Lab2_Computer_Vision_Debiasing.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Lab2_Computer_Vision_Debiasing.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "Ag_e7xtTzT1W",
        "QOpPUH3FR179",
        "NDj7KBaW8Asz"
      ],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/zain910128/MS-MIT_Intro_to_Deep_Learning-Labs/blob/master/Lab2_Computer_Vision_Debiasing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ag_e7xtTzT1W",
        "colab_type": "text"
      },
      "source": [
        "<table align=\"center\">\n",
        "  <td align=\"center\"><a target=\"_blank\" href=\"http://introtodeeplearning.com\">\n",
        "        <img src=\"http://introtodeeplearning.com/images/colab/mit.png\" style=\"padding-bottom:5px;\" />\n",
        "      Visit MIT Deep Learning</a></td>\n",
        "  <td align=\"center\"><a target=\"_blank\" href=\"https://colab.research.google.com/github/aamini/introtodeeplearning/blob/master/lab2/Part2_Debiasing.ipynb\">\n",
        "        <img src=\"http://introtodeeplearning.com/images/colab/colab.png?v2.0\"  style=\"padding-bottom:5px;\" />Run in Google Colab</a></td>\n",
        "  <td align=\"center\"><a target=\"_blank\" href=\"https://github.com/aamini/introtodeeplearning/blob/master/lab2/Part2_Debiasing.ipynb\">\n",
        "        <img src=\"http://introtodeeplearning.com/images/colab/github.png\"  height=\"70px\" style=\"padding-bottom:5px;\"  />View Source on GitHub</a></td>\n",
        "</table>\n",
        "\n",
        "# Copyright Information"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rNbf1pRlSDby",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Copyright 2020 MIT 6.S191 Introduction to Deep Learning. All Rights Reserved.\n",
        "# \n",
        "# Licensed under the MIT License. You may not use this file except in compliance\n",
        "# with the License. Use and/or modification of this code outside of 6.S191 must\n",
        "# reference:\n",
        "#\n",
        "# © MIT 6.S191: Introduction to Deep Learning\n",
        "# http://introtodeeplearning.com\n",
        "#"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QOpPUH3FR179",
        "colab_type": "text"
      },
      "source": [
        "# Laboratory 2: Computer Vision\n",
        "\n",
        "# Part 2: Debiasing Facial Detection Systems\n",
        "\n",
        "In the second portion of the lab, we'll explore two prominent aspects of applied deep learning: facial detection and algorithmic bias. \n",
        "\n",
        "Deploying fair, unbiased AI systems is critical to their long-term acceptance. Consider the task of facial detection: given an image, is it an image of a face?  This seemingly simple, but extremely important, task is subject to significant amounts of algorithmic bias among select demographics. \n",
        "\n",
        "In this lab, we'll investigate [one recently published approach](http://introtodeeplearning.com/AAAI_MitigatingAlgorithmicBias.pdf) to addressing algorithmic bias. We'll build a facial detection model that learns the *latent variables* underlying face image datasets and uses this to adaptively re-sample the training data, thus mitigating any biases that may be present in order  to train a *debiased* model.\n",
        "\n",
        "\n",
        "Run the next code block for a short video from Google that explores how and why it's important to consider bias when thinking about machine learning:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XQh5HZfbupFF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "outputId": "ff94e96e-5cbc-440d-ec1b-05408d702521"
      },
      "source": [
        "import IPython\n",
        "IPython.display.YouTubeVideo('59bMh59JQDo')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "        <iframe\n",
              "            width=\"400\"\n",
              "            height=\"300\"\n",
              "            src=\"https://www.youtube.com/embed/59bMh59JQDo\"\n",
              "            frameborder=\"0\"\n",
              "            allowfullscreen\n",
              "        ></iframe>\n",
              "        "
            ],
            "text/plain": [
              "<IPython.lib.display.YouTubeVideo at 0x7f9af5dd22e8>"
            ],
            "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wCEAAUDBAgHBwgHBwcHBwgHCAcHBwgHCAgHBwgHBwcIBwcI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\n"
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Ezfc6Yv6IhI",
        "colab_type": "text"
      },
      "source": [
        "Let's get started by installing the relevant dependencies:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E46sWVKK6LP9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        },
        "outputId": "7870b63d-7dd2-4b77-b159-2b3f9c4802a4"
      },
      "source": [
        "# Import Tensorflow 2.0\n",
        "%tensorflow_version 2.x\n",
        "import tensorflow as tf\n",
        "\n",
        "import IPython\n",
        "import functools\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from tqdm import tqdm\n",
        "\n",
        "# Download and import the MIT 6.S191 package\n",
        "!pip install mitdeeplearning\n",
        "import mitdeeplearning as mdl"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: mitdeeplearning in /usr/local/lib/python3.6/dist-packages (0.1.2)\n",
            "Requirement already satisfied: regex in /usr/local/lib/python3.6/dist-packages (from mitdeeplearning) (2019.12.20)\n",
            "Requirement already satisfied: numpy in /tensorflow-2.1.0/python3.6 (from mitdeeplearning) (1.18.1)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from mitdeeplearning) (4.28.1)\n",
            "Requirement already satisfied: gym in /usr/local/lib/python3.6/dist-packages (from mitdeeplearning) (0.15.6)\n",
            "Requirement already satisfied: six in /tensorflow-2.1.0/python3.6 (from gym->mitdeeplearning) (1.14.0)\n",
            "Requirement already satisfied: cloudpickle~=1.2.0 in /usr/local/lib/python3.6/dist-packages (from gym->mitdeeplearning) (1.2.2)\n",
            "Requirement already satisfied: scipy in /tensorflow-2.1.0/python3.6 (from gym->mitdeeplearning) (1.4.1)\n",
            "Requirement already satisfied: pyglet<=1.5.0,>=1.4.0 in /usr/local/lib/python3.6/dist-packages (from gym->mitdeeplearning) (1.4.10)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from pyglet<=1.5.0,>=1.4.0->gym->mitdeeplearning) (0.16.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V0e77oOM3udR",
        "colab_type": "text"
      },
      "source": [
        "## 2.1 Datasets\n",
        "\n",
        "We'll be using three datasets in this lab. In order to train our facial detection models, we'll need a dataset of positive examples (i.e., of faces) and a dataset of negative examples (i.e., of things that are not faces). We'll use these data to train our models to classify images as either faces or not faces. Finally, we'll need a test dataset of face images. Since we're concerned about the potential *bias* of our learned models against certain demographics, it's important that the test dataset we use has equal representation across the demographics or features of interest. In this lab, we'll consider skin tone and gender. \n",
        "\n",
        "1.   **Positive training data**: [CelebA Dataset](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html). A large-scale (over 200K images) of celebrity faces.   \n",
        "2.   **Negative training data**: [ImageNet](http://www.image-net.org/). Many images across many different categories. We'll take negative examples from a variety of non-human categories. \n",
        "[Fitzpatrick Scale](https://en.wikipedia.org/wiki/Fitzpatrick_scale) skin type classification system, with each image labeled as \"Lighter'' or \"Darker''.\n",
        "\n",
        "Let's begin by importing these datasets. We've written a class that does a bit of data pre-processing to import the training data in a usable format."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RWXaaIWy6jVw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "62410fb4-d7fc-47ee-b445-3905c2a0198c"
      },
      "source": [
        "# Get the training data: both images from CelebA and ImageNet\n",
        "path_to_training_data = tf.keras.utils.get_file('train_face.h5', 'https://www.dropbox.com/s/l5iqduhe0gwxumq/train_face.h5?dl=1')\n",
        "# Instantiate a TrainingDatasetLoader using the downloaded dataset\n",
        "loader = mdl.lab2.TrainingDatasetLoader(path_to_training_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://www.dropbox.com/s/l5iqduhe0gwxumq/train_face.h5?dl=1\n",
            "1263894528/1263889489 [==============================] - 106s 0us/step\n",
            "Opening /root/.keras/datasets/train_face.h5\n",
            "Loading data into memory...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yIE321rxa_b3",
        "colab_type": "text"
      },
      "source": [
        "We can look at the size of the training dataset and grab a batch of size 100:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DjPSjZZ_bGqe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "number_of_training_examples = loader.get_train_size()\n",
        "(images, labels) = loader.get_batch(100)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sxtkJoqF6oH1",
        "colab_type": "text"
      },
      "source": [
        "Play around with displaying images to get a sense of what the training data actually looks like!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jg17jzwtbxDA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "outputId": "1ffeb6bf-51a5-4ada-f485-7248764b683f"
      },
      "source": [
        "### Examining the CelebA training dataset ###\n",
        "\n",
        "#@title Change the sliders to look at positive and negative training examples! { run: \"auto\" }\n",
        "\n",
        "face_images = images[np.where(labels==1)[0]]\n",
        "not_face_images = images[np.where(labels==0)[0]]\n",
        "\n",
        "idx_face = 37 #@param {type:\"slider\", min:0, max:50, step:1}\n",
        "idx_not_face = 11 #@param {type:\"slider\", min:0, max:50, step:1}\n",
        "\n",
        "plt.figure(figsize=(5,5))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.imshow(face_images[idx_face])\n",
        "plt.title(\"Face\"); plt.grid(False)\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.imshow(not_face_images[idx_not_face])\n",
        "plt.title(\"Not Face\"); plt.grid(False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAATkAAACuCAYAAABN9Xq+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nnn8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 360x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NDj7KBaW8Asz",
        "colab_type": "text"
      },
      "source": [
        "### Thinking about bias\n",
        "\n",
        "Remember we'll be training our facial detection classifiers on the large, well-curated CelebA dataset (and ImageNet), and then evaluating their accuracy by testing them on an independent test dataset. Our goal is to build a model that trains on CelebA *and* achieves high classification accuracy on the the test dataset across all demographics, and to thus show that this model does not suffer from any hidden bias. \n",
        "\n",
        "What exactly do we mean when we say a classifier is biased? In order to formalize this, we'll need to think about [*latent variables*](https://en.wikipedia.org/wiki/Latent_variable), variables that define a dataset but are not strictly observed. As defined in the generative modeling lecture, we'll use the term *latent space* to refer to the probability distributions of the aforementioned latent variables. Putting these ideas together, we consider a classifier *biased* if its classification decision changes after it sees some additional latent features. This notion of bias may be helpful to keep in mind throughout the rest of the lab. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AIFDvU4w8OIH",
        "colab_type": "text"
      },
      "source": [
        "## 2.2 CNN for facial detection \n",
        "\n",
        "First, we'll define and train a CNN on the facial classification task, and evaluate its accuracy. Later, we'll evaluate the performance of our debiased models against this baseline CNN. The CNN model has a relatively standard architecture consisting of a series of convolutional layers with batch normalization followed by two fully connected layers to flatten the convolution output and generate a class prediction. \n",
        "\n",
        "### Define and train the CNN model\n",
        "\n",
        "Like we did in the first part of the lab, we'll define our CNN model, and then train on the CelebA and ImageNet datasets using the `tf.GradientTape` class and the `tf.GradientTape.gradient` method."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "82EVTAAW7B_X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "### Define the CNN model ###\n",
        "\n",
        "n_filters = 12 # base number of convolutional filters\n",
        "\n",
        "'''Function to define a standard CNN model'''\n",
        "def make_standard_classifier(n_outputs=1):\n",
        "  Conv2D = functools.partial(tf.keras.layers.Conv2D, padding='same', activation='relu')\n",
        "  BatchNormalization = tf.keras.layers.BatchNormalization\n",
        "  Flatten = tf.keras.layers.Flatten\n",
        "  Dense = functools.partial(tf.keras.layers.Dense, activation='relu')\n",
        "\n",
        "  model = tf.keras.Sequential([\n",
        "    Conv2D(filters=1*n_filters, kernel_size=5,  strides=2),\n",
        "    BatchNormalization(),\n",
        "    \n",
        "    Conv2D(filters=2*n_filters, kernel_size=5,  strides=2),\n",
        "    BatchNormalization(),\n",
        "\n",
        "    Conv2D(filters=4*n_filters, kernel_size=3,  strides=2),\n",
        "    BatchNormalization(),\n",
        "\n",
        "    Conv2D(filters=6*n_filters, kernel_size=3,  strides=2),\n",
        "    BatchNormalization(),\n",
        "\n",
        "    Flatten(),\n",
        "    Dense(512),\n",
        "    Dense(n_outputs, activation=None),\n",
        "  ])\n",
        "  return model\n",
        "\n",
        "standard_classifier = make_standard_classifier()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c-eWf3l_lCri",
        "colab_type": "text"
      },
      "source": [
        "Now let's train the standard CNN!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "eJlDGh1o31G1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "outputId": "1c563ffe-20fa-4c17-d931-739d7cc35d1a"
      },
      "source": [
        "### Train the standard CNN ###\n",
        "\n",
        "# Training hyperparameters\n",
        "batch_size = 32\n",
        "num_epochs = 2  # keep small to run faster\n",
        "learning_rate = 5e-4\n",
        "\n",
        "optimizer = tf.keras.optimizers.Adam(learning_rate) # define our optimizer\n",
        "loss_history = mdl.util.LossHistory(smoothing_factor=0.99) # to record loss evolution\n",
        "plotter = mdl.util.PeriodicPlotter(sec=2, scale='semilogy')\n",
        "if hasattr(tqdm, '_instances'): tqdm._instances.clear() # clear if it exists\n",
        "\n",
        "@tf.function\n",
        "def standard_train_step(x, y):\n",
        "  with tf.GradientTape() as tape:\n",
        "    # feed the images into the model\n",
        "    logits = standard_classifier(x) \n",
        "    # Compute the loss\n",
        "    loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=y, logits=logits)\n",
        "\n",
        "  # Backpropagation\n",
        "  grads = tape.gradient(loss, standard_classifier.trainable_variables)\n",
        "  optimizer.apply_gradients(zip(grads, standard_classifier.trainable_variables))\n",
        "  return loss\n",
        "\n",
        "# The training loop!\n",
        "for epoch in range(num_epochs):\n",
        "  for idx in tqdm(range(loader.get_train_size()//batch_size)):\n",
        "    # Grab a batch of training data and propagate through the network\n",
        "    x, y = loader.get_batch(batch_size)\n",
        "    loss = standard_train_step(x, y)\n",
        "\n",
        "    # Record the loss and plot the evolution of the loss as a function of training\n",
        "    loss_history.append(loss.numpy().mean())\n",
        "    plotter.plot(loss_history.get())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAD4CAYAAAD2FnFTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSjbwXawAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 3434/3434 [00:33<00:00, 102.10it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAD4CAYAAAD2FnFTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSjbwXawAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AKMdWVHeCxj8",
        "colab_type": "text"
      },
      "source": [
        "### Evaluate performance of the standard CNN\n",
        "\n",
        "Next, let's evaluate the classification performance of our CelebA-trained standard CNN on the training dataset.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "35-PDgjdWk6_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e10e7b94-164b-4069-ef0b-aa9e5e4d173d"
      },
      "source": [
        "### Evaluation of standard CNN ###\n",
        "\n",
        "# TRAINING DATA\n",
        "# Evaluate on a subset of CelebA+Imagenet\n",
        "(batch_x, batch_y) = loader.get_batch(5000)\n",
        "y_pred_standard = tf.round(tf.nn.sigmoid(standard_classifier.predict(batch_x)))\n",
        "acc_standard = tf.reduce_mean(tf.cast(tf.equal(batch_y, y_pred_standard), tf.float32))\n",
        "\n",
        "print(\"Standard CNN accuracy on (potentially biased) training set: {:.4f}\".format(acc_standard.numpy()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Standard CNN accuracy on (potentially biased) training set: 0.9988\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qu7R14KaEEvU",
        "colab_type": "text"
      },
      "source": [
        "We will also evaluate our networks on an independent test dataset containing faces that were not seen during training. For the test data, we'll look at the classification accuracy across four different demographics, based on the Fitzpatrick skin scale and sex-based labels: dark-skinned male, dark-skinned female, light-skinned male, and light-skinned female. \n",
        "\n",
        "Let's take a look at some sample faces in the test set. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "vfDD8ztGWk6x",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "outputId": "ad8615c3-cf9b-4843-cbdc-b3421f49459f"
      },
      "source": [
        "### Load test dataset and plot examples ###\n",
        "\n",
        "test_faces = mdl.lab2.get_test_faces()\n",
        "keys = [\"Light Female\", \"Light Male\", \"Dark Female\", \"Dark Male\"]\n",
        "for group, key in zip(test_faces,keys): \n",
        "  plt.figure(figsize=(5,5))\n",
        "  plt.imshow(np.hstack(group))\n",
        "  plt.title(key, fontsize=15)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAATkAAABpCAYAAABBGFSyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAATkAAABpCAYAAABBGFSyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/aJKlwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAATkAAABpCAYAAABBGFSyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAATkAAABpCAYAAABBGFSyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+7Zv+/aetv0gt2/7tm/vafv/AfnFi+GQu3GUAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uo1z3cdbEUMM",
        "colab_type": "text"
      },
      "source": [
        "Now, let's evaluated the probability of each of these face demographics being classified as a face using the standard CNN classifier we've just trained. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GI4O0Y1GAot9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "51ea1b65-e00a-473f-f46f-5b444806fe27"
      },
      "source": [
        "### Evaluate the standard CNN on the test data ### \n",
        "\n",
        "standard_classifier_logits = [standard_classifier(np.array(x, dtype=np.float32)) for x in test_faces]\n",
        "standard_classifier_probs = tf.squeeze(tf.sigmoid(standard_classifier_logits))\n",
        "\n",
        "# Plot the prediction accuracies per demographic\n",
        "xx = range(len(keys))\n",
        "yy = standard_classifier_probs.numpy().mean(1)\n",
        "plt.bar(xx, yy)\n",
        "plt.xticks(xx, keys)\n",
        "plt.ylim(max(0,yy.min()-yy.ptp()/2.), yy.max()+yy.ptp()/2.)\n",
        "plt.title(\"Standard classifier predictions\");"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEICAYAAABRSj9aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j0Cvvt90DoAm",
        "colab_type": "text"
      },
      "source": [
        "Take a look at the accuracies for this first model across these four groups. What do you observe? Would you consider this model biased or unbiased? What are some reasons why a trained model may have biased accuracies? "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0AKcHnXVtgqJ",
        "colab_type": "text"
      },
      "source": [
        "## 2.3 Mitigating algorithmic bias\n",
        "\n",
        "Imbalances in the training data can result in unwanted algorithmic bias. For example, the majority of faces in CelebA (our training set) are those of light-skinned females. As a result, a classifier trained on CelebA will be better suited at recognizing and classifying faces with features similar to these, and will thus be biased.\n",
        "\n",
        "How could we overcome this? A naive solution -- and on that is being adopted by many companies and organizations -- would be to annotate different subclasses (i.e., light-skinned females, males with hats, etc.) within the training data, and then manually even out the data with respect to these groups.\n",
        "\n",
        "But this approach has two major disadvantages. First, it requires annotating massive amounts of data, which is not scalable. Second, it requires that we know what potential biases (e.g., race, gender, pose, occlusion, hats, glasses, etc.) to look for in the data. As a result, manual annotation may not capture all the different features that are imbalanced within the training data.\n",
        "\n",
        "Instead, let's actually **learn** these features in an unbiased, unsupervised manner, without the need for any annotation, and then train a classifier fairly with respect to these features. In the rest of this lab, we'll do exactly that."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nLemS7dqECsI",
        "colab_type": "text"
      },
      "source": [
        "## 2.4 Variational autoencoder (VAE) for learning latent structure\n",
        "\n",
        "As you saw, the accuracy of the CNN varies across the four demographics we looked at. To think about why this may be, consider the dataset the model was trained on, CelebA. If certain features, such as dark skin or hats, are *rare* in CelebA, the model may end up biased against these as a result of training with a biased dataset. That is to say, its classification accuracy will be worse on faces that have under-represented features, such as dark-skinned faces or faces with hats, relevative to faces with features well-represented in the training data! This is a problem. \n",
        "\n",
        "Our goal is to train a *debiased* version of this classifier -- one that accounts for potential disparities in feature representation within the training data. Specifically, to build a debiased facial classifier, we'll train a model that **learns a representation of the underlying latent space** to the face training data. The model then uses this information to mitigate unwanted biases by sampling faces with rare features, like dark skin or hats, *more frequently* during training. The key design requirement for our model is that it can learn an *encoding* of the latent features in the face data in an entirely *unsupervised* way. To achieve this, we'll turn to variational autoencoders (VAEs).\n",
        "\n",
        "![The concept of a VAE](http://kvfrans.com/content/images/2016/08/vae.jpg)\n",
        "\n",
        "As shown in the schematic above and in Lecture 4, VAEs rely on an encoder-decoder structure to learn a latent representation of the input data. In the context of computer vision, the encoder network takes in input images, encodes them into a series of variables defined by a mean and standard deviation, and then draws from the distributions defined by these parameters to generate a set of sampled latent variables. The decoder network then \"decodes\" these variables to generate a reconstruction of the original image, which is used during training to help the model identify which latent variables are important to learn. \n",
        "\n",
        "Let's formalize two key aspects of the VAE model and define relevant functions for each.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KmbXKtcPkTXA",
        "colab_type": "text"
      },
      "source": [
        "### Understanding VAEs: loss function\n",
        "\n",
        "In practice, how can we train a VAE? In learning the latent space, we constrain the means and standard deviations to approximately follow a unit Gaussian. Recall that these are learned parameters, and therefore must factor into the loss computation, and that the decoder portion of the VAE is using these parameters to output a reconstruction that should closely match the input image, which also must factor into the loss. What this means is that we'll have two terms in our VAE loss function:\n",
        "\n",
        "1.  **Latent loss ($L_{KL}$)**: measures how closely the learned latent variables match a unit Gaussian and is defined by the Kullback-Leibler (KL) divergence.\n",
        "2.   **Reconstruction loss ($L_{x}{(x,\\hat{x})}$)**: measures how accurately the reconstructed outputs match the input and is given by the $L^1$ norm of the input image and its reconstructed output.  \n",
        "\n",
        "The equations for both of these losses are provided below:\n",
        "\n",
        "$$ L_{KL}(\\mu, \\sigma) = \\frac{1}{2}\\sum\\limits_{j=0}^{k-1}\\small{(\\sigma_j + \\mu_j^2 - 1 - \\log{\\sigma_j})} $$\n",
        "\n",
        "$$ L_{x}{(x,\\hat{x})} = ||x-\\hat{x}||_1 $$ \n",
        "\n",
        "Thus for the VAE loss we have: \n",
        "\n",
        "$$ L_{VAE} = c\\cdot L_{KL} + L_{x}{(x,\\hat{x})} $$\n",
        "\n",
        "where $c$ is a weighting coefficient used for regularization. \n",
        "\n",
        "Now we're ready to define our VAE loss function:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S00ASo1ImSuh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "### Defining the VAE loss function ###\n",
        "\n",
        "''' Function to calculate VAE loss given:\n",
        "      an input x, \n",
        "      reconstructed output x_recon, \n",
        "      encoded means mu, \n",
        "      encoded log of standard deviation logsigma, \n",
        "      weight parameter for the latent loss kl_weight\n",
        "'''\n",
        "def vae_loss_function(x, x_recon, mu, logsigma, kl_weight=0.0005):\n",
        "  # TODO: Define the latent loss. Note this is given in the equation for L_{KL}\n",
        "  # in the text block directly above\n",
        "  latent_loss = .5*tf.keras.backend.sum(tf.keras.backend.exp(logsigma)+(mu*mu)-1-logsigma)\n",
        "\n",
        "  # TODO: Define the reconstruction loss as the mean absolute pixel-wise \n",
        "  # difference between the input and reconstruction. Hint: you'll need to \n",
        "  # use tf.reduce_mean, and supply an axis argument which specifies which \n",
        "  # dimensions to reduce over. For example, reconstruction loss needs to average \n",
        "  # over the height, width, and channel image dimensions.\n",
        "  # https://www.tensorflow.org/api_docs/python/tf/math/reduce_mean\n",
        "  reconstruction_loss = tf.math.reduce_mean(abs(x-x_recon), axis=None)\n",
        "\n",
        "\n",
        "  # TODO: Define the VAE loss. Note this is given in the equation for L_{VAE}\n",
        "  # in the text block directly above\n",
        "  vae_loss = kl_weight*latent_loss + reconstruction_loss\n",
        "  \n",
        "  return vae_loss"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E8mpb3pJorpu",
        "colab_type": "text"
      },
      "source": [
        "Great! Now that we have a more concrete sense of how VAEs work, let's explore how we can leverage this network structure to train a *debiased* facial classifier."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DqtQH4S5fO8F",
        "colab_type": "text"
      },
      "source": [
        "### Understanding VAEs: reparameterization \n",
        "\n",
        "As you may recall from lecture, VAEs use a \"reparameterization  trick\" for sampling learned latent variables. Instead of the VAE encoder generating a single vector of real numbers for each latent variable, it generates a vector of means and a vector of standard deviations that are constrained to roughly follow Gaussian distributions. We then sample from the standard deviations and add back the mean to output this as our sampled latent vector. Formalizing this for a latent variable $z$ where we sample $\\epsilon \\sim \\mathcal{N}(0,(I))$ we have: \n",
        "\n",
        "$$ z = \\mathbb{\\mu} + e^{\\left(\\frac{1}{2} \\cdot \\log{\\Sigma}\\right)}\\circ \\epsilon $$\n",
        "\n",
        "where $\\mu$ is the mean and $\\Sigma$ is the covariance matrix. This is useful because it will let us neatly define the loss function for the VAE, generate randomly sampled latent variables, achieve improved network generalization, **and** make our complete VAE network differentiable so that it can be trained via backpropagation. Quite powerful!\n",
        "\n",
        "Let's define a function to implement the VAE sampling operation:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cT6PGdNajl3K",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "### VAE Reparameterization ###\n",
        "\n",
        "\"\"\"Reparameterization trick by sampling from an isotropic unit Gaussian.\n",
        "# Arguments\n",
        "    z_mean, z_logsigma (tensor): mean and log of standard deviation of latent distribution (Q(z|X))\n",
        "# Returns\n",
        "    z (tensor): sampled latent vector\n",
        "\"\"\"\n",
        "def sampling(z_mean, z_logsigma):\n",
        "  # By default, random.normal is \"standard\" (ie. mean=0 and std=1.0)\n",
        "  batch, latent_dim = z_mean.shape\n",
        "  epsilon = tf.random.normal(shape=(batch, latent_dim))\n",
        "\n",
        "  # TODO: Define the reparameterization computation!\n",
        "  # Note the equation is given in the text block immediately above.\n",
        "  z = z_mean+tf.math.exp(.5*z_logsigma)*epsilon\n",
        "  return z"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qtHEYI9KNn0A",
        "colab_type": "text"
      },
      "source": [
        "## 2.5 Debiasing variational autoencoder (DB-VAE)\n",
        "\n",
        "Now, we'll use the general idea behind the VAE architecture to build a model, termed a [*debiasing variational autoencoder*](https://lmrt.mit.edu/sites/default/files/AIES-19_paper_220.pdf) or DB-VAE, to mitigate (potentially) unknown biases present within the training idea. We'll train our DB-VAE model on the facial detection task, run the debiasing operation during training, evaluate on the PPB dataset, and compare its accuracy to our original, biased CNN model.    \n",
        "\n",
        "### The DB-VAE model\n",
        "\n",
        "The key idea behind this debiasing approach is to use the latent variables learned via a VAE to adaptively re-sample the CelebA data during training. Specifically, we will alter the probability that a given image is used during training based on how often its latent features appear in the dataset. So, faces with rarer features (like dark skin, sunglasses, or hats) should become more likely to be sampled during training, while the sampling probability for faces with features that are over-represented in the training dataset should decrease (relative to uniform random sampling across the training data). \n",
        "\n",
        "A general schematic of the DB-VAE approach is shown here:\n",
        "\n",
        "![DB-VAE](https://raw.githubusercontent.com/aamini/introtodeeplearning/2019/lab2/img/DB-VAE.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ziA75SN-UxxO",
        "colab_type": "text"
      },
      "source": [
        "Recall that we want to apply our DB-VAE to a *supervised classification* problem -- the facial detection task. Importantly, note how the encoder portion in the DB-VAE architecture also outputs a single supervised variable, $z_o$, corresponding to the class prediction -- face or not face. Usually, VAEs are not trained to output any supervised variables (such as a class prediction)! This is another key distinction between the DB-VAE and a traditional VAE. \n",
        "\n",
        "Keep in mind that we only want to learn the latent representation of *faces*, as that's what we're ultimately debiasing against, even though we are training a model on a binary classification problem. We'll need to ensure that, **for faces**, our DB-VAE model both learns a representation of the unsupervised latent variables, captured by the distribution $q_\\phi(z|x)$, **and** outputs a supervised class prediction $z_o$, but that, **for negative examples**, it only outputs a class prediction $z_o$."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XggIKYPRtOZR",
        "colab_type": "text"
      },
      "source": [
        "### Defining the DB-VAE loss function\n",
        "\n",
        "This means we'll need to be a bit clever about the loss function for the DB-VAE. The form of the loss will depend on whether it's a face image or a non-face image that's being considered. \n",
        "\n",
        "For **face images**, our loss function will have two components:\n",
        "\n",
        "\n",
        "1.   **VAE loss ($L_{VAE}$)**: consists of the latent loss and the reconstruction loss.\n",
        "2.   **Classification loss ($L_y(y,\\hat{y})$)**: standard cross-entropy loss for a binary classification problem. \n",
        "\n",
        "In contrast, for images of **non-faces**, our loss function is solely the classification loss. \n",
        "\n",
        "We can write a single expression for the loss by defining an indicator variable $\\mathcal{I}_f$which reflects which training data are images of faces ($\\mathcal{I}_f(y) = 1$ ) and which are images of non-faces ($\\mathcal{I}_f(y) = 0$). Using this, we obtain:\n",
        "\n",
        "$$L_{total} = L_y(y,\\hat{y}) + \\mathcal{I}_f(y)\\Big[L_{VAE}\\Big]$$\n",
        "\n",
        "Let's write a function to define the DB-VAE loss function:\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VjieDs8Ovcqs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "### Loss function for DB-VAE ###\n",
        "\n",
        "\"\"\"Loss function for DB-VAE.\n",
        "# Arguments\n",
        "    x: true input x\n",
        "    x_pred: reconstructed x\n",
        "    y: true label (face or not face)\n",
        "    y_logit: predicted labels\n",
        "    mu: mean of latent distribution (Q(z|X))\n",
        "    logsigma: log of standard deviation of latent distribution (Q(z|X))\n",
        "# Returns\n",
        "    total_loss: DB-VAE total loss\n",
        "    classification_loss = DB-VAE classification loss\n",
        "\"\"\"\n",
        "def debiasing_loss_function(x, x_pred, y, y_logit, mu, logsigma):\n",
        "\n",
        "  # TODO: call the relevant function to obtain VAE loss\n",
        "  vae_loss = vae_loss_function(x, x_pred, mu, logsigma) # TODO\n",
        "\n",
        "  # TODO: define the classification loss using sigmoid_cross_entropy\n",
        "  # https://www.tensorflow.org/api_docs/python/tf/nn/sigmoid_cross_entropy_with_logits\n",
        "  classification_loss = tf.nn.sigmoid_cross_entropy_with_logits(y,y_logit)\n",
        "\n",
        "  # Use the training data labels to create variable face_indicator:\n",
        "  #   indicator that reflects which training data are images of faces\n",
        "  face_indicator = tf.cast(tf.equal(y, 1), tf.float32)\n",
        "\n",
        "  # TODO: define the DB-VAE total loss! Use tf.reduce_mean to average over all\n",
        "  # samples\n",
        "  total_loss = classification_loss + face_indicator*vae_loss\n",
        "\n",
        "  return total_loss, classification_loss"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YIu_2LzNWwWY",
        "colab_type": "text"
      },
      "source": [
        "### DB-VAE architecture\n",
        "\n",
        "Now we're ready to define the DB-VAE architecture. To build the DB-VAE, we will use the standard CNN classifier from above as our encoder, and then define a decoder network. We will create and initialize the two models, and then construct the end-to-end VAE. We will use a latent space with 100 latent variables.\n",
        "\n",
        "The decoder network will take as input the sampled latent variables, run them through a series of deconvolutional layers, and output a reconstruction of the original input image."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JfWPHGrmyE7R",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "### Define the decoder portion of the DB-VAE ###\n",
        "\n",
        "n_filters = 12 # base number of convolutional filters, same as standard CNN\n",
        "latent_dim = 100 # number of latent variables\n",
        "\n",
        "def make_face_decoder_network():\n",
        "  # Functionally define the different layer types we will use\n",
        "  Conv2DTranspose = functools.partial(tf.keras.layers.Conv2DTranspose, padding='same', activation='relu')\n",
        "  BatchNormalization = tf.keras.layers.BatchNormalization\n",
        "  Flatten = tf.keras.layers.Flatten\n",
        "  Dense = functools.partial(tf.keras.layers.Dense, activation='relu')\n",
        "  Reshape = tf.keras.layers.Reshape\n",
        "\n",
        "  # Build the decoder network using the Sequential API\n",
        "  decoder = tf.keras.Sequential([\n",
        "    # Transform to pre-convolutional generation\n",
        "    Dense(units=4*4*6*n_filters),  # 4x4 feature maps (with 6N occurances)\n",
        "    Reshape(target_shape=(4, 4, 6*n_filters)),\n",
        "\n",
        "    # Upscaling convolutions (inverse of encoder)\n",
        "    Conv2DTranspose(filters=4*n_filters, kernel_size=3,  strides=2),\n",
        "    Conv2DTranspose(filters=2*n_filters, kernel_size=3,  strides=2),\n",
        "    Conv2DTranspose(filters=1*n_filters, kernel_size=5,  strides=2),\n",
        "    Conv2DTranspose(filters=3, kernel_size=5,  strides=2),\n",
        "  ])\n",
        "\n",
        "  return decoder"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yWCMu12w1BuD",
        "colab_type": "text"
      },
      "source": [
        "Now, we will put this decoder together with the standard CNN classifier as our encoder to define the DB-VAE. Note that at this point, there is nothing special about how we put the model together that makes it a \"debiasing\" model -- that will come when we define the training operation. Here, we will define the core VAE architecture by subclassing the `Model` class; defining encoding, reparameterization, and decoding operations; and calling the network end-to-end."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hHdzYzMIMPpd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        },
        "outputId": "8e4d4308-1410-42ae-e1d9-d0ac240b433f"
      },
      "source": [
        "tf.expand_dims(dbvae.encoder(images)[:, 0],-1)\n",
        "#dbvae.encoder(images)[:, 0]\n",
        "dbvae.encoder(images)[:, 1:100+1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(100, 100), dtype=float32, numpy=\n",
              "array([[-0.05622524, -0.01872196, -0.00151447, ..., -0.05676345,\n",
              "         0.02213193, -0.05447293],\n",
              "       [-0.00885922, -0.00232552, -0.01006855, ..., -0.01833864,\n",
              "        -0.00135343, -0.02155299],\n",
              "       [-0.03432852, -0.00862842, -0.00146498, ..., -0.03777209,\n",
              "         0.01876762, -0.03428186],\n",
              "       ...,\n",
              "       [-0.02279926, -0.01388779,  0.00707213, ..., -0.015466  ,\n",
              "         0.00547478, -0.03022928],\n",
              "       [-0.02630973, -0.00490545, -0.00398932, ..., -0.03342671,\n",
              "         0.01456618, -0.0277404 ],\n",
              "       [-0.00980289, -0.00222404, -0.01356081, ..., -0.02929009,\n",
              "         0.00201951, -0.02203253]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dSFDcFBL13c3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "### Defining and creating the DB-VAE ###\n",
        "\n",
        "class DB_VAE(tf.keras.Model):\n",
        "  def __init__(self, latent_dim):\n",
        "    super(DB_VAE, self).__init__()\n",
        "    self.latent_dim = latent_dim\n",
        "\n",
        "    # Define the number of outputs for the encoder. Recall that we have \n",
        "    # `latent_dim` latent variables, as well as a supervised output for the \n",
        "    # classification.\n",
        "    num_encoder_dims = 2*self.latent_dim + 1\n",
        "\n",
        "    self.encoder = make_standard_classifier(num_encoder_dims)\n",
        "    self.decoder = make_face_decoder_network()\n",
        "  \n",
        "  # function to feed images into encoder, encode the latent space, and output\n",
        "  #   classification probability \n",
        "  def encode(self, x):\n",
        "    # encoder output\n",
        "    encoder_output = self.encoder(x)\n",
        "\n",
        "    # classification prediction\n",
        "    y_logit = tf.expand_dims(encoder_output[:, 0], -1)\n",
        "    # latent variable distribution parameters\n",
        "    z_mean = encoder_output[:, 1:self.latent_dim+1]\n",
        "    z_logsigma = encoder_output[:, self.latent_dim+1:]\n",
        "    \n",
        "    return y_logit, z_mean, z_logsigma\n",
        "\n",
        "  # VAE reparameterization: given a mean and logsigma, sample latent variables\n",
        "  def reparameterize(self, z_mean, z_logsigma):\n",
        "    # TODO: call the sampling function defined above\n",
        "    \n",
        "    z = sampling(z_mean, z_logsigma)\n",
        "    return z\n",
        "\n",
        "  # Decode the latent space and output reconstruction\n",
        "  def decode(self, z):\n",
        "    # TODO: use the decoder to output the reconstruction\n",
        "    reconstruction = self.decoder(z)\n",
        "    return reconstruction\n",
        "\n",
        "  # The call function will be used to pass inputs x through the core VAE\n",
        "  def call(self, x): \n",
        "    # Encode input to a prediction and latent space\n",
        "    y_logit, z_mean, z_logsigma = self.encode(x)\n",
        "\n",
        "    # TODO: reparameterization\n",
        "    z = self.reparameterize( z_mean, z_logsigma)\n",
        "\n",
        "    # TODO: reconstruction\n",
        "    recon = self.decode( z)\n",
        "    return y_logit, z_mean, z_logsigma, recon\n",
        "\n",
        "  # Predict face or not face logit for given input x\n",
        "  def predict(self, x):\n",
        "    y_logit, z_mean, z_logsigma = self.encode(x)\n",
        "    return y_logit\n",
        "\n",
        "dbvae = DB_VAE(latent_dim)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M-clbYAj2waY",
        "colab_type": "text"
      },
      "source": [
        "As stated, the encoder architecture is identical to the CNN from earlier in this lab. Note the outputs of our constructed DB_VAE model in the `call` function: `y_logit, z_mean, z_logsigma, z`. Think carefully about why each of these are outputted and their significance to the problem at hand.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nbDNlslgQc5A",
        "colab_type": "text"
      },
      "source": [
        "### Adaptive resampling for automated debiasing with DB-VAE\n",
        "\n",
        "So, how can we actually use DB-VAE to train a debiased facial detection classifier?\n",
        "\n",
        "Recall the DB-VAE architecture: as input images are fed through the network, the encoder learns an estimate $\\mathcal{Q}(z|X)$ of the latent space. We want to increase the relative frequency of rare data by increased sampling of under-represented regions of the latent space. We can approximate $\\mathcal{Q}(z|X)$ using the frequency distributions of each of the learned latent variables, and then define the probability distribution of selecting a given datapoint $x$ based on this approximation. These probability distributions will be used during training to re-sample the data.\n",
        "\n",
        "You'll write a function to execute this update of the sampling probabilities, and then call this function within the DB-VAE training loop to actually debias the model. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fej5FDu37cf7",
        "colab_type": "text"
      },
      "source": [
        "First, we've defined a short helper function `get_latent_mu` that returns the latent variable means returned by the encoder after a batch of images is inputted to the network:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ewWbf7TE7wVc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Function to return the means for an input image batch\n",
        "def get_latent_mu(images, dbvae, batch_size=1024):\n",
        "  N = images.shape[0]\n",
        "  mu = np.zeros((N, latent_dim))\n",
        "  for start_ind in range(0, N, batch_size):\n",
        "    end_ind = min(start_ind+batch_size, N+1)\n",
        "    batch = (images[start_ind:end_ind]).astype(np.float32)/255.\n",
        "    _, batch_mu, _ = dbvae.encode(batch)\n",
        "    mu[start_ind:end_ind] = batch_mu\n",
        "  \n",
        "  return mu"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wn4yK3SC72bo",
        "colab_type": "text"
      },
      "source": [
        "Now, let's define the actual resampling algorithm `get_training_sample_probabilities`. Importantly note the argument `smoothing_fac`. This parameter tunes the degree of debiasing: for `smoothing_fac=0`, the re-sampled training set will tend towards falling uniformly over the latent space, i.e., the most extreme debiasing."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WEnSpNe6Wv9T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1d26bcf2-4238-4260-d7a5-79159623ca01"
      },
      "source": [
        "images.shape\n",
        "np.zeros((100, latent_dim)).shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(100, 100)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PRt--0YBTpXZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# For understanding how to histogram to invert probabilities of sampling (to be used in next cell)\n",
        "\n",
        "hd = [.1, 2, 1,1]\n",
        "hden,hbin = np.histogram(hd, bins=[0., 1., 2., 3.])\n",
        "hbin[0] = -float('inf')\n",
        "hbin[-1] = float('inf')\n",
        "hbid = np.digitize(hd, hbin)\n",
        "print ('hbin',hbin)\n",
        "print('data',hd)\n",
        "print('hbid',hbid)                                    # bin locations of data points [1 3 2 2]\n",
        "print ('hden',hden)                                   # density in each bin [1 2 1]\n",
        "print('''density in each point's bin''',hden[hbid-1]) # =  hden[  [1 3 2 2] -1  ]  = [1 1 2 2]\n",
        "print('Pick',1/hden[hbid-1])                          # prob for picking data [1.  1.  0.5 0.5]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HiX9pmmC7_wn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "### Resampling algorithm for DB-VAE ###\n",
        "\n",
        "'''Function that recomputes the sampling probabilities for images within a batch\n",
        "      based on how they distribute across the training data'''\n",
        "def get_training_sample_probabilities(images, dbvae,bins=10, smoothing_fac=0.001): \n",
        "    print(\"Recomputing the sampling probabilities\")\n",
        "    \n",
        "    # TODO: run the input batch and get the latent variable means\n",
        "    mu = get_latent_mu(images, dbvae, batch_size) # TODO\n",
        "    \n",
        "    # sampling probabilities for the images\n",
        "    training_sample_p = np.zeros(mu.shape[0])\n",
        "    \n",
        "    # consider the distribution for each latent variable \n",
        "    for i in range(latent_dim):\n",
        "      \n",
        "        latent_distribution = mu[:,i]\n",
        "        # generate a histogram of the latent distribution\n",
        "        hist_density, bin_edges =  np.histogram(latent_distribution, density=True, bins=bins)\n",
        "\n",
        "        # find which latent bin every data sample falls in \n",
        "        bin_edges[0] = -float('inf')\n",
        "        bin_edges[-1] = float('inf')\n",
        "        \n",
        "        # TODO: call the digitize function to find which bins in the latent distribution \n",
        "        #    every data sample falls in to\n",
        "        # https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.digitize.html\n",
        "        bin_idx = np.digitize(latent_distribution, bin_edges) # TODO\n",
        "\n",
        "        # smooth the density function\n",
        "        hist_smoothed_density = hist_density + smoothing_fac\n",
        "        hist_smoothed_density = hist_smoothed_density / np.sum(hist_smoothed_density)\n",
        "\n",
        "        # invert the density function \n",
        "        p = 1.0/(hist_smoothed_density[bin_idx-1])\n",
        "        \n",
        "        # TODO: normalize all probabilities\n",
        "        p = p/np.sum(p)\n",
        "        \n",
        "        # TODO: update sampling probabilities by considering whether the newly\n",
        "        #     computed p is greater than the existing sampling probabilities.\n",
        "        training_sample_p = np.maximum(training_sample_p,p)\n",
        "        \n",
        "    # final normalization\n",
        "    training_sample_p /= np.sum(training_sample_p)\n",
        "    \n",
        "    return training_sample_p"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pF14fQkVUs-a",
        "colab_type": "text"
      },
      "source": [
        "Now that we've defined the resampling update, we can train our DB-VAE model on the CelebA/ImageNet training data, and run the above operation to re-weight the importance of particular data points as we train the model. Remember again that we only want to debias for features relevant to *faces*, not the set of negative examples. Complete the code block below to execute the training loop!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RnDkBaVU5qRN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dbvae(x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xwQs-Gu5bKEK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 786
        },
        "outputId": "e722ba72-1ba0-412f-b13a-745324a23a19"
      },
      "source": [
        "### Training the DB-VAE ###\n",
        "\n",
        "# Hyperparameters\n",
        "batch_size = 32\n",
        "learning_rate = 5e-4\n",
        "latent_dim = 100\n",
        "\n",
        "# DB-VAE needs slightly more epochs to train since its more complex than \n",
        "# the standard classifier so we use 6 instead of 2\n",
        "num_epochs = 6  \n",
        "\n",
        "# instantiate a new DB-VAE model and optimizer\n",
        "dbvae = DB_VAE(100)\n",
        "optimizer = tf.keras.optimizers.Adam(learning_rate)\n",
        "\n",
        "# To define the training operation, we will use tf.function which is a powerful tool \n",
        "#   that lets us turn a Python function into a TensorFlow computation graph.\n",
        "@tf.function\n",
        "def debiasing_train_step(x, y):\n",
        "  \n",
        "  with tf.GradientTape() as tape:\n",
        "    # Feed input x into dbvae. Note that this is using the DB_VAE call function!\n",
        "    y_logit, z_mean, z_logsigma, x_recon = dbvae.call(x)\n",
        "    \n",
        "    '''TODO: call the DB_VAE loss function to compute the loss'''\n",
        "    loss, class_loss = debiasing_loss_function(x, x_recon, y, y_logit, z_mean, z_logsigma)\n",
        "    print('\\nDone LOSS')\n",
        "  '''TODO: use the GradientTape.gradient method to compute the gradients.\n",
        "     Hint: this is with respect to the trainable_variables of the dbvae.'''\n",
        "  grads = tape.gradient(loss, dbvae.trainable_variables)\n",
        "\n",
        "  # apply gradients to variables\n",
        "  optimizer.apply_gradients(zip(grads, dbvae.trainable_variables))\n",
        "  return loss\n",
        "\n",
        "# get training faces from data loader\n",
        "all_faces = loader.get_all_train_faces()\n",
        "\n",
        "if hasattr(tqdm, '_instances'): tqdm._instances.clear() # clear if it exists\n",
        "\n",
        "# The training loop -- outer loop iterates over the number of epochs\n",
        "for i in range(num_epochs):\n",
        "\n",
        "  IPython.display.clear_output(wait=True)\n",
        "  print(\"Starting epoch {}/{}\".format(i+1, num_epochs))\n",
        "\n",
        "  # Recompute data sampling proabilities\n",
        "  '''TODO: recompute the sampling probabilities for debiasing'''\n",
        "  p_faces = get_training_sample_probabilities(all_faces,dbvae)\n",
        "  \n",
        "  # get a batch of training data and compute the training step\n",
        "  for j in tqdm(range(loader.get_train_size() // batch_size)):\n",
        "    # load a batch of data\n",
        "    (x, y) = loader.get_batch(batch_size, p_pos=p_faces)\n",
        "    # loss optimization\n",
        "    loss = debiasing_train_step(x, y)\n",
        "    \n",
        "    # plot the progress every 200 steps\n",
        "    if j % 500 == 0: \n",
        "      mdl.util.plot_sample(x, y, dbvae)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Starting epoch 6/6\n",
            "Recomputing the sampling probabilities\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r  0%|          | 0/3434 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJEAAABVCAYAAABJoQaBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBEKUfbf/5WoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 144x72 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 14%|█▍        | 484/3434 [00:03<00:18, 160.10it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJEAAABVCAYAAABJoQaBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0\ndHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nO29adBl61Xf91vrefY+57xjT7dvT3dE\nV7pXQmCEIsIQgh1MIIoDDomCXQzGxo5x8clVqSKVD8mHOOXYH1yuVLnKVGVQUpUyuGIgBYoxYhDI\nduBCIUWDEZLuIN2x53c85+z9PGvlw3rO243s291XjV6uUa+q7jO8++yzn73XXsN//dc64u48kAdy\nP6J/3AfwQP7tlwdK9EDuWx4o0QO5b3mgRA/kvuWBEj2Q+5YHSvRA7lvuS4lE5LtF5DMi8jkR+ck/\nqoN6K8tX45rvJvLl4kQikoA/AP4s8BLwLPAX3P3Tf3SH99aSr8Y134vcjyV6H/A5d3/O3QfgHwHf\n+0dzWG9Z+Wpc810l38dnLwJfvO31S8A3felGIvLXgL/WXn7j2mTKxvo6KSkisLGxTtf3OI7E9rgb\nbvFaVbFqiMQOtD2R9p87rP4oIlgtiCrguGZEE+CxoUg8Xx0bEvswp5QR1YSqggju3r5DGcclfT/h\nsUcvsbu3z5/6unf5xz/x6avA37zbmnNO33hqewMHxFcH3mR1KNKOZbUwB9XbN4w1isdH5LZN//AO\nb3t5tEyPpx7PvD3edhpWW9y2Az86N6vzdHPvgPli+SVfFnI/SnRP4u4/BfwUwKTr/V2PP8m3ve/f\nYXt7k62tCd/0Le9l68QJXITcZbIIN195ka3ZGuNoKErfJUQhp46UEkk7VFOcTXdcBJE4EQqMpaBi\nXN8/5PRTz1CopLJEVREVzBwRCYUBuq5j99p1Sql0a1P6vsfMqLViqYM6crC7y//7sU/z4V/9KH//\n7/4PnHnk7S/ey5ofPnPC/4v3fwfi3i6KIxrHq66YOApkSZCUJKBJyblD3ElZERM0pyMlVI0LryZI\nl1E4UjA81mY4mGBqmBluUOpIrUIphUrFDVyMUuLvaHyBuCNJURNMhH7S8T//9C+94TW+HyV6GXjk\ntteX2ntvKCJw8cI5Bir9rOebv/W9bGxtUwZD+o5+bZ1h5wbTyRaH80N6zajGxe66DiEhCCl1YbEs\n7iFFESROnlX6boqZcXK9Y/7855GTp5iePUtZHJKTMpv1DMslkiagMNrA+unTHBwcUEejmpNTIufM\ncjlCP2W6YazPJrzy2mUsHUUBd18zzQK0u1vC9CGiCE7WhAApJXKK57nPdCmHRcqKiqCS0eRHFksk\nLnbOCVCOYttm8qRZWHcoZjgVqx2lFsYE1RO1OkZFqFituCZqhdCueJDslHrnuPl+lOhZ4CkReaKd\nyB8A/uIdT6gKGyc2Wdua8czTTyHaUzEkwaRPLHd3UAMVpe8nqFW6TlGNk6QKSROycl96dEqP3ksp\nxcJSwquxtjZlcbBH3V/HspL7GWOtdF2P9BPMwYpTx8JkOoFJh1sBKiKKi9IJWMo8+ehFnnvueV55\n9bX40ntYszuIOkq4SPem+AIioSBhZRURCWWSRKeKK2RPqPrR9oaTRI8+q0cW1bGVu/TmvhMYBkVw\nyVT3sODuJDdGKu6JIo5mDaUTx5OACyQBh1rtS53mH5IvO7B29wL8BPBLwL8CfsbdP3Wnz6TcMS8j\np09vc/r0CVKOk6mdYmUgmzE/PMRrQc0RlOalQpFwzG31/e1fU5xVHNNOaq2VnBKqOe7Wg0M6hFoh\npZ5KZqgwma4h3pG7KZCo1UASLonqoczhDpwnnnySv/3f/7d8/wd+COBd97JmAMwwF8StXUAQE1xA\nxBEXRCBJPKoKSYUkGcmhAJriZoo1KeHNFU2CJEFVmiIqKqGQovFcVdt5AUQjHkVbDCj0InQqoI7g\nJAAx3AzByMn/tdDrdrmvmMjdPwR86F6377qOzYdO8DVPPkk/6+gnE8xCCVSEagMJR92ag6JZoLjT\nBEMFai3NOoGmTLUaytQslrsjqoy1MMkzJhJua3Fjh40L66TpFKyCVdyE6fomdZiDC32nzOdzctdH\n/AGIKV7h+pWr/Nnv/Ha+58+9nxMnz3/S3f/WXRfdgn+hUD2uhBNWVHFEEoi2BEDIEgqkzaJq0nBr\nSZtr1PgcYcEUJcktW6A4VQx1xdv2SRzxsPBQcRFSFrwKkPAeSqlkEWrWuJGqhaVXiXjpDh7tKx5Y\n3y4pJx46c4aHT5xGUwaU1PVMJlNsWFL2dhATqA4qpJxJ2VEKSVJkMVJjcW6oJswqEG7A4Sh4VlW6\ntI5MpmSBYaysz5Tx6uvUtW1OPPF2lof7pEmPmKEkcjcymrPWZ8bDJWuzDZbDAaJK7tfZPPUw4+Im\n41jufdEOoBhGEonAUARNQiKTckIVOslogiQawXRqMZ8bzi0ri1tYHjiyZKqCm4T1cMBC+4+C7SRQ\ngQRSW8ZrkFO83RHuK2IfJ6EUMcwheXz+TlHR8ZY9zOlFWduYRaCcc6TstWCLHbQusXFsVsiPAjxF\nwOIexECPXJodBZRH7q1ZKHOoZlQUm84gZUwSuVtjuXOdw91rdP2EWmu4icmU0Y1SKqoZzQJ9j7s3\nS+mYwN7Nm3Sa7nnJDpEplXZ8VgFHKrg6ah7LTOFKUEFdWmwYMc8qjlfC0ohLbMcKEnHwChAZnbRz\nhiAukcXhSNtnEie1v2NgK0sjjpjjUlsqYJh7/P0OcqxKNI4jN69eYyzO2vpWnAABG5fYzhXmB/vU\ncdlOjgG1YUXhBrqccXeqFcwMq9YsT5juSH8T2k3Q3HM4LkESa7mnm04QUUZzvIzc+OJzcYFrnPwi\nEbBO+glCYhiGhh1pU7RElzqKV4b5/E2s2jGrSF6BLrLKpElGKINGfqkawfIKBBMCG8K1YUOCtMQi\nuaCaWpZHuEQIqyOxTbi8cHur7Uwi6nFpwbcbuN3CjlTB47N6pIphod5IjlWJaikkzWhOGCCrgPhw\nh4ObN7ER3CXS4dsAxJUpD9cFboVShvB6R5mbhktzwxUkKZtrJ3Cc3d1dvFTqWOm9srW1TTbj8OY1\n+klPKQU3R1NCVCmlMJmusZwfxElSZTkOmDmzte2m4Pcu7uFuhAY4alwrk2ZNbBXvScSE4ogSAa4q\nSRWVhCaNeEmFME8tK5V20wlo9VsZa3z8KGNzBbNmtaphHi7LRXAzQFCrDcENJXN3jrKbN5BjjYm0\n63j6mXeycfIkYx1REp0aO198nqEaucstbgCRREoR66xAQTMnpx4zkJzQnKnV6LqOsRSyhIK6K8VA\nk9ElQXPPcrE4utPGYclUE8PrLzHbPklKOe7ENEE0MpxaMyz2ICXcK+pKGRfMth9i58odoaF/fd0t\nvtEUIXG4lIh7VIgAWZqyNKXpWoyXk4IJOSc0gZpGvKOC1wAjHQdr2GuzHarhJk2EXMNdYkISw0Qj\nE2uvcUezkEywFvB7Vaq2rMDulOAfsyXqJz3nzj9MLTXSZjfEI1BOTFCZUGtkI3UsYX28RiwhApJB\nO1wTaIoL0yzRKqV1iztOcsLzlDJWhmGg7zpEnFqWraQRWVAdSzPr8VDGAUSwMVzpdLYegFzDpJIK\na+sbb2rdbtYucNzh7uAt9hHV5n5WLqul/AEMhVtJYVGSJCRJKJsrOSnJhUxYKGnpvB6VUeI7aEqK\nx/HjgZiHpYtwQUyaSxPcWx7oBnILVnkjOVYlSllZ+sCwHMg5Iy2isy6AMIgLuXP1Gp/95O9z7fJ1\nRo/yw0pRHNAGCSCCtc9JDuUqQ6GThKD0fY9kBRVKCxSzOEkiLqIW6rAgeSUyxS4CUBFy7uj6nmEc\nm9I547hAU8/61vabWveqzmcWOJCsYBdxtIXe4qsKlmAIXgNxtpVLqUat8RhIvVPdMVbeZ1VbW8Ec\ncWmdsOBuzS25tRKRhvuzW+cQiXX6CvFuONwdkUaOW4lUWJQl4+IQEDwrXitdPwWipmVWefm11zhc\nLnn2t5/l07/3SUoZAcMCQG1+OmKl1O5YNyOnRD9bZ1kKImBmUW/rOgKwCexIKLgPqBfm169yeHiI\nWUAGkdlZuEoSLOdoTVEEVmU5LNk/OLznNXtbdxYhChQNI2qAIEQw7dqsYVzGiFdwvDg1kju81ni0\nSD/j04aK40cX2tq5adBAtchS3du+NdyZCI5QI+HFGiJOUoQUKY1EaiNyZzU53pgIZe/aTTj/GGVY\nMukzlDmqmQIsd/e4uXOVdzzzDvpJz3I58sKLL/Ebv/47XLp0kc3Tm5w+fZpZP2lwi2Kj45NM7jJj\nKaRpR+4z4+isr/cUhNQp9fAAVeVgZ4fd69c5nB9y7dp1zj7+BOff9Q1sTaeUwZHcIR4pbi0VL4V+\nOmWs4FQ6Hance2AdFieyKsVREXJamaPIkhyhOlAqqkatSpG6gpSQUbGUjpgPrjnQaaCKNhcVQbK5\nMdSKmFNxSjGW48DK2FSLtN2hWaCABFyUrGH5jEJ2MEmoRxZ8JzleJRLhpee/yPu+7j24OzlnqgmL\n5RJ32Dvc4+TJ7VapT2ys9zz99rfxscWSz734ImcOz3D5ylW+9umn0dSCTgzGRF3VzsYBHwPJ3r9+\nmdFgb3cHrSN6uMenf/8zlFI4mC/ocuaCGD7OoYwUsaP61KSbUCcTRGuk6BIF0fl8zmy29qbWLeaR\nPWmDwMOD4Wq4K1aMsQqWIqBNWgNA9CiFgFBTputSYGhdxEkURTKM7ngNN1QLUUT2kVKdWoxlWYaV\nlwi242JoZGmSwurVCqJYrWEBJfZpwlEN7Y3kWJUosJ2gQtDijJQzi709sMT6bMLGxnaY92oB2afE\nU089xbUrV9kbluwezLly5RqnT23jGfIkjK+PA9plxhbD5FxhHNl75YuMw8Crr7zGMDrPf/FlLly4\nwLkLFzh15jSnzlzk8NoNNk6fpZ+uh7s0KLWwqo57LaBQx4E+BSTwZkSTRO0LWg1LGiLdzgmCe8FH\ngEptqb/TKCModI6bkRolRFvZw2pkT95CAUdYlhGrI2N1xrGwrAFPrChV0iwjRAXAjsKDlil6ohig\nFhUEbybxDeRYlQiH3deu0fU9IhV1R/qetbU1dnYOQBrFY6y4BgVBG3Xj0iPnuXbjJsthyuuvvcq4\n2OPcxQv4OOK5w81Y6zoKhA+xws6VV7n++mU+/9znyV3HsKxsziY8euEcZ86do+8nJFWmfRdIrRua\nMomCATl3LOY1sBuMpEKphZwn97xkQVoZwoAWcxkRY6HU6rgYWhVr9A4To7hDBWsMAKuF3CW6kqHr\nSFnQmqCTqNYXqFLx4pRhybKMlBpcoaGODWIIxVVVtAsFMW3ItVuAkBYxlwiYr4q2d46sj7d2pplL\nly5RhwWTtRmdBgWjLnfjQLXjleefY9YJ7sqzv/v/8a5v+HrWT55iNpsy6TvWt05wIGB1YPf6DTZP\nbJNIdP2MUgqShPn+TTJOlxOb2zPe/e6nKbVSDaaTNVJKfPw3f4O3vf0dTLc2kO0tdOcma5KZriVG\nA68LSgUVo4wjzhCwgTs+3iVd+RLRFkAfQdUY6hEPqQpWmt2RsNQuHrHL6NRkYDCoMqlKFcO90mVB\nxRDNiCvVnWKFMhrz5ciiDJSxUqszUtCi6ERJ3lif1jDEGqUiHERri50iHtN4kyMQ/Y3W9+Wpw5cn\nSYSHzpymyzm4Os2M6nSdJEHDGEqleuL6/oILX/M2PvLsx5meOsenX3iFpfcsZQKpp3rm+o2bHB4e\n4h6+O0/XcCt0ucOsYD6wGCujC556lmQuX9/j959/iVeu3ODFl1/i2pVXONzfZefGdfrJhHFYklQp\nZSAnGEttEAPtMSCHe5ZICsOFiAbJLiVoRVRtwCAS7srMGEthLIXqlVoiXrGxBuY1FkqJdL86VLyB\ns01BJYLr4oWRyiiFWgpLr4ylUOpIKVEjrCUKtslX1GRFEqA5sCIN2CC9lSyRubO5tQXQKs+G1JGZ\nVhYqvPLFlzjY3eETn3uBeYUbBwtu7B/wob/5X2HLgUcuXeI//4+/h6e/5lEk9QyLJZdfv8Kp0w8F\n6NiBzQfclpTlgr0bO40CqrhmDsaR3/vs87z02qssDud8/uYuX/f4eZ5IMy6de4ScMovlYRQebYhS\nhUgEmeMYJzMnzN5kFd8dV0csLpJ4FEargxej4iyXpYGwJaxHsSDsCfQi5K4LhUuFMibcDJWKNran\nCGCK10I1YxwJS1RgviwYpdFBYDLt0CFQ8C53aJ+P4v3kDeBVje9IjfR2Bz069sB6vphH0bShFgik\nvif3iScenfGbH/k1Xttb8vzrNykO27OOJ86c4dTZh/nd517k537hZ/mJ//LHuXTuYXZEmO/vMJSB\n2WTG8voNJFA69q/vUK2SJ2vk6Rr71fmXv/VRfv5Dv4IvDtj3KWdPbfDy5df40aefodTKfLHPsDxE\n6JEK5iOYUUohU6nmWJ0eAaP3JAKkFMXMRBRcW6kjiUCNKnkthcViQfXIqMYS8CgijFnYyIqRbhVP\nAWsMx9xS/2IVsuBimNVI981YDkMUnquhncR5yUoaM0NnkeUm6KSjasRquRKVAedWcfYN5FiV6HB+\nyJNPPdYooHG3mxWk26LroqPie7//+/iuUvjEH7zA9cs7nN3a4vXDPT7+27/Jd777Efyw8NjXfiPr\nLBnLgpOnTxGJrzJZnzK/8Ro3r11nTmV97SRbD53ht375V9m+cJEf/KEf5uDyZT75e7/Nx167ySPv\nPM84JP7xh36NH9g6zda589QysNb1LGu4LQG63DMOc3xYItP+rmWAL5UMZImqe0qgkug0BRYlyqRW\nbKqIzo4YCyvAVEVJKbPe9aTkCInpJNPP+uAj9TnwIxOydDgw6yeYK1iEBmuTKdVG6hgE/D5FfOZV\n6adKngQTIqdE50GlKQpQwYUKRwXxN1rfsUlKiZx7UkpUKw11jZqY+xIR6ACZTHjq0Ut80YPtJ1cW\nnD/7KI8//jhpOmN++SX2dnfJfatmy3oEhtUow4DXytp0ja0zp9i7scPph86yOJxz/bkXeO+f/g+5\n9vxnuXjuFHW+yzd883fz8pXXee21V/na8RnKcqD0pQWagiQlG5ShsQsax+nNiEdJKvjUjWKRNGyx\nqrcKRE/RW3FiztaoskrWRFYlK5CEPvckEiknMokkGZdCpsPV6bqOmQnVK2Yp0GzLMAMjQM8kFSOR\nFHpNeBISiml0fohaSyx4a2Vnk8kE80BSkw0oNQqEbm2hgeh2ObO5NuPJJ85jZkw3pnzX9/wZru7s\nkcTY2JrQnb6ADwO7V66gZx4KAjrgYyUnRXMiuXHqkYtMTm6znC8YqrP7yU/x733P97O+sU5OHZqc\np9/5Np546klw2D5xosUCEft0MmWwOTpZw8shXka67t5TfCCCaAk+s4qQUrQA3SKVQep6vCHDYlBa\nDS+liJ86TVHbSom+sRtySqSsBKMm4RTQzNR6smhzv0ank4bNtZKIrKgeguQoRNvqpqkJkUgkckv/\nXaIg+0ZyrErU9z2lFvp+AosBkajkL5cjSTNuhVpHJimzvibM+nWSKg8/tA0IW5fO0k+mmMBib5eD\n3RtILXRdT049TmEcR3pVEGMYD2Bf2FzfZjZZQzC+89vfx/58jpRCKUtOntpiMpmx307s7u4+a7Oe\nbjrFh6g/9d0MxJiPW/hwiNi9Z2eC0ImSVVuXxi0ifihU0ENmqgEqigcJbVUIrQI5mhYSQatVEbou\nkyRFJb/FmJlQTPqeonE+3FodTaQVYVeAeXRweHujWA1AUqOpAF0F64LepQp7vGCjQEpCP5lSlwdh\ngUoFCeZgtVUqLHSpw9TJXWPp6VpkDLVwuL/D8nBOIuESLEfHG+HKkJwRDeqteqvU91Okn7AuQj/N\nZE3UMpK7jKngJXhJ0/UZXkcSwmgWfJ2qlDIndRMWBzvk1L+pNZtD1NsFl+AHWTSVgGsjo626b70l\nHlFoltRcnKZGk9XGxQ4LodxCk5O0ZkgJbvoKLKwrUlrzq6JOtVvdMrXx1d1KAJ6E8tuKbXAXOvDx\nZme1NppFogCaesb5NZbLJe5TssfdqgbSBS/mqN0lGVYCeaUsEa+oaABlgCmY1WgPEo7asLvcI+pQ\nBpIIaTJBJz2Y05mRFKoVTqxNydpRqtHnfBtVVekmwWOq85GUM7eVzO9JpHF9VjeIaPTQ3WrjidLI\nKsvKjQIbSaAfUV1dosQR2GUir0j/7TvcJFyQgqwotS50jcvkzfZEh403ikjQcYKbJY2DpihxrTCo\n3NnyHi9RX4Rsjkw6hiTkw0L53Oe5+lsfRYZAR63VdyII74L66h0+WjPHQcrq+0z1ghM8ay8DVhfx\nPQ5d7m7FGKLRImMjapXszrRToGBWUM2szWYMyyUAo0MJJldUyqmYCJPZ+q3e/jez7EbdWNFYnOAx\nY9Iwo7jzddWUqPloLoBqxjUFINLcDOhRmh+7kWZZJPgF0tiPJNBMShmVFO4UJYu271vt77YmUKKx\nQFNkacHZfgtRQTqMjYN9fu/7f5TNt10if8NjvPSF5+DGDV797Me48OTb8H6LpGtRRBUh5WifNjNS\nymDOUoRxsaTWQup7zCopdVE4LKVt25NytGEjiaQ9l19/ndNnzpDFKbWStfV7SeJw/4DZ9gmWbvT9\negTXeQoO5gkfl2ifMenwN5niu9DoF4052NqcWtCBklqrEOBK6iJQFgu02y1iGJcaN0YKGNwbkBmt\nry2bq1GbS1kbtWjFP1cMQVvXScKxKo2fHsontnKLoZSIN7ryWwhsnHaZM5vCmR//AHlrm/n1V9m+\nvM7V4TJrfUdq1elIPqVRMKJUkNJKkVamPkzwxuYGqZHW4w7PlGGXsRO0W8MMum6KqXLq/EXUo9Ca\nJQOCpsxYKlmU5f4BvpFIHuUORUNB+wme0hGfe293502tO+56AtJo9A+HIzajA6jhNZFzuKJo2pCW\nbkcrXq2thFJpHbC3Al5FKNbgAo/2IG9DIxIcuWBXiXZqC05TlRZ7xU6QGkVgvIEBjW58JzlWdyYS\nmco4zplff53D115m98ZNdhdLZmcfYpQFtYyE3zZWQxpWvWUiQi2VlDtMMt1sxmRts8UZMeghdR21\nVIblgrJY4iRSNyXN1tDJNALUvqPmDs/aWo2FnBXqQFksqGMJlDp3DcvJpNzHiU2JzY03R49FOCKF\nhbsJ+uuRPYvaTBDkWzdG1OgMx/DaiGSrAFniRrBqeAl0erQa5ZrqTTkdq80KWks62v6lhQUGR5NG\n3Jol8rCNkbQZpi0JuMPyjlWJzIzF8gDtMuXwBvs3X+OFLzzPsLFJWj9BHaLY6NVQp/FjHLcRkYpg\nJFUgOj02NzeCqy3hIqrVaPuRxDAfWe4vwK0RO8K65I0NmE2RyRraTTGFKs7BwR5WC4vlAYeLPbRP\npMkET320Nmm41rEKdrduvttEuFW8F1/9ixYhcb91Ed1vNQquYiizo9Ye8YrXZkE8+uTdgzJiqwZI\nM4zGcKxOWvWVrdjb3hKtVrXX294PuuwqHTO80ADccH13qvQcqztb7N7gsx/+Bb743Ge5fPMG5889\nxqOPPMbFv/iXcO15+RM3mc0P8WEWPGcyXgy6DpUcpticYTky37vJ9okNNGc8T5ltn2K+vwe8GrN8\nUKrA3v4e29MZOnf6bp2lCV2/Ri+FOoxILWQx6uKA9Y0NlqVQ54csUk9OI33ft57/nlpKAKWpu+c1\nNxJja3uWI/A3up6D8qEkSrWjQQqDtx66ZjnMpcVhxmiCe20EMiWnSmG1jUe3rYerG4nu12j6DK63\nNWUJHjesXgXdI5QuUPNbfPa7TJY5Zpwo96xfepRv+Pr3sffKi0zEuXnlKpOtEywWS7qtbYaDm9RS\nwpVVR7uEShc0jFUjsRbmOzfZT1EKEHP2S4HlwN61V3BT1jc3YnBBlyH1lGGB2AEy6REv7O/vMe0S\nbiOlFJImLl9+jXTyDFuzh7HJjOoWHSk+MuknqBjLK0sm3ZvAiVbiEaOINwS4db/iROtUdTy1VFuE\n2qzNKhC31u1SzBAPuofUIJKJh0LYqpujCpIVcUMkB3vSPYq37hSPmmCtwSAwq1iN7pHqbSiWrOYS\ntVjzDnKs7my6sc6j3/xnOP3wec6cOkWtC8rmJl03pcsdF9/+LsZxjPpVKQzjwDiMlHFsxVCjWiXJ\nhCSZq6+8yuUXnmPYuc5w/XV2XvoM+1euI8vC4Y1dbDGwvrFN389Ik5bCL/YZ9m9iw4JxfkhdLlAr\nWBk53N1BcXb3dhB3xlJZdUWgylAj7npTfCLCQ8QQihW7q7kNaW1CTcFukemjXGENCPTIwiOTEm+t\n5EZ1i/hvrFgp1LFgxbBWCXBC2VbxkFnET3h0gJgEbFItBntVa/2A2gY4pBUk8ZYi6id6nINhyeFy\nj52dPS5815+PIM8NnWxS8oxhcYhIoLUAuUaWVlu782Tas3bqDNdeKxzuHHBl+ALSKcMwIN0ak40t\nhsMFV19+nRNPvxe3kZwz/WTC/MYN1mYzepw6VNARK7dirjoM7A3XmZ06HdAA4EkpbfDD2uYm+U3g\nRA3vC0uQDLEUhHzkKEV35Aj48wb0mSdor93tqDnTSotxahDvxQJIrM2liXiwFxv+k0SiEu/R41aJ\noLwiQc0lCPmh001bW0s1Bqp+VCB5I7knJRKRF4A9YhJJcff3isgp4KeBx4EXgA+4+4077sihlpHd\nV17kyv7Ao3/+r7K2fQas0OdEsX1OPP44e5/+NHv9Lin3dH3PCXmI1BvSZSQJWZ2HL17g9MNnGeaH\nMURqObD58DppukY3mbDhcDZ35LJgGKxNUHPUncODHXIGNaPW6IC1ceDk5ibLcWD75Cbv+aZvZ2Nj\ng5QzXd/zL/75R7l+4yo//EN/mRdf/ALElLiTd10zLcWHAAMlRreEpdCjTCvahiQyM4nJHeaOSg2Q\nUdpkDyUaE7MejY5ZIdHa0kBLUWlzdWpjCpjFaL3asjeDcKm+mm8QVstd0KyR2RGkNtGmVG8gb8ad\n/Wl3/1Pu/t72+ieBX3H3p4Bfaa/vIk7d22f/+jW2nnkP3cmTLOs8YgQV5leuwHSCTTNdN0UQylCC\n41xbRmLOWIOwpUlZW+tZP1znb9IAAA0jSURBVHWK6fYJkmQ2tk/RT9dIXd9OXJRZgno60k8yqk4p\nQ4B+Kx/ijnkDINvF+dDP/TS/9dFf5V989COIOH/v7/1PfMe//2185Jd+DuKmusdh6IG1aGMWri59\nAIURBLu0cXlwm+tqOHJL65wGHkubCJdo0IbQddGXlnKKgmwmEHflqGhhRJNkJG3RJh0T2QCFdp8F\nal2sDRvzu9rd+4mJvhf4YHv+QeD77vYBM2OxOOD5vQWPvvubWSPKGbVUai3s7+7RTyZoN8PhCFgc\nhwFKhbHiZcS9RE99HY6mgUw31kh9h3XRxdn3PamLjK64M47LuMvrGA0SLozL4WhYRMQlShmX8b04\nXiuL+bzV+zK/+Iv/D3/pR36Ek6dOAVy7lzVDXLDVkFIzael4THiNliTCKlQ/ulGM1iZdpc2VbHFM\nbYrvYK6sZoB4bbEbiq8gg8pR67VVi960Ssu2hGIxMK4FT8Swk7BWVcI9xs7/aPhEDvwzCYj0H7YR\nuw+7+6vt768BD/+bPnj7TOcLZ05SDnZ4/OJ5PvHBf8CGFBbLQ576wb+KYMzWpiiO9RPG/TnSZyRB\nHZaUnKEKue9AJqjEMEyVnvXJOgUnW7iLLAmjkrJTbAyQUAwvBcoItdK1uUZmxmrO48HeHt2sslgO\nuDn/yQd+CFXlx//GX+fHfuyvcPnyFc6dPcfezmWAEXjsbmve2phFa7KmFkxHwu8paB6WwuI4Eu1B\nKxig0S8qFh8RYgoIEiUQDzcjVY+KuYEYRkjj7Xmtq1E9HFk6awE6reM1ijDCqEIypVhFq2Ftv63j\n/76V6Nvc/WUROQv8soj8/u1/dHeX1SDpL5HbZzq/85Fz/sLHn+Vtj38NFx87z3SyyWK+x8GNV8jT\nLaZb6xzu3cSnU2yxzlgHJl1mcXDIuFhi7vQ1ceL1Snphj/KxL8DugrJ5iv1xiZzb4uY0cS5PYK2n\nf/xh+nPrHGz3sJmwbVgMI+PhIUWd9c1N1vMM3Oi6xOb6BpZg98qr/B9//2/xzHv/Xa7u7vMXfvAv\n89RTbwdgsJF0ixpx1zWfe+ikSysrmN8qcLp5zCfyGPTlEq+hZWrFqMWopYRbrtFzL76aO5SiUN14\nSiaFhFPMKMVBIq3HBbo2HLQNCk0ptblErWjrJTK9EQQDC9quRBN6M3z3ySdy95fb42UR+Vni5wle\nF5Hz7v6qiJwHLt9tP7UUTq5vQ+pIXY9OtlCBcX6ApEkUBq2yXC5Yn63j8yDH9xtreDU6Sbz8W5/n\n8v/yL5moowwoSr4x4YRv0b98mbW6y40M03GLHZly9j1PMp7qWD+5zuJk5rLusfGui5x+8kIb91eo\no7WpI87y8IDp+ikeu3SBYRg4f/4sf+7938Ozzz7L2bNnefmlVzizniCYvHdds9DKDm6r8JpkrT1Z\nIVtzSzkhtV2R5r4KMZirUrGhRNCLIZrIycniuCiWMlagJHCvDKXcVrQNWEIRuhzsASOyuOIxEMIr\nFIhBYKl1wmLUKqFU3Jm5cNeYSETWRWRz9Rz4LuCTwP8N/Ejb7EeAn7/bvqwW1tYmTNY3ybN18loM\nlfJSjshRZhXphbw2ZTXwUiXRr6+xcfIkT77va9ntlS9wyOtufGxaef1b38nvv22Nzz7Z82uPKr8+\nHfil7nk+bp/l13/7I3zyw7/Ob/z8h3jtn32cx7/2nTz01JOk2TQmh91ekVdhmjJ7N6+zXC6xxR57\n167yK7/6azzzjqd4//u/m3/8f/0T+ukM4PS9rDnCjwb/mrXJ+o212FyJr1BkbcFzy7SDL+VRNlE/\nQpKlAZJG41ohaF7lT7esG7SeMln1lXEUJnuKTNUcJMWAz0AVAjeSahEXujcX/MZyL5boYeBnG9s/\nA/+nu/9TEXkW+BkR+SvAi8AH7mFfTGZr5NkGebqFdGskLlPKQO+rILOw1vXsHezTpYyNC6waE81Y\nAt1KPJw3EDY5PTnNM+fWufr402y+e4v9mzt8y3rHTqp88p9+mMdfytzc7LgoE4apcPpdjyNPbCBd\n0ApV9A9xjtwq8/kh167t8Tf+678TCi7KB/6z/5Tv+PZv4z3f+I380A//KB/83/53gC3gb9/LmleM\nA1IE1ohGU4EG9TQQ42AsUqPQKtqjvqTrO+pY6dpkNQiSWUpdDPXMwRCoDVMSX2FNoSCqIDnG9aW0\nop+AlLCGbrcmMtoKNzLHU7AGpHG77yR3VSJ3fw74+n/D+9eA/+BeTuJKJl3H7MRpUjW8n1K21ig3\nlTI/RN2QcWzLEdIkkU5usvjCy8gwR+bQ10ruOk799W/lxj/4CGn3C3S2xtYvXuXsyYc4/9QlTr3j\nPVzWOSdurCMMXHj7Y9h7L+Df+iiy2aFdpPsOSKltWnwrKQwj5s5j58/xC//w71In65y+eBHXRJLK\n9uaUf/IzH2TY2+f8U1/3B+5+/a6LbmWN6qBjuIeK4JJInVGCBwJa8OLkrMEwSka2GEs8zXY0wVWr\nIlLaTGptdA9W3JAYqdd1eDLUIlNr4/jBndImg4yl8amLB6jpFj/J0HrvvVrQeP2W9XojOd7amYbL\nQgTvhOlkytwrtSwZy7iadBDvL5bYYknaXGfYuRYji2WILox3Pcz6T3wLr/yv/5ynrg2k9Sk7L79E\n6oRXf+9TnPBEvxgYe6P7sffgJzfwSY7gdcXj9qY8OFYr2tDd+XKJHi5Y29hmtjYjqbIYR6wMmDg2\n+Jsue7Q2zcZGbOhvahcpOaJBz7BkFIMsMQ3OVaJNiERWYlaQxNQOc9rIGqe6rAiPINoosomj2Y4i\njSEQLU+r3/qoQyuNUBkNBqtxTqxFOeq416Oi7hvJsSqRIKTcU3ykSx3L/ZsMy0WwrGoNaD5nRndq\nGehyRmdT6g1hHCqqcQG178jPXOLcf/d9vP47L7Dx6gHdvDBPhVOPX2LY7Fg8vM72ux9jOL1GjLWr\nwRZsPGdrP5Gw6vOyUinDkq6bsJjf5ObONU6srzM/2KegDMsFVUA9x+9l3PuiA0muAThGkUNaBiSk\nGrydoMDFUAdPUQYhOW4JbRRHFw1gEgg82Y6A0eqgFq7HXVBPrc4W/PPaWPe1OlY9MCI3XBWrGrMz\nW2G4SMRSVlrp6c4w0XGPlnF6rVi/ieAMiwO8LKnZgvFZYwZZyh3rGxvMD5eM7nQCpRR0HOn6jknf\nY71QRVn71negm2vkNGW2BKYdKcMkVbouBybSRgurSCObZagj1rpc3byh4gUxoQDPfepf8e2PfQ21\nFlw6YuSckZMcDWO/tzXTaBYxv9pppDIBMaOmRgZrE+ydRNcq+tGqEz8xsaqBRaU/uEPS5hk0VhCe\nVsNJtVFoBUmBlHv7iS5tP5NF6z7R6kczG5PKaloftUqwLVc8ljvIsSqRCywNSAkbDqiLQ5bzA3Rt\nxuHhYQzqBMblki4J3nfMlwd05y5w8PILQYdQYTqN3yTzSTQZSt/FzKCtDu06chc/uRS/KTaS+0nQ\nS+pA9orXIfrfx5EyX2BlJE8nXH3ldZ57/lOcfOwJHjt3hoOdm+imMd3aPlI+s4Lme5+oD1FOab8R\n0MC7FvFCQ6dhFGsKZliNzls3SOq4J7TL0GpqJEG8jU22W35mNSk4BsRHpuUNO6pmlGrNddWjQV0V\n6HNirN6C+uAuxWT9+F2Q8S6zB47XnamSt8+STpyKRSwO8awUl6PWIBHBlgVVZ3GwhyPMtk5yeOMG\ny/1duj4IYZJy3JUomjokTcgpkycT8mSG4wylkL20dHhonRGG1Eodonpfh4FxecCN61e58vILnDj1\nEJcuPsa16zfYu3mDM9vbUI0yLOj6TWwcoY73vmgHG5yUWv9XC4mslrZeDytXcgRDxHCFWhvQl6Fq\nKEHO6SiNX3GtV+7GEVKj0Do03lIMh1jRPMYSowNLG+FjOGqwrI55idjN4vfRzCu1EvO079IB+2X/\nkPCXIyKyB3zm2L7wzcsZ4Oo9bvuYuz90t43+BK35Ddd7vDERfOY2FsBbTkTkd74Cx/cnfs3H27z4\nQP5EygMleiD3LcetRD91zN/3ZuUrcXx/4td8rIH1A/mTKQ/c2QO5b3mgRA/kvuXYlEhEvltEPiMi\nnxOReyS4f0WP5wUR+YSIfExEfqe9d0pEfllEPtseT97nd3x1rPnW78t/5f4R1LjPA08CPfBx4J3H\n8d13OKYXgDNf8t7fAX6yPf9J4H98sOa7/zsuS/Q+4HPu/py7D8A/IrpF3mrypjtY7iBfNWs+LiW6\nCHzxttcvtff+OGXVwfK7rTsD7rGD5R7lq2bNx132eCvJl93B8m+xfEXWfFyW6GXgkdteX2rv/bGJ\n39bBAvyhDhaAe+1guYN81az5uJToWaJ3/QkR6YEfILpF/ljkj7KD5Q7y1bPmY8wM/iPgD4iM5b/5\nY85SniSypY8Dn1odD9EG9CvAZ4EPA6cerPnu/x6UPR7IfcsDxPqB3Lc8UKIHct/yQIkeyH3LAyV6\nIPctD5Togdy3PFCiB3Lf8kCJHsh9y/8PurMPnrjJZZkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 144x72 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 29%|██▉       | 996/3434 [00:06<00:14, 162.60it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJEAAABVCAYAAABJoQaBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 144x72 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 43%|████▎     | 1491/3434 [00:09<00:11, 166.10it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJEAAABVCAYAAABJoQaBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0\ndHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nO29a6xk2XXf91tr7/OounVf/ZxuznAe\nJEWJIilZkpkgIRQERiLZMGAZgQ0nX/whgD8FCWB9iBDrQxLAgBMgDwRBAgtQAAmG4TgJhBiwbItW\nFBtwjISUZFriczjkcKZ7pmf6dW/fW49zzt5r5cPedXsgcfqhJptjsVejuk5V3ao6+5x11uO//muV\nuDvP5Jk8iej3eweeyb/68kyJnskTyzMleiZPLM+U6Jk8sTxTomfyxPJMiZ7JE8sTKZGI/KyIfE1E\nviEiv/Dd2qkPsvwgrvlhIn9UnEhEAvB14N8BrgGfB/59d//yd2/3Pljyg7jmR5EnsUSfAb7h7t90\n9xH4O8Cf++7s1gdWfhDX/FCJT/DeDwFvvufxNeBf+4N/JCJ/Bfgr9cFPatPgZuCOm4OABEVECEFR\nVdoYcUs0Ar0KizYSYkCCEkIghICIAIKKQijbiCIa6zb1XnB3BAccLONuuGfSlDhdrjkdRywpmzGz\n9gwGng0EVIW2jQgwn/dYNq5cueQ3bty8BfzVh605hvCTh/s71B2q/9cNv/+EiNx/rT6+/6dytiR/\nz9v/sAiIn/3RmY85e1w2ivPx97zu3+Fz5f6+IRydnLLejN/xa59EiR5J3P2XgF8CCH3n8dwhNkzk\naUJSRlFkv+Pi5YtgzsXDPV65dBFb32Jxepcf2V/w2Y9fZf/SLt3uDvPdcyx2d2mbHhza0CK7EZNI\nbBfIzkWkmeEioEqgwXMCJiBjw4ppdYSN92Ca+MZXX+e1d97hzW+f8ruv3uR3bcPr145YHa3xtGbn\nYEajzkdeepEbb7zJvOn4Mz/z0/y3/+OvfvtR1nzp/IH/hZ/5N4mxKLS446qIOOIgKigQNaJRCQBB\naZsWzNCg4E7QcoEVxRNUwbODalUuRygXzPYR5nhwci4XrONYhilNOE42A3Hy5CS3MwVUBImKmuAi\nhKj8L//bb77vOX4SJboOvPCex8/X595XPEMcYFobZIfQIH2DSMRzIBrstjN2JLG8+Q4X9jp2GqUL\nDW1u6KzFcyZoQACJAWkCGIQYy9XrCi64RkoI06Bth/sINiIx0OwooW2x9ZpPf/yH+PDBHtd37/Bj\nL1/hY9/4Fr9y7TrXWsVcibFjyhPXbtxiMxnr0yPuHd975DULoKGcaFHFzQniIALuRA24OxqERgOK\nEJtI2zRgjkaF7OUzgoI5osVquTqqgm+VRwXMiqIpRYlEyGJkSwjKRAYEc6nWx7DgyGRkwK3cBMMy\nECFPkffYtT8kT6JEnwc+JiIv1wP5l4D/4MFvcZIn0jQgMYA6YPRdh7ixWMxZnRwztROzFnZboVHD\nHSyBZAUDvFxFqoojBA2A4C7EGJEQcYprE2nAQYi4GEhCXHHvQI0kI/3BAedEaI6P+dTpIZ955UXu\nfukNNhpomobsmdC2LGLDrde/zd2796DoxyOsGSQ4KsVlo17crhTlCqogThMDSnkcVWlUITiBiLSG\nioAECIYUewWh+kMBdwO0WCZx8PJd9VmCRswEaRz3iLhh7rgHVEFjxFMmAxac4FIspluxUg+QP3Jg\n7e4J+I+AfwR8Bfi77v6lh71vGgZiE7EaF5k5h+f2SWngYG/OrFFsdcJB39AGRYOAlqtYRRAzxmEo\nBlukLNBrPESxQuXMKWgDoS0HXxVHCVg1+0r2SI4duW3pDhccXtrjlcuHfPzqea7udYQmoBqYzXZo\nQmR3MeeFD5/nN/7J/wvwo4+6Zowal1l1F6BojTkMrMZL24hDpG4XRfC6viCCUq1wtUBaP0YEVLTY\ni/p525BLtv8LGFJcI1pcqQhiRS1FFUFQo7g2ywhOVN4vCAOeMCZy918Hfv2R/94MESfnVM1wJobI\nlEbaNjBsVhwECDYSRIkhIkGZSFjITDbS0YB7MecC741ORbUEzeaIloMAufp6oToVMMN8IkQjGGjf\nod5At8Pw3MhHXzjiI19vePNkYLlcM1/MWa/X9I3wwktX+eQnf4j/43//rd9397/+0EWX849o2VAt\n7ijGUE6ShhLjiKIqhHoTKbGSqpYTWZMO9+IKywWRy7ZIPRiCotXianFtUt2WbR1SZlLBUGIOeCgX\nsrvT4KgY2bQcq+iEoGSXB+nQ9z6wfq+IgKcJEcUtE9t5VQFj3neMyxPm5/Y4ILATna4R5n2LNi0J\nx6NCF+gWOxAC2TIxRhINQXucgKQJ1WruLWCyLCbeDM8Z8YxJLm4hO+5tcR8RVjawu7PLZz71w9w6\nGfjSnd/h2tGam8tTdmYz5l1PK3Namsdat6KURNQxdzBBzVAvFlLFiRqrohRLEWKo8ZMUz4ScZaRn\nMZGEmoQKbsVFukFyzp63beJaLZsQEIGghriSBTRDMCFLLpZSHJEStmou3/8gOPHplj3ckZzLpgRE\nA0Fb8rAmuhFVaFRpg7Loe+ZdpA1K0IhqQGMElOyGmSE1fQ8xlsyFmri6b+14DbZLCisqZE+41qtZ\nAkiDIIybAYggQheED1+6wPnFnCZGxBwzY70euXvvlPzA6/IPrpkSe6SEmWOWEbcS9KohZiUWFis3\ndxRHKiwh6ojXFN8dISPb9Xmx7CLlQhRxHEPFUanxtUkJPd3Ldxb8ongrrHxmjalcDfVcNBEHN0y2\nGvn+8lQtkQMhBMwNCGgIINA0LapKH5Subei9xC5d0xNjg3u5gq1iGy7Vd8cGc2GyRLCAGzQtZwqD\nO1IzObwEI7HpsFwDUTe0AbHiOrMFQmhoNXEwb+gCtEEZYwMurDdrjqae3fX60RctRbE1FOuooqgo\nIRaXIRXz0jOXpjXwVrRaIA9S4hURxOUsdvHq9rxaJ9Ua95xZDSuBtQnuoSiYCqpaYiNCOSfqeBDU\nAikI4lbjLamfso3RvrM89QKsGYAgQZjPOpomFncSlFnbop5p1Zm1kSaU7EhViTESYgEdY9PSdDOQ\nQNv3NUuDEAMmFTjb2t+Kq6DFNLlEhBaXpp7gXP9EMHdUAkGF3b7j3M4MT+nsYKaUWE8Tt47vPvqC\nvYRk5cxavYCqVTBHzCEXyySAmKEY7lZcLw5muOXyfiuW1rbbOIiVoB1HasKyzWKFgg+5Gy5ejYyh\nZkXFzGpab3VfnezgnslWLD7VQr6fPFVLVM6aEoIiUTldHtPMGsRn7M5mTKfH7J7f5WKbubgTWSx2\n2elnNG1LN5/R9B2x7+m6vmReKrRtT0ZxDRAVDQFXwaWcFEIsQSYCjSOmoCNBBY9KtiWeHXGji7BR\nxTSwP4v8xEcu8n9/4yZtE+gYuXThPGHeIml47GVng4iUfyIEUUQd1XJyVCGIIBLq64I79X4bcCti\nIKIEhey5xH81wVAVjFgsDkLOW4ATPFT8Rw3xkh0HFFNQLRiSZceEotiE6vqKJXuQPGVLJMSmK18s\nQoxCCAVvTSmRUylJLGY9ezs79E1D13U0bUtse9q+o+07iC3EBm3nuHZIiFhsIERc432rE1vKddKA\ntiBdOVuqSIjlOQ2INojGYomaFgFiiJzb3yOosrNYVKwqce/oeGtOH3XJdb33H7qABUGlKLhKuO+u\npKQaonrmtkLwolwaCFFqxhZoQkQpClkQ7WJFY8WLVEPBp0JxlbJVxK0i16xLpL5HqiKroAIB0PCe\nnX8fecqWCLIZIQqxgnjuAZPAiEEe6VTo+o7QdczmhzR9T7uY08xmhBhpY0SaBpo5GlqkBqIWykFW\nKVlQtefVCtVrpZ48F0WkQaKg3mM1Nig404BICeQXfUfbNAQNeNsQ24aeQN93j77g6nHcHbMSf5BD\nRZMzAS0wl5Wz5jUJsFxc0DYxCGJYlhJsS4lZrAKuJQYuwXhBnQ3xgoS7SPksK+5s61bdS4Qp7njd\nvxLhW61pOu4ZcT2DVN5PnrISlbrRZEawEp94Lq5oyon9xQJiw1FSbOOsZODy3h7a9UiMlHDAyBTQ\nMTMRRWhCYJub6TYd9XIQfJpAMiKKSCAriERMwIfEuDHylOibetBrDCWqtCLEoCxXKzqEzXrNYJmD\neftYq1alxhsFx9GgxKCldiahWJ+KPstZOr5NDuQ+mlhWWJIKETxoWbd4hYm8QmJyv7zqvr2cQBRX\nxzOg5VhaxZHMi+KVkMPINSkx1eot3l+NnjJOVCr1SKRtW5JlzJ28XiH9jKP1mrdON3zp1WvEoBzu\n7iA5c/ncHk1Qnr98mZdefIE/8akZ+/u7JV5oIlNsiwmOxZqJBIQGGU9xjyDOuBoYxpHrb9/i5OSU\n9WqJ4WzSwPnz51jefZf1yT0+/dLz7MSWoA0XD3bZbFZoM2ecErHpGCYjpfQYi6a6Dz0rtgatJ3mL\nMDskAywhAik7QQqQIKEg9W6lCCsiaAgFvRbDvGBLmGEIZpkhZ9QhY7gLwziVABnOjrmjBbOiuK2S\njSkJCGaEAhqAOSn7e2v+f0ieborvRp5G+p0eM2OaJmLbMqWMOwzjQBDlZ376szQRFju7HB8fc3Lz\nHTbrDcfLgddef4uZOC9cvcxmfY/Ll55j98Jl4iygZiBtzaYyoo6NA3du3+bam2+yPF1xdDySk3Bw\neMBP/NSnWZw7QFQYlkeMyxP+n9/8B3zyYx+lnXXs7+4x+kRrxkYzm/WKdQDLj6FEXjMfBAnFdbkZ\nZKnYkFfXpQU9zhUIlGIVNZRqekqZtimUlKaJ0ARyhqhOcsg1U0vZyUNmkkxKJTAe0kTKGVSwbBUS\n0OKupATQlhNowHIu7gzIqdBhsn/AYqKgQt/1DNNIjJG+79EYkKYlhJadNnBORi7vLbhy2BGvfIi3\nz7UcL9fcOVmyPL3NG99MHL19nd2djuFkyY8eXsAmA82YNkjcYnHG8c13ufXmm9x75ybJE8/t7nB4\ncIHzH7pKYE3egIoRYmK2CHzq05/kzdde46MvvVwC17bU5AI1yxMnVsznUWVbowqipUYaBKLiuV7f\nIphnJJU4x3OJTYrXKZkaXp7XUJgA6iUANqm1t2067jBZwi0zZidnY8hTiYnKUTkrBrvXQN5BQ8Bc\nCltAtH4XpFzd8QPkqSuRk0s2ECPJMk2MuCi0Hfv7eyz6Fk7vcHhuxvDOG4R5y/N7+1w9t8vRyZqY\njdevX2M4XtOmOUsz/N6SNBe07dDYo57wbOSUOLr5Fuuj23Q2ENx47vIeWTLL5W1i3pDZoY8RC6Vi\nffHiRU5v3SaNE/NZR9RA1EjyQNM2dFHYDOPjLdqK1UEVNzCcjCE42QXHCFbiD8xwFVLOeHJyECZ3\nUpqITSCKkpsWjaBJ8TYgCikVdDknI00jY8qklHATpno8QiwBvamXVD+DbZMOy7iUUotkx6TEUiFs\nk5MPSkyEYK6spkTbNdhoZM/MQoTBmXkkDZkv3r3Bu0drdpvA7qJhM7xNjMpi3nP+3DkuLHYIzFAy\nkjfcvPE6s3N79LuH9AdXS2yQJ8SMvcZpFw1hrpgq716/Tug6rr/1ZWaLA/ZnC7omMjt/iMbAoolc\nuXyFzekpipPHgfOXd1nfWbGeRjYZZofnHn3NQi2oBjRydvUHpNTTFDyV7LDEOF6q6F4ytC3AODm0\nnkkUbk8eFJNY0ndXsgmZiTRlNkNmyGXbXcgkNJegXb1YI/GCWWFO8hKQh1ipbWIVsyqVhVxLK+8n\nT9cS1SxjGAZCVJqmFDKzGwRhNSTuTSNHd+7x9p1TXjx/gY8ffpg/9Wf/NEJid9Zy68Z1Xvv/vkDf\nNVie2JwuWS/v0cwizHfJlBKGC0hODKsNhjOMEyebxPWjNS//0Ef41L/+J+nnPZJGvvrF3+FLr32B\nSxfPc+XiBRbzEtBPKeE5MaaEIqQ0oX3H48BrDhBqYC2l2HpGB85esjWtqHEGS07SzFQr65Ydrdsp\nBZxMCopRFbDWB4N6LcKWGlzGyDXmSuOEWaDJTnRBTAu46Y6EiNqWlxTQkDGJ99N/E+JD/NnTVSIv\n1IbQlhRZROi7Dg3b+peRiBzTcu75F3n+Yz/MW7fe5fffOuL0+C5XLp3nyoUPsZx+lyyFxmEGm3tL\ndvd2iOLE2BXAMAfWp/cKGyI0rNKGtczYffFj/P3f/gqHF+6wv7/Hxz/2PJd//Md4afMxvvw7v8vd\nu0dgTt9ExCEKBc8RIXYNDKnwax5RCmXI8VCD6eBodqgBsaZMcmMYEiQjeQZzhsmYyChCI06zLQqq\nkVTxnEtcVINtvGRilp2UMtPkTGPCXVmvE8km4qpYwaZviGOpHHRtSeFLXW6LUGfQQFat9PUPUGDt\nFO5KoNShtmi0agkem7ZBmpaTrPzLb73FF774Kjat+dXf+OcEM37+r/48v/Cf/yK//jf/G9587St8\n80v/AjHl9HjJ/uGqpKZNh3YzfHA0RNq+x3ImzoWTuyP/xV//73grG8+/8Dy7Ox2LRlCb+OnPfIZP\nf/SHufG138G6wIX2IqFpaN0xJkIakL5hZsLO7DGoIJXrrUgh11UyWEnVHcngWchTYjMMpFyq+mOa\nyDUFbyLsxULXdUJBkEXPyjyxKfSOnBIay/dsa21jNjbDwJQLiKiNMjMjRCUQGJKxO+8JQYjakikZ\nW/SCR4GTHpDew/fBEoXasaEqJVV12N/bQ/KKNkTW64m3bh6j2nJ+7xzPPf8RvvylLzNvWn75V3+Z\nn/9rv8j//Kt/i9e/8VV+8T/9jzm+8QbLt97CXRmGxI7n4hcKdsv+Cy+hseHWq99kEZ2rL1ygPXZO\n3r6Fx8B//z/9D/zT3/ot/uk//hz//Dd+g7/xn/0nzA9niAund06JsWWaDJsyLS1x1tK2j4FYA01F\nQKOGUiuTSFRBXchAQyL3ikvPLAoBZZMSiKOuhBjZaVtUCkI961u6WUfQQNPEUouUYjXBmLctoHhu\nMCKzpiXlEZuAIDShZJlmQjcLxLYAoCGG4u4QkkHIqeBJKfMgWtpTjokE1ZYgUNZhBDGmcUmnE6Gb\nQQcHbUMeJmR9TL655rM/9BKLtuPFixfZXP8Wf/7P/xxiazyP9F3DJm7BuxI7FI6MENvIZhJmXcPV\n56/yzq27fPqVKwxDw6yfsdiZ8X/92t/i7t3b/OxP/0n2Dg+5eOkSuTNyduRkjQA5ZaIEGncIMAyP\nXoAVKNxmKAGM17UTCKXEVcDRSo4T89Kl0jSFV6RKkEijhUaDCm1sCXK/NhYkYBiRiGGEEOmjMKmW\nYmtvkAPWC1bdVpBSImmaSBcChFJLywLZpFYG7pPbeIA1evplD2AzrDl3eEjOiRCENkbmTWDeNvRB\n+ZGL+6TVmovnDpmpcXF/wb/xJz7N3u4uly9fod3fQZJwfPuIRoTZrKPtuuLXlfs+PAZijijKwe4h\naTXxc//WZ3nn1h1SzjQxcP5wxu7uj9HNF4R2UZmSufDTQuFlT5aZxYYuRFaemM1mj7VqqSeouLHC\nJ4qx8McDAXMntB2erIQ9wBbObJrSnBDreyUW0l4IkRiVGEPp1yOCZNBI9pZGrbQEeXnvmSFxr3BD\npVg1gaha03lFk2IkUg2o3Wv55QHy9KkgKmRzYtNw5+Ydzh/s0JDYa1s+euEivQ288omX2J837PTK\n3s6M/QsX6BdzJEZCOMFO1sx65fyiQcZEn3aJbbkKC1d/ezW3xCiYRJAZ517YY//Ch/jo6gTPE0My\n6Bqabo7GSN86uBKbFksjTYRZ13F3uUT6BYnAlSvnCzPykVcstFrOWFPoiQRVuorvNLUmWKrrBUU2\nKn2DQmGV4OBSrU/J6JomFB66SoELVImAuqNtRwqC+wSUGMiqJTnjaCsUlKggszllDDDNjBU9L8Zd\nCxn4AcH196V2FuKMGCNNE7GUCB4JZNK0pG8aPnThkEUvnL94SNtHup1dJDagDU0/YzGbkac1w5gw\nEtuy5DY28EpC0xCZpKDYJiUrbKPiXUDcmWkoRW0NhOC4TWSNBU3HMc2QMnsIBGMMLbFt0eZxAuvK\nHHHHY2VlCmelhdJAUHjYEgrY14ie8cqCRNwyFkJhE7iXRpZKopPaoGhWjkEpUZSMK1tRNMK2JEQF\nGw08FJQcJefCgDSrFD33Untz31ZuH7jEp45Ypykxmy/IVqgIIQhNELq2BZuYdTOuPPcc6gO7u3ug\nxk4/Q2JLiC1tv4MEx5IXGoUpE0YbBZVAoTlMlYIhRC/kMxMBMbSbYUotWEIIzX0qROVsNxSGoeRS\ntGxjwMzoYmScEu8ubz/WmsNZGV8IQqWoFnanhmJ1CrcqFKWq/XRuVlqlvLSGewmwKBY9EAtmUtp+\ngpLdaYIWagxKUwl5kcJUdNFaoS8fkXMJ+M0KA1KlKG4tGpXmT6uW/QHy1In6iNDPZ2w2A569UlIh\n50SaJtqmQ0TZ2dkhBqWfzWmajr7t6NqGEI1kAzmXdhszSG5oEwlNh7mANJhG0Ihlw1LpNdPQYnGO\ndAu0L/cmDU4EaXBpUIdWQKcE04SZs95smLUN6+UpTddx597JYy27UFnrIaBQehwtqLGXGpZvW5pE\nEWJhPgoEKUh7xs+sU6n+e/2cUp7IVpo8s3uhd0j5DieUVmwJhfxGKKi1lVKGSeFQeyq0WHMne2VJ\nmp/FTg+Sp4sTqZDE6Oct7958h2AgbQ9B6bWjiQ2r1cB6vWbe9OQxEbuWdn8XiaEQsFTpknNydIt3\nrr0F08Sly5eZH5yn2TnEwwyPfeHlND3kVNDgNBGlQfo5rgtKx4Pjq1NEU7E8rjAMMG3IY2azNEzK\ndSaeiG6crjZMj3nYzorg6mBaSRa5YD4YQZRGIhoLOS0EAwmIRUKIkHNZg3rtv7eCN52VMIol3TbY\nmoFGwbOjaqUBIAgEwUat4LQjScheEPNt1ral2RZ2ZWE6dttmh/eRpx4TuQjL09Piwx2mKTEMhs8B\nETbTyHK5YhYyXdypvedbOmuphqdp4vrr34aUaTTQhJYutCXeUinoHFJIOrFDLUFOpOmEJswg9liI\nYJXg7hm3jOSiQKTEOIycLidSysWtWmKxNydP6bGr+GGbNZbWC9wDSBneUKaBCBIMtaYURiWULE4V\nQ+nVmbZxjwhihQkgWgJ3r5X+XDMpdUeq0inV3VEyTYlWmm4p7dNWW6RFHUHRXNur2YYL24ba91ej\np+rOStxRuMOWSzXfsjFOI6LK7eO7nKxXlW9t5JyJNTbIltFYDsjp3buMyxN2Ysu86bApkcdUYoFp\ng9uE5QmfEiZN4SZjyLTET+7CsCpdHJXgXuKfCdJUrvo0wLBh3EwgRjMLJZ1W6BW6xz1qcr+NZ0vu\nslTchpuVW6bgXJW+Kp6ZLIGnimLn4hbdKtU2YyljyXBLTHVkTs7l3jBy2k4Cqc+bnVFlxZxJQFIN\nzr1U9bc03uyZ7KkwQB/iz55yFR98yixPT2tXpaOqTHmim82YbE0yY7VeM3bK+tQ5ODiAXPrDPBXo\n/ua1t7DlmjDbZ963pM3E8vgEiy062yuZiEaiWSG5x1Jc9EmxoxvouMLnc+gWxT3YBKwRS+Tk6DiQ\nl8e8+847NG3LbNaTB2NnscNqOiObPrKoC661JagGGVpdkla3IZFaQQ9se+fVOTvBeGn3UYfaeYhT\nulQL/7XGQSJsKeYB7vOjpdBxVQXzUimIJqQgZViGxMJNQop7T9VCmtfmgfeXp09K61vG9QY3xwKs\nVmuuPrfHatgQY8/pZsXdk2P2ZGQR5gx3bzK/dECnDWZr1kdHvP2tr3L87hGrkxX3ViN/7x99gZ/8\niR/lsz/1I7z84/fo9w5ZDcY//Puf49w48cmf/EkufuRlunOHTOrY8hbdpoHZHtRxNZ4TYiO2XjGd\nnHD79l1++/d+D8dZn56yu5hzcrqmP3eJmzdef6w1p1zQ9BKzFOuQcEJ2pmCoKRqsVtDrpA6pqLEV\n1mLOGfwPUFVFiMGYpPKmpbYrOmyHZIkXPjdWGii3mZZXKu12lkyxNtW1U4NpKZYzbzm87yNP2RI5\ncd4yvn0H7TpS7Y9SUYZxLAe69PExTCNms2Kap5E8FRdlU2Jnd5/VCdy8eUKIPT/1yR9ifXTK17/8\nFc5/6FIZQNDM+fSnPsGnXn6Jd2/dRJpAmPX4xshphHFVM6EZuCA542lELTGOieOjI27fOUJjS9c1\nhBA4Pj1B95zlZnq8hdeA2LScfnUvGaNKIc9JJo9K6gqHqGkcF6003EBOqdBlkDJSB4Dt4KpSPjIr\nXaxiGbfCw8aN0DRn7dtBSsblGKiQpkzWMjXOs5X2dHF8KqBtSoaIAg+OAZ+qEqkK2XJpLswZJDAM\nIyClbJFG2rYFKRZqnOZYmvBpRIaJlEamaeLwwnm6bsHu0TH3jk+5+tzHOTldsrenrJZL9i4JB+fO\nc/Hlj3Lr7k0Wn/xh2t19IBJzIqREHiYsrQsqbBGZMj6eMm2WrFenHB/d43h5ymjGYneHaRhIuaDJ\n6OMetpIZnc22wmsK7uQaLJsaloury9lLv1fphUWD4FZIbFqgnkqyd8iFeC9ueIGhy8QVoQbTtZ5Y\nwckKoBUoJFJmPklp9nRRPBUGpgESAJfajP0BQawVoQvKuq3cmKBsx741TcPJ6T36tmUYEykY4zQx\nTQm1CRs3BfzLhrRKtwjsxR2ambA6PebSlUNmuzv0iz2yR4aU6YfE/PxzhH7GpIEkQqel/oR3JXRI\nAyENMI6QRsbNmpO7d7l7+xZHd+7StOdIaWJKidl8xjAOtUP0MUQK3cMjYFKopwaQzxiOWvvxy7kt\ncZfnDEELuX7Lqbb7fGvzbaZViVVK0bBQSWperF4h8FvpmsVqPxqklDEVbLTqwuo9oFVRhRKLPXG3\nh4i8DpxQ+BXJ3X9KRM4B/yvwEvA68Bfd/YFN6l0T2GmEtRXt9imdpbIAwzASdnYYpgmLgTxllstT\n8vKEabVmyrBJCZdAPz/P3n6Lu6GxY7F/yOxgl2V2RomECxexdk5vPTop1kC2CUKPK2Qvk0jk9BS3\nTLKJzWbN6Y23eOPaDf7yL3+OMRnEY64F5eoLh2SHd752ndVqDWVK3OHD1gxUYnzeYq2FslrnJRrl\n+ZxLgO1e8LRIIe3HmoCUBHrYZkMAAA3wSURBVLzMOLWKgKttx82EkuYHxa2UfPCAqZGkgAA1t8NS\nUa4sqWwHyFIA2eT303vfjsMReShR/3GS1X/b3X/c3X+qPv4F4Dfd/WPAb9bHD5QQlK4JdF1XOy63\nA5a2rwdSTsWsAmNOjGni3npgPY4s12vWmw3LdcIlgW/oyRwu5vRNh2qLxI7Z7j4JZcyO+QhhQmQk\nMOHThjStGIYlKQ0gmWwj07TBLbNcT9w5WmLmXJnP+eGPvMwnfvRlVJWj26f0O3P2L12BclE90jB0\nd6uj8+pkMy0MQy3p1P0mXa8npDIvQg2OtdJfVQSNtXNEldiUelsMStNWWkgIpcwSqVNCSnPidsiW\nh+pOAXTb7OhkcbxW67ezdnXbfi0PHnL1JDjRnwN+pW7/CvBzD3tDUGXRBZrY13FvARymlBk3qQx/\n0rY08jWRpMoEDCmxHgaWq1NO7h0xnRyzPl3CZOTNhuWt2wy3b3Lv2jXmEtBhJKREnwZkOIXpFJlW\naN6QxyVuY8FWxhVTHjGbmKYBzxObIXG0GktJIbbszDvu3DkiaMPJvRXz3T2mKQPcfpQ1Q/E0Odcu\njwQ5W2EzbgPasfCXbCqMT0sFF5uykSdIOZOmTMqZnFJxO17aebbxkU0l0zeH5AUbmlK5WTbyVKxQ\nTuU1z05K1a2mqsx1wmzpkysYlLndb216H3nUmMiB35ACGPzNOmL3sru/XV+/AVz+Tm9870znxazl\n4kHHq6/erUBjJk+ZRmOZN+hGyhPJhc0gDFGR3ZY0LgkS2Jwcs1oPDJvE8u6KezFycW+Pi+c6YIWo\nsrn2LXLbMjUdcbHLkMvwphgi7XyGp4kpjYgbm82avdmMKU8EVXLKnC5X3D46wcx4++QeN7/0NfpZ\nw/MfXhR6aWVlUmYav/iwNe8v5rUduna7howS8QiaSlu3SgBxslgdlydnJy2Jle4PLZX/UN1MwZq8\nTJalcJaoRXezUldDKrhYpmCVWpj42fQ0DeVzvBKYXKS0N20LfFpre98lnOiz7n5dRC4BnxORr773\nRXd3eR9E6r0zna9c3PMX9nrmrbHJgZwzwzBw5+iYg52enZ05g23wuIeFSPLIkAKpP0+KLW+/O/DV\nr32b5VvH7IepjsBUPvGpT7M42KXvAruLHSQGNuPEvF3Q9ZG1DAybwl8O+wcsLpxjf3eX4B3ZG9Ay\nEzGnzOvXX+fVa9f4+KU9bP8CcMrXv3WHYSzAyWq9qhPbyvIevuZz5XIXsMpbdiknnliIZiiIKBZA\nXLAANpW+ObdMTiWFj0qp8ruU4V1SlEqkdNaqljbplLaF07LP2tSJHyHWcTZag2agTgEpZbzKLMCx\nWuOjBuEPkkdyZ+5+vd6/C/wa5ecJ3hGRK2U/5Arw7sM+x8yIU2b0oQwQECHnzDiOrDdrlqs12QIa\nGnIWhmkCcdp+Tj9bMFvscfHic9gm04UOM/jSV97hc//sd7B+n9mlFxnaQ+LBVfYvfwxrL/Dlr13n\nyuUrnDs4pNOG1179FtPkrDYjTdcRNBJCi3lkSM7NlfHOidHP9sk2MsqCxe6CzTDR9R2rzYrJMkDz\nKGsWamXdSlaU3YoFTkbKVpWkPK9TIaJhjqXE5JlxNKY8MW02LFcj69WK9TgwTiNpHBingTFPDJuB\nzTgxjRPDtGGcJsZpLBluTmflkFz3Q0TIJnUUYN2/2n1brFd1fVanhDzAGD3UEonIDqDuflK3/13g\nvwT+HvCXgb9R7//Ph32WO8xiS+x60roU/rIZMTZshoH1NDCbXeJkueFDV85jwxHZR2Le0PcNLzx3\njt6M3eMBtQ0vv/IR/r2/cJXlONLu79Pt7jKl0lPe7ezQHwQ++8k/i/SJ/YuX2U/OVSLrZEzplBSm\nQgCzjFlmmhLvHJ+yssBqGlmnzGADm/WGqx/+EF3b8c03rtHt9gDngb/90DVDnalY3M02lRctBWhD\n74/JU2ofWpkhHaWg3TKVgFhyqbCrQYhaP0+LZYrFR5mWqbB2Nppv2zxZKvkula+kEKtuiILk+5NE\nHIFc5kXWX6ngSRHry8CvVRg9An/b3f+hiHwe+Lsi8h8C3wb+4sM+KOVM1/Q0s47NsDmrnWloGKeJ\nNkRu3znl5St7jMMaycaUnU3ywimKHXF2zP6Fljwp905vc7hoeeWVjxIuXyZ3PdL0+GR4CEjTIbOI\nru7gJMacmR8cksexDPOclqQp1dk/I8Ow4u2jE+6sB77+7hEZA1Gee+4S5y+cZ3d/j2+89i2W794A\n2KNcQA8VK+wNJBq5puA5GSaBVr1MPLN41q3hKoi2BN+gbWTaUmytBMCtUjJR95pBCZNnVALumaSx\nxDlWSH/EMkE/EErQ7aUinL2Q8BI1tbdS8qCi35Yr88KfMLB2928CP/Ydnr8N/KlHOYhnB9OExUK5\nerDHtwdjGkdshNUqMU0jL774HO/eOOLtW2sWuuLSTsvdkyV7t46YkhDahvlih9Vez+rmHWQ0vvnW\nG7RXDtkZOrQ/oMmR9XrJvIkMmxPyaab1NSKRZrbDxhIeM8GVPCmSVqxOT7lz55h/+eo3efXeyKrp\naRc9i3ML5mGXD3/4EiJw7c0bXH3lI9w9OuL07be/7u53Hr5qL5PqBUIqcLO5YhLQaKUW5gIy1fpW\niXjbaGANEpQupIIj4UhWVFKZsG+FC60O7oEQBE+xWKvGIWmdygaBOryzBthbV2oCPpWMLFUa7xnt\ntnbGfrcC6++KGCUtlTTRdS33HESc5bDhoO+ZspA8c+tkyWGvaB6JIXDv+Ji269msNzjO6Bnd6Rk2\nE9O04Z1Xv8Fzy5HDi5eQWc9uCMhyIoiytIlpFon9oly1MRAtkYaJxoxpfcq9k3u8+c5NvnXjJvcm\nuHVySlJhsX9AOs1nuM694yPuHa2ZzWacPsa6XYsCbOdVOYY0Zf5ioUQLakIOJQ3bgnyFJy6IxzJh\nrRLuFSUbxRJZGf0i7gWJ19LnJgpWB5lYLeZuR+w5ia3zErs/vKHsmVWaMLXSUbtGHiBPVYmmZBxv\nRuazhoW13NHjohTDSLYZb71zh92dOVGVe1OiW2VmTeTm3duM7uzs7NI2DW3TME0T7HRoEsZxzTvX\n3uDW9bfYu3rAZhjpujlN37N/9TKpgRxLxiJeZmm3As7EyfqEe0d3uXHrDt98+xa3jpesBifMOrq2\nZ/d8yzCVIelGIkYY1svHWrd4GVJFKBwiQbApkVBaC6CJFCJNkkJaI2M5lNbrFFGsDlOXWlWv8aQb\nXlNyp8QvgpMNQopn7EkPBSEPGgsGRUHNzXKdl8R9DqyV3rNACarPwIYHcIqeOrPxdDNy7nDB6zdv\n0Hcdw7Bhvdlgep7r19/mU5/4GAnn1r27hE6ZSSC2TuxnOMK87TnY2WHWtGymiWntdHsLxiGTZ3D7\n9ruIC7MpsQiKeGYW5oSmBS2NgwlYj4lhNbG5fcTRzVu8/vYNfvfumrsnFHdiAgMMOrB/uGC5OmE1\nnBJiV8ohj7XwUjf0aj1cjISiYkxSUvaGgtcYQjTBYnlPGb+nxRK54Joxi/WnJ7TO9i4uUWtnayNl\n9jUWS+GXUtV3Su04FKZZoYPk++9HCvYkUn6KQdXJWzX6oLQMJYO7pxPnnzuPyg2mKRFiZBw2nCxX\nLPYO2AzGON/B24nXTu7w9tEJn4rnOd58myvnzvPCc5eJu3vsdj0tgX6np583IBHVhlRJ6aHpkBCY\nL/aQpi+cZAFPK1JeFgR7eYtf/+03+GdffZPPf+sGby83ZOrEjCYy5cx8vsM4CVefe56vfPUNxnGs\n9IjHkcITAmpfXMmuxAqVHmp7TsWThmRorO1MjYOV3wJRCaiHahQUJNeRyoWwZ6ES2WoV35JhAdJU\nkOds4FqQ8JTsrPg95YR5xq0M0HItHb9ZSobo+QPkznDjtTff5kf2d2lbJWsuRVjPrE5PuPLC86Rp\nYkhOE1tOJxiTcOPWKVcunGO9ydy+ecTV84f0i5bYtcx2d5jtLkAaQuiwblb4wyFCiEhoSztyTkAm\np6FU69cnbO4d8fmvv8HvXT/h+omzToLkxLzvQITsBaMxj3zlK6/SNB2r1ZrZYwz+dC8/SqdQB7WX\nDGtKqQJ9VmcyNjSxUC5CoBLGigXKeCGXNYpa9SwqMAW0qQh2K5X2XyyXuECTyFPCxElWZkHlsdQr\nx5wrJiUVpyoMxrzFirTgWCHEQn95QGz9R/4h4T+KiMgJ8LWn9oWPLxeAW4/4ty+6+8WH/dEfozW/\n73qfNj32a+9hAXzgRES+8D3Yvz/2a366zYvP5I+lPFOiZ/LE8rSV6Jee8vc9rnwv9u+P/ZqfamD9\nTP54yjN39kyeWJ4p0TN5YnlqSiQiPysiXxORb4jIIxHcv8f787qI/J6I/AsR+UJ97pyIfE5EXq33\nh0/4HT8Yay4/z/S9vVHIl68BrwAt8EXgE0/jux+wT68DF/7Ac/818At1+xeA/+rZmh9+e1qW6DPA\nN9z9m+4+An+H0i3yQZPH7mB5gPzArPlpKdGHgDff8/hafe77KdsOlt+u3RnwiB0sjyg/MGt+6lNB\nPkDyR+5g+VdYvidrflqW6DrwwnseP1+f+76Jf5c6WB4gPzBrflpK9HlK7/rLItICf4nSLfJ9ERHZ\nEZHd7Talg+X3ud/BAo/YwfIA+cFZ81PMDP4M8HVKxvLXvs9ZyiuUbOmLwJe2+0NpA/pN4FXgHwPn\nnq354bdnZY9n8sTyDLF+Jk8sz5TomTyxPFOiZ/LE8kyJnskTyzMleiZPLM+U6Jk8sTxTomfyxPL/\nA6cbEArebcN2AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 144x72 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 58%|█████▊    | 1990/3434 [00:12<00:08, 163.12it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJEAAABVCAYAAABJoQaBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 144x72 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 72%|███████▏  | 2484/3434 [00:16<00:05, 163.46it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJEAAABVCAYAAABJoQaBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+UwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 144x72 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 87%|████████▋ | 2993/3434 [00:19<00:02, 159.25it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJEAAABVCAYAAABJoQaBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLlv+P2rl2R9ekOBYAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 144x72 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 3434/3434 [00:22<00:00, 155.23it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uZBlWDPOVcHg",
        "colab_type": "text"
      },
      "source": [
        "Wonderful! Now we should have a trained and (hopefully!) debiased facial classification model, ready for evaluation!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Eo34xC7MbaiQ",
        "colab_type": "text"
      },
      "source": [
        "## 2.6 Evaluation of DB-VAE on Test Dataset\n",
        "\n",
        "Finally let's test our DB-VAE model on the test dataset, looking specifically at its accuracy on each the \"Dark Male\", \"Dark Female\", \"Light Male\", and \"Light Female\" demographics. We will compare the performance of this debiased model against the (potentially biased) standard CNN from earlier in the lab."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uuRq0zi2B1zP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200
        },
        "outputId": "ff4afd13-09f4-41cc-8cc4-7a584aadc40e"
      },
      "source": [
        "dbvae_probs.numpy()\n",
        "dbvae_logits\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-206-d42556c9977c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mdbvae_probs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mdbvae_logits\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtest_faces\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: count() takes exactly one argument (0 given)"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bgK77aB9oDtX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "8bbec911-1c5a-4913-c2bb-bc402e705254"
      },
      "source": [
        "dbvae_logits = [dbvae.predict(np.array(x, dtype=np.float32)) for x in test_faces]\n",
        "dbvae_probs = tf.squeeze(tf.sigmoid(dbvae_logits))\n",
        "\n",
        "xx = np.arange(len(keys))\n",
        "plt.bar(xx, standard_classifier_probs.numpy().mean(1), width=0.2, label=\"Standard CNN\")\n",
        "plt.bar(xx+0.2, dbvae_probs.numpy().mean(1), width=0.2, label=\"DB-VAE\")\n",
        "plt.xticks(xx, keys); \n",
        "plt.title(\"Network predictions on test dataset\")\n",
        "plt.ylabel(\"Probability\"); plt.legend(bbox_to_anchor=(1.04,1), loc=\"upper left\");\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAf8AAAEICAYAAABRZv0fAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rESoXRPQo_mq",
        "colab_type": "text"
      },
      "source": [
        "## 2.7 Conclusion \n",
        "\n",
        "We encourage you to think about and maybe even address some questions raised by the approach and results outlined here:\n",
        "\n",
        "*  How does the accuracy of the DB-VAE across the four demographics compare to that of the standard CNN? Do you find this result surprising in any way?\n",
        "*  How can the performance of the DB-VAE classifier be improved even further? We purposely did not optimize hyperparameters to leave this up to you! If you want to go further, try to optimize your model to achieve the best performance. **[Email us](mailto:introtodeeplearning-staff@mit.edu) a copy of your notebook with the 2.6 bar plot executed, and we'll give out prizes to the best performers!** \n",
        "*  In which applications (either related to facial detection or not!) would debiasing in this way be desired? Are there applications where you may not want to debias your model? \n",
        "* Do you think it should be necessary for companies to demonstrate that their models, particularly in the context of tasks like facial detection, are not biased? If so, do you have thoughts on how this could be standardized and implemented?\n",
        "* Do you have ideas for other ways to address issues of bias, particularly in terms of the training data?\n",
        "\n",
        "Hopefully this lab has shed some light on a few concepts, from vision based tasks, to VAEs, to algorithmic bias. We like to think it has, but we're biased ;). \n",
        "\n",
        "![Faces](https://media1.tenor.com/images/44e1f590924eca94fe86067a4cf44c72/tenor.gif?itemid=3394328)"
      ]
    }
  ]
}