeDuTe6+O8Jas4/mphaRiEUZECOANXHLDeG6/ZjZaKAOeCZcNR5oNLOHzew1M7s1bJF0P+5qM5tvZvM3bcqzges0N7WIRCxTOqmnAw+6e+f41UXAGcDXgJOBI4Gruh/k7ve6e72719fU1PRVrZmhbCAU9VMLQkQiE2VArAWOiFseGa5LZDrh5aVQA7AwvDzVBvweODGSKrOVWdCKUECISESiDIh5wDgzqzOzEoIQmNl9JzM7GhgEvNTt2IFm1tks+CCwuPuxeU9zU4tIhCILiPCb/xeBJ4AlwG/dfZGZ3WxmF8ftOh14wN097th2gstLT5vZG4AB/xVVrVlLc1OLSIQiu80VwN1nAbO6rbuh2/JNPRw7GzgusuJyQaw2aEG4B5ecRERSKFM6qeVQxGqhbQ80Nx54XxGRg6SAyGaVmllORKKjgMhmmptaRCKkgMhmmptaRCKkgMhmXeMxqQUhIqmngMhmxf2g3yC1IEQkEgqIbBer1XhMIhIJBUS209SjIhIRBUS2U0CISEQUENmushaaNkBH+4H3FRE5CAqIbBcbBt4BTRvTXYmI5BgFRLbrmptat7qKSGopILJd17MQutVVRFJLAZHtOuem1nAbIpJiCohs178GrFAtCBFJOQVEtisohIqhutVVRFJOAZELNDe1iERAAZELKodruA0RSTkFRC5QC0JEIpBUQJjZw2Z2gZkpUDJRrDaYdrR1T7orEZEckuwH/o+BTwDLzewWM5sQYU1ysLomDlIrQkRSJ6mAcPen3P2TwInAauApM/uzmf2DmRVHWaAkoXNuavVDiEgKJX3JyMyqgKuAzwCvAT8kCIzZkVQmyVMLQkQiUJTMTmb2CDAB+BVwkbt3fhL9xszmR1WcJEkBISIRSCoggDvc/dlEG9y9PoX1yKEoGwBF/fQ0tYikVLKXmCaZ2cDOBTMbZGb/HFFN0k1LWzubdrb0vINZ0A+h8ZhEJIWSDYh/cvfGzgV33wb8UzQlSae29g5+M+9dzr51Dmd+/1nWbN3d886xWrUgRCSlkg2IQjOzzgUzKwRKDnSQmU0zs6VmtsLMrk+w/XYzWxj+LDOzxm7bK82swcx+lGSdOaGjw3nsr+v48O3P8fWH3qAmVooZfO+xxT0fFKvVnBAiklLJ9kE8TtAh/ZNw+bPhuh6FIXIX8CGgAZhnZjPdvetTzt2vjdv/S8AJ3U7zPeC5JGvMeu7OnKWbuPWJpSxev4PxQyu498qT+NCkodw9dyXff3wpc5Zu5KwJQ/Y/ODYsaEG4B5ecREQOU7IB8XWCUPh8uDwb+OkBjpkCrHD3VQBm9gBwCdDT1+DLgRs7F8zsJGAoQRDlfEf4K29v5dYn3mLe6m2MGlzO7R8/nouPH0FhQfBh/48fqON38xu4+Q+LOW1sNSVF3Rp/lcOhrRn2bIPywWn4DUQk1yQVEO7eAdwd/iRrBLAmbrkBOCXRjmY2GqgDngmXC4D/BK4Azu3pDczsauBqgFGjRh1EaZnjzbXbufWJpcxdtokhsVL+5dJjuaz+iP0CoLSokBsvmsRVP5vHjBff5nNTx+57oviZ5RQQIpICyT4HMQ74d2ASUNa53t2PTFEd04EH3b09XP5nYJa7N1gvl0vc/V7gXoD6+npPUS19YsXGndw2exmz3niPgeXFfPP8o7ny/WPoV1LY4zFnTRjCuROHcsfTy7l08giGDSj728b4uamHToq4ehHJB8leYvoZweWf24GzgX/gwB3ca4Ej4pZHhusSmQ58IW75VOCM8FbaCqDEzJrcfb+O7mzTsG03P3hqOQ8vaKBfcSFfPmccnzmjjsqy5EYsueHCSZx7+1z+bdYS7rg8rstGc1OLSIolGxD93P1pMzN3fwe4ycxeBW7o5Zh5wDgzqyMIhukEA/7tw8yOBgYBL3WuC8d96tx+FVCf7eGwaWcLdz27gl//5R3MjE+fXsfnzxpLVUXpQZ1nVFU5n5s6ljueXs4nTxnFKUdWBRtiGo9JRFIr2YBoCfsFlpvZFwk+8Ct6O8Dd28J9nwAKgRnuvsjMbgbmu/vMcNfpwAPunlWXiJK1fXcr9z6/khkvrGZveweX1R/Bl885itoB/Q75nJ+fOpaHXm3gxpmLeOxLH6CosACKy6DfIA23ISIpY8l8LpvZycASYCDBraeVwK3u/nK05SWvvr7e58/PnGGhdu9t42cvruYnc1eys6WNi48fzlfOHU9ddf+UnP/xN9fzufsWcNNFk7jq9Lpg5Y9Pg0Gj4fL7U/IeIpL7zOzVnoZMOmALInye4ePu/jWgiaD/QXrQ1t7B/a+8yw+fXsHmphbOnTiEr354AhNrK1P6Ph85ZhhnjKvmttnLuPD44VRXlGpmORFJqQM+SR3eWfSBPqgl672wfDPn3/E833l0EWNr+vPQ50/jp586OeXhAGBm3HjRMeze286tjy8NVlbWqg9CRFIm2T6I18xsJvA7YFfnSnd/OJKqsszqzbv4lz8u4aklGxg1uLzr6efebtFNhaOGVPDpD9Rx73OruPyUUUyO1cKujdDeBoXJ/tWKiCSW7KdIGbAF+GDcOgfyOiB2Nrfyo2dXMOOFtykpLODr047m0x8YQ2lRz88ypNqXPngUv39tLTc++iaPTKmlwDuCkKgc3mc1iEhuSvZJavU7xOnocB58tYHvP7GUzU0t/K+TRnLdRyYwpLLswAenWKysmG+cfzTX/uZ1XhxTxBkQ9EMoIETkMCX7JPXPCFoM+3D3T6e8ogw3b/VWvvuHRby5dgcnjR7EjKvqOW7kwAMfGKFLJ4/gf/7yLvcsWBMExI71wUAnIiKHIdlLTI/FvS4DPgrk1djSaxv3cMuf3uIPr6+jdkAZP5w+mYuPHx55P0MyzIybLj6GT9+5CkrRnUwikhLJXmJ6KH7ZzO4HXoikogyzZ28798xdyU+eW4k7XHPOOD479UjKSzKrE/iY4QP4yJT30bawgMb1q6lOd0EikvUO9VNuHJBgUoLc4e7MfH0dt/zpLdZvb+bC42q5/ryjGTmoPN2l9ej/fGQiW14fyOK3lnLWxZ4RrRsRyV7J9kHsZN8+iPcI5ojISX9taOTmPyxm/jvbOGZ4JT+cfgJT6jJ/CO2B5SVsHjCCwq3vMfP1dVwyWR0RInLokr3EFIu6kEywcWcztz6+lAcXNFDVv4T/97H38fcnHdE1aU82qBo2itFNb3LdrCWcM3EoFaWZdSlMRLJHUnNSm9lHzWxA3PJAM7s0urL6VktbO3fPWcnZt87h9wvXcvUZR/Ls187i4yePyqpwALDK4Ywo3MaGHS3c+czydJcjIlks2a+XN7r7I50L7t5oZjcCv4+mrL7z7pbdXDnjL7yzZTfnThzKty6YmLIB9dIiNoyivTu4/IRqZrzwNpfVH8HYml4H3hURSSipFkQP++XEtYvhA8uYVFvJLz89hZ9+qj67wwG6HpD72qmVlBUVctPMReToSOoiErFkA2K+md1mZmPDn9uAV6MsrK8UFRZw9xUnceb4mnSXkhrhzHJVHVu59kPjeX75Zp5cvCHNRYlINko2IL4E7AV+AzwANLPvFKGSKbrmpl7PlaeOZvzQCr732GKaW9t7P05EpJukAsLdd7n79e5e7+4nu/s33X3XgY+UPtc1N/V6igsLuOniY2jYtod75q5Mb10iknWSvYtptpkNjFseZGZPRFeWHLKyAVBc3jUvxGljq7nwuFrunrOSNVt3p7k4EckmyV5iqnb3xs4Fd99Gjj9JnbXM9ptZ7lsXTKTAjO89tjiNhYlItkk2IDrMbFTngpmNIcHorpIhYsP3CYjaAf344geP4snFG5i7bFMaCxORbJJsQHwLeMHMfmVm9wFzgW9EV5YclgRzU3/mjDrGVJXz3ZmL2NvWkabCRCSbJNtJ/ThQDywF7ge+CuyJsC45HJ1zU8c9/1BaVMiNFx3Dqs27mPHi22ksTkSyRbKd1J8BniYIhq8BvwJuiq4sOSyxWmhvgT3b9ll99tFDOHfiEO58ejnvbW9OU3Eiki2SvcR0DXAy8I67nw2cADT2foikTaw2+G+CiYO+c+EkWjucf//Tkj4uSkSyTbIB0ezuzQBmVurubwEToitLDksvATG6qj+fPfNIHl24jr82KONFpGfJBkRD+BzE74HZZvYo8E50ZclhqQwDYkfiqUc/c8aRFBYYTy7SEBwi0rNkO6k/6u6N7n4T8B3gv4GcGe4751R0Pk39XsLNA/oVc+KogbrlVUR6lWwLoou7z3X3me6+N4qCJAWKy6DfYNi5rsddpo6v4Y2129nc1NKHhYlINjnogDgYZjbNzJaa2Qozuz7B9tvNbGH4s8zMGsP1k83sJTNbZGZ/NbOPR1lnTorV9tiCAJg6PngQ/vnlakWISGKRBYSZFQJ3AecBk4DLzWxS/D7ufq27T3b3ycCdwMPhpt3A/3b3Y4BpwA/ix4KSJFTWwo6eWxDHDK+kqn8Jc5cqIEQksShbEFOAFe6+Krwc9QBwSS/7X07wEB7uvszdl4ev1wEbgRyZsKGPxIb12oIoKDDOHF/Dc8s309GhUVNEZH9RBsQIYE3cckO4bj9mNhqoA55JsG0KUALsN161mV1tZvPNbP6mTfomvI/YcNi1Edrbetxl6vgatu7ay5vrtvdhYSKSLSLtgzgI04EH3X2fWW3MrJbgqe1/cPf9BhBy93vDOSrqa2rUwNhHbBh4RxASPThjXDVm6DKTiCQUZUCsBY6IWx4ZrktkOuHlpU5mVgn8EfiWu78cSYW5LJybuqdnIQCqKkp534gBut1VRBKKMiDmAePMrM7MSghCYGb3nczsaGAQ8FLcuhLgEeCX7v5ghDXmrriZ5XozdXwNC97dxvbdrX1QlIhkk8gCwt3bgC8CTwBLgN+6+yIzu9nMLo7bdTrwgLvH95ReBpwJXBV3G+zkqGrNSXFzU/dm6vgaOhxeXLm5D4oSkWxSFOXJ3X0WMKvbuhu6Ld+U4Lj7gPuirC3n9a8GK+z1VleAyUcMJFZWxNylmzj/fbV9VJyIZINM6aSWVCsoPOCtrgBFhQWcMa6aucs2sW8jTkTynQIil8WG9TrcRqep42t4b0czyzY09UFRIpItFBC57ADDbXQ6c3xwi/DcZT3fEisi+UcBkctitb3e5tqpdkA/JgyN6XZXEdmHAiKXVdZCy3bYu+uAu06dUMO8t7exq6XnJ69FJL8oIHJZ18xyB77MNHV8DXvbO3h51ZaIixKRbKGAyGW9TD3aXf2YQfQrLtRlJhHpooDIZbHepx6NV1pUyGljqxQQItJFAZHLKpNvQUDQD/HOlt2s3nzgPgsRyX0KiFxWWgnF5ckHRNftrmpFiIgCIreZhc9CJBcQo6v6M6aqXAEhIoACIvcl+SxEp6nja/jzys00t7YfeGcRyWkKiFxXmXwLAuCsCUNobu1g3uqtERYlItlAAZHrOgfsS3IgvlOOHExJUYFmmRMRBUTOiw2H9hbYsy2p3ctLijilbrD6IUREAZHzOmeWO8C8EPGmjq9h+cYm1jbuiagoEckGCohc1zk3dRLDbXTqvN31ObUiRPKaAiLXdQ23kXwL4qghFQwfUKZ+CJE8p4DIdZ2XmA6iBWFmTJ1Qw4srNtPa3hFRYSKS6RQQua6oFMqrDqoPAoLLTDtb2njt3caIChORTKeAyAdJziwX77SjqiksMM0yJ5LHFBD5IFZ7UH0QAJVlxZw0apBudxXJYwqIfND5sNxBmjqhhjfX7mDTzpYIirm4pl4AAA0cSURBVBKRTKeAyAeVw6FpI7S3HtRhnbe7Pr9crQiRfKSAyAexYYAHIXEQJtVWUl1RostMInlKAZEPYp0PyyU/aB9AQYFx5rganlu2ifaO5MZyEpHcoYDIB13PQhxcQEDQD7Ftdytvrt2e4qJEJNNFGhBmNs3MlprZCjO7PsH2281sYfizzMwa47Z9ysyWhz+firLOnNc53MZBzAvR6QNHVWOmWeZE8lFkAWFmhcBdwHnAJOByM5sUv4+7X+vuk919MnAn8HB47GDgRuAUYApwo5kNiqrWnFdeDQVFh9SCqKoo5bgRAxQQInkoyhbEFGCFu69y973AA8Alvex/OXB/+PojwGx33+ru24DZwLQIa81tBQVQMeyQAgKCu5lee3cb23cf3F1QIpLdogyIEcCauOWGcN1+zGw0UAc8czDHmtnVZjbfzOZv2qRvuL2KHUZATKihw+GFFZtTXJSIZLJM6aSeDjzo7gc1EbK73+vu9e5eX1NTE1FpOaLy4Oamjnf8yIFUlhVp2A2RPBNlQKwFjohbHhmuS2Q6f7u8dLDHSjIOYTymTkWFBZwxroa5yzbhSU5dKiLZL8qAmAeMM7M6MyshCIGZ3Xcys6OBQcBLcaufAD5sZoPCzukPh+vkUMVqoWU77N11SIdPHV/Dhh0tLN2wM8WFiUimiiwg3L0N+CLBB/sS4LfuvsjMbjazi+N2nQ484HFfTd19K/A9gpCZB9wcrpND1TVx0KG1Is4Mh93QJEIi+aMoypO7+yxgVrd1N3RbvqmHY2cAMyIrLt9UhgGxYx1UjT3ow4cNKOPoYTHmLtvEZ6ce/PEikn0ypZNaonaYLQgILjPNW72VXS1tKSpKRDKZAiJfHMLc1N1NHV9Da7vz0sotKSpKRDKZAiJflMaguP9htSBOGjOI8pJCPVUtkicUEPnCLHwW4tBbEKVFhZw2too5yzbqdleRPKCAyCeH8SxEp6nja1izdQ+rt+xOUVEikqkUEPnkEOam7m7q+CEAzF2qp6pFcp0CIp90zk19GJeHRlWVU1fdnznqhxDJeQqIfFI5HNr3wu7De+Zw6vgaXl61hebWgxo6S0SyjAIinxzGzHLxpk6oobm1g1fe1sPtIrlMAZFPDnFu6u7eX1dFSVGBbncVyXEKiHySohZEv5JCTqkbrIAQyXEKiHzSGRCHOC9EvKnja1ixsYmGbbrdVSRXKSDySVEplFcddgsC4KwJweiuzy3TLHMiuUoBkW9iw1MSEGNrKhgxsJ9mmRPJYQqIfHMYc1PHMzPOHF/Diyu20NrekYLCRCTTKCDyzWHMTd3d1PE1NLW0seCdbSk5n4hkFgVEvonVwq5N0N562Kc67agqigpMdzOJ5CgFRL6J1QIOTRsO+1SVZcWcOHqQAkIkRykg8k0KZpaLN3V8DYvW7WDjzuaUnE9EMocCIt/Ez02dAlPHB7e7Pq/bXUVyjgIi36S4BTGptpLqilJdZhLJQQqIfFNeDQVFhz0vRKeCAuPM8dU8v3wT7R2aZU4klygg8k1BAVQMS9mtrhBcZtq2u5U31m5P2TlFJP2K0l2ApEFlbUoelut0xrgazODa3yxk3JAKamKlf/upKGVIZRk1sVKqK0ooLSpM2fuKSLQUEPkoNgw2LU3Z6Qb3L+FrH57Ay6u28M6W3cx/Zxtbd+1NuO+AfsVdwVETK2VIfJjEhcqg8hIKCixlNYrIwVNA5KPYcFg1N6Wn/MLZR/GFs4/qWm5t72BL01427WxhU1MzG3e0hK/D/+5s4fWGRjbuaGFPgpnpigqMirIiFBEiB3bsiAH86h9PSfl5FRD5KDYMWnZASxOUVkTyFsWFBQwbUMawAWXAgF73bWpp6wqN4KeZTU0t7Gxui6Q2kVwzclC/SM4baUCY2TTgh0Ah8FN3vyXBPpcBNwEOvO7unwjXfx+4gKAjfTZwjbvrNplUqOycWe49KD2q9337QEVpERWlRdRV9093KSISJ7KAMLNC4C7gQ0ADMM/MZrr74rh9xgHfAE53921mNiRcfxpwOnBcuOsLwFRgTlT15pXOiYPu+zsojuabh4j0oaHHwN/PSPlpo2xBTAFWuPsqADN7ALgEWBy3zz8Bd7n7NgB375xcwIEyoAQwoBg4/MGDJDDyZDjhCmjZme5KRCQVBo6O5LRRBsQIYE3ccgPQvRdlPICZvUhwGeomd3/c3V8ys2eB9QQB8SN3X9L9DczsauBqgFGjRqX+N8hVJf3hkrvSXYWIZLh0PyhXBIwDzgIuB/7LzAaa2VHARGAkQdB80MzO6H6wu9/r7vXuXl9TU9OHZYuI5L4oA2ItcETc8shwXbwGYKa7t7r728AygsD4KPCyuze5exPwJ+DUCGsVEZFuogyIecA4M6szsxJgOjCz2z6/J2g9YGbVBJecVgHvAlPNrMjMigk6qPe7xCQiItGJLCDcvQ34IvAEwYf7b919kZndbGYXh7s9AWwxs8XAs8B17r4FeBBYCbwBvE5w++sfoqpVRET2Z7nyaEF9fb3Pnz8/3WWIiGQVM3vV3esTbUt3J7WIiGQoBYSIiCSkgBARkYRypg/CzDYB7xzGKaqBTJ5YOdPrg8yvMdPrA9WYCpleH2RWjaPdPeGDZDkTEIfLzOb31FGTCTK9Psj8GjO9PlCNqZDp9UF21Ai6xCQiIj1QQIiISEIKiL+5N90FHECm1weZX2Om1weqMRUyvT7IjhrVByEiIompBSEiIgkpIEREJKG8Dwgzm2ZmS81shZldn+56ujOzI8zsWTNbbGaLzOyadNeUiJkVmtlrZvZYumtJJJxn5EEze8vMlphZRg0fb2bXhn+/b5rZ/WZWlgE1zTCzjWb2Zty6wWY228yWh/8dlIE13hr+Pf/VzB4xs4GZVmPctq+amYejWWecvA6IuHmzzwMmAZeb2aT0VrWfNuCr7j4JeD/whQysEeAaMntI9h8Cj7v70cDxZFCtZjYC+DJQ7+7HEsyuOD29VQHwc2Bat3XXA0+7+zjg6XA5nX7O/jXOBo519+MI5pj5Rl8X1c3P2b9GzOwI4MME0xtkpLwOCOLmzXb3vUDnvNkZw93Xu/uC8PVOgg+2Eemtal9mNhK4APhpumtJxMwGAGcC/w3g7nvdvTG9Ve2nCOhnZkVAObAuzfXg7s8BW7utvgT4Rfj6F8ClfVpUN4lqdPcnw+kGAF4mmKwsbXr4cwS4Hfi/QMbeKZTvAZFo3uyM+vCNZ2ZjgBOAv6S3kv38gOB/9I50F9KDOmAT8LPwMthPzax/uovq5O5rgf8g+Ca5Htju7k+mt6oeDXX39eHr94Ch6SwmCZ8mmJEyo5jZJcBad3893bX0Jt8DImuYWQXwEPAVd9+R7no6mdmFwEZ3fzXdtfSiCDgRuNvdTwB2kf5LI13C6/iXEATZcKC/mV2R3qoOzIN75DP226+ZfYvgEu2v011LPDMrB74J3JDuWg4k3wMimXmz0y6cdvUh4Nfu/nC66+nmdOBiM1tNcInug2Z2X3pL2k8D0ODunS2vBwkCI1OcC7zt7pvcvRV4GDgtzTX1ZIOZ1QKE/92Y5noSMrOrgAuBT3rmPew1luDLwOvhv5uRwAIzG5bWqhLI94BIZt7stDIzI7h2vsTdb0t3Pd25+zfcfaS7jyH483vG3TPq26+7vwesMbMJ4apzgMVpLKm7d4H3m1l5+Pd9DhnUid7NTOBT4etPAY+msZaEzGwawSXPi919d7rr6c7d33D3Ie4+Jvx30wCcGP5/mlHyOiB6mjc7vVXt53TgSoJv5gvDn/PTXVQW+hLwazP7KzAZ+Lc019MlbNk8CCwgmIe9gAwYisHM7gdeAiaYWYOZ/SNwC/AhM1tO0PK5JQNr/BEQA2aH/17uycAas4KG2hARkYTyugUhIiI9U0CIiEhCCggREUlIASEiIgkpIEREJCEFhEgGMLOzMnUkXMlfCggREUlIASFyEMzsCjN7JXwA6yfhPBhNZnZ7OJ/D02ZWE+472cxejpuXYFC4/igze8rMXjezBWY2Njx9RdycFb8On6oWSRsFhEiSzGwi8HHgdHefDLQDnwT6A/Pd/RhgLnBjeMgvga+H8xK8Ebf+18Bd7n48wZhLnaOjngB8hWBukiMJnqIXSZuidBcgkkXOAU4C5oVf7vsRDFbXAfwm3Oc+4OFwDoqB7j43XP8L4HdmFgNGuPsjAO7eDBCe7xV3bwiXFwJjgBei/7VEElNAiCTPgF+4+z4zlJnZd7rtd6jj17TEvW5H/z4lzXSJSSR5TwN/b2ZDoGt+5tEE/47+PtznE8AL7r4d2GZmZ4TrrwTmhrMCNpjZpeE5SsP5AUQyjr6hiCTJ3Reb2beBJ82sAGgFvkAwAdGUcNtGgn4KCIbDvicMgFXAP4TrrwR+YmY3h+f4X334a4gkTaO5ihwmM2ty94p01yGSarrEJCIiCakFISIiCakFISIiCSkgREQkIQWEiIgkpIAQEZGEFBAiIpLQ/wf6WYCKw4EPoAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 588
        },
        "id": "MusUFDbmQRhc",
        "outputId": "8d7afa38-4f13-4764-ecc5-ebc266127a84"
      },
      "source": [
        "# summarize val loss and accuracy for ResNet\n",
        "summarizeLossAccuracy(history_resnet)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy', 'lr'])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 588
        },
        "id": "E3vTEWLYQYzT",
        "outputId": "bbf2e5e1-ff02-49e5-d126-5737d5e7ed4c"
      },
      "source": [
        "# summarize val loss and accuracy for flipped\n",
        "summarizeLossAccuracy(history_flipped)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dict_keys(['loss', 'accuracy', 'precision', 'val_loss', 'val_accuracy', 'val_precision'])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 588
        },
        "id": "fSskQo51Qe1I",
        "outputId": "c343139d-814e-4054-8d5c-6ca643354b3f"
      },
      "source": [
        "# summarize val loss and accuracy for noise\n",
        "summarizeLossAccuracy(history_noise)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dict_keys(['loss', 'accuracy', 'precision', 'val_loss', 'val_accuracy', 'val_precision'])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 588
        },
        "id": "xxPvN5_lQnc_",
        "outputId": "2475f0bd-d3ee-48e0-c663-59b7e86cfa5a"
      },
      "source": [
        "# summarize val loss and accuracy for blur\n",
        "summarizeLossAccuracy(history_blur)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dict_keys(['loss', 'accuracy', 'precision', 'val_loss', 'val_accuracy', 'val_precision'])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kgUQzCAqAbU1uFwb3NTUKGGVmb5jZ22Y2s7UfZGbXmdlSM1taVFQUpXLDd9WpQzh/bD9++vxaVm7fH3Y5IpIgwh4sTgZGAmcDVwC/MbO8ljs55x5wzk11zk3t06dPF5fYdcyMn86eQH5mKjctXE55dW3YJYlIAohmEGwHBje5PSi4r6lC4GnnXI1z7iNgPT4YElZ+Zip3zpnEh3vK+NEzq8MuR0QSQDSDYAkw0syGm1kqMBd4usU+T+FbA5hZb3xX0YdRrCkmzBjRm+vPOpEFi7fx3Ac7wy5HROJc1ILAOVcL3AC8AKwBHnHOrTKz281sVrDbC0Cxma0GXgFuds4VR6umWPLtC0YxcVAu8574gB0lFWGXIyJxzGJt9cupU6e6pUuXhl1Gl9i8p4yL732dkwty+dNXTyMpYmGXJCIxyszedc5NbW1b2IPF0oZhvTO5/ZLxvPPRXu5ftDHsckQkTikIurnZUwqYNXEgd720gWVb94VdjojEIQVBN2dm/PjS8QzITeOmhcsprawJuyQRiTMKghiQk5bCPXMnsaOkktueWhl2OSISZxQEMeKUofncdN5InlqxgyeXF4ZdjojEEQVBDPnGOSOYPiyf255axZbisrDLEZE4oSCIIUkR4665k4gY3LhwBTV19WGXJCJxQEEQYwry0rlj9gTe21bC3S+tD7scEYkDCoIYdPHJA7h86mB+uWgTb23SgdgicnwUBDHqB7PGMrxXJv/y8Ar2lVWHXY6IxDAFQYzKSE3m3ismU1xWxbwn3ifWlgoRke5DQRDDxhfk8t0LR/PCql0sWLzt6A8QEWmFgiDGXfuJ4Zw5sje3P7OKDbtKwy5HRGKQgiDGRSLGf82ZSGZqMjcuXEFlTV3YJYlIjFEQxIG+2Wn8/PMTWLPzAD97fl3Y5YhIjFEQxIlzR/fjmjOGMf+Nj3hl3e6wyxGRGKIgiCPzLhrN6P7Z3PzoexSVVoVdjojECAVBHElLSeLeKyZTWlnLdx59j/p6TSkVkaNTEMSZUf2yufUzY3l1fRH/8+bmsMsRkRigIIhDV506hPPH9uOnz61l5fb9YZcjIt2cgiAOmRk/nT2Bnpkp3LRwOeXVtWGXJCLdmIIgTuVnpnLnnEl8uKeMHz2zJuxyRKQbUxDEsRkjenP9WSeyYPFWnl+5M+xyRKSbUhDEuW9fMIqJg3L53uMfsKOkIuxyRKQbUhDEuZSkCPfMnUxNXT3/8vAK6jSlVERaUBAkgGG9M7n9kvG889Fe7l+0MexyRKSbURAkiNlTCpg1cSB3vbSBZVv3hV2OiHQjCoIEYWb8+NLxDMhN46aFyymtrAm7JBHpJhQECSQnLYV75k5iR0kl3/+/VWGXIyLdRLuCwMxuMrMc8x40s2VmdkG0i5POd8rQfG46byRPLt/Ok8sLwy5HRLqB9rYIvuycOwBcAPQErgbuiFpVElXfOGcE04flc9tTq9hSXBZ2OSISsvYGgQX/Xgz83jm3qsl9EmOSIsZdcycRMbhp4Qpq6urDLklEQtTeIHjXzF7EB8ELZpYN6NMjhhXkpXPH7Ams2FbC3S+tD7scEQlRe4PgWmAeMM05Vw6kAF+KWlXSJS4+eQCXTx3MLxdt4q1NxWGXIyIhaW8QnA6sc86VmNlVwK2A1jeOAz+YNZbhvTL5l4dXUFJeHXY5IhKC9gbB/UC5mU0Evg1sAn4Xtaqky2SkJnPvFZMpLqvie4+/j3NagkIk0bQ3CGqd/4S4BPhv59x9QHb0ypKuNL4gl+9eOJoXVu1iweJtYZcjIl2svUFQama34KeN/sXMIvhxgjaZ2UwzW2dmG81sXivbrzGzIjNbEVy+0rHypbNc+4nhnDmyN7c/s4qNu0vDLkdEulB7g+ByoAp/PMHHwCDg5209wMySgPuAi4CxwBVmNraVXR92zk0KLr9tf+nSmSIR47/mTCQzNZl/XrCCypq6sEsSkS7SriAIPvz/COSa2WeASufc0cYIpgMbnXMfOueqgYX4riXppvpmp/Hzz09gzc4D/Oz5dWGXIyJdpL1LTMwBFgOfB+YA75jZZUd5WAHQtMO5MLivpdlm9r6ZPWZmg4/w/NeZ2VIzW1pUVNSekuUYnTu6H9ecMYz5b3zEK+t2h12OiHSB5Hbu96/4Ywh2A5hZH+Al4LHjfP4/Awucc1Vmdj3wEHBuy52ccw8ADwBMnTpV01qibN5Fo3n7w2JuWrCcT43px5gBOYwZkMPoAdn0zuoRdnki0snaGwSRQyEQKOborYntQNNv+IOC+xo455oexfRb4GftrEeiKC0liV9+YQo/+csa3ti0hyeWN/7Z+mT38MHQP7shIE7ok0lKkhayFYlV7Q2C583sBWBBcPty4NmjPGYJMNLMhuMDYC5wZdMdzGyAc+7QWdVnAWvaWY9E2Ql9snjwmmkAFB+sYu3HpazZeYA1O/2//7OpmOpgjaLUpAgj+mYFwdAYEPmZqWH+CiLSTtbeA4jMbDYwI7j5unPuyXY85mLgbiAJmO+c+4mZ3Q4sdc49bWb/gQ+AWmAv8HXn3Nq2fubUqVPd0qVL21WzRE9NXT0fFpUF4XCANUFQFJVWNezTL6cHo/vnNATE2AE5DO+dSbJaDyJdzszedc5NbXVbrB1JqiDo3vYcrGLtztKGgFi98wCbig5SU+f/n6UmRxjVL4sx/XMY3SQg8jLUehCJpmMOAjMrBVrbwQDnnMvpnBLbT0EQe6pr69lUdJA1Ow806WI6wJ6DjWsbDchNY3STcYcxA7IZ3juLpIhWOxfpDG0FQZtjBM45LSMhxy01OdLwAd/U7tLKZq2HtR+X8vqGPdTW++8ePZIjnNQ/O2g9BCHRP4fcjKMe1C4iHdDewWKRTtc3O42+2WmcNapPw31VtXVs3H2QNTtLWbvzAGs+PsBf1+zi4aWNh6QMzE1rNqV1zIAchvXKVOtB5BgpCKRb6ZGcxLiBuYwbmNtwn3OOotIqVjeZtbT24wMsWl9EXdB6SE9JYlT/7GbTWk/qn01uuloPIkejIJBuz8zom5NG35w0zj6pb8P9lTWHWg+NAfH8qo9ZuKSx9VCQl86YATmMHZAdDE7nMDQ/g4haDyINFAQSs9JSkhhfkMv4guath10HqoIprY0B8fLaXQSNB9JTkvzYQxAQh1oP2WlqPUhi0vRRSQiVNXVs2HWwYUrr2iAk9lfUNOwzOD+9YVrroYAY3FOtB4kPxzxrSCRepKUkcfKgXE4e1Lz1sHN/ZcOMpdXB7KWX1jS2HjJTG1sPh6a1ntQ/h6weeutI/FCLQKSFiuo61u8qPeyo6dLK2oZ9hvbKaH7cQ/8cBuenY6bWg3RPahGIdEB6ahITB+cxcXBew33OObaXVDQe9xB0Lb24eheuSethUM8MCnqmMzAvjYK8DAbmpTGoZzoD89Lpm52mKa7SLSkIRNrBzBjUM4NBPTP41Nh+DfeXV9ey7uNS1uwsZf2uUraXVLB9XwXvbtnXbPwBIDli9M9NoyAv3V+CgCjIa/w3PTWpq381EQWByPHISE1m8pCeTB7S87BtB6tq2VFS0RAOh67vKKng7Q+L+fhAZcNYxCG9MlObh0PPdAqatC7yM1PV/SSdTkEgEiVZPZIZ1S+bUf1aX6mltq6ejw9UsqOkku0l5ewoqaRwnw+LjUUHeXV9ERUtzh2dlhJpCIqCwwIjnf65aTo3hHSYgkAkJMlJkYbuJsg/bLtzjpLyGt+iaKVV0XLhPgAz6Jed1qzbqSCv+W0dLyEtKQhEuikzo2dmKj0zU5sdNNdUZU0dO0oqGloV20sqGwLjvW0lPL9yZ8MS4IdkpyU3G6do2arok9VDx04kGAWBSAxLS0nihD5ZnNAnq9Xt9fWOooNVFLZoTWwPuqAWb97bbFosQEqSMSD3yOMUA/PSSUvRoHY8URCIxLFIxOiXk0a/nDROGXr4gDbAgcqaoFVxKCAqGwLjjY172FVaScvDjXpnpTab7dS0RVGQl05eRooGtWOIgkAkweWkpZDTP4XR/Vs/z1R1bT27DlQe3qooqWDdrlJeXrubqtr6Zo/JSE1iYF4r4xS5PjD656TplKXdiIJARNqUmhxhcH4Gg/MzWt3unGNvWXVDQPjAaJwJtXL7fvaWNR/Ujhj0y0mjV1YqPTP8JT8zuJ6Z0nA7LyOl4X51R0WPgkBEjouZ0SurB72yejBhUF6r+1RU1zVrSRz6d19ZNXvLa9hSXM6+8urDxiuaSk9J8qEQBEXLsMjLSCW/RZAoPNoncYKgqhTKiiD/hLArEUk46alJjOibxYi+rQ9qH1JdW09JRTUl5TXsLatmX1k1+8pr2FdeHYRG431b95azt+zo4dEzI4WemalNwsLf9q2PVL+9SYskEY/uTpwgePtX8OodMPlq+OR3IWdg2BWJSAupyZGGU5i2V01dPSVNwmJfuQ+KlkGyt6yabUF4HGgjPNJSIuRnBKGReXhYNO2u6pnpWyGxHh6JEwSTr4KDH8O7D8F7C2DaV+AT34LMXmFXJiLHISUpQp/sHvTJ7tHux9TW1VNSUdMQFHsbAiRoeZTVUFLuWyDbSyrYW1Z92NpRTfVIjhxhjKN5C6RpkKSnJHWbmVWJtwz1vs2w6Kfw/kJIyYDTv+Evaa0fsCMiAj489lccal00765q3pXV2AopKW87PBpaFZkpTcY4fAukIViCcDne8GhrGerEC4JDdq+FV34Ca56G9J4w45sw/TpIbX1mhIhIRzWGR2P3VEl5kxZH0+AIQmV/Rc1hx20c8sNZ4/jiGcOOqRYFQVt2LIeXfwwbX4KsfnDWzTDli5Cc2nnPISLSTnX1jv0VNU1CI2hxlFfziRG9j7jcyNEoCNpjy5vwt9th61uQNwTOvgUmXA6R2B4EEhGBtoNAh/YdMvQM+NJz8IXHfVfRU1+HX54Gq56C+vqjP15EJEYpCJoyg5GfgutehTm/Awwe/SL85mzY8FeO2HEnIhLDFAStMYOxl8A/vQWf+xVU7IM/Xgb/c5HvQhIRiSMKgrZEkmDSFXDDu3Dxf8Lej3wY/P4f/CCziEgcUBC0R3IqTP8q3Lgczr8ddiyDB86Gh6/y01BFRGKYgqAjUjNgxk1w03vwyXmw6RW4/3R48mv+QDURkRikIDgWablwzi1w0/v+qORVT8IvpsIz34IDO8OuTkSkQxQExyOzF1zwY99lNOVqWPYQ3DsJXrwVyorDrk5EpF0UBJ0hZyB85i64YSmM/Ry8+d9wz0RYdAdUHgi7OhGRNikIOlP+cPiHX/tppyeeDYv+wwfCG/dCTUXY1YmItCqqQWBmM81snZltNLN5bew328ycmbV6+HPM6TsGLv8DfPVlGDgJ/nob3DsZlvwWaquP/niJLfX1ULjU/333bQm7GpEOi9paQ2aWBKwHzgcKgSXAFc651S32ywb+AqQCNzjn2lxIKGprDUXT5r/D334E296GvKHBOkZztI5RLKsuhw8XwbpnYf0LULY72GAw8gKYdi2M+JT+xtJttLXWUDRPTDMd2Oic+zAoYiFwCbC6xX4/An4K3BzFWsI17BPw5ef9MhUv/wie+hq8cTec868w5rP+SGbp/ko/hvXPw7rnfAjUVkJqtl+W5KSLof/JsPJxWPY7+NMcv3jhKV/yZ8XL6hN29SJHFM0WwWXATOfcV4LbVwOnOuduaLLPFOBfnXOzzWwR8J3WWgRmdh1wHcCQIUNO2bIlhpvf9fWw5v/g5Z9A8QYYMAnOuw1OPE+B0N04B7tWwrrn/Tf/Hcv8/XlDYNRFcNJFMHTG4UuW19XA2mdgyYOw+XWIpPglS6ZdC0NO199ZQhFWi6BNZhYB7gSuOdq+zrkHgAfAdw1Ft7Ioi0Rg3KUw+rPw/sN+ZtEfZvsPlHNvg6Gnh11hYqut8l15h77579/m7y+YCufe6r/59x3b9od5Uor/G4+7FIrWwdL5sGIBrHwM+ozxgTDhckjL6ZrfSeQootkiOB34N+fchcHtWwCcc/8R3M4FNgEHg4f0B/YCs9oaJ4jJMYK21Fb5roRXf+b7mUec7z9wBk4Ku7LEUb4XNrzov/VvfBmqSyE5HU48x3/rH3khZPc7vueoLvPdRksehJ0rICUTJnwepl4LAyZ0zu8h0oZQTkxjZr7pOEcAABCXSURBVMn4weLzgO34weIrnXOrjrD/Io7QNdRU3AXBIdVlsPgB+PvdUFnij0c451+hz6iwK4tPezb4b/zrnvOD+K4esvrDqAv9t/7hZ0XvtKXb34Ul830LobYSBk2DaV/xf/OUtOg8pyS80M5QZmYXA3cDScB859xPzOx2YKlz7ukW+y4ikYPgkIoSeOs+ePuXUFMOE6+AT34Peg4Nu7LYVlcL296B9cGHf/FGf3+/k+Gkmf6b/4DJvuuuq1Ts811GSx/09aTnw+Qv+AHmXid2XR2SEHSqylhUtgf+fhcs/o3/tnrKNXDWdyC7f9iVxY7KA7Dpb36wd8ML/oM3kgLDzwwGe2f6gd+wOQcfveaPQ1j7F3B1cOK5vtto1ExICm0oT+KIgiCW7d8Or/0Mlv0eklLh1OtgxjchIz/syrqnkq3+g3/9c/DR61Bf4089OvJC/8F/4nnde5D2wE4/ZvTu/0LpDsgp8F8CpvyjvgTIcVEQxIPiTX6G0QePQo9sOOOf4bSv++uJrL4edi4P+vufh10f+Pt7jfDdPSddDIOmx9636rpaP3Np6YOw6WWIJMPoT/tWwvCzNAVVOkxBEE92rYZXfuLnqWf0gk98y09HTEkPu7KuU1MBH74aHNX7PBzcBRbxc/RHBf39vUeGXWXnKd4UTEH9o+/e6jUSpn7Znz0vvWfY1UmMUBDEo8J34eXb/RGu2QPhkzf7I1iTUsKuLDpKd/kP/fXP+xMC1Vb4o3pHnOe/9Y88P/67y2oqYNVTvpVQuMRPcR0/G6Z9GQpOCbs66eYUBPHso9f8OkaFi6HnMDj7/8HJl8X+GjfOwe7V/lv/uudhe/A3zx0cdPlcBEM/cfhRvYli5/s+EN5/FGrKYOBk3200fnb0pr1KTFMQxDvn/AFRf/uR7yPvM8YflDb607HVl1xbDVve8P3965/zA7/gv+0eWtKh37jY+p2irXI/vP+In3FUtNafPW/ilb7rSMegSBMKgkRRXw+rn4RX/t3PSx84xa9jdMI53ffDs3wvbHwpOKr3b1B1AJLTfM0nzfR9/potc3TOwZY3fSth9dN+ttSwM/340ejPxG+XobSbgiDR1NXCewv8LKMDhf4D4dzbYMipYVfmFW9qPKp361t+3nxm3+CD/yI44Wx1bxyPg7th+e9h6f/C/q2Q1Q+mfBFO+SLkDgq7OgmJgiBR1Vb5+eiv/RzKivxc+nNv7fq1berrYNvixqN696z39/cd19jfP3BK1x7Vmwjq63xra8lv/RLoZj5op30ZTjhXr3eCURAkuuoyeOdX8MY9vk953KV+HaNoTrGsKvXz39c958cvyov9Ub3DZvhZPqNmatmMrrRvs/9SsOz3UL4Heg6HqV+CSVdBZq+wq5MuoCAQr6IE3vwFvH2/n3456Uq/jlFnLbOwv7Cxy2fz61BXDWl5fiG3UTP9VM+03M55Ljk2tVWw5s9+FdStb0JSDxj3OT/jaPD07juWJMdNQSDNHSyCv9/puwzAL3J25rc7vtRyfb1fUnl9cOKWj4OjevNPbDyqd/CpsXdUb6LYtdofqPbeQr/0dr+TfbfRyXOgR1bY1UknUxBI60q2+XWMlv8RknvAqdfDGTe2fWBWTYU/dmHdcz4ASnf6o3oHn9r44R9PR/UmgqqD8MEjfmnsXR/4A/UmXu5bCf3Ghl2ddBIFgbSteJOfcrryMeiRCzP+GU79euO3woO7/Qna1z/v+/1ryiE1y3f1jLrIn6xd/cyxzzl/xPKSB2HVk1BX5ZftmHotjJ3lvyxIzFIQSPt8vNKvY7TuWcjoDRPmQOFS/+GAg5xBwbf+mX5Kqj4Y4ldZsV/baOl82PeR//8w5WrfjahB/pikIJCO2bbEr2P00Wt+6YJDs3z6n6zBxERTXw8fvuy7jdY/51sNI8/3rYSR58f+UiYJREEgx6amUqdOlEb7C+Hdh2DZQ37F19wh/iC1Kf8IWX3Drk6OQkEgIp2nrsafSW3pg77VGEmBMZ/1y1kMnaFWYzfVVhBoXp+IdExSij/2YNznYM+GxnMlrHoC+oz23UYTL9cxIzFELQIROX7V5bDycd9K2LEcUjL9cujTroUBE8OuTlDXkIh0pe3LfCB88Lg/gr1gqg+EcZcm1pn0uhkFgYh0vYp9/qjlJQ9C8QZ/Ws1JX/DnSuh1YtjVJRwFgYiExzk/qLz0QT/IXF/rzzcx9cv+oMTUzLArTAgaLBaR8JjBCZ/0l9KPYdnv/Eqoj1wNkWQYMAmGnuFnHA051bccpEupRSAiXa+uFj561Z+adMubsP1dv1ot5k9HOvQMfxlyRscXQ5RWqUUgIt1LUrLvFhpxnr9dU+HDYMubPhyW/wEWP+C39RrR2GIYekbnLZsuDRQEIhK+lHQY9gl/AX/Q2s73G1sMq//PdymBX/PqUIth6Ay/2q0OYjsu6hoSke6vvh52r25sMWx5E8p2+20ZvZu3GPqN0xpIrVDXkIjEtkgE+o/3l1Ov8zORijf5s6wdCoc1T/t9e+TAkNMaw2HAJEhODbf+bk5BICKxxwx6j/CXKf/o7yvZBlvfCloMb/lzZQMkp8PgaY0thoKpkJoRXu3dkIJAROJD3mB/mTDH3z5YFARD0GJYdAfg/CJ5Ayc3n7Ka4OsiaYxARBJDRQlsW9w4xrBjOdTX+FOt9hvf2GIYegZk9g672k6nI4tFRFqqLoftSxtbDNuW+LWRAHqPaj4AnTso3Fo7gQaLRURaSs2A4Wf5C0BtNexcEQTDm7DySX8ENPhjFw6FwpAz/FpJcTRlVS0CEZHW1NfBrlXNp6yW7/HbMvs2bzH0HetnNnVjahGIiHRUJAkGTPCX077mp6zu2eBDYetbsPkNWP2U3zct17cUGqasTvAn8IkRCgIRkfYwgz6j/GXql/x9JVubtxjWP+fvT8mAwdObTFk9pVufiyGqQWBmM4F7gCTgt865O1ps/xrwDaAOOAhc55xbHc2aREQ6Td4Qf5k4198u3RUc5BZMW33l3wEHSak+DA7NShp8KvTIDrX0pqI2RmBmScB64HygEFgCXNH0g97McpxzB4Lrs4B/cs7NbOvnaoxARGJGxT7Y+k5ji2HnCn8+BotA/wlNBqBPh8xeUS0lrDGC6cBG59yHQRELgUuAhiA4FAKBTCC2Rq5FRNqS3hNOmukvANVlULikcWbS0gfh7fv8tj5jmiymdwbkDOyyMqMZBAXAtia3C4FTW+5kZt8AvgWkAue29oPM7DrgOoAhQ7QErYjEqNRMOOFsfwGorfIHth1qMbz/iA8HgJ7Dmh/k1nN41KasRrNr6DJgpnPuK8Htq4FTnXM3HGH/K4ELnXNfbOvnqmtIROJWXS3s+iAYYwjCoWKv35Y9AC74MZx82TH96LC6hrYDg5vcHhTcdyQLgfujWI+ISPeWlOzXQRo4GU7/J7/89p71jaGQFZ2ztUUzCJYAI81sOD4A5gJXNt3BzEY65zYENz8NbEBERLxIBPqO9pdp10btaaIWBM65WjO7AXgBP310vnNulZndDix1zj0N3GBmnwJqgH1Am91CIiLS+aJ6HIFz7lng2Rb3fb/J9Zui+fwiInJ03XtxDBERiToFgYhIglMQiIgkOAWBiEiCUxCIiCQ4BYGISIKLuTOUmVkRsOUYH94b2NOJ5XQW1dUxqqvjumttqqtjjqeuoc65Pq1tiLkgOB5mtvRIa22ESXV1jOrquO5am+rqmGjVpa4hEZEEpyAQEUlwiRYED4RdwBGoro5RXR3XXWtTXR0TlboSaoxAREQOl2gtAhERaUFBICKS4OIyCMxsppmtM7ONZjavle09zOzhYPs7Zjasm9R1jZkVmdmK4PKVLqprvpntNrOVR9huZnZvUPf7Zjalm9R1tpntb/J6fb+1/Tq5psFm9oqZrTazVWZ22FLqYbxe7awrjNcrzcwWm9l7QV0/bGWfLn8/trOuUN6PwXMnmdlyM3umlW2d/3o55+Lqgj8JzibgBCAVeA8Y22KffwJ+FVyfCzzcTeq6BvjvEF6zs4ApwMojbL8YeA4w4DTgnW5S19nAM138Wg0ApgTXs4H1rfwdu/z1amddYbxeBmQF11OAd4DTWuwTxvuxPXWF8n4MnvtbwJ9a+3tF4/WKxxbBdGCjc+5D51w1/lzIl7TY5xLgoeD6Y8B5ZmbdoK5QOOdeA/a2scslwO+c9zaQZ2YDukFdXc45t9M5tyy4XgqsAQpa7Nblr1c76+pywWtwMLiZElxazlDp8vdjO+sKhZkNwp+697dH2KXTX694DIICYFuT24Uc/oZo2Mc5VwvsB3p1g7oAZgfdCY+Z2eAo19Re7a09DKcHzfvnzGxcVz5x0CSfjP822VSor1cbdUEIr1fQzbEC2A381Tl3xNerC9+P7akLwnk/3g18F6g/wvZOf73iMQhi2Z+BYc65CcBfaUx9ad0y/PopE4FfAE911RObWRbwOPBN59yBrnreozlKXaG8Xs65OufcJGAQMN3MxnfF8x5NO+rq8vejmX0G2O2cezfaz9VUPAbBdqBpcg8K7mt1HzNLBnKB4rDrcs4VO+eqgpu/BU6Jck3t1Z7XtMs55w4cat47f37sFDPrHe3nNbMU/IftH51zT7SySyiv19HqCuv1avL8JcArwMwWm8J4Px61rpDejzOAWWa2Gd99fK6Z/aHFPp3+esVjECwBRprZcDNLxQ+mPN1in6eBLwbXLwNedsHIS5h1tehHnoXv5+0Ongb+MZgNcxqw3zm3M+yizKz/ob5RM5uO//8c1Q+Q4PkeBNY45+48wm5d/nq1p66QXq8+ZpYXXE8HzgfWttity9+P7akrjPejc+4W59wg59ww/GfEy865q1rs1umvV/LxPLg7cs7VmtkNwAv4mTrznXOrzOx2YKlz7mn8G+b3ZrYRPxg5t5vUdaOZzQJqg7quiXZdAGa2AD+jpLeZFQI/wA+e4Zz7FfAsfibMRqAc+FI3qesy4OtmVgtUAHO7INBnAFcDHwT9ywD/DxjSpK4wXq/21BXG6zUAeMjMkvDB84hz7pmw34/trCuU92Nrov16aYkJEZEEF49dQyIi0gEKAhGRBKcgEBFJcAoCEZEEpyAQEUlwCgKRLmR+BdDDVpQUCZOCQEQkwSkIRFphZlcF69WvMLNfBwuUHTSzu4L16/9mZn2CfSeZ2dvB4mRPmlnP4P4RZvZSsMjbMjM7MfjxWcEiZmvN7I9dsPKtSJsUBCItmNkY4HJgRrAoWR3wBSATf3TnOOBV/JHOAL8DvhcsTvZBk/v/CNwXLPJ2BnBomYnJwDeBsfjzU8yI+i8l0oa4W2JCpBOch19gbEnwZT0dv1RxPfBwsM8fgCfMLBfIc869Gtz/EPComWUDBc65JwGcc5UAwc9b7JwrDG6vAIYBf4/+ryXSOgWByOEMeMg5d0uzO81ua7Hfsa7PUtXkeh16H0rI1DUkcri/AZeZWV8AM8s3s6H498tlwT5XAn93zu0H9pnZmcH9VwOvBmcJKzSzzwU/o4eZZXTpbyHSTvomItKCc261md0KvGhmEaAG+AZQhj+Bya34rqLLg4d8EfhV8EH/IY2rjV4N/DpYObIG+HwX/hoi7abVR0XaycwOOueywq5DpLOpa0hEJMGpRSAikuDUIhARSXAKAhGRBKcgEBFJcAoCEZEEpyAQEUlw/x9hn/G2icXjbgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oLGXqMAGjC7m"
      },
      "source": [
        "# Error Bars for Retrain Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wvrbgd56jYi3"
      },
      "source": [
        "## Load Models\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "klqGxywAjK2X"
      },
      "source": [
        "model_inception = tf.keras.models.load_model('./retrain_inception_resnet/inception')\n",
        "model_resnet    = tf.keras.models.load_model('./retrain_inception_resnet/resnet')\n",
        "\n",
        "model_retrain_5     = tf.keras.models.load_model('./retrain_imbalance/5.hdf5')\n",
        "model_retrain_flip  = tf.keras.models.load_model('./retrain_imbalance/flip')\n",
        "model_retrain_noise = tf.keras.models.load_model('./retrain_imbalance/noise')\n",
        "model_retrain_blur  = tf.keras.models.load_model('./retrain_imbalance/blur')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TPoesQ3Vjd2a"
      },
      "source": [
        "## Load Histories"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e9-vLkAWjdav"
      },
      "source": [
        "import pickle\n",
        "\n",
        "history_inception = pickle.load(open('./retrain_inception_resnet/inception_history', \"rb\"))\n",
        "resnet_inception  = pickle.load(open('./retrain_inception_resnet/resnet_history', \"rb\"))\n",
        "\n",
        "history_5 = pickle.load(open('./retrain_imbalance/5_history', \"rb\"))\n",
        "history_flip = pickle.load(open('./retrain_imbalance/flip_history', \"rb\"))\n",
        "history_noise = pickle.load(open('./retrain_imbalance/noise_history', \"rb\"))\n",
        "history_blur = pickle.load(open('./retrain_imbalance/blur_history', \"rb\"))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rXPyZX_LjkSy"
      },
      "source": [
        "## Predict and Evaluate from Models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9OC-8vKTq0wP"
      },
      "source": [
        "def getAccuracyErrors(predict, acc):\n",
        "  # number of samples\n",
        "  n = predict.shape[0]\n",
        "\n",
        "  # assuming 95% confidence interval\n",
        "  Z = 1.96\n",
        "\n",
        "  radius = Z * np.sqrt((acc * (1 - acc)) / n)\n",
        "  lower = acc - radius\n",
        "  upper = acc + radius\n",
        "\n",
        "  return radius, lower, upper"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4unsZRNBjoc7",
        "outputId": "18cf309f-794e-496b-8584-a3126e7a9384"
      },
      "source": [
        "# predict from the default model\n",
        "pred_5 = model_retrain_5.predict(test_data)\n",
        "\n",
        "eval_5 = model_retrain_5.evaluate(test_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "117/117 [==============================] - 8s 66ms/step - loss: 2.8559 - accuracy: 0.3605 - precision: 0.7358\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_4MBQ1hdpdMU",
        "outputId": "b87287e3-5c8f-477e-dfd4-9e2c576713bd"
      },
      "source": [
        "# obtain the model loss, accuracy, and precision\n",
        "print(eval_5)\n",
        "\n",
        "loss_5 = eval_5[0]\n",
        "accuracy_5 = eval_5[1]\n",
        "precision_5 = eval_5[2]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[2.855910062789917, 0.36047133803367615, 0.7358490824699402]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cs0c0JW2r5vk",
        "outputId": "1a035f65-90cd-4063-df3b-e2b86bd9effe"
      },
      "source": [
        "radius_5, lower_5, upper_5 = getAccuracyErrors(pred_5, accuracy_5)\n",
        "\n",
        "print('radius: ', radius_5)\n",
        "print('lower bound: ', lower_5)\n",
        "print('upper bounds: ', upper_5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "radius:  0.02177956935596049\n",
            "lower bound:  0.33869176867771567\n",
            "upper bounds:  0.38225090738963663\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kf6C6Pj7j8sp",
        "outputId": "224b57cc-d368-4baf-b522-c3dcd9ce7d57"
      },
      "source": [
        "pred_retrain_flip = model_retrain_flip.predict(test_data)\n",
        "\n",
        "eval_retrain_flip = model_retrain_flip.evaluate(test_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "117/117 [==============================] - 8s 65ms/step - loss: 4.0951 - accuracy: 0.3771 - precision: 0.6333\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2LJ0mkscpmCD",
        "outputId": "cb0b48a9-c7b4-4329-c2d8-5afa8d2f2413"
      },
      "source": [
        "# obtain the model loss, accuracy, and precision\n",
        "print(eval_retrain_flip)\n",
        "\n",
        "loss_retrain_flip = eval_retrain_flip[0]\n",
        "accuracy_retrain_flip = eval_retrain_flip[1]\n",
        "precision_retrain_flip = eval_retrain_flip[2]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[4.095114231109619, 0.37707552313804626, 0.6333333253860474]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pzhp0n9qrxhD",
        "outputId": "9075f3d5-ba68-4fdc-abfd-53c18deb9901"
      },
      "source": [
        "radius_retrain_flip, lower_retrain_flip, upper_retrain_flip = getAccuracyErrors(pred_retrain_flip, accuracy_retrain_flip)\n",
        "\n",
        "print('radius: ', radius_retrain_flip)\n",
        "print('lower bound: ', lower_retrain_flip)\n",
        "print('upper bounds: ', upper_retrain_flip)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "radius:  0.021984458935883536\n",
            "lower bound:  0.35509106420216274\n",
            "upper bounds:  0.3990599820739298\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NGLETM2Fj9Rh",
        "outputId": "5feeac71-0480-448d-efde-e41993869634"
      },
      "source": [
        "pred_retrain_noise = model_retrain_noise.predict(test_data)\n",
        "\n",
        "eval_retrain_noise = model_retrain_noise.evaluate(test_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "117/117 [==============================] - 8s 65ms/step - loss: 4.7992 - accuracy: 0.3931 - precision: 0.6483\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kgb3UeTMpo1d",
        "outputId": "04429ee2-0097-43e2-84cd-858c754e74b3"
      },
      "source": [
        "# obtain the model loss, accuracy, and precision\n",
        "print(eval_retrain_noise)\n",
        "\n",
        "loss_retrain_noise = eval_retrain_noise[0]\n",
        "accuracy_retrain_noise = eval_retrain_noise[1]\n",
        "precision_retrain_noise = eval_retrain_noise[2]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[4.799220085144043, 0.39314407110214233, 0.6482758522033691]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bJ4o6WHos5ly",
        "outputId": "293cf687-aa31-4689-e1b0-d5dd8b7ff13d"
      },
      "source": [
        "radius_retrain_noise, lower_retrain_noise, upper_retrain_noise = getAccuracyErrors(pred_retrain_noise, accuracy_retrain_noise)\n",
        "\n",
        "print('radius: ', radius_retrain_noise)\n",
        "print('lower bound: ', lower_retrain_noise)\n",
        "print('upper bounds: ', upper_retrain_noise)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "radius:  0.022156572450724138\n",
            "lower bound:  0.3709874986514182\n",
            "upper bounds:  0.41530064355286644\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qvtKvqMuj9tJ",
        "outputId": "e4f5e2db-6fdb-4472-9dec-508fe19b8b5c"
      },
      "source": [
        "pred_retrain_blur = model_retrain_blur.predict(test_data)\n",
        "\n",
        "eval_retrain_blur = model_retrain_blur.evaluate(test_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "117/117 [==============================] - 8s 64ms/step - loss: 7.6598 - accuracy: 0.3878 - precision: 0.6319\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KvR1Iodbprku",
        "outputId": "5ceb0cc5-4ef2-4225-a06d-094a92ebaceb"
      },
      "source": [
        "# obtain the model loss, accuracy, and precision\n",
        "print(eval_retrain_blur)\n",
        "\n",
        "loss_retrain_blur = eval_retrain_blur[0]\n",
        "accuracy_retrain_blur = eval_retrain_blur[1]\n",
        "precision_retrain_blur = eval_retrain_blur[2]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[7.659763813018799, 0.38778790831565857, 0.6319444179534912]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2B-q07c7tImQ",
        "outputId": "eccf7ae7-41d2-4d53-c109-3a8f81de0570"
      },
      "source": [
        "radius_retrain_blur, lower_retrain_blur, upper_retrain_blur = getAccuracyErrors(pred_retrain_blur, accuracy_retrain_blur)\n",
        "\n",
        "print('radius: ', radius_retrain_blur)\n",
        "print('lower bound: ', lower_retrain_blur)\n",
        "print('upper bounds: ', upper_retrain_blur)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "radius:  0.022102021419511522\n",
            "lower bound:  0.36568588689614706\n",
            "upper bounds:  0.4098899297351701\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NI53PoFsmGbm"
      },
      "source": [
        "def confIntervalBarChart(bars, yer, labels):\n",
        "  barWidth = 0.3\n",
        "\n",
        "  # x position of the bars\n",
        "  r1 = np.arange(len(bars))\n",
        "  r2 = [x + barWidth for x in r1]\n",
        "\n",
        "  # create blur bars\n",
        "  plt.bar(r1, bars, width=barWidth, color='cyan', edgecolor='black', yerr=yer, capsize=7)\n",
        "\n",
        "  # general layout\n",
        "  plt.xticks([r + barWidth for r in range(len(bars))], labels)\n",
        "  plt.ylabel('accuracy')\n",
        "\n",
        "  # Show graphic\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "hp9aaEUTmSra",
        "outputId": "25925109-af04-42ff-8357-8a9b1646e62f"
      },
      "source": [
        "allBars = [\n",
        "           accuracy_5,\n",
        "           accuracy_retrain_flip,\n",
        "           accuracy_retrain_noise,\n",
        "           accuracy_retrain_blur\n",
        "           ]\n",
        "\n",
        "allYers = [\n",
        "           radius_5,\n",
        "           radius_retrain_flip,\n",
        "           radius_retrain_noise,\n",
        "           radius_retrain_blur\n",
        "           ]\n",
        "\n",
        "allLabels = ['No Augmentation', 'Flip', 'Noise', 'Blur']\n",
        "\n",
        "# display confidence intervals for all 5 models at once\n",
        "confIntervalBarChart(allBars, allYers, allLabels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "ZRiookYO5rX0",
        "outputId": "ea093d95-bcb1-403d-d9b1-048e66a9432b"
      },
      "source": [
        "allBars2 = [\n",
        "           0.6529,\n",
        "           0.6535,\n",
        "           0.3996,\n",
        "           0.3519,\n",
        "           0.3621\n",
        "           ]\n",
        "\n",
        "allYers2 = [\n",
        "           0.02159,\n",
        "           0.02158,\n",
        "           0.02219,\n",
        "           0.02176,\n",
        "           0.02163\n",
        "           ]\n",
        "\n",
        "allLabels2 = ['InceptionV3', 'ResNet', 'Flip', 'Noise', 'Blur']\n",
        "\n",
        "# display confidence intervals for all 5 models at once\n",
        "confIntervalBarChart(allBars2, allYers2, allLabels2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}