introtodeeplearning/lab2/Part2_Debiasing.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ag_e7xtTzT1W"
   },
   "source": [
    "<table align=\"center\">\n",
    "  <td align=\"center\"><a target=\"_blank\" href=\"http://introtodeeplearning.com\">\n",
    "        <img src=\"https://i.ibb.co/Jr88sn2/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=\"https://i.ibb.co/2P3SLwK/colab.png\"  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=\"https://i.ibb.co/xfJbPmL/github.png\"  height=\"70px\" style=\"padding-bottom:5px;\"  />View Source on GitHub</a></td>\n",
    "</table>\n",
    "\n",
    "# Copyright Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-01T12:23:50.967168Z",
     "start_time": "2021-03-01T12:23:50.965053Z"
    },
    "id": "rNbf1pRlSDby"
   },
   "outputs": [],
   "source": [
    "# Copyright 2021 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",
    "#"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QOpPUH3FR179"
   },
   "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",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-01T12:23:53.473364Z",
     "start_time": "2021-03-01T12:23:53.153215Z"
    },
    "id": "XQh5HZfbupFF"
   },
   "outputs": [
    {
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\n",
      "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 0x7f715588f750>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import IPython\n",
    "IPython.display.YouTubeVideo('59bMh59JQDo')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3Ezfc6Yv6IhI"
   },
   "source": [
    "Let's get started by installing the relevant dependencies:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-01T12:29:17.557632Z",
     "start_time": "2021-03-01T12:29:15.773524Z"
    },
    "id": "E46sWVKK6LP9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: mitdeeplearning in /home/explore/miniconda3/envs/mit_6S191/lib/python3.7/site-packages (0.2.0)\r\n",
      "Requirement already satisfied: numpy in /home/explore/miniconda3/envs/mit_6S191/lib/python3.7/site-packages (from mitdeeplearning) (1.19.2)\r\n",
      "Requirement already satisfied: regex in /home/explore/miniconda3/envs/mit_6S191/lib/python3.7/site-packages (from mitdeeplearning) (2020.11.13)\r\n",
      "Requirement already satisfied: gym in /home/explore/miniconda3/envs/mit_6S191/lib/python3.7/site-packages (from mitdeeplearning) (0.18.0)\r\n",
      "Requirement already satisfied: tqdm in /home/explore/miniconda3/envs/mit_6S191/lib/python3.7/site-packages (from mitdeeplearning) (4.56.0)\r\n",
      "Requirement already satisfied: scipy in /home/explore/miniconda3/envs/mit_6S191/lib/python3.7/site-packages (from gym->mitdeeplearning) (1.6.0)\r\n",
      "Requirement already satisfied: Pillow<=7.2.0 in /home/explore/miniconda3/envs/mit_6S191/lib/python3.7/site-packages (from gym->mitdeeplearning) (7.2.0)\r\n",
      "Requirement already satisfied: cloudpickle<1.7.0,>=1.2.0 in /home/explore/miniconda3/envs/mit_6S191/lib/python3.7/site-packages (from gym->mitdeeplearning) (1.6.0)\r\n",
      "Requirement already satisfied: pyglet<=1.5.0,>=1.4.0 in /home/explore/miniconda3/envs/mit_6S191/lib/python3.7/site-packages (from gym->mitdeeplearning) (1.5.0)\r\n",
      "Requirement already satisfied: future in /home/explore/miniconda3/envs/mit_6S191/lib/python3.7/site-packages (from pyglet<=1.5.0,>=1.4.0->gym->mitdeeplearning) (0.18.2)\r\n"
     ]
    }
   ],
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "V0e77oOM3udR"
   },
   "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",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-01T12:32:37.670652Z",
     "start_time": "2021-03-01T12:30:27.454583Z"
    },
    "id": "RWXaaIWy6jVw"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://www.dropbox.com/s/hlz8atheyozp1yx/train_face.h5?dl=1\n",
      "1263894528/1263889489 [==============================] - 115s 0us/step\n",
      "Opening /home/explore/.keras/datasets/train_face.h5\n",
      "Loading data into memory...\n"
     ]
    }
   ],
   "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/hlz8atheyozp1yx/train_face.h5?dl=1')\n",
    "# Instantiate a TrainingDatasetLoader using the downloaded dataset\n",
    "loader = mdl.lab2.TrainingDatasetLoader(path_to_training_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yIE321rxa_b3"
   },
   "source": [
    "We can look at the size of the training dataset and grab a batch of size 100:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-01T12:34:51.261450Z",
     "start_time": "2021-03-01T12:34:51.251471Z"
    },
    "id": "DjPSjZZ_bGqe"
   },
   "outputs": [],
   "source": [
    "number_of_training_examples = loader.get_train_size()\n",
    "(images, labels) = loader.get_batch(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sxtkJoqF6oH1"
   },
   "source": [
    "Play around with displaying images to get a sense of what the training data actually looks like!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-01T12:34:55.965629Z",
     "start_time": "2021-03-01T12:34:55.798102Z"
    },
    "id": "Jg17jzwtbxDA"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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 = 23 #@param {type:\"slider\", min:0, max:50, step:1}\n",
    "idx_not_face = 6 #@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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NDj7KBaW8Asz"
   },
   "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"
   },
   "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",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-01T12:41:42.007827Z",
     "start_time": "2021-03-01T12:41:41.992628Z"
    },
    "id": "82EVTAAW7B_X"
   },
   "outputs": [],
   "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()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c-eWf3l_lCri"
   },
   "source": [
    "Now let's train the standard CNN!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-01T12:43:35.558281Z",
     "start_time": "2021-03-01T12:41:45.644288Z"
    },
    "id": "eJlDGh1o31G1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3434/3434 [00:56<00:00, 61.20it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AKMdWVHeCxj8"
   },
   "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",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-01T12:43:48.207844Z",
     "start_time": "2021-03-01T12:43:47.346404Z"
    },
    "id": "35-PDgjdWk6_"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standard CNN accuracy on (potentially biased) training set: 0.9980\n"
     ]
    }
   ],
   "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()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Qu7R14KaEEvU"
   },
   "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",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-01T12:44:00.331389Z",
     "start_time": "2021-03-01T12:43:59.962195Z"
    },
    "id": "vfDD8ztGWk6x"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uo1z3cdbEUMM"
   },
   "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",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-01T12:45:02.272025Z",
     "start_time": "2021-03-01T12:45:02.179382Z"
    },
    "id": "GI4O0Y1GAot9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "j0Cvvt90DoAm"
   },
   "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"
   },
   "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 one 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"
   },
   "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](https://i.ibb.co/3s4S6Gc/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"
   },
   "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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KV1khmq5wt-K"
   },
   "source": [
    "The equation for the latent loss is provided by:\n",
    "\n",
    "$$L_{KL}(\\mu, \\sigma) = \\frac{1}{2}\\sum_{j=0}^{k-1} (\\sigma_j + \\mu_j^2 - 1 - \\log{\\sigma_j})$$\n",
    "\n",
    "The equation for the reconstruction loss is provided by:\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. Now we're ready to define our VAE loss function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-01T12:56:00.172669Z",
     "start_time": "2021-03-01T12:56:00.169603Z"
    },
    "id": "S00ASo1ImSuh"
   },
   "outputs": [],
   "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 = 0.5*(np.sum(np.exp(logsigma)+np.square(mu)-1-logsigma))# TODO\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.reduce_mean(x-x_recon, 0) # TODO\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 # TODO\n",
    "\n",
    "    return vae_loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E8mpb3pJorpu"
   },
   "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"
   },
   "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",
    "$$z = \\mu + e^{\\left(\\frac{1}{2} \\cdot \\log{\\Sigma}\\right)}\\circ \\epsilon$$\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",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-01T12:58:58.677650Z",
     "start_time": "2021-03-01T12:58:58.668191Z"
    },
    "id": "cT6PGdNajl3K"
   },
   "outputs": [],
   "source": [
    "### VAE Reparameterization ###\n",
    "\n",
    "def sampling(z_mean, z_logsigma):\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",
    "    # 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+np.exp(0.5*z_logsigma)*epsilon# TODO\n",
    "    return z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qtHEYI9KNn0A"
   },
   "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"
   },
   "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"
   },
   "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",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-01T13:03:23.440885Z",
     "start_time": "2021-03-01T13:03:23.427654Z"
    },
    "id": "VjieDs8Ovcqs"
   },
   "outputs": [],
   "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 = tf.reduce_mean(classification_loss + face_indicator*vae_loss, 0) # TODO\n",
    "\n",
    "    return total_loss, classification_loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YIu_2LzNWwWY"
   },
   "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",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-01T13:03:55.051238Z",
     "start_time": "2021-03-01T13:03:55.038843Z"
    },
    "id": "JfWPHGrmyE7R"
   },
   "outputs": [],
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yWCMu12w1BuD"
   },
   "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 sublassing the `Model` class; defining encoding, reparameterization, and decoding operations; and calling the network end-to-end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-01T13:09:11.519212Z",
     "start_time": "2021-03-01T13:09:11.479921Z"
    },
    "id": "dSFDcFBL13c3"
   },
   "outputs": [],
   "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",
    "    z = sampling(z_mean, z_logsigma) # TODO\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) # TODO\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 = reparameterize(z_mean, z_logsigma)# TODO\n",
    "\n",
    "    # TODO: reconstruction\n",
    "    recon = decode(z) # TODO\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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "M-clbYAj2waY"
   },
   "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"
   },
   "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"
   },
   "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",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-01T13:09:25.580660Z",
     "start_time": "2021-03-01T13:09:25.565474Z"
    },
    "id": "ewWbf7TE7wVc"
   },
   "outputs": [],
   "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",
    "  return mu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wn4yK3SC72bo"
   },
   "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",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-01T13:15:26.073938Z",
     "start_time": "2021-03-01T13:15:26.068671Z"
    },
    "id": "HiX9pmmC7_wn"
   },
   "outputs": [],
   "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) # 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, bins) # 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 /= np.sum(p)# TODO\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.max(p, training_sample_p)# TODO\n",
    "        \n",
    "    # final normalization\n",
    "    training_sample_p /= np.sum(training_sample_p)\n",
    "\n",
    "    return training_sample_p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pF14fQkVUs-a"
   },
   "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",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-01T13:19:31.910700Z",
     "start_time": "2021-03-01T13:19:25.678277Z"
    },
    "id": "xwQs-Gu5bKEK"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting epoch 1/6\n",
      "Recomputing the sampling probabilities\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "object of too small depth for desired array",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-24-0a72ed3a0ba4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     47\u001b[0m   \u001b[0;31m# Recompute data sampling proabilities\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m   \u001b[0;34m'''TODO: recompute the sampling probabilities for debiasing'''\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m   \u001b[0mp_faces\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_training_sample_probabilities\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_faces\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdbvae\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# TODO\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m   \u001b[0;31m# get a batch of training data and compute the training step\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-23-0479e158706d>\u001b[0m in \u001b[0;36mget_training_sample_probabilities\u001b[0;34m(images, dbvae, bins, smoothing_fac)\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0;31m#    every data sample falls in to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m         \u001b[0;31m# https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.digitize.html\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m         \u001b[0mbin_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdigitize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlatent_distribution\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# TODO\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m         \u001b[0;31m# smooth the density function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mdigitize\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/mit_6S191/lib/python3.7/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36mdigitize\u001b[0;34m(x, bins, right)\u001b[0m\n\u001b[1;32m   4774\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"x may not be complex\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4775\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4776\u001b[0;31m     \u001b[0mmono\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_monotonicity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbins\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4777\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmono\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4778\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"bins must be monotonically increasing or decreasing\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: object of too small depth for desired array"
     ]
    }
   ],
   "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(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) # TODO\n",
    "  \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) # TODO\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) # TODO\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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uZBlWDPOVcHg"
   },
   "source": [
    "Wonderful! Now we should have a trained and (hopefully!) debiased facial classification model, ready for evaluation!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Eo34xC7MbaiQ"
   },
   "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",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-01T13:19:47.052278Z",
     "start_time": "2021-03-01T13:19:46.918108Z"
    },
    "id": "bgK77aB9oDtX"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rfvSjHy3+wQ9+8M8nnnhiv/w6s2fPbn3HHXfs/9xzz82dO3furJNOOmnN5s2b38t5bdu2jbStqK6uVu3yFi12TItPPvlkh+eee65DVVXV7Dlz5szq16/fpk2bNrUAaN26dU3ueX5JO6yfr0+fPptfeeWVveqr06VLl21nnHHGqh/+8IcFE/w111zz9kMPPdRlw4YNO5XHszzyHwzMi4g3IuJd4BHgtHrqjwAezjAeMzNr5qZOndpm+vTpbWrnJ0+e3K579+7vAuy9997b1qxZ0wJg9erVZe3atavp1KnTtgULFrR89tlnOzbU9jHHHLPu3nvv7QzJ1fhr164tA3jnnXfKOnbsuK1Dhw41kydPbjt16tS9C61/3HHHbXj55Zc7LF26tGzLli16/PHHd/hSAnDNNdcsve2227pNmzatDcC2bdu4/vrrdxji//a3v/32uHHjyrdt27bDN4r9999/26mnnrr6oYce6pJfVowsr/avABbkzC8EhhSqKGkvYCgwso7yy4DLAHr27Nm0UZqZ2U4r5qd5TWnt2rVlX/va13quXbu2rKysLHr16rVl3LhxbwFceOGFK4YNG9a7a9euW1955ZW5/fv339i7d+/De/bsuWXgwIHrG2p79OjRi88888yPVFZW9jv22GPXd+vW7V2AM888c83YsWPL+/TpU3nIIYdsHjBgwIZC6x900EFbr7766sXHHHNMv/Ly8q1HHHHExkKJe8iQIZtuueWWBSNGjPjIpk2bWkji5JNPXpNfr1u3btXDhg1b/bOf/azguf9vf/vbS8eNG1deqKwhyr2asSlJOhv414i4NJ0/HxgcEV8tUPcc4IsRcWpD7Q4aNCiqqqqaPF6z5sQ/9bPGkjQpIgZ90HamTp06f8CAASuaIibL1tSpU7sMGDCgV6GyLIf9FwI9cua7A3XdhGA4HvI3MzPbJbJM/hOB3pIOltSaJMGPz68kqSNwPPDbDGMxMzOzVGbn/COiWtJI4CmgDLg3ImZKujwtH5NWPQN4OiIKnkMxMzOzppXp7X0jYgIwIW/ZmLz5+4D7sozDzMzM3uc7/JmZmZUYJ38zM7MS46f6mZnZzru+Y5M+0pfr1zR434CysrKBvXv33lRdXa2ysrIYMWLEyu985ztvl5WV8eSTT3YYMWLEIRUVFe/W1NTQpUuX6l/+8pdv1D4eF2DOnDmtjzvuuL6LFy+eVlb2/u3x+/btW3nXXXfNP/HEEzfecMMNXW+++ebuixcvntq5c+dtwHZt164zevToBaeffvq6Ju2DXcBH/mZm1qy0adOmZvbs2bPmzZs3889//vPcp59+uuNVV1313lP7Bg0atH727Nmz5s6dO+uoo47akH+L3MMOO+zdbt26vfuHP/zhvafuTZ48ue2GDRtanHjiiRsBfvWrX3Xu37//hgcffHDf3HVr2659NcfED07+ZmbWjFVUVFTfc88983/+8593ramp2a6spqaGdevWle233347PPf+rLPOWvXQQw+99yC5Bx54oNMZZ5yxCmDmzJltNm7c2OLGG29c9Nhjj30oHzbn5G9mZs1aZWXluzU1NSxatKglQFVVVfu+fftWHnjggUe88MILHUaOHLnDHQkvuOCCVU8//fS+W7duBeCJJ57Y7/zzz18FydMAP//5z68aOnTo+jfffLNtbbu5bde+Zs6c2Sa/7ebAyd/MzJq93FvV1w7NL126dNq55567cuTIkd3z6/fs2bO6d+/em8ePH7/PSy+91K5ly5Zx9NFHbwZ4/PHHO11wwQWrysrKGDZs2Or7779/v/y2a1+HH374ll2yg03MF/yZmVmzNmvWrNZlZWVUVFRUT506dbuyM888852zzz77EIBPfvKTvVesWNFqwIABGx599NG3zj777FUPP/xwp65du24988wzVwG88sor7d566602Q4cO7QOwdetW9ejRY8s111yzfJfvWIac/M3MrNlavHhxyy996UsHXXzxxctatNhxMPsvf/lL+4MOOmgLwF//+tfXc8vOP//81TfddFNF27Zta5555pk5APfff3+nK6+8cvH3v//9pbX1KioqPjp37tzWGe/KLuXkb2ZmO6+In+Y1tS1btrTo27dvZe1P/c4555yV3/3ud9+uLa89Lx8RdOjQYdu99947v1A7Xbp02XbkkUeuX7FiRau+ffu+C/DEE090evLJJ7f7kjBs2LDV48aN63TsscduqG27tuzqq69ecvHFF6/OaFcz4+RvZmbNyrZt2+r8wnHKKaesW7du3ZRi23rmmWf+kTu/aNGi6fl17rnnnoW1041pe0/mC/7MzMxKjJO/mZlZiXHyNzOzxqipqanR7g7C6pe+RzV1lTv5m5lZY8xYvnx5R38B2HPV1NRo+fLlHYEZddXxBX9mZla06urqS5cuXXrP0qVL++MDyD1VDTCjurr60roqOPmbmVnRBg4cuAz43O6Owz4Yf2szMzMrMU7+ZmZmJcbD/tbkeo36fZO0M3/0Z5ukHTMz216mR/6ShkqaI2mepFF11DlB0hRJMyU9l2U8ZmZmluGRv6Qy4E7gU8BCYKKk8RExK6fOvsBPgKER8U9JXbOKx8zMzBJZDvsPBuZFxBsAkh4BTgNm5dQ5F/hNRPwTICKWZRiPh6PNzMzIdti/AliQM78wXZarD7CfpGclTZJ0QaGGJF0mqUpS1fLlH6pHKpuZme1yWSb/Qnd/irz5lsBA4LPAvwLfkdRnh5UixkbEoIgYVF5e3vSRmpmZlZAsh/0XAj1y5rsDiwvUWRERG4ANkp4HBgBzM4zLzMyspGV55D8R6C3pYEmtgeHA+Lw6vwX+RVJLSXsBQ4DXMozJzMys5GV25B8R1ZJGAk8BZcC9ETFT0uVp+ZiIeE3SH4BpJPcivici6nwQgZnZruSLhO3DKtOb/ETEBGBC3rIxefM/AH6QZRxmZmb2Pt/e18zMrMQ4+ZuZmZUYJ38zM7MS4+RvZmZWYpz8zczMSoyTv5mZWYlx8jczMysxTv5mZmYlxsnfzMysxDj5m5mZlRgnfzMzsxLj5G9mZlZinPzNzMxKjJO/mZlZiXHyNzMzKzFO/mZmZiXGyd/MzKzEOPmbmZmVGCd/MzOzEuPkb2ZmVmIyTf6ShkqaI2mepFEFyk+QtEbSlPR1XZbxmJmZGbTMqmFJZcCdwKeAhcBESeMjYlZe1Rci4pSs4jAzM7PtZXnkPxiYFxFvRMS7wCPAaRluz8zMzIqQZfKvABbkzC9Ml+U7VtJUSf8n6fBCDUm6TFKVpKrly5dnEauZmVnJyGzYH1CBZZE3/ypwUESsl/QZ4Amg9w4rRYwFxgIMGjQov41d7/qOTdTOmqZpx2xn+bO8a7ifbQ+TZfJfCPTIme8OLM6tEBFrc6YnSPqJpC4RsSLDuKy58B9MM7NMZDnsPxHoLelgSa2B4cD43AqSDpCkdHpwGs/KDGMyMzMreZkd+UdEtaSRwFNAGXBvRMyUdHlaPgY4C/iypGpgEzA8Inb/sL6ZmdmHWJbD/kTEBGBC3rIxOdN3AHdkGYOZmZltz3f4MzMzKzFO/mZmZiXGyd/MzKzEOPmbmZmVmKKSv6RTJPmLgpmZ2YdAsQl9OPC6pP+W1C/LgMzMzCxbRSX/iPgicBTwD+Dnkv6W3m+/Q6bRmZmZWZMreig/vRXvr0meztcNOAN4VdJXM4rNzMzMMlDsOf/PSXoc+DPQChgcEcOAAcBVGcZnZmZmTazYO/ydBfwoIp7PXRgRGyVd0vRhmZmZWVaKHfZfkp/4Jd0CEBHPNHlUZmZmlplik/+nCiwb1pSBmJmZ2a5R77C/pC8D/wEcImlaTlEH4MUsAzMzM7NsNHTO/yHg/4DvA6Nylq+LiFWZRWVmZmaZaSj5R0TMl/SV/AJJnfwFwMzMrPkp5sj/FGASEIByygL4SEZxmZmZWUbqTf4RcUr678G7JhwzMzPLWkMX/H2svvKIeLVpwzEzM7OsNTTsf2s9ZQGc1ISxmJmZ2S7Q0LD/iR+kcUlDgR8DZcA9ETG6jnpHAy8D50TErz7INs3MzKx+DQ37nxQRf5b0+ULlEfGbetYtA+4kuUHQQmCipPERMatAvVuApxobvJmZmTVeQ8P+x5M8zOfUAmUB1Jn8gcHAvIh4A0DSI8BpwKy8el8leVrg0cUEbGZmZh9MQ8P+303/vXgn2q4AFuTMLwSG5FaQVEHyaOCTqCf5S7oMuAygZ8+eOxGKmZmZ1Sr2kb6dJd0u6VVJkyT9WFLnhlYrsCzy5v8HuDoittXXUESMjYhBETGovLy8mJDNzMysDsU+0vcR4HngzHT+POBR4OR61lkI9MiZ7w4szqszCHhEEkAX4DOSqiPiiSLjMjMzs0YqNvl3iojv5czfJOn0BtaZCPSWdDCwCBgOnJtbIffmQZLuA5504jczM8tWsY/0/Yuk4ZJapK8vAL+vb4WIqAZGklzF/xrwWETMlHS5pMs/WNhmZma2sxr6qd863r+n/zeAX6RFLYD1wHfrWz8iJgAT8paNqaPuRUVFbGZmZh9IQ1f7d9hVgZiZmdmuUew5fyTtB/QG2tYui4jnswjKzMzMslNU8pd0KfCfJFfsTwGOAf6G7+1vZmbW7BR7wd9/ktyE5630fv9HAcszi8rMzMwyU2zy3xwRmwEktYmI2cBh2YVlZmZmWSn2nP9CSfsCTwB/lLSaHW/YY2ZmZs1AUck/Is5IJ6+X9BegI/CHzKIyMzOzzDTmav+PAZ8k+d3/ixHxbmZRmZmZWWaKfbDPdcA4oDPJPfh/LunaLAMzMzOzbBR75D8COCrnor/RwKvATVkFZmZmZtko9mr/+eTc3AdoA/yjyaMxMzOzzDV0b///JTnHvwWYKemP6fyngL9mH56ZmZk1tYaG/avSfycBj+csfzaTaMzMzCxzDT3YZ1zttKTWQJ90dk5EbM0yMDMzM8tGsff2P4Hkav/5JI/37SHpQj/Yx8zMrPkp9mr/W4FPR8QcAEl9gIeBgVkFZmZmZtko9mr/VrWJHyAi5gKtsgnJzMzMslTskf8kST8DHkjnzyO5CNDMzMyamWKT/+XAV4CvkZzzfx74SVZBmZmZWXYaTP6SWgCTIqI/cFv2IZmZmVmWGjznHxE1wFRJPRvbuKShkuZImidpVIHy0yRNkzRFUpWkTzZ2G2ZmZtY4xQ77dyO5w9/fgQ21CyPic3WtIKkMuJPkboALgYmSxkfErJxqzwDjIyIkHQE8BvRt5D6YmZlZIxSb/G/YibYHA/Mi4g0ASY8ApwHvJf+IWJ9Tf2+SWwebmZlZhhq6t39bkov9DgWmAz+LiOoi264AFuTMLwSGFNjGGcD3ga7AZ+uI4zLgMoCePRt99sHMzMxyNHTOfxwwiCTxDyO52U+xVGDZDkf2EfF4RPQFTge+V6ihiBgbEYMiYlB5eXkjQjAzM7N8DQ37V0bERwHS3/n/vRFtLwR65Mx3BxbXVTkinpd0iKQuEbGiEdsxMzOzRmjoyP+9h/c0Yri/1kSgt6SD04cCDQfG51aQdKgkpdMfA1oDKxu5HTMzM2uEho78B0ham04LaJfOC4iI2KeuFSOiWtJI4CmgDLg3ImZKujwtHwOcCVwgaSuwCTgnInzRn5mZWYYaeqRv2QdpPCImABPylo3Jmb4FuOWDbMPMzMwap9gH+5iZmdmHhJO/mZlZiXHyNzMzKzFO/mZmZiXGyd/MzKzEOPmbmZmVGCd/MzOzEuPkb2ZmVmKc/M3MzEqMk7+ZmVmJcfI3MzMrMU7+ZmZmJcbJ38zMrMQ4+ZuZmZUYJ38zM7MS4+RvZmZWYpz8zczMSoyTv5mZWYlx8jczMysxmSZ/SUMlzZE0T9KoAuXnSZqWvl6SNCDLeMzMzCzD5C+pDLgTGAZUAiMkVeZVexM4PiKOAL4HjM0qHjMzM0tkeeQ/GJgXEW9ExLvAI8BpuRUi4qWIWJ3Ovgx0zzAeMzMzI9vkXwEsyJlfmC6ry78B/5dhPGZmZga0zLBtFVgWBStKJ5Ik/0/WUX4ZcBlAz549myo+MzOzkpTlkf9CoEfOfHdgcX4lSUcA9wCnRcTKQg1FxNiIGBQRg8rLyzMJ1szMrFRkmfwnAr0lHSypNTAcGJ9bQVJP4DfA+RExN8NYzMzMLJXZsH9EVEsaCTwFlAH3RsRMSZen5WOA64DOwE8kAVRHxKCsYjIzM7Nsz/kTEROACXnLxuRMXwpcmmUMZmZmtj3f4c/MzKzEOPmbmZmVGCd/MzOzEuPkb2ZmVmKc/M3MzEqMk7+ZmVmJcfI3MzMrMU7+ZmZmJcbJ38zMrMQ4+ZuZmZUYJ38zM7MS4+RvZmZWYpz8zczMSoyTv5mZWYlx8jczMysxTv5mZmYlxsnfzMysxDj5m5mZlRgnfzMzsxLj5G9mZlZiMk3+koZKmiNpnqRRBcr7SvqbpC2SrsoyFjMzM0u0zKphSWXAncCngIXAREnjI2JWTrVVwNeA07OKw8zMzLaX5ZH/YGBeRLwREe8CjwCn5VaIiGURMRHYmmEcZmZmliPL5F8BLMiZX5guazRJl0mqklS1fPnyJgnOzMysVGWZ/FVgWexMQxExNiIGRcSg8vLyDxiWmZlZacsy+S8EeuTMdwcWZ7g9MzMzK0KWyX8i0FvSwZJaA8OB8Rluz8zMzIqQ2dX+EVEtaSTwFFAG3BsRMyVdnpaPkXQAUAXsA9RI+jpQGRFrs4rLzMys1GWW/AEiYgIwIW/ZmJzppSSnA8zMzGwX8R3+zMzMSoyTv5mZWYlx8jczMysxTv5mZmYlxsnfzMysxDj5m5mZlRgnfzMzsxLj5G9mZlZinPzNzMxKjJO/mZlZiXHyNzMzKzFO/mZmZiXGyd/MzKzEOPmbmZmVGCd/MzOzEuPkb2ZmVmKc/M3MzEqMk7+ZmVmJcfI3MzMrMU7+ZmZmJSbT5C9pqKQ5kuZJGlWgXJJuT8unSfpYlvGYmZlZhslfUhlwJzAMqARGSKrMqzYM6J2+LgPuyioeMzMzS2R55D8YmBcRb0TEu8AjwGl5dU4D7o/Ey8C+krplGJOZmVnJa5lh2xXAgpz5hcCQIupUAEtyK0m6jGRkAGC9pDlNG+p2ugAr6qugptrSDU3WUnPkfs6e+3jXaC79fFBThWHNX5bJv9CnNHaiDhExFhjbFEE1RFJVRAzaFdsqZe7n7LmPdw33szVHWQ77LwR65Mx3BxbvRB0zMzNrQlkm/4lAb0kHS2oNDAfG59UZD1yQXvV/DLAmIpbkN2RmZmZNJ7Nh/4ioljQSeAooA+6NiJmSLk/LxwATgM8A84CNwMVZxdMIu+T0grmfdwH38a7hfrZmRxE7nGI3MzOzDzHf4c/MzKzEOPmbmZmVmD0y+UtaX2DZ5ZIuaGC9iyTdUUfZt+pZb76k6ZKmpK+PNz7q4kh6VtIe/bOg3dT/L+QtmyJpRgPb69VQnT2BpG3p/syUNFXSNyQ16v9eMfua1tmU8zmekl5sm4lCn5M90S7u/5D0vZxlXSRtrev/RU69Ov/vmGUhy9/5N6n0AsEP4lvAzfWUnxgR9d6oo5Ttgv7vIKlHRCyQ1O8DbmtPsykijgSQ1BV4COgIfLeYldNbZRfrH7Xbsvfsyv5/AzgF+E46fzYwsxHrm+0Se+SRfyGSrpd0VTp9dPogoL9J+kHeN/IDJf1B0uuS/jutPxpol377f7DI7R2StjNJ0guS+qbL75N0l6S/SHpD0vGS7pX0mqT7cta/S1JVerRxQx3b+HS6D69K+qWk9jvZPZnbBf3/GHBOOj0CeDhn273S9+DV9LXDyIyksjSWiWls/94U+93UImIZyd0qRya/cC28b5JOSD9jDwHTc9uQ9BFJkyUdXcw26/qcpSMuN6dlVZI+JukpSf9Q+qscSe0lPZOuO11S/i26a7fxzZy+L/h53xPsgv7fBLym90f3ziH5bNeue6qkV9L1/yRp//wGJJVL+nXanxMlfaKJdt/sfRGxx72A9QWWXQ9clU7PAD6eTo8GZqTTF5F88+4ItAXeAnrU1WZO2/NJ/oNPAV5Jlz0D9E6nhwB/TqfvI3lOgUieTbAW+CjJF6lJwJFpvU7pv2XAs8AR6fyzwCCSW4I+D+ydLr8auG539/1u7P8+wEvp/GSSh0HVtrsX0Dad7g1UpdO9cupcBlybTrcBqoCDd3df1tOfq4H969m3E4ANtftQu6/AYWn/HFmgzV4kyWdK+rqzvs9Z2u9fTqd/BEwDOgDlwLJ0eUtgn3S6C8nPcpW7X8CnSX7upvT/wZPAcbu733dT/88APgf8kOSmZc+k/y/uSOvsl9N/lwK35vzfqa3zEPDJdLon8Nru7kO/PnyvZjPsX0vSvkCHiHgpXfQQyTBbrWciYk1adxbJ/awX0LD3hv3TI6OPA7+U3rsDcZucur+LiJA0HXg7Iqan680k+QMwBfiCkmcStAS6kSSzaTltHJMuezHdRmvgb0XEuVtl2P+rgNWShgOvkdz3oVYr4A5JRwLbSL4o5Ps0cISks9L5jiR/zN8sYtu7Q+0Hq759+3tE5MZfDvwWODMi6hpK3m7YX9Ip1P85q73x1nSgfUSsA9ZJ2py+1xuAmyUdB9SQPHtjf2BpThufTl+T0/n2JH3/fD37v7tl1f8AfwC+B7wNPJpX1h14VMkDzFpT+PN5MlCZ87dnH0kd0vfGrEk0u+RPw8/I2JIzvY2d28cWwDtR97nT2m3U5G2vBmgp6WDgKuDoiFidng5om9eGgD9GxIidiG93yrL/HyU5Wr0ob/kVJH9IB5C8N5vriOurEfFUI7a3W0j6CEnfLCM571zXvm3IW3UNyRepT1D8eeSGPmf1fpaB80iS3sCI2CppPoU/y9+PiLuLjGm3yrr/I+JdSZOAK4HDgVNziv8XuC0ixks6gWRELV8L4NiI2FT0Tpk1UrM5518rIlaTHJkcky4aXuSqWyW1KnIba4E3JZ0NkJ4bHNCIMPch+cOxJj2nN6xAnZeBT0g6NN3GXpIKHdHuUTLu/8eB/ya5K2SujsCSiKgBzic5lZLvKeDLtduQ1EfS3kXGtstIKgfGkAzxBsXtW613gdNJbol9bpGb/KCfs44kpwC2SjqRwk+Gewq4JOdaggolF9btcXZh/98KXB0RK/OWdwQWpdMX1rHu08DInJiPbGBbZo22px757yVpYc78bXnl/wb8VNIGknPoa4pocywwTdKrEXFeEfXPA+6SdC3J0OAjwNQi1iMipkqaTHJ08AbwYoE6yyVdBDwsqfaUwrXA3GK2kbHd0v/psOYtADlDngA/AX6dfhn7CzsekQHcQ3LK5VUlKy8n+UO9J2gnaQrJ56gaeID3+7SYfXtPRGxIh/L/KGlDRPy2gfof9HP2IPA7SVUkp7NmF9jG00p+ofG39H1bD3yR5Mh6T7DL+z89LVBodOB6ktOJi0i+mB1coM7XgDslTSP5G/08cHm9e2jWSM3y9r6S2kfE+nR6FNAtIv5zN4dVMtz/ZmbN25565N+Qz0q6hiT+t9jxHLFly/1vZtaMNcsjfzMzM9t5ze6CPzMzM/tgnPzNzMxKjJO/mZlZiXHyNzMzKzFO/mZmZiXm/wNQVhyK2EDDLgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rESoXRPQo_mq"
   },
   "source": [
    "## 2.7 Conclusion and submission information\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!\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",
    "Try to optimize your model to achieve improved performance. **MIT students and affiliates will be eligible for prizes during the IAP offering.** To enter the competition, MIT students and affiliates should upload the following to the course Canvas:\n",
    "\n",
    "* iPython notebook with the code you used to generate your results;\n",
    "* copy of the bar plot from section 2.6 showing the performance of your model;\n",
    "* a description and/or diagram of the architecture and hyperparameters you used -- if there are any additional or interesting modifications you made to the template code, please include these in your description;\n",
    "* discussion of why these modifications helped improve performance.\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",
    "<img src=\"https://i.ibb.co/BjLSRMM/ezgif-2-253dfd3f9097.gif\" />"
   ]
  }
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