From 9c61e423cba7f7ce081ce3638139b86f162caef1 Mon Sep 17 00:00:00 2001
From: Ava Amini <apsoleimany@gmail.com>
Date: Tue, 10 Jan 2023 21:10:38 -0500
Subject: [PATCH 01/22] fixing links
---
.../Lab3_Part_1_Introduction_to_CAPSA.ipynb | 44 +++++++++++++++++--
1 file changed, 40 insertions(+), 4 deletions(-)
diff --git a/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb b/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb
index c3d6d3fd..9d1e5cde 100644
--- a/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb
+++ b/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb
@@ -1,12 +1,50 @@
{
"cells": [
+ {
+ "cell_type": "markdown",
+ "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/2023/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.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/2023/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.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"
+ ],
+ "metadata": {
+ "id": "SWa-rLfIlTaf"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Copyright 2023 MIT 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 MIT Introduction\n",
+ "# to Deep Learning must reference:\n",
+ "#\n",
+ "# © MIT Introduction to Deep Learning\n",
+ "# http://introtodeeplearning.com\n",
+ "#"
+ ],
+ "metadata": {
+ "id": "-LohleBMlahL"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
{
"cell_type": "markdown",
"metadata": {
"id": "ckzz5Hus-hJB"
},
"source": [
- "## Part 1: Introduction to CAPSA"
+ "# Part 1: Introduction to CAPSA"
]
},
{
@@ -22,7 +60,6 @@
]
},
{
- "attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "o02MyoDrnNqP"
@@ -778,7 +815,6 @@
]
},
{
- "attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "CkpvkOL06jRd"
@@ -814,4 +850,4 @@
},
"nbformat": 4,
"nbformat_minor": 0
-}
+}
\ No newline at end of file
From 367603f5a7474e48a5e0fa2d5f5e2bbe1600291b Mon Sep 17 00:00:00 2001
From: Ava Amini <apsoleimany@gmail.com>
Date: Tue, 10 Jan 2023 21:55:02 -0500
Subject: [PATCH 02/22] updated/final text up to 1.2
---
.../Lab3_Part_1_Introduction_to_CAPSA.ipynb | 92 ++++++++++++-------
1 file changed, 58 insertions(+), 34 deletions(-)
diff --git a/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb b/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb
index 9d1e5cde..976a7c51 100644
--- a/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb
+++ b/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb
@@ -44,19 +44,16 @@
"id": "ckzz5Hus-hJB"
},
"source": [
- "# Part 1: Introduction to CAPSA"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "gTpt_Hj5j-FZ"
- },
- "source": [
- "As we saw in lecture 6, it is critical to be able to estimate bias and uncertainty robustly: we need benchmarks that uniformly measure how uncertain a given model is, and we need principled ways of measuring bias and uncertainty. To that end, in this lab, we'll utilize [CAPSA](https://github.com/themis-ai/capsa), a risk-estimation wrapping library developed by [Themis AI](https://themisai.io/). CAPSA supports the estimation of three different types of *risk*, defined as measures of how trustworthy our model is. These are:\n",
- "1. Representation bias: using a histogram estimation approach, CAPSA calculates how likely combinations of features are to appear in a given dataset. Often, certain combinations of features are severely underrepresented in datasets, which means models learn them less well. Since evaluation metrics are often also biased in the same manner, these biases are not caught through traditional validation pipelines.\n",
- "2. Aleatoric uncertainty: we can estimate the uncertainty in *data* by learning a layer that predicts a standard deviation for every input. This is useful to determine when sensors have noise, classes in datasets have low separations, and generally when very similar inputs lead to drastically different outputs.\n",
- "3. Epistemic uncertainty: also known as predictive or model uncertainty, epistemic uncertainty captures the areas of our underlying data distribution that the model has not yet learned. Areas of high epistemic uncertainty can be due to out of distribution (OOD) samples or data that is harder to learn.\n"
+ "# Laboratory 3: Debiasing, Uncertainty, and Robustness\n",
+ "\n",
+ "# Part 1: Introduction to Capsa\n",
+ "\n",
+ "In this lab, we'll explore different ways to make deep learning models more **robust** and **trustworthy**.\n",
+ "\n",
+ "To achieve this it is critical to be able to identify and diagnose issues of bias and uncertainty in deep learning models, as we explored in the Facial Detection Lab 2. We need benchmarks that uniformly measure how uncertain a given model is, and we need principled ways of measuring bias and uncertainty. To that end, in this lab, we'll utilize [CAPSA](https://github.com/themis-ai/capsa), a risk-estimation wrapping library developed by [Themis AI](https://themisai.io/). CAPSA supports the estimation of three different types of ***risk***, defined as measures of how robust and trustworthy our model is. These are:\n",
+ "1. **Representation bias**: reflects how likely combinations of features are to appear in a given dataset. Often, certain combinations of features are severely under-represented in datasets, which means models learn them less well and can thus lead to unwanted bias.\n",
+ "2. **Data uncertainty**: reflects noise in the data, for example when sensors have noisy measurements, classes in datasets have low separations, and generally when very similar inputs lead to drastically different outputs. Also known as *aleatoric* uncertainty. \n",
+ "3. **Model uncertainty**: captures the areas of our underlying data distribution that the model has not yet learned or has difficulty learning. Areas of high model uncertainty can be due to out-of-distribution (OOD) samples or data that is harder to learn. Also known as *epistemic* uncertainty."
]
},
{
@@ -65,11 +62,15 @@
"id": "o02MyoDrnNqP"
},
"source": [
- "The core ideology behind CAPSA is that models can be *wrapped* in a way that makes them *risk-aware*. \n",
+ "## CAPSA overview\n",
+ "\n",
+ "This lab introduces `CAPSA` and its functionalities, to next build automated tools that use `CAPSA` to mitigate the underlying issues of bias and uncertainty.\n",
+ "\n",
+ "The core idea behind `CAPSA` is that any deep learning model of interest can be ***wrapped*** -- just like wrapping a gift -- to be made ***aware of its own risks***. Risk is captured in representation bias, data uncertainty, and model uncertainty.\n",
"\n",
"\n",
"\n",
- "This means that CAPSA augments or modifies the user's original model minimally to create a risk-aware variant while preserving the model's underlying structure and training pipeline. CAPSA is a one-line addition to any training workflow in Tensorflow. In this part of the lab, we'll apply CAPSA's risk estimation methods to a toy regression task to further explore the notions of bias and uncertainty. "
+ "This means that `CAPSA` takes the user's original model as input, and modifies it minimally to create a risk-aware variant while preserving the model's underlying structure and training pipeline. `CAPSA` is a one-line addition to any training workflow in TensorFlow. In this part of the lab, we'll apply `CAPSA`'s risk estimation methods to a simple regression problem to further explore the notions of bias and uncertainty. "
]
},
{
@@ -78,7 +79,7 @@
"id": "hF0uSqk-nwmA"
},
"source": [
- "Let's first install necessary dependencies:"
+ "Let's get started by installing the necessary dependencies:"
]
},
{
@@ -112,17 +113,24 @@
}
],
"source": [
+ "# Import Tensorflow 2.0\n",
+ "%tensorflow_version 2.x\n",
"import tensorflow as tf\n",
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "!pip install capsa\n",
"\n",
- "from capsa import *\n",
+ "import IPython\n",
+ "import functools\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "from tqdm import tqdm\n",
"from helper import gen_data_regression\n",
"\n",
+ "# Download and import the MIT Introduction to Deep Learning package\n",
"!pip install mitdeeplearning\n",
"import mitdeeplearning as mdl\n",
- "import tqdm"
+ "\n",
+ "# Download and import CAPSA\n",
+ "!pip install capsa\n",
+ "from capsa import *"
]
},
{
@@ -131,8 +139,11 @@
"id": "xzEcxjKHn8gc"
},
"source": [
- "### 1.1 Datasets \n",
- "Next, let's construct a dataset that we'll analyze. As shown in lecture, we'll look at the curve `y = x^3` with epistemic and aleatoric noise added to certain parts of the dataset. The blue points below are the test data: note that there are regions where we have no train data but we have test data! Do you expect these areas to have higher or lower uncertainty? What type of uncertainty?"
+ "## 1.1 Dataset\n",
+ "\n",
+ "We will build understanding of bias and uncertainty by training a neural network for a simple 2D regression task: modeling the function $y = x^3$. We will use `CAPSA` to analyze this dataset and the performance of the model. Noise and missing-ness will be injected into the dataset.\n",
+ "\n",
+ "Let's generate the dataset and visualize it:"
]
},
{
@@ -178,6 +189,20 @@
"plt.show()"
]
},
+ {
+ "cell_type": "markdown",
+ "source": [
+ "In the plot above, the orange points are the training data, which will be used as inputs to train the neural network model. The blue points are the test data, which will be used to evaluate the performance of the model. Write short (~1 sentence) answers to the questions below to complete the `TODO`s:\n",
+ "\n",
+ "#### **TODO: Inspecting the 2D regression dataset**\n",
+ "1. What are your observations about where the train data and test data lie relative to each other?\n",
+ "2. What, if any, areas do you expect to have high/low aleatoric (data) uncertainty?\n",
+ "3. What, if any, areas do you expect to have high/low epistemic (model) uncertainty?"
+ ],
+ "metadata": {
+ "id": "Fz3UxT8vuN95"
+ }
+ },
{
"cell_type": "markdown",
"metadata": {
@@ -313,7 +338,13 @@
},
"source": [
"### 1.3 Bias Identification\n",
- "Now that we've seen what the predictions from this model look like, let's see what the uncertainty and bias look like! To do this, we'll wrap a model first with a `HistogramWrapper`. For low-dimensional data, the HistogramWrapper bins the input directly into discrete categories and measures the density. "
+ "Now that we've seen what the predictions from this model look like, let's see what the uncertainty and bias look like! To do this, we'll wrap a model first with a `HistogramWrapper`. For low-dimensional data, the HistogramWrapper bins the input directly into discrete categories and measures the density. \n",
+ "\n",
+ "--- FROM INTRO ---\n",
+ "\n",
+ "using a histogram estimation approach, CAPSA calculates how likely combinations of features are to appear in a given dataset. \n",
+ "\n",
+ " Since evaluation metrics are often also biased in the same manner, these biases are not caught through traditional validation pipelines."
]
},
{
@@ -497,7 +528,9 @@
},
"source": [
"## 1.3 Aleatoric Estimation\n",
- "Now, let's do the same thing but for aleatoric estimation! The method we use here is Mean and Variance Estimation (MVE) since we're trying to estimate both mean and variance for every input. As presented in lecture 5, we measure the accuracy of these predictions negative likelihood loss in addition to mean squared error. However, capsa *automatically* does this for us, so we only have to specify the loss function that we want to use for evaluating the predictions, not the uncertainty."
+ "Now, let's do the same thing but for aleatoric estimation! The method we use here is Mean and Variance Estimation (MVE) since we're trying to estimate both mean and variance for every input. As presented in lecture 5, we measure the accuracy of these predictions negative likelihood loss in addition to mean squared error. However, capsa *automatically* does this for us, so we only have to specify the loss function that we want to use for evaluating the predictions, not the uncertainty.\n",
+ "\n",
+ "we can estimate data uncertainty by learning a layer that predicts a standard deviation for every input."
]
},
{
@@ -825,15 +858,6 @@
"\n",
""
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "bs4mAQ5c6cMY"
- },
- "outputs": [],
- "source": []
}
],
"metadata": {
From 3eb396a6788ae71ebcb154da17c09a42fa441429 Mon Sep 17 00:00:00 2001
From: Ava Amini <apsoleimany@gmail.com>
Date: Tue, 10 Jan 2023 22:20:05 -0500
Subject: [PATCH 03/22] final text and code comments up to 1.3
---
.../Lab3_Part_1_Introduction_to_CAPSA.ipynb | 103 ++++++++----------
1 file changed, 46 insertions(+), 57 deletions(-)
diff --git a/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb b/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb
index 976a7c51..cc5e9ede 100644
--- a/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb
+++ b/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb
@@ -172,6 +172,7 @@
}
],
"source": [
+ "# Get the data for the cubic function, injected with noise and missing-ness\n",
"def gen_data(x_min, x_max, n, train=True):\n",
" x = np.random.triangular(x_min, 2, x_max, size=(n, 1))\n",
"\n",
@@ -180,10 +181,11 @@
"\n",
" return x, y\n",
"\n",
- "x, y = gen_data(-4, 4, 2000)\n",
- "x_val, y_val = gen_data(-6, 6, 500)\n",
- "plt.scatter(x_val,y_val, s=1.5, label='test data')\n",
- "plt.scatter(x,y, s=1.5, label='train data')\n",
+ "# Plot the dataset and visualize the train and test datapoints\n",
+ "x_train, y_train = gen_data(-4, 4, 2000) # train data\n",
+ "x_test, y_test = gen_data(-6, 6, 500) # test data\n",
+ "plt.scatter(x_train, y_train, s=1.5, label='train data')\n",
+ "plt.scatter(x_test, y_test, s=1.5, label='test data')\n",
"\n",
"plt.legend()\n",
"plt.show()"
@@ -209,8 +211,9 @@
"id": "mXMOYRHnv8tF"
},
"source": [
- "### 1.2 Vanilla regression\n",
- "Let's define a small model that can predict `y` given `x`: this is a classical regression task!"
+ "## 1.2 Regression on cubic dataset\n",
+ "\n",
+ "Next we will define a small dense neural network model that can predict `y` given `x`: this is a classical regression task! We will build the model and use the [`model.fit()`](https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit) function to train the model -- normally, without any risk-awareness -- using the train dataset that we visualized above."
]
},
{
@@ -221,7 +224,10 @@
},
"outputs": [],
"source": [
- "def create_standard_classifier():\n",
+ "### Define and train a dense NN model for the regression task###\n",
+ "\n",
+ "'''Function to define a small dense NN'''\n",
+ "def create_dense_NN():\n",
" return tf.keras.Sequential(\n",
" [\n",
" tf.keras.Input(shape=(1,)),\n",
@@ -231,7 +237,17 @@
" ]\n",
" )\n",
"\n",
- "standard_classifier = create_standard_classifier()"
+ "dense_NN = create_dense_NN()\n",
+ "\n",
+ "# Build the model for regression, defining the loss function and optimizer\n",
+ "dense_NN.compile(\n",
+ " optimizer=tf.keras.optimizers.Adam(learning_rate=2e-3),\n",
+ " loss=tf.keras.losses.MeanSquaredError(), # MSE loss for the regression task\n",
+ ")\n",
+ "\n",
+ "# TODO: Train the model for 10 epochs. Use model.fit().\n",
+ "loss_history = dense_NN.fit(x_train, y_train, epochs=10) \n",
+ "# loss_history = # TODO"
]
},
{
@@ -240,54 +256,9 @@
"id": "ovwYBUG3wTDv"
},
"source": [
- "Let's first train this model normally, without any wrapping. Which areas would you expect the model to do well in? Which areas should it do worse in?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "oPNxsGBRwaNA",
- "outputId": "0598cef9-350c-4785-a7a9-51ed3b54fd4b"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 1/10\n",
- "63/63 [==============================] - 1s 2ms/step - loss: 5.5708\n",
- "Epoch 2/10\n",
- "63/63 [==============================] - 0s 2ms/step - loss: 4.3687\n",
- "Epoch 3/10\n",
- "63/63 [==============================] - 0s 2ms/step - loss: 3.9064\n",
- "Epoch 4/10\n",
- "63/63 [==============================] - 0s 2ms/step - loss: 3.1653\n",
- "Epoch 5/10\n",
- "63/63 [==============================] - 0s 4ms/step - loss: 2.1027\n",
- "Epoch 6/10\n",
- "63/63 [==============================] - 0s 3ms/step - loss: 1.6488\n",
- "Epoch 7/10\n",
- "63/63 [==============================] - 0s 2ms/step - loss: 1.3093\n",
- "Epoch 8/10\n",
- "63/63 [==============================] - 0s 2ms/step - loss: 1.1078\n",
- "Epoch 9/10\n",
- "63/63 [==============================] - 0s 2ms/step - loss: 0.9919\n",
- "Epoch 10/10\n",
- "63/63 [==============================] - 0s 3ms/step - loss: 0.8937\n"
- ]
- }
- ],
- "source": [
- "standard_classifier.compile(\n",
- " optimizer=tf.keras.optimizers.Adam(learning_rate=2e-3),\n",
- " loss=tf.keras.losses.MeanSquaredError(),\n",
- ")\n",
+ "Now, we are ready to evaluate our neural network. We use the test data to assess performance on the regression task, and visualize the predicted values against the true values.\n",
"\n",
- "history = standard_classifier.fit(x, y, epochs=10)\n"
+ "Given your observation of the data in the previous plot, where do you expect the model to perform well? Let's test the model and see:"
]
},
{
@@ -326,11 +297,29 @@
}
],
"source": [
- "plt.scatter(x_val, y_val, s=0.5, label='truth')\n",
- "plt.scatter(x_val, standard_classifier(x_val), s=0.5, label='predictions')\n",
+ "# Pass the test data through the network and predict the y values\n",
+ "y_predicted = dense_NN(x_test)\n",
+ "\n",
+ "# Visualize the true (x, y) pairs for the test data vs. the predicted values\n",
+ "plt.scatter(x_test, y_test, s=0.5, label='truth')\n",
+ "plt.scatter(x_test, y_predicted, s=0.5, label='predictions')\n",
"plt.legend()"
]
},
+ {
+ "cell_type": "markdown",
+ "source": [
+ "\n",
+ "Write short (~1 sentence) answers to the questions below to complete the `TODO`s:\n",
+ "\n",
+ "#### **TODO: Analyzing the performance of standard regression model**\n",
+ "1. Where does the model perform well? How does this relate to aleatoric and epistemic uncertainty?\n",
+ "2. Where does the model perform poorly? How does this relate to aleatoric and epistemic uncertainty?"
+ ],
+ "metadata": {
+ "id": "7Vktjwfu0ReH"
+ }
+ },
{
"cell_type": "markdown",
"metadata": {
From 8d5c2da86064244c57f4fa3988cada7d7013ed7a Mon Sep 17 00:00:00 2001
From: Ava Amini <apsoleimany@gmail.com>
Date: Tue, 10 Jan 2023 23:48:38 -0500
Subject: [PATCH 04/22] finalize text up to epistemic
---
.../Lab3_Part_1_Introduction_to_CAPSA.ipynb | 249 ++++++------------
1 file changed, 85 insertions(+), 164 deletions(-)
diff --git a/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb b/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb
index cc5e9ede..a5838aa1 100644
--- a/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb
+++ b/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb
@@ -194,9 +194,12 @@
{
"cell_type": "markdown",
"source": [
- "In the plot above, the orange points are the training data, which will be used as inputs to train the neural network model. The blue points are the test data, which will be used to evaluate the performance of the model. Write short (~1 sentence) answers to the questions below to complete the `TODO`s:\n",
+ "In the plot above, the orange points are the training data, which will be used as inputs to train the neural network model. The blue points are the test data, which will be used to evaluate the performance of the model.\n",
"\n",
"#### **TODO: Inspecting the 2D regression dataset**\n",
+ "\n",
+ " Write short (~1 sentence) answers to the questions below to complete the `TODO`s:\n",
+ "\n",
"1. What are your observations about where the train data and test data lie relative to each other?\n",
"2. What, if any, areas do you expect to have high/low aleatoric (data) uncertainty?\n",
"3. What, if any, areas do you expect to have high/low epistemic (model) uncertainty?"
@@ -245,8 +248,8 @@
" loss=tf.keras.losses.MeanSquaredError(), # MSE loss for the regression task\n",
")\n",
"\n",
- "# TODO: Train the model for 10 epochs. Use model.fit().\n",
- "loss_history = dense_NN.fit(x_train, y_train, epochs=10) \n",
+ "# TODO: Train the model for 30 epochs. Use model.fit().\n",
+ "loss_history = dense_NN.fit(x_train, y_train, epochs=30) \n",
"# loss_history = # TODO"
]
},
@@ -309,10 +312,11 @@
{
"cell_type": "markdown",
"source": [
+ "\n",
+ "#### **TODO: Analyzing the performance of standard regression model**\n",
"\n",
"Write short (~1 sentence) answers to the questions below to complete the `TODO`s:\n",
"\n",
- "#### **TODO: Analyzing the performance of standard regression model**\n",
"1. Where does the model perform well? How does this relate to aleatoric and epistemic uncertainty?\n",
"2. Where does the model perform poorly? How does this relate to aleatoric and epistemic uncertainty?"
],
@@ -326,14 +330,13 @@
"id": "7MzvM48JyZMO"
},
"source": [
- "### 1.3 Bias Identification\n",
- "Now that we've seen what the predictions from this model look like, let's see what the uncertainty and bias look like! To do this, we'll wrap a model first with a `HistogramWrapper`. For low-dimensional data, the HistogramWrapper bins the input directly into discrete categories and measures the density. \n",
+ "## 1.3 Evaluating bias\n",
"\n",
- "--- FROM INTRO ---\n",
+ "Now that we've seen what the predictions from this model look like, we will identify and quantify bias and uncertainty in this problem. We first consider bias.\n",
"\n",
- "using a histogram estimation approach, CAPSA calculates how likely combinations of features are to appear in a given dataset. \n",
+ "Recall that *representation bias* reflects how likely combinations of features are to appear in a given dataset. `Capsa` calculates how likely combinations of features are by using a histogram estimation approach: the `HistogramWrapper`. For low-dimensional data, the `HistogramWrapper` bins the input directly into discrete categories and measures the density. \n",
"\n",
- " Since evaluation metrics are often also biased in the same manner, these biases are not caught through traditional validation pipelines."
+ "We start by taking our `dense_NN` and wrapping it with the `HistogramWrapper`:"
]
},
{
@@ -344,10 +347,14 @@
},
"outputs": [],
"source": [
- "standard_classifier = create_standard_classifier()\n",
- "bias_wrapped_classifier = HistogramWrapper(standard_classifier, \n",
- " queue_size=2000, # how many samples to track\n",
- " target_hidden_layer=False) # for low-dimensional data, we can estimate densities directly from data\n"
+ "### Wrap the dense network for bias estimation ###\n",
+ "\n",
+ "standard_dense_NN = create_dense_NN()\n",
+ "bias_wrapped_dense_NN = HistogramWrapper(\n",
+ " standard_dense_NN, # the original model\n",
+ " queue_size=2000, # how many samples to track\n",
+ " target_hidden_layer=False # for low-dimensional data, we can estimate densities directly from data\n",
+ " ) \n"
]
},
{
@@ -356,7 +363,7 @@
"id": "UFHO7LKcz8uP"
},
"source": [
- "Now that we've wrapped the classifier, let's re-train it to update the biases as we train. We can use the exact same training pipeline as above to accomplish this!"
+ "Now that we've wrapped the classifier, let's re-train it to update the bias estimates as we train. We can use the exact same training pipeline, using `compile` to build the model and `model.fit()` to train the model:"
]
},
{
@@ -452,12 +459,16 @@
}
],
"source": [
- "bias_wrapped_classifier.compile(\n",
+ "### Compile and train the wrapped model! ###\n",
+ "\n",
+ "# Build the model for regression, defining the loss function and optimizer\n",
+ "bias_wrapped_dense_NN.compile(\n",
" optimizer=tf.keras.optimizers.Adam(learning_rate=2e-3),\n",
- " loss=tf.keras.losses.MeanSquaredError(),\n",
+ " loss=tf.keras.losses.MeanSquaredError(), # MSE loss for the regression task\n",
")\n",
"\n",
- "history = bias_wrapped_classifier.fit(x, y, epochs=30)"
+ "# Train the wrapped model for 30 epochs.\n",
+ "loss_history_bias_wrap = bias_wrapped_dense_NN.fit(x_train, y_train, epochs=30)"
]
},
{
@@ -466,7 +477,9 @@
"id": "_6iVeeqq0f_H"
},
"source": [
- "To access the bias for a given testing input, we can simply call the method as we would normally. In addition to outputting the prediction, this risk-aware model now also outputs an additional bias score per output."
+ "We can now use our wrapped model to assess the bias for a given test input. With the wrapping capability, `Capsa` neatly allows us to output a *bias score* along with the predicted target value. This bias score reflects the density of data surrounding an input point -- the higher the score, the greater the data representation and density. The wrapped, risk-aware model outputs the predicted target and bias score after it is called!\n",
+ "\n",
+ "Let's see how it is done:"
]
},
{
@@ -505,174 +518,77 @@
}
],
"source": [
- "predictions, bias = bias_wrapped_classifier(np.sort(x_val))\n",
- "plt.scatter(np.sort(x_val), bias, label='bias', s=0.5)\n",
+ "### Generate and visualize bias scores for data in test set ###\n",
+ "\n",
+ "# Call the risk-aware model to generate scores\n",
+ "predictions, bias = bias_wrapped_dense_NN(np.sort(x_test))\n",
+ "\n",
+ "# Visualize the relationship between the input data x and the bias\n",
+ "plt.scatter(np.sort(x_test), bias, label='bias', s=0.5)\n",
"plt.legend()"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "id": "PvS8xR_q27Ec"
- },
"source": [
- "## 1.3 Aleatoric Estimation\n",
- "Now, let's do the same thing but for aleatoric estimation! The method we use here is Mean and Variance Estimation (MVE) since we're trying to estimate both mean and variance for every input. As presented in lecture 5, we measure the accuracy of these predictions negative likelihood loss in addition to mean squared error. However, capsa *automatically* does this for us, so we only have to specify the loss function that we want to use for evaluating the predictions, not the uncertainty.\n",
+ "#### **TODO: Evaluating bias with wrapped regression model**\n",
"\n",
- "we can estimate data uncertainty by learning a layer that predicts a standard deviation for every input."
- ]
+ "Write short (~1 sentence) answers to the questions below to complete the `TODO`s:\n",
+ "\n",
+ "1. How does the bias score relate to the train/test data density from the first plot?\n",
+ "2. What is one limitation of the Histogram approach that simply bins the data based on frequency?"
+ ],
+ "metadata": {
+ "id": "HpDMT_1FERQE"
+ }
},
{
- "cell_type": "code",
- "execution_count": null,
+ "cell_type": "markdown",
"metadata": {
- "id": "sxmm-2sd3G9u"
+ "id": "PvS8xR_q27Ec"
},
- "outputs": [],
"source": [
- "standard_classifier = create_standard_classifier()\n",
- "mve_wrapped_classifier = MVEWrapper(standard_classifier)\n"
+ "# 1.4 Estimating data uncertainty\n",
+ "\n",
+ "Next we turn our attention to uncertainty, first focusing on the uncertainty in the data -- the aleatoric uncertainty.\n",
+ "\n",
+ "As introduced in Lecture 5 on Robust & Trustworthy Deep Learning, in regression we can estimate aleatoric uncertainty by training the model to predict both a target value and a variance for every input. Because we estimate both a mean and variance for every input, this method is called Mean Variance Estimation (MVE). MVE involves modifying the output layer to predict both the mean and variance, and changing the loss to reflect the prediction likelihood.\n",
+ "\n",
+ "`Capsa` automatically implements these changes for us: we can wrap a given model using `MVEWrapper` to use MVE to estimate aleatoric uncertainty. All we have to do is define the model and the loss function to evaluate its predictions!\n",
+ "\n",
+ "Let's take our standard network, wrap it with `MVEWrapper`, build the wrapped model, and then train it for the regression task. Finally, we evaluate performance of the resulting model by quantifying the aleatoric uncertainty across the data space: "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "Yr0yIJEc26yM",
- "outputId": "5dc23258-613b-4a83-a672-70da11ebbe81"
+ "id": "sxmm-2sd3G9u"
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 1/30\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "WARNING:tensorflow:Gradients do not exist for variables ['dense_62/kernel:0', 'dense_62/bias:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n",
- "WARNING:tensorflow:Gradients do not exist for variables ['dense_62/kernel:0', 'dense_62/bias:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "63/63 [==============================] - 1s 2ms/step - mve_compiled_loss: 5.2917 - mve_wrapper_loss: 8.2990\n",
- "Epoch 2/30\n",
- "63/63 [==============================] - 0s 2ms/step - mve_compiled_loss: 1.5322 - mve_wrapper_loss: 2.2633\n",
- "Epoch 3/30\n",
- "63/63 [==============================] - 0s 2ms/step - mve_compiled_loss: 0.6495 - mve_wrapper_loss: 0.4517\n",
- "Epoch 4/30\n",
- "63/63 [==============================] - 0s 2ms/step - mve_compiled_loss: 0.4826 - mve_wrapper_loss: -0.0290\n",
- "Epoch 5/30\n",
- "63/63 [==============================] - 0s 2ms/step - mve_compiled_loss: 0.4571 - mve_wrapper_loss: -0.2686\n",
- "Epoch 6/30\n",
- "63/63 [==============================] - 0s 2ms/step - mve_compiled_loss: 0.4070 - mve_wrapper_loss: -0.3623\n",
- "Epoch 7/30\n",
- "63/63 [==============================] - 0s 2ms/step - mve_compiled_loss: 0.3092 - mve_wrapper_loss: -0.4281\n",
- "Epoch 8/30\n",
- "63/63 [==============================] - 0s 3ms/step - mve_compiled_loss: 0.3229 - mve_wrapper_loss: -0.4038\n",
- "Epoch 9/30\n",
- "63/63 [==============================] - 0s 2ms/step - mve_compiled_loss: 0.3236 - mve_wrapper_loss: -0.5268\n",
- "Epoch 10/30\n",
- "63/63 [==============================] - 0s 2ms/step - mve_compiled_loss: 0.3022 - mve_wrapper_loss: -0.5458\n",
- "Epoch 11/30\n",
- "63/63 [==============================] - 0s 2ms/step - mve_compiled_loss: 0.3035 - mve_wrapper_loss: -0.6220\n",
- "Epoch 12/30\n",
- "63/63 [==============================] - 0s 2ms/step - mve_compiled_loss: 0.3110 - mve_wrapper_loss: -0.5680\n",
- "Epoch 13/30\n",
- "63/63 [==============================] - 0s 2ms/step - mve_compiled_loss: 0.2720 - mve_wrapper_loss: -0.4468\n",
- "Epoch 14/30\n",
- "63/63 [==============================] - 0s 2ms/step - mve_compiled_loss: 0.2848 - mve_wrapper_loss: -0.5656\n",
- "Epoch 15/30\n",
- "63/63 [==============================] - 0s 2ms/step - mve_compiled_loss: 0.3078 - mve_wrapper_loss: -0.6007\n",
- "Epoch 16/30\n",
- "63/63 [==============================] - 0s 2ms/step - mve_compiled_loss: 0.2827 - mve_wrapper_loss: -0.6292\n",
- "Epoch 17/30\n",
- "63/63 [==============================] - 0s 2ms/step - mve_compiled_loss: 0.3168 - mve_wrapper_loss: -0.6420\n",
- "Epoch 18/30\n",
- "63/63 [==============================] - 0s 2ms/step - mve_compiled_loss: 0.2910 - mve_wrapper_loss: -0.6672\n",
- "Epoch 19/30\n",
- "63/63 [==============================] - 0s 2ms/step - mve_compiled_loss: 0.3076 - mve_wrapper_loss: -0.5917\n",
- "Epoch 20/30\n",
- "63/63 [==============================] - 0s 2ms/step - mve_compiled_loss: 0.3097 - mve_wrapper_loss: -0.6985\n",
- "Epoch 21/30\n",
- "63/63 [==============================] - 0s 2ms/step - mve_compiled_loss: 0.2982 - mve_wrapper_loss: -0.5248\n",
- "Epoch 22/30\n",
- "63/63 [==============================] - 0s 2ms/step - mve_compiled_loss: 0.2912 - mve_wrapper_loss: -0.5999\n",
- "Epoch 23/30\n",
- "63/63 [==============================] - 0s 2ms/step - mve_compiled_loss: 0.3003 - mve_wrapper_loss: -0.5714\n",
- "Epoch 24/30\n",
- "63/63 [==============================] - 0s 2ms/step - mve_compiled_loss: 0.3314 - mve_wrapper_loss: -0.6600\n",
- "Epoch 25/30\n",
- "63/63 [==============================] - 0s 2ms/step - mve_compiled_loss: 0.2974 - mve_wrapper_loss: -0.5685\n",
- "Epoch 26/30\n",
- "63/63 [==============================] - 0s 2ms/step - mve_compiled_loss: 0.3157 - mve_wrapper_loss: -0.6695\n",
- "Epoch 27/30\n",
- "63/63 [==============================] - 0s 2ms/step - mve_compiled_loss: 0.2832 - mve_wrapper_loss: -0.6686\n",
- "Epoch 28/30\n",
- "63/63 [==============================] - 0s 2ms/step - mve_compiled_loss: 0.3066 - mve_wrapper_loss: -0.6361\n",
- "Epoch 29/30\n",
- "63/63 [==============================] - 0s 2ms/step - mve_compiled_loss: 0.2880 - mve_wrapper_loss: -0.6316\n",
- "Epoch 30/30\n",
- "63/63 [==============================] - 0s 2ms/step - mve_compiled_loss: 0.3322 - mve_wrapper_loss: -0.5759\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
- "mve_wrapped_classifier.compile(\n",
+ "### Estimating data uncertainty with Capsa wrapping ###\n",
+ "\n",
+ "standard_dense_NN = create_dense_NN()\n",
+ "# Wrap the dense network for aleatoric uncertainty estimation\n",
+ "mve_wrapped_NN = MVEWrapper(standard_dense_NN)\n",
+ "\n",
+ "# Build the model for regression, defining the loss function and optimizer\n",
+ "mve_wrapped_NN.compile(\n",
" optimizer=tf.keras.optimizers.Adam(learning_rate=1e-2),\n",
- " loss=tf.keras.losses.MeanSquaredError(),\n",
+ " loss=tf.keras.losses.MeanSquaredError(), # MSE loss for the regression task\n",
")\n",
"\n",
- "history = mve_wrapped_classifier.fit(x, y, epochs=30)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 283
- },
- "id": "k_m_7H4P1ADv",
- "outputId": "c215f212-1bb6-45aa-beab-0565ed20362b"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x7fe12a1e10a0>"
- ]
- },
- "execution_count": 126,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "outputs = mve_wrapped_classifier(x_val)\n",
- "plt.scatter(x_val, outputs.aleatoric, label='aleatoric uncertainty', s=0.5)\n",
+ "# Train the wrapped model for 30 epochs.\n",
+ "loss_history_mve_wrap = mve_wrapped_NN.fit(x_train, y_train, epochs=30)\n",
+ "\n",
+ "# Call the uncertainty-aware model to generate scores for the test data\n",
+ "outputs = mve_wrapped_NN(x_test)\n",
+ "# Capsa makes the aleatoric uncertainty an attribute of the prediction!\n",
+ "aleatoric_unc = outputs.aleatoric\n",
+ "\n",
+ "# Visualize the aleatoric uncertainty across the data space\n",
+ "plt.scatter(x_test, aleatoric, label='aleatoric uncertainty', s=0.5)\n",
"plt.legend()"
]
},
@@ -682,7 +598,12 @@
"id": "ZFeArgRX9U9s"
},
"source": [
- "We can see that in the areas of high label noise-- where small changes in the input lead to large changes in the output-- aleatoric uncertainty spikes!"
+ "#### **TODO: Estimating aleatoric uncertainty**\n",
+ "\n",
+ "Write short (~1 sentence) answers to the questions below to complete the `TODO`s:\n",
+ "\n",
+ "1. For what values of $x$ is the aleatoric uncertainty high or increasing suddenly?\n",
+ "2. How does your answer in (1) relate to how the $x$ values are distributed?"
]
},
{
From 4150f1858f4c2d01a6168521e5e803212a344d6d Mon Sep 17 00:00:00 2001
From: Sadhana Lolla <lolla.sadhana@gmail.com>
Date: Wed, 11 Jan 2023 00:07:07 -0500
Subject: [PATCH 05/22] changed dataset
---
.../solutions/Lab3_Bias_And_Uncertainty.ipynb | 104 +++++++++---------
1 file changed, 52 insertions(+), 52 deletions(-)
diff --git a/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb b/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb
index 33cbff92..e5af74eb 100644
--- a/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb
+++ b/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb
@@ -19,28 +19,28 @@
},
{
"cell_type": "code",
- "source": [
- "!git clone https://github.com/slolla/capsa-intro-deep-learning.git\n",
- "!cd capsa-intro-deep-learning/ && git checkout HistogramVAEWrapper\n"
- ],
+ "execution_count": 1,
"metadata": {
- "id": "5Ll7uZ8q72hm",
- "outputId": "56b3117b-e344-481b-a9fc-2798b76d7a60",
"colab": {
"base_uri": "https://localhost:8080/"
- }
+ },
+ "id": "5Ll7uZ8q72hm",
+ "outputId": "56b3117b-e344-481b-a9fc-2798b76d7a60"
},
- "execution_count": 1,
"outputs": [
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"fatal: destination path 'capsa-intro-deep-learning' already exists and is not an empty directory.\n",
"Already on 'HistogramVAEWrapper'\n",
"Your branch is up to date with 'origin/HistogramVAEWrapper'.\n"
]
}
+ ],
+ "source": [
+ "!git clone https://github.com/slolla/capsa-intro-deep-learning.git\n",
+ "!cd capsa-intro-deep-learning/ && git checkout HistogramVAEWrapper\n"
]
},
{
@@ -54,23 +54,18 @@
},
{
"cell_type": "code",
- "source": [
- "%cd capsa-intro-deep-learning/\n",
- "%pip install -e .\n",
- "%cd .."
- ],
+ "execution_count": 2,
"metadata": {
- "id": "SjAn-WZK9lOv",
- "outputId": "35e24600-85b4-4320-c436-061856e56861",
"colab": {
"base_uri": "https://localhost:8080/"
- }
+ },
+ "id": "SjAn-WZK9lOv",
+ "outputId": "35e24600-85b4-4320-c436-061856e56861"
},
- "execution_count": 2,
"outputs": [
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"/content/capsa-intro-deep-learning\n",
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
@@ -85,29 +80,27 @@
"/content\n"
]
}
+ ],
+ "source": [
+ "%cd capsa-intro-deep-learning/\n",
+ "%pip install -e .\n",
+ "%cd .."
]
},
{
"cell_type": "code",
- "source": [
- "!git clone https://github.com/aamini/introtodeeplearning.git\n",
- "!cd introtodeeplearning/ && git checkout 2023\n",
- "%cd introtodeeplearning/\n",
- "%pip install -e .\n",
- "%cd .."
- ],
+ "execution_count": 3,
"metadata": {
- "id": "3pzGVPrh-4LQ",
- "outputId": "f4588f12-d290-4746-d819-501a0e3ba390",
"colab": {
"base_uri": "https://localhost:8080/"
- }
+ },
+ "id": "3pzGVPrh-4LQ",
+ "outputId": "f4588f12-d290-4746-d819-501a0e3ba390"
},
- "execution_count": 3,
"outputs": [
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"fatal: destination path 'introtodeeplearning' already exists and is not an empty directory.\n",
"Already on '2023'\n",
@@ -133,6 +126,13 @@
"/content\n"
]
}
+ ],
+ "source": [
+ "!git clone https://github.com/aamini/introtodeeplearning.git\n",
+ "!cd introtodeeplearning/ && git checkout 2023\n",
+ "%cd introtodeeplearning/\n",
+ "%pip install -e .\n",
+ "%cd .."
]
},
{
@@ -182,16 +182,16 @@
"cell_type": "code",
"execution_count": 7,
"metadata": {
- "id": "HIA6EA1D71EW",
- "outputId": "df98738c-00d5-4987-bd58-938dd17c8ef4",
"colab": {
"base_uri": "https://localhost:8080/"
- }
+ },
+ "id": "HIA6EA1D71EW",
+ "outputId": "df98738c-00d5-4987-bd58-938dd17c8ef4"
},
"outputs": [
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"Opening /root/.keras/datasets/train_face_2023_v2.h5\n",
"Loading data into memory...\n",
@@ -204,10 +204,11 @@
"batch_size = 32\n",
"\n",
"# Get the training data: both images from CelebA and ImageNet\n",
- "path_to_training_data = tf.keras.utils.get_file('train_face_2023_v2.h5', 'https://www.dropbox.com/s/b5z1cd317y5u1tr/train_face_2023_v2.h5?dl=1')\n",
+ "path_to_training_data = tf.keras.utils.get_file('train_face_perturbed_small.h5', 'https://www.dropbox.com/s/tbra3danrk5x8h5/train_face_2023_perturbed_small.h5?dl=1')\n",
"# Instantiate a DatasetLoader using the downloaded dataset\n",
"train_loader = lab3.DatasetLoader(path_to_training_data, training=True, batch_size= batch_size)\n",
- "test_loader = lab3.DatasetLoader(path_to_training_data, training=False, batch_size = batch_size)"
+ "test_loader = lab3.DatasetLoader(path_to_training_data, training=False, batch_size = batch_size)\n",
+ "train_imgs = train_loader.get_all_faces()"
]
},
{
@@ -367,31 +368,31 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "id": "NmshVdLM71Ed",
- "outputId": "48155283-4767-46e7-e84b-dfd3ac8c1917",
"colab": {
"base_uri": "https://localhost:8080/"
- }
+ },
+ "id": "NmshVdLM71Ed",
+ "outputId": "48155283-4767-46e7-e84b-dfd3ac8c1917"
},
"outputs": [
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"Epoch 1/6\n"
]
},
{
- "output_type": "stream",
"name": "stderr",
+ "output_type": "stream",
"text": [
"WARNING:tensorflow:Gradients do not exist for variables ['dense_1/kernel:0', 'dense_1/bias:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n",
"WARNING:tensorflow:Gradients do not exist for variables ['dense_1/kernel:0', 'dense_1/bias:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n"
]
},
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
" 102/2404 [>.............................] - ETA: 5:58 - vae_compiled_loss: 0.8147 - vae_compiled_binary_accuracy: 0.4792 - vae_wrapper_loss: 3385.2124"
]
@@ -707,8 +708,7 @@
"dbvae = HistogramVAEWrapper(standard_classifier, latent_dim=100, num_bins=5, queue_size=2000, decoder=make_face_decoder_network())\n",
"dbvae.compile(optimizer=tf.keras.optimizers.Adam(1e-4),\n",
" loss=tf.keras.losses.BinaryCrossentropy(),\n",
- " metrics=[tf.keras.metrics.BinaryAccuracy()])\n",
- "train_imgs = train_loader.get_all_faces()"
+ " metrics=[tf.keras.metrics.BinaryAccuracy()])"
]
},
{
@@ -832,6 +832,11 @@
}
],
"metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "provenance": []
+ },
+ "gpuClass": "standard",
"kernelspec": {
"display_name": "Python 3",
"language": "python",
@@ -848,13 +853,8 @@
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.10"
- },
- "colab": {
- "provenance": []
- },
- "accelerator": "GPU",
- "gpuClass": "standard"
+ }
},
"nbformat": 4,
"nbformat_minor": 0
-}
\ No newline at end of file
+}
From 2fb127cfd48339576a3a08fb1e007778ce1163e8 Mon Sep 17 00:00:00 2001
From: Sadhana Lolla <lolla.sadhana@gmail.com>
Date: Wed, 11 Jan 2023 00:08:26 -0500
Subject: [PATCH 06/22] added support for negative sampling
---
mitdeeplearning/lab3.py | 3 ++-
1 file changed, 2 insertions(+), 1 deletion(-)
diff --git a/mitdeeplearning/lab3.py b/mitdeeplearning/lab3.py
index a33f4886..ee7b9213 100644
--- a/mitdeeplearning/lab3.py
+++ b/mitdeeplearning/lab3.py
@@ -76,6 +76,7 @@ def __init__(self, data_path, batch_size, training=True):
self.train_inds = np.concatenate((self.pos_train_inds, self.neg_train_inds))
self.batch_size = batch_size
self.p_pos = np.ones(self.pos_train_inds.shape) / len(self.pos_train_inds)
+ self.p_neg = np.ones(self.neg_train_inds.shape) / len(self.neg_train_inds)
def get_train_size(self):
return self.pos_train_inds.shape[0] + self.neg_train_inds.shape[0]
@@ -88,7 +89,7 @@ def __getitem__(self, index):
self.pos_train_inds, size=self.batch_size // 2, replace=False, p=self.p_pos
)
selected_neg_inds = np.random.choice(
- self.neg_train_inds, size=self.batch_size // 2, replace=False
+ self.neg_train_inds, size=self.batch_size // 2, replace=False, p = self.p_neg
)
selected_inds = np.concatenate((selected_pos_inds, selected_neg_inds))
From 7940aaccfa96ed32dbc12400eb890c3808c5244e Mon Sep 17 00:00:00 2001
From: Ava Amini <apsoleimany@gmail.com>
Date: Wed, 11 Jan 2023 00:23:38 -0500
Subject: [PATCH 07/22] complete (near final) text and structure
---
.../Lab3_Part_1_Introduction_to_CAPSA.ipynb | 124 +++++++++++++-----
1 file changed, 93 insertions(+), 31 deletions(-)
diff --git a/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb b/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb
index a5838aa1..fa037bca 100644
--- a/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb
+++ b/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb
@@ -50,7 +50,7 @@
"\n",
"In this lab, we'll explore different ways to make deep learning models more **robust** and **trustworthy**.\n",
"\n",
- "To achieve this it is critical to be able to identify and diagnose issues of bias and uncertainty in deep learning models, as we explored in the Facial Detection Lab 2. We need benchmarks that uniformly measure how uncertain a given model is, and we need principled ways of measuring bias and uncertainty. To that end, in this lab, we'll utilize [CAPSA](https://github.com/themis-ai/capsa), a risk-estimation wrapping library developed by [Themis AI](https://themisai.io/). CAPSA supports the estimation of three different types of ***risk***, defined as measures of how robust and trustworthy our model is. These are:\n",
+ "To achieve this it is critical to be able to identify and diagnose issues of bias and uncertainty in deep learning models, as we explored in the Facial Detection Lab 2. We need benchmarks that uniformly measure how uncertain a given model is, and we need principled ways of measuring bias and uncertainty. To that end, in this lab, we'll utilize [Capsa](https://github.com/themis-ai/capsa), a risk-estimation wrapping library developed by [Themis AI](https://themisai.io/). Capsa supports the estimation of three different types of ***risk***, defined as measures of how robust and trustworthy our model is. These are:\n",
"1. **Representation bias**: reflects how likely combinations of features are to appear in a given dataset. Often, certain combinations of features are severely under-represented in datasets, which means models learn them less well and can thus lead to unwanted bias.\n",
"2. **Data uncertainty**: reflects noise in the data, for example when sensors have noisy measurements, classes in datasets have low separations, and generally when very similar inputs lead to drastically different outputs. Also known as *aleatoric* uncertainty. \n",
"3. **Model uncertainty**: captures the areas of our underlying data distribution that the model has not yet learned or has difficulty learning. Areas of high model uncertainty can be due to out-of-distribution (OOD) samples or data that is harder to learn. Also known as *epistemic* uncertainty."
@@ -64,13 +64,13 @@
"source": [
"## CAPSA overview\n",
"\n",
- "This lab introduces `CAPSA` and its functionalities, to next build automated tools that use `CAPSA` to mitigate the underlying issues of bias and uncertainty.\n",
+ "This lab introduces Capsa and its functionalities, to next build automated tools that use Capsa to mitigate the underlying issues of bias and uncertainty.\n",
"\n",
- "The core idea behind `CAPSA` is that any deep learning model of interest can be ***wrapped*** -- just like wrapping a gift -- to be made ***aware of its own risks***. Risk is captured in representation bias, data uncertainty, and model uncertainty.\n",
+ "The core idea behind Capsa is that any deep learning model of interest can be ***wrapped*** -- just like wrapping a gift -- to be made ***aware of its own risks***. Risk is captured in representation bias, data uncertainty, and model uncertainty.\n",
"\n",
"\n",
"\n",
- "This means that `CAPSA` takes the user's original model as input, and modifies it minimally to create a risk-aware variant while preserving the model's underlying structure and training pipeline. `CAPSA` is a one-line addition to any training workflow in TensorFlow. In this part of the lab, we'll apply `CAPSA`'s risk estimation methods to a simple regression problem to further explore the notions of bias and uncertainty. "
+ "This means that Capsa takes the user's original model as input, and modifies it minimally to create a risk-aware variant while preserving the model's underlying structure and training pipeline. Capsa is a one-line addition to any training workflow in TensorFlow. In this part of the lab, we'll apply Capsa's risk estimation methods to a simple regression problem to further explore the notions of bias and uncertainty. "
]
},
{
@@ -128,9 +128,9 @@
"!pip install mitdeeplearning\n",
"import mitdeeplearning as mdl\n",
"\n",
- "# Download and import CAPSA\n",
+ "# Download and import Capsa\n",
"!pip install capsa\n",
- "from capsa import *"
+ "import capsa"
]
},
{
@@ -317,8 +317,8 @@
"\n",
"Write short (~1 sentence) answers to the questions below to complete the `TODO`s:\n",
"\n",
- "1. Where does the model perform well? How does this relate to aleatoric and epistemic uncertainty?\n",
- "2. Where does the model perform poorly? How does this relate to aleatoric and epistemic uncertainty?"
+ "1. Where does the model perform well?\n",
+ "2. Where does the model perform poorly?"
],
"metadata": {
"id": "7Vktjwfu0ReH"
@@ -334,9 +334,9 @@
"\n",
"Now that we've seen what the predictions from this model look like, we will identify and quantify bias and uncertainty in this problem. We first consider bias.\n",
"\n",
- "Recall that *representation bias* reflects how likely combinations of features are to appear in a given dataset. `Capsa` calculates how likely combinations of features are by using a histogram estimation approach: the `HistogramWrapper`. For low-dimensional data, the `HistogramWrapper` bins the input directly into discrete categories and measures the density. \n",
+ "Recall that *representation bias* reflects how likely combinations of features are to appear in a given dataset. Capsa calculates how likely combinations of features are by using a histogram estimation approach: the `capsa.HistogramWrapper`. For low-dimensional data, the `capsa.HistogramWrapper` bins the input directly into discrete categories and measures the density. \n",
"\n",
- "We start by taking our `dense_NN` and wrapping it with the `HistogramWrapper`:"
+ "We start by taking our `dense_NN` and wrapping it with the `capsa.HistogramWrapper`:"
]
},
{
@@ -350,11 +350,11 @@
"### Wrap the dense network for bias estimation ###\n",
"\n",
"standard_dense_NN = create_dense_NN()\n",
- "bias_wrapped_dense_NN = HistogramWrapper(\n",
+ "bias_wrapped_dense_NN = capsa.HistogramWrapper(\n",
" standard_dense_NN, # the original model\n",
" queue_size=2000, # how many samples to track\n",
" target_hidden_layer=False # for low-dimensional data, we can estimate densities directly from data\n",
- " ) \n"
+ " )\n"
]
},
{
@@ -477,7 +477,7 @@
"id": "_6iVeeqq0f_H"
},
"source": [
- "We can now use our wrapped model to assess the bias for a given test input. With the wrapping capability, `Capsa` neatly allows us to output a *bias score* along with the predicted target value. This bias score reflects the density of data surrounding an input point -- the higher the score, the greater the data representation and density. The wrapped, risk-aware model outputs the predicted target and bias score after it is called!\n",
+ "We can now use our wrapped model to assess the bias for a given test input. With the wrapping capability, Capsa neatly allows us to output a *bias score* along with the predicted target value. This bias score reflects the density of data surrounding an input point -- the higher the score, the greater the data representation and density. The wrapped, risk-aware model outputs the predicted target and bias score after it is called!\n",
"\n",
"Let's see how it is done:"
]
@@ -554,9 +554,9 @@
"\n",
"As introduced in Lecture 5 on Robust & Trustworthy Deep Learning, in regression we can estimate aleatoric uncertainty by training the model to predict both a target value and a variance for every input. Because we estimate both a mean and variance for every input, this method is called Mean Variance Estimation (MVE). MVE involves modifying the output layer to predict both the mean and variance, and changing the loss to reflect the prediction likelihood.\n",
"\n",
- "`Capsa` automatically implements these changes for us: we can wrap a given model using `MVEWrapper` to use MVE to estimate aleatoric uncertainty. All we have to do is define the model and the loss function to evaluate its predictions!\n",
+ "Capsa automatically implements these changes for us: we can wrap a given model using `capsa.MVEWrapper` to use MVE to estimate aleatoric uncertainty. All we have to do is define the model and the loss function to evaluate its predictions!\n",
"\n",
- "Let's take our standard network, wrap it with `MVEWrapper`, build the wrapped model, and then train it for the regression task. Finally, we evaluate performance of the resulting model by quantifying the aleatoric uncertainty across the data space: "
+ "Let's take our standard network, wrap it with `capsa.MVEWrapper`, build the wrapped model, and then train it for the regression task. Finally, we evaluate performance of the resulting model by quantifying the aleatoric uncertainty across the data space: "
]
},
{
@@ -571,7 +571,7 @@
"\n",
"standard_dense_NN = create_dense_NN()\n",
"# Wrap the dense network for aleatoric uncertainty estimation\n",
- "mve_wrapped_NN = MVEWrapper(standard_dense_NN)\n",
+ "mve_wrapped_NN = capsa.MVEWrapper(standard_dense_NN)\n",
"\n",
"# Build the model for regression, defining the loss function and optimizer\n",
"mve_wrapped_NN.compile(\n",
@@ -582,7 +582,7 @@
"# Train the wrapped model for 30 epochs.\n",
"loss_history_mve_wrap = mve_wrapped_NN.fit(x_train, y_train, epochs=30)\n",
"\n",
- "# Call the uncertainty-aware model to generate scores for the test data\n",
+ "# Call the uncertainty-aware model to generate outputs for the test data\n",
"outputs = mve_wrapped_NN(x_test)\n",
"# Capsa makes the aleatoric uncertainty an attribute of the prediction!\n",
"aleatoric_unc = outputs.aleatoric\n",
@@ -612,8 +612,11 @@
"id": "6FC5WPRT5lAb"
},
"source": [
- "## 1.4 Epistemic Estimation\n",
- "Finally, let's do the same thing but for epistemic estimation! In this example, we'll use ensembles, which essentially copy the model `N` times and average predictions across all runs for a more robust prediction, and also calculate the variance of the `N` runs. Feel free to play around with any of the epistemic methods shown in the github repository! Which methods perform the best? Why do you think this is?"
+ "# 1.5 Estimating model uncertainty\n",
+ "\n",
+ "Finally, we use Capsa for estimating the uncertainty underlying the model predictions -- the epistemic uncertainty. In this example, we'll use ensembles, which essentially copy the model `N` times and average predictions across all runs for a more robust prediction, and also calculate the variance of the `N` runs to estimate the uncertainty.\n",
+ "\n",
+ "Capsa provides a neat wrapper, `capsa.EnsembleWrapper`, to make an ensemble from an input model. Just like with aleatoric estimation, we can take our standard dense network model, wrap it with `capsa.EnsembleWrapper`, build the wrapped model, and then train it for the regression task. Finally, we evaluate the resulting model by quantifying the epistemic uncertainty on the test data:"
]
},
{
@@ -695,15 +698,29 @@
}
],
"source": [
- "standard_classifier = create_standard_classifier()\n",
- "ensemble_wrapper = EnsembleWrapper(standard_classifier, num_members=5)\n",
+ "### Estimating model uncertainty with Capsa wrapping ###\n",
+ "\n",
+ "standard_dense_NN = create_dense_NN()\n",
+ "# Wrap the dense network for epistemic uncertainty estimation with a 5-member Ensemble\n",
+ "ensemble_NN = capsa.EnsembleWrapper(standard_dense_NN, num_members=5)\n",
"\n",
- "ensemble_wrapper.compile(\n",
+ "# Build the model for regression, defining the loss function and optimizer\n",
+ "ensemble_NN.compile(\n",
" optimizer=tf.keras.optimizers.Adam(learning_rate=3e-3),\n",
- " loss=tf.keras.losses.MeanSquaredError(),\n",
+ " loss=tf.keras.losses.MeanSquaredError(), # MSE loss for the regression task\n",
")\n",
"\n",
- "history = ensemble_wrapper.fit(x, y, epochs=30)"
+ "# Train the wrapped model for 30 epochs.\n",
+ "loss_history_ensemble = ensemble_NN.fit(x_train, y_train, epochs=30)\n",
+ "\n",
+ "# Call the uncertainty-aware model to generate outputs for the test data\n",
+ "outputs = ensemble_NN(x_test)\n",
+ "# Capsa makes the epistemic uncertainty an attribute of the prediction!\n",
+ "epistemic_unc = outputs.epistemic\n",
+ "\n",
+ "# Visualize the epistemic uncertainty across the data space\n",
+ "plt.scatter(x_test, epistemic, label='epistemic uncertainty', s=0.5)\n",
+ "plt.legend()"
]
},
{
@@ -749,13 +766,18 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "id": "VU6eMpYX9m9N"
- },
"source": [
- "## Conclusion\n",
- "As expected, areas where there is no training data have very high epistemic uncertainty, since all of the testing data is OOD. If our training data contained more samples from this region, would you expect the epistemic uncertainty to decrease?"
- ]
+ "#### **TODO: Estimating epistemic uncertainty**\n",
+ "\n",
+ "Write short (~1 sentence) answers to the questions below to complete the `TODO`s:\n",
+ "\n",
+ "1. For what values of $x$ is the epistemic uncertainty high or increasing suddenly?\n",
+ "2. How does your answer in (1) relate to how the $x$ values are distributed (refer back to original plot)? Think about both the train and test data.\n",
+ "3. How could you reduce the epistemic uncertainty in regions where it is high?"
+ ],
+ "metadata": {
+ "id": "N4LMn2tLPBdg"
+ }
},
{
"cell_type": "markdown",
@@ -763,8 +785,48 @@
"id": "CkpvkOL06jRd"
},
"source": [
+ "# 1.6 Conclusion\n",
+ "\n",
+ "You've just analyzed the bias, aleatoric uncertainty, and epistemic uncertainty for your first risk-aware model! This is a task that data scientists do constantly to determine methods of improving their models and datasets.\n",
+ "\n",
+ "## NOTE TO ADDRESS: THIS CAN BE ELIMINATED COMPLETELY IF IT IS TOO MUCH FOR COMPETITION!\n",
+ "### 1.6.1 Submission information\n",
+ "To be eligible for the Debiasing Faces Lab prize, you must submit a document of your answers to the short-answer `TODO`s with your complete lab submission. **Name your file in the following format: `[FirstName]_[LastName]_Debiasing_Report.pdf`.**\n",
+ "\n",
+ "Upload your document write-up as part of your complete lab submission for the Debiasing Faces Lab ([submission upload link](https://www.dropbox.com/request/TTYz3Ikx5wIgOITmm5i2)).\n",
+ "\n",
+ "Please see the short-answer `TODO`s replicated again here:\n",
+ "\n",
+ "#### **TODO: Inspecting the 2D regression dataset**\n",
+ "\n",
+ "1. What are your observations about where the train data and test data lie relative to each other?\n",
+ "2. What, if any, areas do you expect to have high/low aleatoric (data) uncertainty?\n",
+ "3. What, if any, areas do you expect to have high/low epistemic (model) uncertainty?\n",
+ "\n",
+ "#### **TODO: Analyzing the performance of standard regression model**\n",
+ "\n",
+ "1. Where does the model perform well?\n",
+ "2. Where does the model perform poorly?\n",
+ "\n",
+ "#### **TODO: Evaluating bias**\n",
+ "\n",
+ "1. How does the bias score relate to the train/test data density from the first plot?\n",
+ "2. What is one limitation of the Histogram approach that simply bins the data based on frequency?\n",
+ "\n",
+ "#### **TODO: Estimating aleatoric uncertainty**\n",
+ "\n",
+ "1. For what values of $x$ is the aleatoric uncertainty high or increasing suddenly?\n",
+ "2. How does your answer in (1) relate to how the $x$ values are distributed?\n",
+ "\n",
+ "#### **TODO: Estimating epistemic uncertainty**\n",
+ "\n",
+ "1. For what values of $x$ is the epistemic uncertainty high or increasing suddenly?\n",
+ "2. How does your answer in (1) relate to how the $x$ values are distributed (refer back to original plot)? Think about both the train and test data.\n",
+ "3. How could you reduce the epistemic uncertainty in regions where it is high?\n",
+ "\n",
+ "### 1.6.2 Moving forward\n",
"\n",
- "You've just analyzed the bias, aleatoric uncertainty, and epistemic uncertainty for your first risk-aware model! This is a task that data scientists do constantly to determine methods of improving their models and datasets. In the next part, you'll continue to build off of these concepts to *mitigate* these risks, in addition to diagnosing them!\n",
+ "In the next part of the lab, you'll continue to build off of these concepts to *mitigate* these risks, in addition to diagnosing them!\n",
"\n",
""
]
From 225971e1a62a412bda59fe9dc1dd208713dd9258 Mon Sep 17 00:00:00 2001
From: Alexander Amini <xan.amini@gmail.com>
Date: Wed, 11 Jan 2023 01:01:31 -0500
Subject: [PATCH 08/22] Created using Colaboratory
---
.../solutions/Lab3_Bias_And_Uncertainty.ipynb | 520 ++++++++++++------
1 file changed, 337 insertions(+), 183 deletions(-)
diff --git a/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb b/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb
index e5af74eb..8380628a 100644
--- a/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb
+++ b/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb
@@ -17,32 +17,6 @@
"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": 1,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "5Ll7uZ8q72hm",
- "outputId": "56b3117b-e344-481b-a9fc-2798b76d7a60"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "fatal: destination path 'capsa-intro-deep-learning' already exists and is not an empty directory.\n",
- "Already on 'HistogramVAEWrapper'\n",
- "Your branch is up to date with 'origin/HistogramVAEWrapper'.\n"
- ]
- }
- ],
- "source": [
- "!git clone https://github.com/slolla/capsa-intro-deep-learning.git\n",
- "!cd capsa-intro-deep-learning/ && git checkout HistogramVAEWrapper\n"
- ]
- },
{
"cell_type": "markdown",
"metadata": {
@@ -54,57 +28,31 @@
},
{
"cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "SjAn-WZK9lOv",
- "outputId": "35e24600-85b4-4320-c436-061856e56861"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "/content/capsa-intro-deep-learning\n",
- "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
- "Obtaining file:///content/capsa-intro-deep-learning\n",
- " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
- "Installing collected packages: capsa\n",
- " Attempting uninstall: capsa\n",
- " Found existing installation: capsa 0.1.2\n",
- " Can't uninstall 'capsa'. No files were found to uninstall.\n",
- " Running setup.py develop for capsa\n",
- "Successfully installed capsa-0.1.2\n",
- "/content\n"
- ]
- }
- ],
"source": [
- "%cd capsa-intro-deep-learning/\n",
- "%pip install -e .\n",
- "%cd .."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
+ "!git clone -b 2023 https://github.com/aamini/introtodeeplearning.git\n",
+ "%cd introtodeeplearning/\n",
+ "%pip install -e ."
+ ],
"metadata": {
+ "id": "3pzGVPrh-4LQ",
+ "outputId": "2049b9c3-1816-43a3-acda-fbd18df44e7c",
"colab": {
"base_uri": "https://localhost:8080/"
- },
- "id": "3pzGVPrh-4LQ",
- "outputId": "f4588f12-d290-4746-d819-501a0e3ba390"
+ }
},
+ "execution_count": 1,
"outputs": [
{
- "name": "stdout",
"output_type": "stream",
+ "name": "stdout",
"text": [
- "fatal: destination path 'introtodeeplearning' already exists and is not an empty directory.\n",
- "Already on '2023'\n",
- "Your branch is up to date with 'origin/2023'.\n",
+ "Cloning into 'introtodeeplearning'...\n",
+ "remote: Enumerating objects: 2354, done.\u001b[K\n",
+ "remote: Counting objects: 100% (337/337), done.\u001b[K\n",
+ "remote: Compressing objects: 100% (162/162), done.\u001b[K\n",
+ "remote: Total 2354 (delta 207), reused 284 (delta 172), pack-reused 2017\u001b[K\n",
+ "Receiving objects: 100% (2354/2354), 136.06 MiB | 27.14 MiB/s, done.\n",
+ "Resolving deltas: 100% (1334/1334), done.\n",
"/content/introtodeeplearning\n",
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Obtaining file:///content/introtodeeplearning\n",
@@ -113,35 +61,37 @@
"Requirement already satisfied: regex in /usr/local/lib/python3.8/dist-packages (from mitdeeplearning==0.3.0) (2022.6.2)\n",
"Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from mitdeeplearning==0.3.0) (4.64.1)\n",
"Requirement already satisfied: gym in /usr/local/lib/python3.8/dist-packages (from mitdeeplearning==0.3.0) (0.25.2)\n",
- "Requirement already satisfied: importlib-metadata>=4.8.0 in /usr/local/lib/python3.8/dist-packages (from gym->mitdeeplearning==0.3.0) (5.2.0)\n",
"Requirement already satisfied: gym-notices>=0.0.4 in /usr/local/lib/python3.8/dist-packages (from gym->mitdeeplearning==0.3.0) (0.0.8)\n",
- "Requirement already satisfied: cloudpickle>=1.2.0 in /usr/local/lib/python3.8/dist-packages (from gym->mitdeeplearning==0.3.0) (1.5.0)\n",
+ "Requirement already satisfied: cloudpickle>=1.2.0 in /usr/local/lib/python3.8/dist-packages (from gym->mitdeeplearning==0.3.0) (2.2.0)\n",
+ "Requirement already satisfied: importlib-metadata>=4.8.0 in /usr/local/lib/python3.8/dist-packages (from gym->mitdeeplearning==0.3.0) (6.0.0)\n",
"Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.8/dist-packages (from importlib-metadata>=4.8.0->gym->mitdeeplearning==0.3.0) (3.11.0)\n",
"Installing collected packages: mitdeeplearning\n",
- " Attempting uninstall: mitdeeplearning\n",
- " Found existing installation: mitdeeplearning 0.3.0\n",
- " Can't uninstall 'mitdeeplearning'. No files were found to uninstall.\n",
" Running setup.py develop for mitdeeplearning\n",
- "Successfully installed mitdeeplearning-0.3.0\n",
- "/content\n"
+ "Successfully installed mitdeeplearning-0.3.0\n"
]
}
- ],
- "source": [
- "!git clone https://github.com/aamini/introtodeeplearning.git\n",
- "!cd introtodeeplearning/ && git checkout 2023\n",
- "%cd introtodeeplearning/\n",
- "%pip install -e .\n",
- "%cd .."
]
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 17,
"metadata": {
- "id": "2PdAhs1371EU"
+ "id": "2PdAhs1371EU",
+ "outputId": "dd327495-e85d-4849-9487-4f71535b6cae",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
},
- "outputs": [],
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
+ "Requirement already satisfied: capsa in /usr/local/lib/python3.8/dist-packages (0.1.3)\n"
+ ]
+ }
+ ],
"source": [
"# Import Tensorflow 2.0\n",
"#%tensorflow_version 2.x\n",
@@ -153,10 +103,13 @@
"import numpy as np\n",
"from tqdm import tqdm\n",
"from capsa import *\n",
+ "\n",
"# Download and import the MIT 6.S191 package\n",
- "from mitdeeplearning import lab3 \n",
+ "import mitdeeplearning as mdl\n",
+ "\n",
"# Download and import capsa\n",
- "#!pip install capsa\n"
+ "!pip install capsa\n",
+ "import capsa"
]
},
{
@@ -180,19 +133,21 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 6,
"metadata": {
+ "id": "HIA6EA1D71EW",
+ "outputId": "162f7d36-81aa-4fb9-b265-af9a8fa9395d",
"colab": {
"base_uri": "https://localhost:8080/"
- },
- "id": "HIA6EA1D71EW",
- "outputId": "df98738c-00d5-4987-bd58-938dd17c8ef4"
+ }
},
"outputs": [
{
- "name": "stdout",
"output_type": "stream",
+ "name": "stdout",
"text": [
+ "Downloading data from https://www.dropbox.com/s/b5z1cd317y5u1tr/train_face_2023_v2.h5?dl=1\n",
+ "1350735194/1350735194 [==============================] - 32s 0us/step\n",
"Opening /root/.keras/datasets/train_face_2023_v2.h5\n",
"Loading data into memory...\n",
"Opening /root/.keras/datasets/train_face_2023_v2.h5\n",
@@ -204,11 +159,10 @@
"batch_size = 32\n",
"\n",
"# Get the training data: both images from CelebA and ImageNet\n",
- "path_to_training_data = tf.keras.utils.get_file('train_face_perturbed_small.h5', 'https://www.dropbox.com/s/tbra3danrk5x8h5/train_face_2023_perturbed_small.h5?dl=1')\n",
+ "path_to_training_data = tf.keras.utils.get_file('train_face_2023_v2.h5', 'https://www.dropbox.com/s/b5z1cd317y5u1tr/train_face_2023_v2.h5?dl=1')\n",
"# Instantiate a DatasetLoader using the downloaded dataset\n",
- "train_loader = lab3.DatasetLoader(path_to_training_data, training=True, batch_size= batch_size)\n",
- "test_loader = lab3.DatasetLoader(path_to_training_data, training=False, batch_size = batch_size)\n",
- "train_imgs = train_loader.get_all_faces()"
+ "train_loader = mdl.lab3.DatasetLoader(path_to_training_data, training=True, batch_size=batch_size)\n",
+ "test_loader = mdl.lab3.DatasetLoader(path_to_training_data, training=False, batch_size=batch_size)"
]
},
{
@@ -248,7 +202,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 50,
"metadata": {
"id": "5hQb75Vm71EZ"
},
@@ -256,10 +210,8 @@
"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",
+ "def make_standard_classifier(n_outputs=1, n_filters=12):\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",
@@ -279,6 +231,9 @@
" Conv2D(filters=6*n_filters, kernel_size=3, strides=2),\n",
" BatchNormalization(),\n",
"\n",
+ " Conv2D(filters=8*n_filters, kernel_size=3, strides=2),\n",
+ " BatchNormalization(),\n",
+ "\n",
" Flatten(),\n",
" Dense(512),\n",
" Dense(n_outputs, activation=None),\n",
@@ -306,7 +261,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 51,
"metadata": {
"id": "zTat3K8E71Eb"
},
@@ -323,10 +278,11 @@
" # 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",
+ " Dense(units=2*2*8*n_filters), # 4x4 feature maps (with 6N occurances)\n",
+ " Reshape(target_shape=(2, 2, 8*n_filters)),\n",
"\n",
" # Upscaling convolutions (inverse of encoder)\n",
+ " Conv2DTranspose(filters=6*n_filters, kernel_size=3, strides=2),\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",
@@ -338,23 +294,16 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 52,
"metadata": {
"id": "i4JmvmMA71Ec"
},
"outputs": [],
"source": [
- "standard_classifier = make_standard_classifier()\n",
- "wrapped_classifier = HistogramVAEWrapper(standard_classifier, num_bins=5, queue_size=20000, latent_dim = 100, decoder=make_face_decoder_network())"
+ "model = make_standard_classifier()\n",
+ "wrapped_model = HistogramVAEWrapper(model, num_bins=5, queue_size=20000, latent_dim = 32, decoder=make_face_decoder_network())"
]
},
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "valYm5LH71Ec"
- },
- "source": []
- },
{
"cell_type": "markdown",
"metadata": {
@@ -366,43 +315,53 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 53,
"metadata": {
+ "id": "NmshVdLM71Ed",
+ "outputId": "873a0931-c1ae-43cc-f8f0-53b11f30ae4a",
"colab": {
"base_uri": "https://localhost:8080/"
- },
- "id": "NmshVdLM71Ed",
- "outputId": "48155283-4767-46e7-e84b-dfd3ac8c1917"
+ }
},
"outputs": [
{
- "name": "stdout",
"output_type": "stream",
+ "name": "stdout",
"text": [
"Epoch 1/6\n"
]
},
{
- "name": "stderr",
"output_type": "stream",
+ "name": "stderr",
"text": [
- "WARNING:tensorflow:Gradients do not exist for variables ['dense_1/kernel:0', 'dense_1/bias:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n",
- "WARNING:tensorflow:Gradients do not exist for variables ['dense_1/kernel:0', 'dense_1/bias:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n"
+ "WARNING:tensorflow:Gradients do not exist for variables ['dense_21/kernel:0', 'dense_21/bias:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n",
+ "WARNING:tensorflow:Gradients do not exist for variables ['dense_21/kernel:0', 'dense_21/bias:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n"
]
},
{
- "name": "stdout",
"output_type": "stream",
+ "name": "stdout",
"text": [
- " 102/2404 [>.............................] - ETA: 5:58 - vae_compiled_loss: 0.8147 - vae_compiled_binary_accuracy: 0.4792 - vae_wrapper_loss: 3385.2124"
+ "2747/2747 [==============================] - 30s 10ms/step - vae_compiled_loss: 0.5694 - vae_compiled_binary_accuracy: 0.7284 - vae_wrapper_loss: 983.3300\n",
+ "Epoch 2/6\n",
+ "2747/2747 [==============================] - 32s 12ms/step - vae_compiled_loss: 0.3165 - vae_compiled_binary_accuracy: 0.8775 - vae_wrapper_loss: 397.1720\n",
+ "Epoch 3/6\n",
+ "2747/2747 [==============================] - 29s 10ms/step - vae_compiled_loss: 0.2387 - vae_compiled_binary_accuracy: 0.9106 - vae_wrapper_loss: 330.7421\n",
+ "Epoch 4/6\n",
+ "2747/2747 [==============================] - 28s 10ms/step - vae_compiled_loss: 0.1949 - vae_compiled_binary_accuracy: 0.9289 - vae_wrapper_loss: 300.7650\n",
+ "Epoch 5/6\n",
+ "2747/2747 [==============================] - 29s 10ms/step - vae_compiled_loss: 0.1605 - vae_compiled_binary_accuracy: 0.9416 - vae_wrapper_loss: 280.1750\n",
+ "Epoch 6/6\n",
+ "2747/2747 [==============================] - 33s 12ms/step - vae_compiled_loss: 0.1387 - vae_compiled_binary_accuracy: 0.9469 - vae_wrapper_loss: 253.5578\n"
]
}
],
"source": [
- "learning_rate = 1e-5\n",
+ "learning_rate = 5e-4\n",
"\n",
"# compile model using desired optimizers and losses\n",
- "wrapped_classifier.compile(\n",
+ "wrapped_model.compile(\n",
" optimizer=tf.keras.optimizers.Adam(learning_rate),\n",
" loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n",
" metrics=[tf.keras.metrics.BinaryAccuracy()],\n",
@@ -410,7 +369,7 @@
")\n",
"\n",
"# fit the model to our training data\n",
- "history = wrapped_classifier.fit(\n",
+ "history = wrapped_model.fit(\n",
" train_loader,\n",
" epochs=6,\n",
" batch_size=batch_size,\n",
@@ -428,25 +387,26 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 55,
"metadata": {
- "id": "1dCqvPFH71Ed"
+ "id": "1dCqvPFH71Ed",
+ "outputId": "a3a19ee7-5c8e-4563-d6df-709f39dd357d",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
},
- "outputs": [],
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "22/22 [==============================] - 7s 316ms/step\n"
+ ]
+ }
+ ],
"source": [
"test_imgs = test_loader.get_all_faces() # Get all faces from the testing dataset\n",
- "predictions, _, bias = wrapped_classifier.predict(test_imgs) # use CAPSA-wrapped classifier to obtain estimates for bias and the output"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "Pt7_FlRW71Ee"
- },
- "outputs": [],
- "source": [
- "tf.config.list_physical_devices('GPU')"
+ "predictions, uncertainty, bias = wrapped_model.predict(test_imgs, batch_size=512) # use CAPSA-wrapped classifier to obtain estimates for bias and the output"
]
},
{
@@ -460,7 +420,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 14,
"metadata": {
"id": "OYMRqq5E71Ee"
},
@@ -474,24 +434,80 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 56,
"metadata": {
- "id": "UAYaFUj-71Ee"
+ "id": "UAYaFUj-71Ee",
+ "outputId": "39f4fe7d-ca14-47f4-d229-97d868dcc182",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 287
+ }
},
- "outputs": [],
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "<matplotlib.image.AxesImage at 0x7f8c2c232fd0>"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 56
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {
+ "needs_background": "light"
+ }
+ }
+ ],
"source": [
- "lab3.plot_k(sorted_images[:20]) # These are the samples with the lowest representation (least bias) in our test dataset"
+ "plt.imshow(mdl.util.create_grid_of_images(sorted_images[:20], (4, 5))) # These are the images with the lowest representation in our test dataset"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 57,
"metadata": {
- "id": "CnbR3qAF71Ef"
+ "id": "CnbR3qAF71Ef",
+ "outputId": "6a84a235-88b6-41e1-b616-17c3f9a0096b",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 287
+ }
},
- "outputs": [],
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "<matplotlib.image.AxesImage at 0x7f8c2d4f26a0>"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 57
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {
+ "needs_background": "light"
+ }
+ }
+ ],
"source": [
- "lab3.plot_k(sorted_images[-20:]) # These are the samples with the highest representation (most bias) in our test dataset"
+ "plt.imshow(mdl.util.create_grid_of_images(sorted_images[-20:], (4, 5))) # These are the samples with the highest representation in our test dataset"
]
},
{
@@ -505,13 +521,40 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 58,
"metadata": {
- "id": "DfzlOhWi71Ef"
+ "id": "DfzlOhWi71Ef",
+ "outputId": "65e20e7a-5e3e-4452-ae49-559a33a063ba",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 299
+ }
},
- "outputs": [],
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {
+ "needs_background": "light"
+ }
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "<Figure size 432x288 with 0 Axes>"
+ ]
+ },
+ "metadata": {}
+ }
+ ],
"source": [
- "averaged_imgs = lab3.plot_accuracy_vs_risk(sorted_images, sorted_biases, sorted_preds, \"Bias vs. Accuracy\")"
+ "averaged_imgs = mdl.lab3.plot_accuracy_vs_risk(sorted_images, sorted_biases, sorted_preds, \"Bias vs. Accuracy\")"
]
},
{
@@ -525,13 +568,42 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "source": [
+ "fig, ax = plt.subplots(figsize=(15,5))\n",
+ "ax.imshow(mdl.util.create_grid_of_images(averaged_imgs, (1,10)))"
+ ],
"metadata": {
- "id": "1cd590UP71Ef"
+ "id": "kn9IpPKYSECg",
+ "outputId": "1685fde2-8f66-4ed6-95c4-2158a0358fbc",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 153
+ }
},
- "outputs": [],
- "source": [
- "lab3.plot_percentile(averaged_imgs)"
+ "execution_count": 33,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "<matplotlib.image.AxesImage at 0x7f8be1776280>"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 33
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "<Figure size 1080x360 with 1 Axes>"
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {
+ "needs_background": "light"
+ }
+ }
]
},
{
@@ -560,40 +632,94 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 59,
"metadata": {
"id": "AwGPvdZm71Eg"
},
"outputs": [],
"source": [
- "predictions, reconstruction_loss, bias = wrapped_classifier.predict(test_imgs) # note that we're estimating both bias and uncertainty in a single shot!\n",
- "\n",
- "epistemic_indices = np.argsort(reconstruction_loss, axis=None) \n",
+ "epistemic_indices = np.argsort(uncertainty, axis=None) \n",
"epistemic_images = test_imgs[epistemic_indices] # sort images by reconstruction loss this time!\n",
- "sorted_epistemic = reconstruction_loss[epistemic_indices]\n",
+ "sorted_epistemic = uncertainty[epistemic_indices]\n",
"sorted_epistemic_preds = predictions[epistemic_indices]"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 60,
"metadata": {
- "id": "kB8Iqrfb71Eg"
+ "id": "kB8Iqrfb71Eg",
+ "outputId": "dc16b7d3-a6f1-4542-dcf2-135e0737e80e",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 287
+ }
},
- "outputs": [],
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "<matplotlib.image.AxesImage at 0x7f8c2d410580>"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 60
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {
+ "needs_background": "light"
+ }
+ }
+ ],
"source": [
- "lab3.plot_k(epistemic_images[:20]) # samples with the LEAST epistemic uncertainty"
+ "plt.imshow(mdl.util.create_grid_of_images(epistemic_images[:20], (4, 5))) # samples with the LEAST epistemic uncertainty"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 62,
"metadata": {
- "id": "miu5h2Pc71Eh"
+ "id": "miu5h2Pc71Eh",
+ "outputId": "00442875-f1bb-4160-d11c-8cfdfb5765ac",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 287
+ }
},
- "outputs": [],
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "<matplotlib.image.AxesImage at 0x7f8c2d5f6430>"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 62
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {
+ "needs_background": "light"
+ }
+ }
+ ],
"source": [
- "lab3.plot_k(epistemic_images[-20:]) # samples with the MOST epistemic uncertainty"
+ "plt.imshow(mdl.util.create_grid_of_images(epistemic_images[-20:], (4, 5))) # samples with the MOST epistemic uncertainty"
]
},
{
@@ -607,11 +733,38 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 63,
"metadata": {
- "id": "rzQwvSvA71Eh"
+ "id": "rzQwvSvA71Eh",
+ "outputId": "4299761d-22b3-4f44-b9b3-d9194d04a6f2",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 299
+ }
},
- "outputs": [],
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {
+ "needs_background": "light"
+ }
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "<Figure size 432x288 with 0 Axes>"
+ ]
+ },
+ "metadata": {}
+ }
+ ],
"source": [
"_ = lab3.plot_accuracy_vs_risk(epistemic_images, sorted_epistemic, sorted_epistemic_preds, \"Epistemic Uncertainty vs. Accuracy\")"
]
@@ -708,7 +861,8 @@
"dbvae = HistogramVAEWrapper(standard_classifier, latent_dim=100, num_bins=5, queue_size=2000, decoder=make_face_decoder_network())\n",
"dbvae.compile(optimizer=tf.keras.optimizers.Adam(1e-4),\n",
" loss=tf.keras.losses.BinaryCrossentropy(),\n",
- " metrics=[tf.keras.metrics.BinaryAccuracy()])"
+ " metrics=[tf.keras.metrics.BinaryAccuracy()])\n",
+ "train_imgs = train_loader.get_all_faces()"
]
},
{
@@ -832,11 +986,6 @@
}
],
"metadata": {
- "accelerator": "GPU",
- "colab": {
- "provenance": []
- },
- "gpuClass": "standard",
"kernelspec": {
"display_name": "Python 3",
"language": "python",
@@ -853,8 +1002,13 @@
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.10"
- }
+ },
+ "colab": {
+ "provenance": []
+ },
+ "accelerator": "GPU",
+ "gpuClass": "standard"
},
"nbformat": 4,
"nbformat_minor": 0
-}
+}
\ No newline at end of file
From 11b35cc2d8022e2db8470581fedb5748b1d97404 Mon Sep 17 00:00:00 2001
From: Ava Amini <apsoleimany@gmail.com>
Date: Wed, 11 Jan 2023 01:03:34 -0500
Subject: [PATCH 09/22] removing todos as part of competition
---
.../Lab3_Part_1_Introduction_to_CAPSA.ipynb | 44 ++-----------------
1 file changed, 3 insertions(+), 41 deletions(-)
diff --git a/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb b/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb
index fa037bca..1e27e635 100644
--- a/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb
+++ b/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb
@@ -248,9 +248,8 @@
" loss=tf.keras.losses.MeanSquaredError(), # MSE loss for the regression task\n",
")\n",
"\n",
- "# TODO: Train the model for 30 epochs. Use model.fit().\n",
- "loss_history = dense_NN.fit(x_train, y_train, epochs=30) \n",
- "# loss_history = # TODO"
+ "# Train the model for 30 epochs using model.fit().\n",
+ "loss_history = dense_NN.fit(x_train, y_train, epochs=30)"
]
},
{
@@ -789,44 +788,7 @@
"\n",
"You've just analyzed the bias, aleatoric uncertainty, and epistemic uncertainty for your first risk-aware model! This is a task that data scientists do constantly to determine methods of improving their models and datasets.\n",
"\n",
- "## NOTE TO ADDRESS: THIS CAN BE ELIMINATED COMPLETELY IF IT IS TOO MUCH FOR COMPETITION!\n",
- "### 1.6.1 Submission information\n",
- "To be eligible for the Debiasing Faces Lab prize, you must submit a document of your answers to the short-answer `TODO`s with your complete lab submission. **Name your file in the following format: `[FirstName]_[LastName]_Debiasing_Report.pdf`.**\n",
- "\n",
- "Upload your document write-up as part of your complete lab submission for the Debiasing Faces Lab ([submission upload link](https://www.dropbox.com/request/TTYz3Ikx5wIgOITmm5i2)).\n",
- "\n",
- "Please see the short-answer `TODO`s replicated again here:\n",
- "\n",
- "#### **TODO: Inspecting the 2D regression dataset**\n",
- "\n",
- "1. What are your observations about where the train data and test data lie relative to each other?\n",
- "2. What, if any, areas do you expect to have high/low aleatoric (data) uncertainty?\n",
- "3. What, if any, areas do you expect to have high/low epistemic (model) uncertainty?\n",
- "\n",
- "#### **TODO: Analyzing the performance of standard regression model**\n",
- "\n",
- "1. Where does the model perform well?\n",
- "2. Where does the model perform poorly?\n",
- "\n",
- "#### **TODO: Evaluating bias**\n",
- "\n",
- "1. How does the bias score relate to the train/test data density from the first plot?\n",
- "2. What is one limitation of the Histogram approach that simply bins the data based on frequency?\n",
- "\n",
- "#### **TODO: Estimating aleatoric uncertainty**\n",
- "\n",
- "1. For what values of $x$ is the aleatoric uncertainty high or increasing suddenly?\n",
- "2. How does your answer in (1) relate to how the $x$ values are distributed?\n",
- "\n",
- "#### **TODO: Estimating epistemic uncertainty**\n",
- "\n",
- "1. For what values of $x$ is the epistemic uncertainty high or increasing suddenly?\n",
- "2. How does your answer in (1) relate to how the $x$ values are distributed (refer back to original plot)? Think about both the train and test data.\n",
- "3. How could you reduce the epistemic uncertainty in regions where it is high?\n",
- "\n",
- "### 1.6.2 Moving forward\n",
- "\n",
- "In the next part of the lab, you'll continue to build off of these concepts to *mitigate* these risks, in addition to diagnosing them!\n",
+ "In the next part of the lab, you'll continue to build off of these concepts to study them in the context of facial detection systems: not only diagnosing issues of bias and uncertainty, but also developing solutions to *mitigate* these risks.\n",
"\n",
""
]
From 109b9657dbc48bb8fcee418da6370f98452d5296 Mon Sep 17 00:00:00 2001
From: Alexander Amini <xan.amini@gmail.com>
Date: Wed, 11 Jan 2023 01:50:30 -0500
Subject: [PATCH 10/22] Created using Colaboratory
---
.../Lab3_Part_1_Introduction_to_CAPSA.ipynb | 438 ++++--------------
1 file changed, 100 insertions(+), 338 deletions(-)
diff --git a/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb b/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb
index 1e27e635..df416fed 100644
--- a/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb
+++ b/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb
@@ -86,32 +86,9 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "NdXF4Reyj6yy",
- "outputId": "e21a92b6-cb80-4da3-9b25-f447bf28482b"
+ "id": "NdXF4Reyj6yy"
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
- "Requirement already satisfied: capsa in /usr/local/lib/python3.8/dist-packages (0.1.2)\n",
- "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
- "Requirement already satisfied: mitdeeplearning in /usr/local/lib/python3.8/dist-packages (0.2.0)\n",
- "Requirement already satisfied: gym in /usr/local/lib/python3.8/dist-packages (from mitdeeplearning) (0.25.2)\n",
- "Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from mitdeeplearning) (1.21.6)\n",
- "Requirement already satisfied: regex in /usr/local/lib/python3.8/dist-packages (from mitdeeplearning) (2022.6.2)\n",
- "Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from mitdeeplearning) (4.64.1)\n",
- "Requirement already satisfied: importlib-metadata>=4.8.0 in /usr/local/lib/python3.8/dist-packages (from gym->mitdeeplearning) (5.2.0)\n",
- "Requirement already satisfied: gym-notices>=0.0.4 in /usr/local/lib/python3.8/dist-packages (from gym->mitdeeplearning) (0.0.8)\n",
- "Requirement already satisfied: cloudpickle>=1.2.0 in /usr/local/lib/python3.8/dist-packages (from gym->mitdeeplearning) (1.5.0)\n",
- "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.8/dist-packages (from importlib-metadata>=4.8.0->gym->mitdeeplearning) (3.11.0)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# Import Tensorflow 2.0\n",
"%tensorflow_version 2.x\n",
@@ -122,7 +99,6 @@
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from tqdm import tqdm\n",
- "from helper import gen_data_regression\n",
"\n",
"# Download and import the MIT Introduction to Deep Learning package\n",
"!pip install mitdeeplearning\n",
@@ -150,51 +126,36 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 265
- },
- "id": "fH40EhC1j9dH",
- "outputId": "c6936767-2162-4b6c-b430-e5717c70bb75"
+ "id": "fH40EhC1j9dH"
},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"# Get the data for the cubic function, injected with noise and missing-ness\n",
"def gen_data(x_min, x_max, n, train=True):\n",
+ " if train: \n",
" x = np.random.triangular(x_min, 2, x_max, size=(n, 1))\n",
+ " else: \n",
+ " x = np.linspace(x_min, x_max, n).reshape(n, 1)\n",
"\n",
- " sigma = np.exp(-(x+1)**2/1) + 0.2 if train else np.zeros_like(x)\n",
- " y = x**3/6 + np.random.normal(0, sigma).astype(np.float32)\n",
+ " sigma = 2*np.exp(-(x+1)**2/1) + 0.2 if train else np.zeros_like(x)\n",
+ " y = x**3/6 + np.random.normal(0, sigma).astype(np.float32)\n",
"\n",
- " return x, y\n",
+ " return x, y\n",
"\n",
"# Plot the dataset and visualize the train and test datapoints\n",
- "x_train, y_train = gen_data(-4, 4, 2000) # train data\n",
- "x_test, y_test = gen_data(-6, 6, 500) # test data\n",
- "plt.scatter(x_train, y_train, s=1.5, label='train data')\n",
- "plt.scatter(x_test, y_test, s=1.5, label='test data')\n",
+ "x_train, y_train = gen_data(-4, 4, 2000, train=True) # train data\n",
+ "x_test, y_test = gen_data(-6, 6, 500, train=False) # test data\n",
"\n",
- "plt.legend()\n",
- "plt.show()"
+ "plt.figure(figsize=(10, 6))\n",
+ "plt.plot(x_test, y_test, c='r', zorder=-1, label='ground truth')\n",
+ "plt.scatter(x_train, y_train, s=1.5, label='train data')\n",
+ "plt.legend()"
]
},
{
"cell_type": "markdown",
"source": [
- "In the plot above, the orange points are the training data, which will be used as inputs to train the neural network model. The blue points are the test data, which will be used to evaluate the performance of the model.\n",
+ "In the plot above, the blue points are the training data, which will be used as inputs to train the neural network model. The red line is the ground truth data, which will be used to evaluate the performance of the model.\n",
"\n",
"#### **TODO: Inspecting the 2D regression dataset**\n",
"\n",
@@ -234,8 +195,9 @@
" return tf.keras.Sequential(\n",
" [\n",
" tf.keras.Input(shape=(1,)),\n",
- " tf.keras.layers.Dense(8, \"relu\"),\n",
- " tf.keras.layers.Dense(8, \"relu\"),\n",
+ " tf.keras.layers.Dense(32, \"relu\"),\n",
+ " tf.keras.layers.Dense(32, \"relu\"),\n",
+ " tf.keras.layers.Dense(32, \"relu\"),\n",
" tf.keras.layers.Dense(1),\n",
" ]\n",
" )\n",
@@ -244,12 +206,12 @@
"\n",
"# Build the model for regression, defining the loss function and optimizer\n",
"dense_NN.compile(\n",
- " optimizer=tf.keras.optimizers.Adam(learning_rate=2e-3),\n",
+ " optimizer=tf.keras.optimizers.Adam(learning_rate=5e-3),\n",
" loss=tf.keras.losses.MeanSquaredError(), # MSE loss for the regression task\n",
")\n",
"\n",
"# Train the model for 30 epochs using model.fit().\n",
- "loss_history = dense_NN.fit(x_train, y_train, epochs=30)"
+ "loss_history = dense_NN.fit(x_train, y_train, epochs=30, verbose=0)"
]
},
{
@@ -267,44 +229,18 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 283
- },
- "id": "fb-EklZywR4D",
- "outputId": "1f913c81-fbef-43dd-a391-b7dc209055fa"
+ "id": "fb-EklZywR4D"
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x7fe11cd8e3a0>"
- ]
- },
- "execution_count": 107,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"# Pass the test data through the network and predict the y values\n",
- "y_predicted = dense_NN(x_test)\n",
+ "y_predicted = dense_NN.predict(x_test)\n",
"\n",
"# Visualize the true (x, y) pairs for the test data vs. the predicted values\n",
- "plt.scatter(x_test, y_test, s=0.5, label='truth')\n",
- "plt.scatter(x_test, y_predicted, s=0.5, label='predictions')\n",
+ "plt.figure(figsize=(10, 6))\n",
+ "plt.scatter(x_train, y_train, s=1.5, label='train data')\n",
+ "plt.plot(x_test, y_test, c='r', zorder=-1, label='ground truth')\n",
+ "plt.plot(x_test, y_predicted, c='b', zorder=0, label='predicted')\n",
"plt.legend()"
]
},
@@ -351,9 +287,10 @@
"standard_dense_NN = create_dense_NN()\n",
"bias_wrapped_dense_NN = capsa.HistogramWrapper(\n",
" standard_dense_NN, # the original model\n",
+ " num_bins=20,\n",
" queue_size=2000, # how many samples to track\n",
- " target_hidden_layer=False # for low-dimensional data, we can estimate densities directly from data\n",
- " )\n"
+ " target_hidden_layer=False # for low-dimensional data (like this dataset), we can estimate biases directly from data\n",
+ ")"
]
},
{
@@ -369,94 +306,9 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "SkyD3rsqy2ff",
- "outputId": "7cd6b5fa-c61a-4306-faed-02a5b9d6a3e3"
+ "id": "SkyD3rsqy2ff"
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 1/30\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "WARNING:tensorflow:Gradients do not exist for variables ['dense_47/kernel:0', 'dense_47/bias:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n",
- "WARNING:tensorflow:Gradients do not exist for variables ['dense_47/kernel:0', 'dense_47/bias:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "63/63 [==============================] - 1s 2ms/step - histogram_compiled_loss: 3.9734 - histogram_wrapper_loss: 7.2317\n",
- "Epoch 2/30\n",
- "63/63 [==============================] - 0s 2ms/step - histogram_compiled_loss: 2.0948 - histogram_wrapper_loss: 4.1785\n",
- "Epoch 3/30\n",
- "63/63 [==============================] - 0s 6ms/step - histogram_compiled_loss: 1.6633 - histogram_wrapper_loss: 3.2580\n",
- "Epoch 4/30\n",
- "63/63 [==============================] - 0s 4ms/step - histogram_compiled_loss: 1.5208 - histogram_wrapper_loss: 2.8870\n",
- "Epoch 5/30\n",
- "63/63 [==============================] - 0s 4ms/step - histogram_compiled_loss: 1.2064 - histogram_wrapper_loss: 2.5474\n",
- "Epoch 6/30\n",
- "63/63 [==============================] - 0s 4ms/step - histogram_compiled_loss: 1.1682 - histogram_wrapper_loss: 2.3297\n",
- "Epoch 7/30\n",
- "63/63 [==============================] - 0s 3ms/step - histogram_compiled_loss: 1.0387 - histogram_wrapper_loss: 2.0440\n",
- "Epoch 8/30\n",
- "63/63 [==============================] - 0s 3ms/step - histogram_compiled_loss: 0.9051 - histogram_wrapper_loss: 1.8478\n",
- "Epoch 9/30\n",
- "63/63 [==============================] - 0s 3ms/step - histogram_compiled_loss: 0.8954 - histogram_wrapper_loss: 1.6332\n",
- "Epoch 10/30\n",
- "63/63 [==============================] - 0s 5ms/step - histogram_compiled_loss: 0.7636 - histogram_wrapper_loss: 1.5712\n",
- "Epoch 11/30\n",
- "63/63 [==============================] - 0s 4ms/step - histogram_compiled_loss: 0.6725 - histogram_wrapper_loss: 1.3582\n",
- "Epoch 12/30\n",
- "63/63 [==============================] - 0s 5ms/step - histogram_compiled_loss: 0.6783 - histogram_wrapper_loss: 1.2359\n",
- "Epoch 13/30\n",
- "63/63 [==============================] - 0s 4ms/step - histogram_compiled_loss: 0.6118 - histogram_wrapper_loss: 1.1157\n",
- "Epoch 14/30\n",
- "63/63 [==============================] - 0s 5ms/step - histogram_compiled_loss: 0.5462 - histogram_wrapper_loss: 1.0705\n",
- "Epoch 15/30\n",
- "63/63 [==============================] - 0s 4ms/step - histogram_compiled_loss: 0.4946 - histogram_wrapper_loss: 0.9810\n",
- "Epoch 16/30\n",
- "63/63 [==============================] - 0s 4ms/step - histogram_compiled_loss: 0.4712 - histogram_wrapper_loss: 0.9213\n",
- "Epoch 17/30\n",
- "63/63 [==============================] - 0s 4ms/step - histogram_compiled_loss: 0.4449 - histogram_wrapper_loss: 0.8751\n",
- "Epoch 18/30\n",
- "63/63 [==============================] - 0s 4ms/step - histogram_compiled_loss: 0.4146 - histogram_wrapper_loss: 0.8342\n",
- "Epoch 19/30\n",
- "63/63 [==============================] - 0s 5ms/step - histogram_compiled_loss: 0.4441 - histogram_wrapper_loss: 0.8335\n",
- "Epoch 20/30\n",
- "63/63 [==============================] - 0s 5ms/step - histogram_compiled_loss: 0.4050 - histogram_wrapper_loss: 0.7910\n",
- "Epoch 21/30\n",
- "63/63 [==============================] - 0s 4ms/step - histogram_compiled_loss: 0.4113 - histogram_wrapper_loss: 0.7864\n",
- "Epoch 22/30\n",
- "63/63 [==============================] - 0s 5ms/step - histogram_compiled_loss: 0.3650 - histogram_wrapper_loss: 0.7556\n",
- "Epoch 23/30\n",
- "63/63 [==============================] - 0s 4ms/step - histogram_compiled_loss: 0.3521 - histogram_wrapper_loss: 0.7350\n",
- "Epoch 24/30\n",
- "63/63 [==============================] - 0s 4ms/step - histogram_compiled_loss: 0.3672 - histogram_wrapper_loss: 0.7575\n",
- "Epoch 25/30\n",
- "63/63 [==============================] - 0s 3ms/step - histogram_compiled_loss: 0.3608 - histogram_wrapper_loss: 0.7124\n",
- "Epoch 26/30\n",
- "63/63 [==============================] - 0s 4ms/step - histogram_compiled_loss: 0.3740 - histogram_wrapper_loss: 0.7006\n",
- "Epoch 27/30\n",
- "63/63 [==============================] - 0s 4ms/step - histogram_compiled_loss: 0.3691 - histogram_wrapper_loss: 0.6984\n",
- "Epoch 28/30\n",
- "63/63 [==============================] - 0s 5ms/step - histogram_compiled_loss: 0.3733 - histogram_wrapper_loss: 0.6849\n",
- "Epoch 29/30\n",
- "63/63 [==============================] - 0s 3ms/step - histogram_compiled_loss: 0.3372 - histogram_wrapper_loss: 0.6665\n",
- "Epoch 30/30\n",
- "63/63 [==============================] - 0s 5ms/step - histogram_compiled_loss: 0.3604 - histogram_wrapper_loss: 0.6658\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"### Compile and train the wrapped model! ###\n",
"\n",
@@ -467,7 +319,9 @@
")\n",
"\n",
"# Train the wrapped model for 30 epochs.\n",
- "loss_history_bias_wrap = bias_wrapped_dense_NN.fit(x_train, y_train, epochs=30)"
+ "loss_history_bias_wrap = bias_wrapped_dense_NN.fit(x_train, y_train, epochs=30, verbose=0)\n",
+ "\n",
+ "print(\"Done training model with Bias Wrapper!\")"
]
},
{
@@ -485,46 +339,27 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 287
- },
- "id": "tZ17eCbP0YM4",
- "outputId": "4da00423-1115-4bf2-95e6-966b8697b5b6"
+ "id": "tZ17eCbP0YM4"
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x7fe11cd97190>"
- ]
- },
- "execution_count": 110,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"### Generate and visualize bias scores for data in test set ###\n",
"\n",
"# Call the risk-aware model to generate scores\n",
- "predictions, bias = bias_wrapped_dense_NN(np.sort(x_test))\n",
+ "predictions, bias = bias_wrapped_dense_NN(x_test)\n",
"\n",
"# Visualize the relationship between the input data x and the bias\n",
- "plt.scatter(np.sort(x_test), bias, label='bias', s=0.5)\n",
- "plt.legend()"
+ "fig, ax = plt.subplots(2, 1, figsize=(8,6))\n",
+ "ax[0].plot(x_test, bias, label='bias')\n",
+ "ax[0].set_ylabel('Estimated Bias')\n",
+ "ax[0].legend()\n",
+ "\n",
+ "# Let's compare against the ground truth density distribution\n",
+ "# should roughly align with our estimated bias in this toy example\n",
+ "ax[1].hist(x_train, 50, label='ground truth')\n",
+ "ax[1].set_xlim(-6, 6)\n",
+ "ax[1].set_ylabel('True Density')\n",
+ "ax[1].legend();"
]
},
{
@@ -582,14 +417,30 @@
"loss_history_mve_wrap = mve_wrapped_NN.fit(x_train, y_train, epochs=30)\n",
"\n",
"# Call the uncertainty-aware model to generate outputs for the test data\n",
- "outputs = mve_wrapped_NN(x_test)\n",
+ "x_test_clipped = np.clip(x_test, x_train.min(), x_train.max())\n",
+ "prediction = mve_wrapped_NN(x_test_clipped)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
"# Capsa makes the aleatoric uncertainty an attribute of the prediction!\n",
- "aleatoric_unc = outputs.aleatoric\n",
+ "pred = np.array(prediction.y_hat).flatten()\n",
+ "unc = np.sqrt(prediction.aleatoric).flatten() # out.aleatoric is the predicted variance\n",
"\n",
"# Visualize the aleatoric uncertainty across the data space\n",
- "plt.scatter(x_test, aleatoric, label='aleatoric uncertainty', s=0.5)\n",
+ "plt.figure(figsize=(10, 6))\n",
+ "plt.scatter(x_train, y_train, s=1.5, label='train data')\n",
+ "plt.plot(x_test, y_test, c='r', zorder=-1, label='ground truth')\n",
+ "plt.fill_between(x_test_clipped.flatten(), pred-2*unc, pred+2*unc, \n",
+ " color='b', alpha=0.2, label='aleatoric')\n",
"plt.legend()"
- ]
+ ],
+ "metadata": {
+ "id": "dT2Rx8JCg3NR"
+ },
+ "execution_count": null,
+ "outputs": []
},
{
"cell_type": "markdown",
@@ -622,86 +473,15 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "SuRlhq2c5Fob",
- "outputId": "b1f81f5a-69da-4e40-af2a-2a908a1da639"
+ "id": "SuRlhq2c5Fob"
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 1/30\n",
- "63/63 [==============================] - 2s 3ms/step - usermodel_0_compiled_loss: 6.5601 - usermodel_1_compiled_loss: 4.9589 - usermodel_2_compiled_loss: 4.8135 - usermodel_3_compiled_loss: 4.3547 - usermodel_4_compiled_loss: 6.3809\n",
- "Epoch 2/30\n",
- "63/63 [==============================] - 0s 3ms/step - usermodel_0_compiled_loss: 3.7732 - usermodel_1_compiled_loss: 4.3968 - usermodel_2_compiled_loss: 2.8938 - usermodel_3_compiled_loss: 3.2580 - usermodel_4_compiled_loss: 4.0894\n",
- "Epoch 3/30\n",
- "63/63 [==============================] - 0s 4ms/step - usermodel_0_compiled_loss: 2.8896 - usermodel_1_compiled_loss: 4.1322 - usermodel_2_compiled_loss: 2.3051 - usermodel_3_compiled_loss: 2.6397 - usermodel_4_compiled_loss: 3.0872\n",
- "Epoch 4/30\n",
- "63/63 [==============================] - 0s 3ms/step - usermodel_0_compiled_loss: 2.4637 - usermodel_1_compiled_loss: 4.0084 - usermodel_2_compiled_loss: 2.0035 - usermodel_3_compiled_loss: 2.2897 - usermodel_4_compiled_loss: 2.5871\n",
- "Epoch 5/30\n",
- "63/63 [==============================] - 0s 3ms/step - usermodel_0_compiled_loss: 2.1638 - usermodel_1_compiled_loss: 3.8279 - usermodel_2_compiled_loss: 1.7763 - usermodel_3_compiled_loss: 2.0321 - usermodel_4_compiled_loss: 2.2477\n",
- "Epoch 6/30\n",
- "63/63 [==============================] - 0s 3ms/step - usermodel_0_compiled_loss: 1.9428 - usermodel_1_compiled_loss: 3.6973 - usermodel_2_compiled_loss: 1.6039 - usermodel_3_compiled_loss: 1.8292 - usermodel_4_compiled_loss: 2.0054\n",
- "Epoch 7/30\n",
- "63/63 [==============================] - 0s 3ms/step - usermodel_0_compiled_loss: 1.7664 - usermodel_1_compiled_loss: 3.5628 - usermodel_2_compiled_loss: 1.4630 - usermodel_3_compiled_loss: 1.6634 - usermodel_4_compiled_loss: 1.8182\n",
- "Epoch 8/30\n",
- "63/63 [==============================] - 0s 3ms/step - usermodel_0_compiled_loss: 1.6222 - usermodel_1_compiled_loss: 3.4514 - usermodel_2_compiled_loss: 1.3476 - usermodel_3_compiled_loss: 1.5290 - usermodel_4_compiled_loss: 1.6662\n",
- "Epoch 9/30\n",
- "63/63 [==============================] - 0s 3ms/step - usermodel_0_compiled_loss: 1.4997 - usermodel_1_compiled_loss: 3.3267 - usermodel_2_compiled_loss: 1.2495 - usermodel_3_compiled_loss: 1.4147 - usermodel_4_compiled_loss: 1.5371\n",
- "Epoch 10/30\n",
- "63/63 [==============================] - 0s 3ms/step - usermodel_0_compiled_loss: 1.3961 - usermodel_1_compiled_loss: 3.2211 - usermodel_2_compiled_loss: 1.1664 - usermodel_3_compiled_loss: 1.3165 - usermodel_4_compiled_loss: 1.4279\n",
- "Epoch 11/30\n",
- "63/63 [==============================] - 0s 3ms/step - usermodel_0_compiled_loss: 1.3066 - usermodel_1_compiled_loss: 3.1169 - usermodel_2_compiled_loss: 1.0947 - usermodel_3_compiled_loss: 1.2315 - usermodel_4_compiled_loss: 1.3337\n",
- "Epoch 12/30\n",
- "63/63 [==============================] - 0s 3ms/step - usermodel_0_compiled_loss: 1.2304 - usermodel_1_compiled_loss: 3.0270 - usermodel_2_compiled_loss: 1.0334 - usermodel_3_compiled_loss: 1.1599 - usermodel_4_compiled_loss: 1.2545\n",
- "Epoch 13/30\n",
- "63/63 [==============================] - 0s 3ms/step - usermodel_0_compiled_loss: 1.1635 - usermodel_1_compiled_loss: 2.9340 - usermodel_2_compiled_loss: 0.9794 - usermodel_3_compiled_loss: 1.0972 - usermodel_4_compiled_loss: 1.1850\n",
- "Epoch 14/30\n",
- "63/63 [==============================] - 0s 3ms/step - usermodel_0_compiled_loss: 1.1056 - usermodel_1_compiled_loss: 2.8478 - usermodel_2_compiled_loss: 0.9327 - usermodel_3_compiled_loss: 1.0430 - usermodel_4_compiled_loss: 1.1251\n",
- "Epoch 15/30\n",
- "63/63 [==============================] - 0s 3ms/step - usermodel_0_compiled_loss: 1.0553 - usermodel_1_compiled_loss: 2.7690 - usermodel_2_compiled_loss: 0.8924 - usermodel_3_compiled_loss: 0.9959 - usermodel_4_compiled_loss: 1.0732\n",
- "Epoch 16/30\n",
- "63/63 [==============================] - 0s 3ms/step - usermodel_0_compiled_loss: 1.0106 - usermodel_1_compiled_loss: 2.6925 - usermodel_2_compiled_loss: 0.8563 - usermodel_3_compiled_loss: 0.9544 - usermodel_4_compiled_loss: 1.0276\n",
- "Epoch 17/30\n",
- "63/63 [==============================] - 0s 3ms/step - usermodel_0_compiled_loss: 0.9704 - usermodel_1_compiled_loss: 2.6262 - usermodel_2_compiled_loss: 0.8240 - usermodel_3_compiled_loss: 0.9167 - usermodel_4_compiled_loss: 0.9862\n",
- "Epoch 18/30\n",
- "63/63 [==============================] - 0s 3ms/step - usermodel_0_compiled_loss: 0.9343 - usermodel_1_compiled_loss: 2.5600 - usermodel_2_compiled_loss: 0.7951 - usermodel_3_compiled_loss: 0.8834 - usermodel_4_compiled_loss: 0.9494\n",
- "Epoch 19/30\n",
- "63/63 [==============================] - 0s 3ms/step - usermodel_0_compiled_loss: 0.9023 - usermodel_1_compiled_loss: 2.4966 - usermodel_2_compiled_loss: 0.7695 - usermodel_3_compiled_loss: 0.8541 - usermodel_4_compiled_loss: 0.9171\n",
- "Epoch 20/30\n",
- "63/63 [==============================] - 0s 3ms/step - usermodel_0_compiled_loss: 0.8727 - usermodel_1_compiled_loss: 2.4440 - usermodel_2_compiled_loss: 0.7459 - usermodel_3_compiled_loss: 0.8270 - usermodel_4_compiled_loss: 0.8870\n",
- "Epoch 21/30\n",
- "63/63 [==============================] - 0s 3ms/step - usermodel_0_compiled_loss: 0.8455 - usermodel_1_compiled_loss: 2.3854 - usermodel_2_compiled_loss: 0.7243 - usermodel_3_compiled_loss: 0.8023 - usermodel_4_compiled_loss: 0.8594\n",
- "Epoch 22/30\n",
- "63/63 [==============================] - 0s 3ms/step - usermodel_0_compiled_loss: 0.8213 - usermodel_1_compiled_loss: 2.3344 - usermodel_2_compiled_loss: 0.7052 - usermodel_3_compiled_loss: 0.7805 - usermodel_4_compiled_loss: 0.8349\n",
- "Epoch 23/30\n",
- "63/63 [==============================] - 0s 3ms/step - usermodel_0_compiled_loss: 0.7981 - usermodel_1_compiled_loss: 2.2851 - usermodel_2_compiled_loss: 0.6867 - usermodel_3_compiled_loss: 0.7593 - usermodel_4_compiled_loss: 0.8112\n",
- "Epoch 24/30\n",
- "63/63 [==============================] - 0s 3ms/step - usermodel_0_compiled_loss: 0.7778 - usermodel_1_compiled_loss: 2.2358 - usermodel_2_compiled_loss: 0.6707 - usermodel_3_compiled_loss: 0.7409 - usermodel_4_compiled_loss: 0.7906\n",
- "Epoch 25/30\n",
- "63/63 [==============================] - 0s 3ms/step - usermodel_0_compiled_loss: 0.7586 - usermodel_1_compiled_loss: 2.1888 - usermodel_2_compiled_loss: 0.6555 - usermodel_3_compiled_loss: 0.7235 - usermodel_4_compiled_loss: 0.7711\n",
- "Epoch 26/30\n",
- "63/63 [==============================] - 0s 3ms/step - usermodel_0_compiled_loss: 0.7409 - usermodel_1_compiled_loss: 2.1461 - usermodel_2_compiled_loss: 0.6415 - usermodel_3_compiled_loss: 0.7074 - usermodel_4_compiled_loss: 0.7532\n",
- "Epoch 27/30\n",
- "63/63 [==============================] - 0s 3ms/step - usermodel_0_compiled_loss: 0.7247 - usermodel_1_compiled_loss: 2.1043 - usermodel_2_compiled_loss: 0.6288 - usermodel_3_compiled_loss: 0.6927 - usermodel_4_compiled_loss: 0.7367\n",
- "Epoch 28/30\n",
- "63/63 [==============================] - 0s 4ms/step - usermodel_0_compiled_loss: 0.7098 - usermodel_1_compiled_loss: 2.0641 - usermodel_2_compiled_loss: 0.6171 - usermodel_3_compiled_loss: 0.6792 - usermodel_4_compiled_loss: 0.7216\n",
- "Epoch 29/30\n",
- "63/63 [==============================] - 0s 3ms/step - usermodel_0_compiled_loss: 0.6950 - usermodel_1_compiled_loss: 2.0264 - usermodel_2_compiled_loss: 0.6053 - usermodel_3_compiled_loss: 0.6658 - usermodel_4_compiled_loss: 0.7067\n",
- "Epoch 30/30\n",
- "63/63 [==============================] - 0s 3ms/step - usermodel_0_compiled_loss: 0.6819 - usermodel_1_compiled_loss: 1.9897 - usermodel_2_compiled_loss: 0.5950 - usermodel_3_compiled_loss: 0.6539 - usermodel_4_compiled_loss: 0.6934\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"### Estimating model uncertainty with Capsa wrapping ###\n",
"\n",
"standard_dense_NN = create_dense_NN()\n",
- "# Wrap the dense network for epistemic uncertainty estimation with a 5-member Ensemble\n",
- "ensemble_NN = capsa.EnsembleWrapper(standard_dense_NN, num_members=5)\n",
+ "# Wrap the dense network for epistemic uncertainty estimation with an Ensemble\n",
+ "ensemble_NN = capsa.EnsembleWrapper(standard_dense_NN)\n",
"\n",
"# Build the model for regression, defining the loss function and optimizer\n",
"ensemble_NN.compile(\n",
@@ -713,55 +493,28 @@
"loss_history_ensemble = ensemble_NN.fit(x_train, y_train, epochs=30)\n",
"\n",
"# Call the uncertainty-aware model to generate outputs for the test data\n",
- "outputs = ensemble_NN(x_test)\n",
- "# Capsa makes the epistemic uncertainty an attribute of the prediction!\n",
- "epistemic_unc = outputs.epistemic\n",
- "\n",
- "# Visualize the epistemic uncertainty across the data space\n",
- "plt.scatter(x_test, epistemic, label='epistemic uncertainty', s=0.5)\n",
- "plt.legend()"
+ "prediction = ensemble_NN(x_test)"
]
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 283
- },
- "id": "HfnPqf8T6TVw",
- "outputId": "4a9fa19d-ae27-477c-bc47-f84d62a3444f"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x7fe127f9f7f0>"
- ]
- },
- "execution_count": 130,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
"source": [
- "outputs = ensemble_wrapper(x_val)\n",
- "plt.scatter(x_val, outputs.epistemic, label='epistemic uncertainty', s=0.5)\n",
+ "# Capsa makes the epistemic uncertainty an attribute of the prediction!\n",
+ "pred = np.array(prediction.y_hat).flatten()\n",
+ "unc = np.array(prediction.epistemic).flatten()\n",
+ "\n",
+ "# Visualize the aleatoric uncertainty across the data space\n",
+ "plt.figure(figsize=(10, 6))\n",
+ "plt.scatter(x_train, y_train, s=1.5, label='train data')\n",
+ "plt.plot(x_test, y_test, c='r', zorder=-1, label='ground truth')\n",
+ "plt.fill_between(x_test.flatten(), pred-20*unc, pred+20*unc, color='b', alpha=0.2, label='epistemic')\n",
"plt.legend()"
- ]
+ ],
+ "metadata": {
+ "id": "eauNoKDOj_ZT"
+ },
+ "execution_count": null,
+ "outputs": []
},
{
"cell_type": "markdown",
@@ -792,6 +545,15 @@
"\n",
""
]
+ },
+ {
+ "cell_type": "code",
+ "source": [],
+ "metadata": {
+ "id": "nIpfPcpjlsKK"
+ },
+ "execution_count": null,
+ "outputs": []
}
],
"metadata": {
From b1c0267373a0dfd0bb70a037a0f28e101150be68 Mon Sep 17 00:00:00 2001
From: Ava Amini <apsoleimany@gmail.com>
Date: Wed, 11 Jan 2023 01:53:12 -0500
Subject: [PATCH 11/22] part2 up to bias identifying
---
.../solutions/Lab3_Bias_And_Uncertainty.ipynb | 105 ++++++++++++------
1 file changed, 71 insertions(+), 34 deletions(-)
diff --git a/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb b/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb
index 8380628a..35533914 100644
--- a/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb
+++ b/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb
@@ -1,20 +1,60 @@
{
"cells": [
+ {
+ "cell_type": "markdown",
+ "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/2023/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.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/2023/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.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"
+ ],
+ "metadata": {
+ "id": "Kxl9-zNYhxlQ"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Copyright 2023 MIT 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 MIT Introduction\n",
+ "# to Deep Learning must reference:\n",
+ "#\n",
+ "# © MIT Introduction to Deep Learning\n",
+ "# http://introtodeeplearning.com\n",
+ "#"
+ ],
+ "metadata": {
+ "id": "aAcJJN3Xh3S1"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
{
"cell_type": "markdown",
"metadata": {
"id": "IgYKebt871EK"
},
"source": [
- "# Laboratory 3: Detecting and mitigating bias and uncertainty in Facial Detection Systems\n",
- "In this lab, we'll continue to explore how to mitigate algorithmic bias in facial recognition systems. In addition, we'll explore the notion of *uncertainty* in datasets, and learn how to reduce both data-based and model-based uncertainty.\n",
+ "# Laboratory 3: Debiasing, Uncertainty, and Robustness\n",
"\n",
- "As we've seen in lecture 5, bias and uncertainty underlie many common issues with machine learning models today, and these are not just limited to classification tasks. Automatically detecting and mitigating uncertainty is crucial to deploying fair and safe models. \n",
+ "# Part 2: Mitigating Bias and Uncertainty in Facial Detection Systems\n",
"\n",
- "In this lab, we'll be using [CAPSA](https://github.com/themis-ai/capsa/), a software package developed by [Themis AI](https://themisai.io/), which automatically *wraps* models to make them risk-aware and plugs into training workflows. We'll explore how we can use CAPSA to diagnose uncertainties, and then develop methods for automatically mitigating them.\n",
+ "In Lab 2, we defined a semi-supervised VAE (SS-VAE) to diagnose feature representation disparities and biases in facial detection systems. In Lab 3 Part 1, we gained experience with Capsa and its ability to build risk-aware models automatically through wrapping. Now in this lab, we will put these two together: using Capsa to build systems that can *automatically* uncover and mitigate bias and uncertainty in facial detection systems.\n",
"\n",
+ "As we have seen, automatically detecting and mitigating bias and uncertainty is crucial to deploying fair and safe models. Building off our foundation with [Capsa](https://github.com/themis-ai/capsa/), developed by [Themis AI](https://themisai.io/), we will now use Capsa for the facial detection problem, in order to diagnose risks in facial detection models. You will then design and create strategies to mitigate these risks, with goal of improving model performance across the entire facial detection dataset.\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:"
+ "**Your goal in this lab -- and the associated competition -- is to design a strategic solution for bias and uncertainty mitigation, using Capsa.** The approaches and solutions with oustanding performance will be recognized with outstanding prizes! Details on the submission process are at the end of this lab.\n",
+ "\n",
+ ""
]
},
{
@@ -23,7 +63,7 @@
"id": "6JTRoM7E71EU"
},
"source": [
- "Let's get started by installing the relevant dependencies:"
+ "Let's get started by installing the necessary dependencies:"
]
},
{
@@ -40,7 +80,7 @@
"base_uri": "https://localhost:8080/"
}
},
- "execution_count": 1,
+ "execution_count": null,
"outputs": [
{
"output_type": "stream",
@@ -74,7 +114,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": null,
"metadata": {
"id": "2PdAhs1371EU",
"outputId": "dd327495-e85d-4849-9487-4f71535b6cae",
@@ -118,22 +158,21 @@
"id": "6VKVqLb371EV"
},
"source": [
- "## 3.1 Datasets\n",
+ "# 3.1 Datasets\n",
"\n",
- "We'll be using the same datasets from lab 2 in this lab. Note that in this dataset, we've intentionally perturbed some of the samples in some ways (it's up to you to figure out how!) that are not necessarily present in the actual dataset. \n",
+ "Since we are again focusing on the facial detection problem, we will use the same datasets from Lab 2. To remind you, we have a dataset of positive examples (i.e., of faces) and a dataset of negative examples (i.e., of things that are not faces).\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",
+ "1. **Positive training data**: [CelebA Dataset](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html). A large-scale dataset (over 200K images) of celebrity faces. \n",
+ "2. **Negative training data**: [ImageNet](http://www.image-net.org/). A large-scale dataset with many images across many different categories. We will take negative examples from a variety of non-human categories.\n",
"\n",
- "Like before, 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.\n",
+ "We will evaluate trained models on an independent test dataset of face images to diagnose and mitigate potential issues with *bias, fairness, and confidence*. This will be a larger test dataset for evaluation purposes.\n",
"\n",
- "Also note that in this lab, we'll be using a much larger test dataset for evaluation purposes."
+ "We begin by importing these datasets. We have defined a `DatasetLoader` class that does a bit of data pre-processing to import the training data in a usable format."
]
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {
"id": "HIA6EA1D71EW",
"outputId": "162f7d36-81aa-4fb9-b265-af9a8fa9395d",
@@ -173,11 +212,9 @@
"source": [
"### Recap: Thinking about bias and uncertainty\n",
"\n",
- "Remember that 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",
- "In addition to thinking about bias, we want to detect areas of high *aleatoric* uncertainty in the dataset, which is defined as data noise: in the context of facial detection, this means that we may have very similar inputs with different labels-- think about the scenario where one face is labeled correctly as a positive, and another face is labeled incorrectly as a negative. \n",
+ "Remember that 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. We want to mitigate the effects of unwanted bias and uncertainty on the model's predictions and performance. and to thus show that this model does not suffer from any hidden bias.\n",
"\n",
- "Finally, we want to look at samples with high *epistemic*, or predictive, uncertainty. These may be samples that are anomalous or out of distribution, samples that contain adversarial noise, or samples that are \"harder\" to learn in some way. Importantly, epistemic uncertainty is not the same as bias! We may have well-represented samples that still have high epistemic uncertainty. "
+ "You may consider the three metrics introduced with Capsa: (1) representation bias, (2) data or aleatoric uncertainty, and (3) model or epistemic uncertainty. Note that all three of these metrics are different! For example, we can have well-represented examples that still have high epistemic uncertainty. "
]
},
{
@@ -202,7 +239,7 @@
},
{
"cell_type": "code",
- "execution_count": 50,
+ "execution_count": null,
"metadata": {
"id": "5hQb75Vm71EZ"
},
@@ -261,7 +298,7 @@
},
{
"cell_type": "code",
- "execution_count": 51,
+ "execution_count": null,
"metadata": {
"id": "zTat3K8E71Eb"
},
@@ -294,7 +331,7 @@
},
{
"cell_type": "code",
- "execution_count": 52,
+ "execution_count": null,
"metadata": {
"id": "i4JmvmMA71Ec"
},
@@ -315,7 +352,7 @@
},
{
"cell_type": "code",
- "execution_count": 53,
+ "execution_count": null,
"metadata": {
"id": "NmshVdLM71Ed",
"outputId": "873a0931-c1ae-43cc-f8f0-53b11f30ae4a",
@@ -387,7 +424,7 @@
},
{
"cell_type": "code",
- "execution_count": 55,
+ "execution_count": null,
"metadata": {
"id": "1dCqvPFH71Ed",
"outputId": "a3a19ee7-5c8e-4563-d6df-709f39dd357d",
@@ -420,7 +457,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": null,
"metadata": {
"id": "OYMRqq5E71Ee"
},
@@ -434,7 +471,7 @@
},
{
"cell_type": "code",
- "execution_count": 56,
+ "execution_count": null,
"metadata": {
"id": "UAYaFUj-71Ee",
"outputId": "39f4fe7d-ca14-47f4-d229-97d868dcc182",
@@ -473,7 +510,7 @@
},
{
"cell_type": "code",
- "execution_count": 57,
+ "execution_count": null,
"metadata": {
"id": "CnbR3qAF71Ef",
"outputId": "6a84a235-88b6-41e1-b616-17c3f9a0096b",
@@ -521,7 +558,7 @@
},
{
"cell_type": "code",
- "execution_count": 58,
+ "execution_count": null,
"metadata": {
"id": "DfzlOhWi71Ef",
"outputId": "65e20e7a-5e3e-4452-ae49-559a33a063ba",
@@ -580,7 +617,7 @@
"height": 153
}
},
- "execution_count": 33,
+ "execution_count": null,
"outputs": [
{
"output_type": "execute_result",
@@ -632,7 +669,7 @@
},
{
"cell_type": "code",
- "execution_count": 59,
+ "execution_count": null,
"metadata": {
"id": "AwGPvdZm71Eg"
},
@@ -646,7 +683,7 @@
},
{
"cell_type": "code",
- "execution_count": 60,
+ "execution_count": null,
"metadata": {
"id": "kB8Iqrfb71Eg",
"outputId": "dc16b7d3-a6f1-4542-dcf2-135e0737e80e",
@@ -685,7 +722,7 @@
},
{
"cell_type": "code",
- "execution_count": 62,
+ "execution_count": null,
"metadata": {
"id": "miu5h2Pc71Eh",
"outputId": "00442875-f1bb-4160-d11c-8cfdfb5765ac",
@@ -733,7 +770,7 @@
},
{
"cell_type": "code",
- "execution_count": 63,
+ "execution_count": null,
"metadata": {
"id": "rzQwvSvA71Eh",
"outputId": "4299761d-22b3-4f44-b9b3-d9194d04a6f2",
From 2ed641d1076165f92a3ae6324d25a6abb61aef4d Mon Sep 17 00:00:00 2001
From: Alexander Amini <xan.amini@gmail.com>
Date: Wed, 11 Jan 2023 01:53:49 -0500
Subject: [PATCH 12/22] Created using Colaboratory
From bcade5634da7df45641c4b0704b78d96fe255ffd Mon Sep 17 00:00:00 2001
From: Alexander Amini <xan.amini@gmail.com>
Date: Wed, 11 Jan 2023 01:59:10 -0500
Subject: [PATCH 13/22] Created using Colaboratory
---
lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb | 7 ++++---
1 file changed, 4 insertions(+), 3 deletions(-)
diff --git a/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb b/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb
index df416fed..c1e2691c 100644
--- a/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb
+++ b/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb
@@ -117,7 +117,7 @@
"source": [
"## 1.1 Dataset\n",
"\n",
- "We will build understanding of bias and uncertainty by training a neural network for a simple 2D regression task: modeling the function $y = x^3$. We will use `CAPSA` to analyze this dataset and the performance of the model. Noise and missing-ness will be injected into the dataset.\n",
+ "We will build understanding of bias and uncertainty by training a neural network for a simple 2D regression task: modeling the function $y = x^3$. We will use Capsa to analyze this dataset and the performance of the model. Noise and missing-ness will be injected into the dataset.\n",
"\n",
"Let's generate the dataset and visualize it:"
]
@@ -131,6 +131,7 @@
"outputs": [],
"source": [
"# Get the data for the cubic function, injected with noise and missing-ness\n",
+ "# This is just a toy dataset that we can use to test some of the wrappers on\n",
"def gen_data(x_min, x_max, n, train=True):\n",
" if train: \n",
" x = np.random.triangular(x_min, 2, x_max, size=(n, 1))\n",
@@ -211,7 +212,7 @@
")\n",
"\n",
"# Train the model for 30 epochs using model.fit().\n",
- "loss_history = dense_NN.fit(x_train, y_train, epochs=30, verbose=0)"
+ "loss_history = dense_NN.fit(x_train, y_train, epochs=30)"
]
},
{
@@ -319,7 +320,7 @@
")\n",
"\n",
"# Train the wrapped model for 30 epochs.\n",
- "loss_history_bias_wrap = bias_wrapped_dense_NN.fit(x_train, y_train, epochs=30, verbose=0)\n",
+ "loss_history_bias_wrap = bias_wrapped_dense_NN.fit(x_train, y_train, epochs=30)\n",
"\n",
"print(\"Done training model with Bias Wrapper!\")"
]
From cc9a46e24b5f531565fe564153f9c7b88d4a0fbb Mon Sep 17 00:00:00 2001
From: Alexander Amini <xan.amini@gmail.com>
Date: Wed, 11 Jan 2023 02:11:38 -0500
Subject: [PATCH 14/22] Created using Colaboratory
---
.../Lab3_Part_1_Introduction_to_CAPSA.ipynb | 14 +++++++++-----
1 file changed, 9 insertions(+), 5 deletions(-)
diff --git a/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb b/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb
index c1e2691c..09b73c8b 100644
--- a/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb
+++ b/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb
@@ -66,11 +66,13 @@
"\n",
"This lab introduces Capsa and its functionalities, to next build automated tools that use Capsa to mitigate the underlying issues of bias and uncertainty.\n",
"\n",
- "The core idea behind Capsa is that any deep learning model of interest can be ***wrapped*** -- just like wrapping a gift -- to be made ***aware of its own risks***. Risk is captured in representation bias, data uncertainty, and model uncertainty.\n",
+ "The core idea behind [Capsa](https://themisai.io/capsa/) is that any deep learning model of interest can be ***wrapped*** -- just like wrapping a gift -- to be made ***aware of its own risks***. Risk is captured in representation bias, data uncertainty, and model uncertainty.\n",
"\n",
"\n",
"\n",
- "This means that Capsa takes the user's original model as input, and modifies it minimally to create a risk-aware variant while preserving the model's underlying structure and training pipeline. Capsa is a one-line addition to any training workflow in TensorFlow. In this part of the lab, we'll apply Capsa's risk estimation methods to a simple regression problem to further explore the notions of bias and uncertainty. "
+ "This means that Capsa takes the user's original model as input, and modifies it minimally to create a risk-aware variant while preserving the model's underlying structure and training pipeline. Capsa is a one-line addition to any training workflow in TensorFlow. In this part of the lab, we'll apply Capsa's risk estimation methods to a simple regression problem to further explore the notions of bias and uncertainty. \n",
+ "\n",
+ "Please refer to [Capsa's documentation](https://themisai.io/capsa/) for additional details."
]
},
{
@@ -270,7 +272,7 @@
"\n",
"Now that we've seen what the predictions from this model look like, we will identify and quantify bias and uncertainty in this problem. We first consider bias.\n",
"\n",
- "Recall that *representation bias* reflects how likely combinations of features are to appear in a given dataset. Capsa calculates how likely combinations of features are by using a histogram estimation approach: the `capsa.HistogramWrapper`. For low-dimensional data, the `capsa.HistogramWrapper` bins the input directly into discrete categories and measures the density. \n",
+ "Recall that *representation bias* reflects how likely combinations of features are to appear in a given dataset. Capsa calculates how likely combinations of features are by using a histogram estimation approach: the `capsa.HistogramWrapper`. For low-dimensional data, the `capsa.HistogramWrapper` bins the input directly into discrete categories and measures the density. More details of the `HistogramWrapper` and how it can be used are [available here](https://themisai.io/capsa/api_documentation/HistogramWrapper.html).\n",
"\n",
"We start by taking our `dense_NN` and wrapping it with the `capsa.HistogramWrapper`:"
]
@@ -389,7 +391,7 @@
"\n",
"As introduced in Lecture 5 on Robust & Trustworthy Deep Learning, in regression we can estimate aleatoric uncertainty by training the model to predict both a target value and a variance for every input. Because we estimate both a mean and variance for every input, this method is called Mean Variance Estimation (MVE). MVE involves modifying the output layer to predict both the mean and variance, and changing the loss to reflect the prediction likelihood.\n",
"\n",
- "Capsa automatically implements these changes for us: we can wrap a given model using `capsa.MVEWrapper` to use MVE to estimate aleatoric uncertainty. All we have to do is define the model and the loss function to evaluate its predictions!\n",
+ "Capsa automatically implements these changes for us: we can wrap a given model using `capsa.MVEWrapper` to use MVE to estimate aleatoric uncertainty. All we have to do is define the model and the loss function to evaluate its predictions! More details of the `MVEWrapper` and how it can be used are [available here](https://themisai.io/capsa/api_documentation/MVEWrapper.html).\n",
"\n",
"Let's take our standard network, wrap it with `capsa.MVEWrapper`, build the wrapped model, and then train it for the regression task. Finally, we evaluate performance of the resulting model by quantifying the aleatoric uncertainty across the data space: "
]
@@ -467,7 +469,9 @@
"\n",
"Finally, we use Capsa for estimating the uncertainty underlying the model predictions -- the epistemic uncertainty. In this example, we'll use ensembles, which essentially copy the model `N` times and average predictions across all runs for a more robust prediction, and also calculate the variance of the `N` runs to estimate the uncertainty.\n",
"\n",
- "Capsa provides a neat wrapper, `capsa.EnsembleWrapper`, to make an ensemble from an input model. Just like with aleatoric estimation, we can take our standard dense network model, wrap it with `capsa.EnsembleWrapper`, build the wrapped model, and then train it for the regression task. Finally, we evaluate the resulting model by quantifying the epistemic uncertainty on the test data:"
+ "Capsa provides a neat wrapper, `capsa.EnsembleWrapper`, to make an ensemble from an input model. Just like with aleatoric estimation, we can take our standard dense network model, wrap it with `capsa.EnsembleWrapper`, build the wrapped model, and then train it for the regression task. More details of the `EnsembleWrapper` and how it can be used are [available here](https://themisai.io/capsa/api_documentation/EnsembleWrapper.html).\n",
+ "\n",
+ "Finally, we evaluate the resulting model by quantifying the epistemic uncertainty on the test data:"
]
},
{
From 0fc4e7964e80ea7962dc8782e6a44b33be0b5282 Mon Sep 17 00:00:00 2001
From: Ava Amini <apsoleimany@gmail.com>
Date: Wed, 11 Jan 2023 02:13:42 -0500
Subject: [PATCH 15/22] updating links
---
lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb b/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb
index 35533914..339db513 100644
--- a/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb
+++ b/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb
@@ -7,9 +7,9 @@
" <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/2023/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb\">\n",
+ " <td align=\"center\"><a target=\"_blank\" href=\"https://colab.research.google.com/github/aamini/introtodeeplearning/blob/2023/lab3/solutions/Lab3_Bias_And_Uncertainty.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/2023/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb\">\n",
+ " <td align=\"center\"><a target=\"_blank\" href=\"https://github.com/aamini/introtodeeplearning/blob/2023/lab3/solutions/Lab3_Bias_And_Uncertainty.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",
From f94db44a440e1f6597c19da525526e6f27bd4537 Mon Sep 17 00:00:00 2001
From: Alexander Amini <xan.amini@gmail.com>
Date: Wed, 11 Jan 2023 02:32:49 -0500
Subject: [PATCH 16/22] v0.4.0
---
setup.py | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/setup.py b/setup.py
index 90d0df82..9f2ffd0b 100644
--- a/setup.py
+++ b/setup.py
@@ -22,13 +22,13 @@ def get_dist(pkgname):
setup(
name = 'mitdeeplearning', # How you named your package folder (MyLib)
packages = ['mitdeeplearning'], # Chose the same as "name"
- version = '0.3.0', # Start with a small number and increase it with every change you make
+ version = '0.4.0', # Start with a small number and increase it with every change you make
license='MIT', # Chose a license from here: https://help.github.com/articles/licensing-a-repository
description = 'Official software labs for MIT Introduction to Deep Learning (http://introtodeeplearning.com)', # Give a short description about your library
author = 'Alexander Amini', # Type in your name
author_email = 'introtodeeplearning-staff@mit.edu', # Type in your E-Mail
url = 'http://introtodeeplearning.com', # Provide either the link to your github or to your website
- download_url = 'https://github.com/aamini/introtodeeplearning/archive/v0.3.0.tar.gz', # I explain this later on
+ download_url = 'https://github.com/aamini/introtodeeplearning/archive/v0.4.0.tar.gz', # I explain this later on
keywords = ['deep learning', 'neural networks', 'tensorflow', 'introduction'], # Keywords that define your package best
install_requires=install_deps,
classifiers=[
From 769b5753b31ddc16f21dfd9a98ddf588ecda7cae Mon Sep 17 00:00:00 2001
From: Ava Amini <apsoleimany@gmail.com>
Date: Wed, 11 Jan 2023 03:10:38 -0500
Subject: [PATCH 17/22] completed bias writeup
---
.../solutions/Lab3_Bias_And_Uncertainty.ipynb | 299 ++++++------------
1 file changed, 89 insertions(+), 210 deletions(-)
diff --git a/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb b/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb
index 339db513..ee46125e 100644
--- a/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb
+++ b/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb
@@ -7,9 +7,9 @@
" <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/2023/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb\">\n",
+ " <td align=\"center\"><a target=\"_blank\" href=\"https://colab.research.google.com/github/aamini/introtodeeplearning/blob/2023/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.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/2023/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb\">\n",
+ " <td align=\"center\"><a target=\"_blank\" href=\"https://github.com/aamini/introtodeeplearning/blob/2023/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.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",
@@ -48,9 +48,9 @@
"\n",
"# Part 2: Mitigating Bias and Uncertainty in Facial Detection Systems\n",
"\n",
- "In Lab 2, we defined a semi-supervised VAE (SS-VAE) to diagnose feature representation disparities and biases in facial detection systems. In Lab 3 Part 1, we gained experience with Capsa and its ability to build risk-aware models automatically through wrapping. Now in this lab, we will put these two together: using Capsa to build systems that can *automatically* uncover and mitigate bias and uncertainty in facial detection systems.\n",
+ "In Lab 2, we defined a semi-supervised VAE (SS-VAE) to diagnose feature representation disparities and biases in facial detection systems. In Lab 3 Part 1, we gained experience with [Capsa](https://github.com/themis-ai/capsa/) and its ability to build risk-aware models automatically through wrapping. Now in this lab, we will put these two together: using Capsa to build systems that can *automatically* uncover and mitigate bias and uncertainty in facial detection systems.\n",
"\n",
- "As we have seen, automatically detecting and mitigating bias and uncertainty is crucial to deploying fair and safe models. Building off our foundation with [Capsa](https://github.com/themis-ai/capsa/), developed by [Themis AI](https://themisai.io/), we will now use Capsa for the facial detection problem, in order to diagnose risks in facial detection models. You will then design and create strategies to mitigate these risks, with goal of improving model performance across the entire facial detection dataset.\n",
+ "As we have seen, automatically detecting and mitigating bias and uncertainty is crucial to deploying fair and safe models. Building off our foundation with Capsa, developed by [Themis AI](https://themisai.io/), we will now use Capsa for the facial detection problem, in order to diagnose risks in facial detection models. You will then design and create strategies to mitigate these risks, with goal of improving model performance across the entire facial detection dataset.\n",
"\n",
"**Your goal in this lab -- and the associated competition -- is to design a strategic solution for bias and uncertainty mitigation, using Capsa.** The approaches and solutions with oustanding performance will be recognized with outstanding prizes! Details on the submission process are at the end of this lab.\n",
"\n",
@@ -210,11 +210,11 @@
"id": "cREmhMWJ71EX"
},
"source": [
- "### Recap: Thinking about bias and uncertainty\n",
+ "### Building robustness to bias and uncertainty\n",
"\n",
- "Remember that 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. We want to mitigate the effects of unwanted bias and uncertainty on the model's predictions and performance. and to thus show that this model does not suffer from any hidden bias.\n",
+ "Remember that 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. We want to mitigate the effects of unwanted bias and uncertainty on the model's predictions and performance. Your goal is to build the best-performing, most robust model, one that achieves high classification accuracy across the entire test dataset.\n",
"\n",
- "You may consider the three metrics introduced with Capsa: (1) representation bias, (2) data or aleatoric uncertainty, and (3) model or epistemic uncertainty. Note that all three of these metrics are different! For example, we can have well-represented examples that still have high epistemic uncertainty. "
+ "To achieve this, you may want to consider the three metrics introduced with Capsa: (1) representation bias, (2) data or aleatoric uncertainty, and (3) model or epistemic uncertainty. Note that all three of these metrics are different! For example, we can have well-represented examples that still have high epistemic uncertainty. Think about how you may use these metrics to improve the performance of your model."
]
},
{
@@ -223,18 +223,13 @@
"id": "1NhotGiT71EY"
},
"source": [
- "# 3.2 Bias\n",
+ "# 3.2 Diagnosing algorithmic bias with Capsa\n",
"\n",
- "In the previous lab, we used a variational autoencoder (VAE) to automatically learn the latent structure of our database, and we developed a scoring mechanism for samples to determine their bias. In this lab, we'll show that we can use CAPSA to do the same thing in one line! Then, our goal will be to continue our implementation of the DB-VAE and 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)."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "niy4he0m71EZ"
- },
- "source": [
- "Just like the last lab, let's define a standard classifier that we'll use as the base encoder of our network."
+ "In Lab 2, we built a semi-supervised variational autoencoder (SS-VAE) to learn the latent structure of our database and to uncover feature representation disparities, inspired by the approach of [uncover hidden biases](http://introtodeeplearning.com/AAAI_MitigatingAlgorithmicBias.pdf). In this lab, we'll show that we can use Capsa to build the same VAE in one line!\n",
+ "\n",
+ "This sets the foundation for quantifying a key risk metric -- representation bias -- for the facial detection problem. In working to improve your model's performance, you will want to consider representation bias carefully and think about how you could mitigate the effect of representation bias.\n",
+ "\n",
+ "Just like in Lab 2, we begin by defining a standard CNN-based classifier. We will then use Capsa to wrap the model and build the risk-aware VAE variant."
]
},
{
@@ -245,7 +240,7 @@
},
"outputs": [],
"source": [
- "### Define the CNN model ###\n",
+ "### Define the CNN classifier model ###\n",
"\n",
"'''Function to define a standard CNN model'''\n",
"def make_standard_classifier(n_outputs=1, n_filters=12):\n",
@@ -284,16 +279,19 @@
"id": "LgTG6buf71Ea"
},
"source": [
- "Let's use CAPSA's `HistogramVAEWrapper` to analyze the latent space distribution as we did previously. The `HistogramVAEWrapper` constructs a histogram with `num_bins` bins across every dimension of the latent space, and then calculates the joint probability of every sample according to the histograms. The samples with the lowest joint probability have the lowest bias, and we want to oversample these. Conversely, we want to undersample the areas of the dataset with the highest bias."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "FivHOdGE71Ea"
- },
- "source": [
- "The `HistogramVAEWrapper` class takes in a number of arguments: namely, the number of bins we want to discretize our distribution into, the number of samples we want to track at any given point, and whether we're using the output of a hidden layer (good for higher-dimensional data) or the input data itself (good for lower-dimensional data). Since this is a variational autoencoder, we need to also pass in a decoder. Let's define the same decoder as the previous lab:"
+ "### Capsa's `HistogramVAEWrapper`\n",
+ "\n",
+ "With our base classifier Capsa allows us to automatically define a VAE implementing that base classifier. Capsa's [`HistogramVAEWrapper`](https://themisai.io/capsa/api_documentation/HistogramVAEWrapper.html) builds this VAE to analyze the latent space distribution, just as we did in Lab 2. \n",
+ "\n",
+ "Specifically, `capsa.HistogramVAEWrapper` constructs a histogram with `num_bins` bins across every dimension of the latent space, and then calculates the joint probability of every sample according to the constructed histograms. The samples with the lowest joint probability have the lowest representation; the samples with the highest joint probability have the highest representation.\n",
+ "\n",
+ "`capsa.HistogramVAEWrapper` takes in a number of arguments including:\n",
+ "1. `base_model`: the model to be transformed into the risk-aware variant.\n",
+ "2. `num_bins`: the number of bins we want to discretize our distribution into. \n",
+ "2. `queue_size`: the number of samples we want to track at any given point.\n",
+ "3. `decoder`: the decoder architecture for the VAE.\n",
+ "\n",
+ "We define the same decoder as in Lab 2:"
]
},
{
@@ -304,9 +302,12 @@
},
"outputs": [],
"source": [
+ "### Define the decoder architecture for the facial detection VAE ###\n",
+ "\n",
"def make_face_decoder_network(n_filters=12):\n",
" # Functionally define the different layer types we will use\n",
- " Conv2DTranspose = functools.partial(tf.keras.layers.Conv2DTranspose, padding='same', activation='relu')\n",
+ " Conv2DTranspose = functools.partial(tf.keras.layers.Conv2DTranspose, \n",
+ " 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",
@@ -329,122 +330,57 @@
" return decoder"
]
},
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "i4JmvmMA71Ec"
- },
- "outputs": [],
- "source": [
- "model = make_standard_classifier()\n",
- "wrapped_model = HistogramVAEWrapper(model, num_bins=5, queue_size=20000, latent_dim = 32, decoder=make_face_decoder_network())"
- ]
- },
{
"cell_type": "markdown",
"metadata": {
- "id": "A527wdyV71Ec"
+ "id": "SzFGcrhv71Ed"
},
"source": [
- "Now, let's train the wrapped classifier! As we did in the previous lab, in addition to updating the weights of the model, the wrapped classifier also tracks feature distributions. We can use the joint probabilities of these feature distributions to determine the bias of a given sample in this dataset. We'll make use of the `Model.fit` API here, but note that we can achieve the same behavior with a custom training loop as well."
+ "We are ready to create the wrapped model using `capsa.HistogramVAEWrapper` by passing in the relevant arguments!\n",
+ "\n",
+ "Just like in the wrappers in the Introduction to Capsa lab, we can take our standard CNN classifier, wrap it with `capsa.HistogramVAEWrapper`, build the wrapped model. The wrapper then enablings semi-supervised training for the facial detection task. As the wrapped model trains, the classifier weights are updated, and the VAE-wrapped model learns to track feature distributions over the latent space. More details of the `HistogramVAEWrapper` and how it can be used are [available here](https://themisai.io/capsa/api_documentation/HistogramVAEWrapper.html).\n",
+ "\n",
+ "We can then evaluate the representation bias of the classifier on the test dataset. By calling the `wrapped_model` on our test data, we can automatically generate representation bias scores that are normally manually calculated. Let's wrap our base CNN classifier using Capsa, train and build the resulting model, and start to process the test data: "
]
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "NmshVdLM71Ed",
- "outputId": "873a0931-c1ae-43cc-f8f0-53b11f30ae4a",
- "colab": {
- "base_uri": "https://localhost:8080/"
- }
- },
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Epoch 1/6\n"
- ]
- },
- {
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "WARNING:tensorflow:Gradients do not exist for variables ['dense_21/kernel:0', 'dense_21/bias:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n",
- "WARNING:tensorflow:Gradients do not exist for variables ['dense_21/kernel:0', 'dense_21/bias:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n"
- ]
- },
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "2747/2747 [==============================] - 30s 10ms/step - vae_compiled_loss: 0.5694 - vae_compiled_binary_accuracy: 0.7284 - vae_wrapper_loss: 983.3300\n",
- "Epoch 2/6\n",
- "2747/2747 [==============================] - 32s 12ms/step - vae_compiled_loss: 0.3165 - vae_compiled_binary_accuracy: 0.8775 - vae_wrapper_loss: 397.1720\n",
- "Epoch 3/6\n",
- "2747/2747 [==============================] - 29s 10ms/step - vae_compiled_loss: 0.2387 - vae_compiled_binary_accuracy: 0.9106 - vae_wrapper_loss: 330.7421\n",
- "Epoch 4/6\n",
- "2747/2747 [==============================] - 28s 10ms/step - vae_compiled_loss: 0.1949 - vae_compiled_binary_accuracy: 0.9289 - vae_wrapper_loss: 300.7650\n",
- "Epoch 5/6\n",
- "2747/2747 [==============================] - 29s 10ms/step - vae_compiled_loss: 0.1605 - vae_compiled_binary_accuracy: 0.9416 - vae_wrapper_loss: 280.1750\n",
- "Epoch 6/6\n",
- "2747/2747 [==============================] - 33s 12ms/step - vae_compiled_loss: 0.1387 - vae_compiled_binary_accuracy: 0.9469 - vae_wrapper_loss: 253.5578\n"
- ]
- }
- ],
"source": [
- "learning_rate = 5e-4\n",
+ "### Estimating representation bias with Capsa HistogramVAEWrapper ###\n",
+ "\n",
+ "model = make_standard_classifier()\n",
+ "# Wrap the CNN classifier for latent encoding with a VAE wrapper\n",
+ "wrapped_model = capsa.HistogramVAEWrapper(model, num_bins=5, queue_size=20000, \n",
+ " latent_dim = 32, decoder=make_face_decoder_network())\n",
"\n",
- "# compile model using desired optimizers and losses\n",
+ "# Build the model for classification, defining the loss function, optimizer, and metrics\n",
"wrapped_model.compile(\n",
- " optimizer=tf.keras.optimizers.Adam(learning_rate),\n",
- " loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n",
- " metrics=[tf.keras.metrics.BinaryAccuracy()],\n",
+ " optimizer=tf.keras.optimizers.Adam(learning_rate=5e-4),\n",
+ " loss=tf.keras.losses.BinaryCrossentropy(from_logits=True), # for classification\n",
+ " metrics=[tf.keras.metrics.BinaryAccuracy()], # for classification\n",
" run_eagerly=True\n",
")\n",
"\n",
- "# fit the model to our training data\n",
+ "# Train the wrapped model for 6 epochs by fitting to the training data\n",
"history = wrapped_model.fit(\n",
" train_loader,\n",
" epochs=6,\n",
" batch_size=batch_size,\n",
- " )"
- ]
- },
- {
- "cell_type": "markdown",
+ " )\n",
+ "\n",
+ "## Evaluation\n",
+ "\n",
+ "# Get all faces from the testing dataset\n",
+ "test_imgs = test_loader.get_all_faces()\n",
+ "\n",
+ "# Call the Capsa-wrapped classifier to generate outputs: predictions, uncertainty, and bias!\n",
+ "predictions, uncertainty, bias = wrapped_model.predict(test_imgs, batch_size=512)"
+ ],
"metadata": {
- "id": "SzFGcrhv71Ed"
+ "id": "YqsBHBf3yUlm"
},
- "source": [
- "Let's see what the bias looks like on our test dataset! Note that in this lab, we're using a much larger test dataset than the one in Lab 2. By calling the `wrapped_classifier` on our test set, we can automatically generate the same bias scores that we manually calculated in the last lab. "
- ]
- },
- {
- "cell_type": "code",
"execution_count": null,
- "metadata": {
- "id": "1dCqvPFH71Ed",
- "outputId": "a3a19ee7-5c8e-4563-d6df-709f39dd357d",
- "colab": {
- "base_uri": "https://localhost:8080/"
- }
- },
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "22/22 [==============================] - 7s 316ms/step\n"
- ]
- }
- ],
- "source": [
- "test_imgs = test_loader.get_all_faces() # Get all faces from the testing dataset\n",
- "predictions, uncertainty, bias = wrapped_model.predict(test_imgs, batch_size=512) # use CAPSA-wrapped classifier to obtain estimates for bias and the output"
- ]
+ "outputs": []
},
{
"cell_type": "markdown",
@@ -452,7 +388,9 @@
"id": "Xtc0kjE471Ee"
},
"source": [
- "Now, we have an estimate for the bias score! Let's visualize what the samples with the highest bias and those with the lowest bias look like. Before you run the next code block, which faces would you expect to be underrepresented in the dataset? Which ones do you think will be overrepresented?"
+ "### Analyzing the representation bias\n",
+ "\n",
+ "Now, we have an estimate for the representation bias score! We can analyze the representation scores to start to think about manifestations of bias in the facial detection dataset. Before you run the next code block, which faces would you expect to be underrepresented in the dataset? Which ones do you think will be overrepresented?"
]
},
{
@@ -463,88 +401,19 @@
},
"outputs": [],
"source": [
- "indices = np.argsort(bias, axis=None) \n",
- "sorted_images = test_imgs[indices] # sort images from lowest to highest bias\n",
- "sorted_biases = bias[indices]\n",
- "sorted_preds = predictions[indices]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "UAYaFUj-71Ee",
- "outputId": "39f4fe7d-ca14-47f4-d229-97d868dcc182",
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 287
- }
- },
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- "<matplotlib.image.AxesImage at 0x7f8c2c232fd0>"
- ]
- },
- "metadata": {},
- "execution_count": 56
- },
- {
- "output_type": "display_data",
- "data": {
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ],
- "image/png": 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\n"
- },
- "metadata": {
- "needs_background": "light"
- }
- }
- ],
- "source": [
- "plt.imshow(mdl.util.create_grid_of_images(sorted_images[:20], (4, 5))) # These are the images with the lowest representation in our test dataset"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "CnbR3qAF71Ef",
- "outputId": "6a84a235-88b6-41e1-b616-17c3f9a0096b",
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 287
- }
- },
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- "<matplotlib.image.AxesImage at 0x7f8c2d4f26a0>"
- ]
- },
- "metadata": {},
- "execution_count": 57
- },
- {
- "output_type": "display_data",
- "data": {
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ],
- "image/png": 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\n"
- },
- "metadata": {
- "needs_background": "light"
- }
- }
- ],
- "source": [
- "plt.imshow(mdl.util.create_grid_of_images(sorted_images[-20:], (4, 5))) # These are the samples with the highest representation in our test dataset"
+ "### Analyzing representation bias scores ###\n",
+ "\n",
+ "# Sort according to lowest to highest representation scores\n",
+ "indices = np.argsort(bias, axis=None) # sort the score values themselves\n",
+ "sorted_images = test_imgs[indices] # sort images from lowest to highest representations\n",
+ "sorted_biases = bias[indices] # order the representation bias scores\n",
+ "sorted_preds = predictions[indices] # order the prediction values\n",
+ "\n",
+ "# Visualize the 20 images with the lowest representation in the test dataset\n",
+ "plt.imshow(mdl.util.create_grid_of_images(sorted_images[:20], (4, 5)))\n",
+ "\n",
+ "# Visualize the 20 images with the highest representation in the test dataset\n",
+ "plt.imshow(mdl.util.create_grid_of_images(sorted_images[-20:0], (4, 5)))"
]
},
{
@@ -553,7 +422,7 @@
"id": "-JYmGMJF71Ef"
},
"source": [
- "Now, we'll spend some time looking at the bias by *percentile* in our dataset. First, let's plot the accuracy as the bias increases. Remember that we use bias to quantify the level of representation in our dataset, so increasing bias means increasing representation. How do you expect the accuracy to change?"
+ "We can also quantify how the representation density relates to the classification accuracy by plotting the two against each other: "
]
},
{
@@ -600,7 +469,7 @@
"id": "i8ERzg2-71Ef"
},
"source": [
- "Now, for a super interesting visualization, let's look at the *percentiles* of bias: what does the average face in the 10th percentile of bias look like? What about the 90th percentile? What changes across these faces?"
+ "These representations scores relate back to data examples, so we can visualize what the average face looks like for a gien *percentile* of representation density:"
]
},
{
@@ -649,7 +518,13 @@
"id": "cRNV-3SU71Eg"
},
"source": [
- "Now that we know what the bias in our dataset looks like, let's adaptively resample from our dataset! Since we can calculate this score on-the-fly *during training*, we can adjust the probability of samples being chosen. But first, let's also take a look at the *epistemic* uncertainty of this dataset"
+ "#### **TODO: Scoring representation densities with Capsa**\n",
+ "\n",
+ "Write short answers to the questions below to complete the `TODO`s:\n",
+ "\n",
+ "1. How does accuracy relate to the representation score? From this relationship, what can you determine about the bias underlying the dataset?\n",
+ "2. What does the average face in the 10th percentile of representation density look like (i.e., the face for which 10% of the data have lower probability of occuring)? What about the 90th percentile? What changes across these faces?\n",
+ "3. What could be potential limitations of the `HistogramVAEWrapper` approach as it is implemented now?"
]
},
{
@@ -822,6 +697,10 @@
},
"source": [
"# 3.4 Resampling based on risk metrics\n",
+ " Then, our goal will be to continue our implementation of the DB-VAE and 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",
+ " and we want to oversample these. Conversely, we want to undersample the areas of the dataset with the highest bias.\n",
+ "\n",
"\n",
"Finally, let's use both the bias score and the reconstruction loss to adaptively resample from our dataset. Since we can calculate this score on-the-fly *during training*, we can adjust the probability of samples being chosen. \n",
"\n",
From faeab1424ef3b1000ed81bb9b9408b358596fab7 Mon Sep 17 00:00:00 2001
From: Ava Amini <apsoleimany@gmail.com>
Date: Wed, 11 Jan 2023 03:31:14 -0500
Subject: [PATCH 18/22] finish epistemic uq section
---
.../solutions/Lab3_Bias_And_Uncertainty.ipynb | 140 ++++++------------
1 file changed, 45 insertions(+), 95 deletions(-)
diff --git a/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb b/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb
index ee46125e..62139c3b 100644
--- a/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb
+++ b/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb
@@ -223,7 +223,7 @@
"id": "1NhotGiT71EY"
},
"source": [
- "# 3.2 Diagnosing algorithmic bias with Capsa\n",
+ "# 3.2 Risk-aware facial detection with Capsa\n",
"\n",
"In Lab 2, we built a semi-supervised variational autoencoder (SS-VAE) to learn the latent structure of our database and to uncover feature representation disparities, inspired by the approach of [uncover hidden biases](http://introtodeeplearning.com/AAAI_MitigatingAlgorithmicBias.pdf). In this lab, we'll show that we can use Capsa to build the same VAE in one line!\n",
"\n",
@@ -340,7 +340,7 @@
"\n",
"Just like in the wrappers in the Introduction to Capsa lab, we can take our standard CNN classifier, wrap it with `capsa.HistogramVAEWrapper`, build the wrapped model. The wrapper then enablings semi-supervised training for the facial detection task. As the wrapped model trains, the classifier weights are updated, and the VAE-wrapped model learns to track feature distributions over the latent space. More details of the `HistogramVAEWrapper` and how it can be used are [available here](https://themisai.io/capsa/api_documentation/HistogramVAEWrapper.html).\n",
"\n",
- "We can then evaluate the representation bias of the classifier on the test dataset. By calling the `wrapped_model` on our test data, we can automatically generate representation bias scores that are normally manually calculated. Let's wrap our base CNN classifier using Capsa, train and build the resulting model, and start to process the test data: "
+ "We can then evaluate the representation bias of the classifier on the test dataset. By calling the `wrapped_model` on our test data, we can automatically generate representation bias and uncertainty scores that are normally manually calculated. Let's wrap our base CNN classifier using Capsa, train and build the resulting model, and start to process the test data: "
]
},
{
@@ -384,14 +384,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "id": "Xtc0kjE471Ee"
- },
"source": [
- "### Analyzing the representation bias\n",
+ "# 3.3 Analyzing representation bias with Capsa\n",
"\n",
- "Now, we have an estimate for the representation bias score! We can analyze the representation scores to start to think about manifestations of bias in the facial detection dataset. Before you run the next code block, which faces would you expect to be underrepresented in the dataset? Which ones do you think will be overrepresented?"
- ]
+ "From the above output, we have an estimate for the representation bias score! We can analyze the representation scores to start to think about manifestations of bias in the facial detection dataset. Before you run the next code block, which faces would you expect to be underrepresented in the dataset? Which ones do you think will be overrepresented?"
+ ],
+ "metadata": {
+ "id": "629ng-_H6WOk"
+ }
},
{
"cell_type": "code",
@@ -422,7 +422,7 @@
"id": "-JYmGMJF71Ef"
},
"source": [
- "We can also quantify how the representation density relates to the classification accuracy by plotting the two against each other: "
+ "We can also quantify how the representation density relates to the classification accuracy by plotting the two against each other:"
]
},
{
@@ -533,105 +533,49 @@
"id": "ww5lx7ue71Eg"
},
"source": [
- "# 3.3 Epistemic Uncertainty\n",
+ "# 3.4 Analyzing epistemic uncertainty with Capsa\n",
"\n",
- "Recall from lecture that *epistemic* uncertainty, or a model's uncertainty in its prediction, can arise from out of distribution data, or samples that are harder to learn. This does not necessarily correlate with bias! Imagine the scenario of training an object detector for self-driving cars: even if the model is presented with many cluttered scenes, these samples still may be harder to learn than scenes with very few objects in them. In this part of the lab, we'll analyze the epistemic uncertainty of the VAE that we've trained on this dataset. \n",
+ "Recall that *epistemic* uncertainty, or a model's uncertainty in its prediction, can arise from out-of-distribution data, missing data, or samples that are harder to learn. This does not necessarily correlate with representation bias! Imagine the scenario of training an object detector for self-driving cars: even if the model is presented with many cluttered scenes, these samples still may be harder to learn than scenes with very few objects in them.\n",
"\n",
- "From lecture 6, we saw that most methods of estimating epistemic uncertainty are *sampling-based*, but we can also use *reconstruction-based* methods. If a model is unable to provide a good reconstruction for a given data point, it has not learned that area of the underlying data distribution well, and therefore has high epistemic uncertainty. \n",
+ "We will now use our VAE-wrapped facial detection classifier to analyze and estimate the epistemic uncertainty of the model trained on the facial detection task.\n",
"\n",
- "Since we've already used a VAE to calculate the histograms for bias quantification, we can use the same VAE to shed insight into epistemic uncertainty! CAPSA helps us do exactly that: when call the model, we get the bias, reconstruction loss, and prediction for every sample."
+ "While most methods of estimating epistemic uncertainty are *sampling-based*, we can also use ***reconstruction-based*** methods -- like using VAEs -- to estimate epistemic uncertainty. If a model is unable to provide a good reconstruction for a given data point, it has not learned that area of the underlying data distribution well, and therefore has high epistemic uncertainty.\n",
+ "\n"
]
},
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "AwGPvdZm71Eg"
- },
- "outputs": [],
+ "cell_type": "markdown",
"source": [
- "epistemic_indices = np.argsort(uncertainty, axis=None) \n",
- "epistemic_images = test_imgs[epistemic_indices] # sort images by reconstruction loss this time!\n",
- "sorted_epistemic = uncertainty[epistemic_indices]\n",
- "sorted_epistemic_preds = predictions[epistemic_indices]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "kB8Iqrfb71Eg",
- "outputId": "dc16b7d3-a6f1-4542-dcf2-135e0737e80e",
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 287
- }
- },
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- "<matplotlib.image.AxesImage at 0x7f8c2d410580>"
- ]
- },
- "metadata": {},
- "execution_count": 60
- },
- {
- "output_type": "display_data",
- "data": {
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ],
- "image/png": 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\n"
- },
- "metadata": {
- "needs_background": "light"
- }
- }
+ "Since we've already used the `HistogramVAEWrapper` to calculate the histograms for representation bias quantification, we can use the exact same VAE wrapper to shed insight into epistemic uncertainty! Capsa helps us do exactly that. When we called the model, we returned the classification prediction, uncertainty, and bias for every sample:\n",
+ "`predictions, uncertainty, bias = wrapped_model.predict(test_imgs, batch_size=512)`.\n",
+ "\n",
+ "Let's analyze these estimated uncertainties:"
],
- "source": [
- "plt.imshow(mdl.util.create_grid_of_images(epistemic_images[:20], (4, 5))) # samples with the LEAST epistemic uncertainty"
- ]
+ "metadata": {
+ "id": "NEfeWo2p7wKm"
+ }
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
- "id": "miu5h2Pc71Eh",
- "outputId": "00442875-f1bb-4160-d11c-8cfdfb5765ac",
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 287
- }
+ "id": "AwGPvdZm71Eg"
},
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- "<matplotlib.image.AxesImage at 0x7f8c2d5f6430>"
- ]
- },
- "metadata": {},
- "execution_count": 62
- },
- {
- "output_type": "display_data",
- "data": {
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ],
- "image/png": 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\n"
- },
- "metadata": {
- "needs_background": "light"
- }
- }
- ],
+ "outputs": [],
"source": [
- "plt.imshow(mdl.util.create_grid_of_images(epistemic_images[-20:], (4, 5))) # samples with the MOST epistemic uncertainty"
+ "### Analyzing epistemic uncertainty estimates ###\n",
+ "\n",
+ "# Sort according to epistemic uncertainty estimates\n",
+ "epistemic_indices = np.argsort(uncertainty, axis=None) # sort the uncertainty values\n",
+ "epistemic_images = test_imgs[epistemic_indices] # sort images from lowest to highest uncertainty\n",
+ "sorted_epistemic = uncertainty[epistemic_indices] # order the uncertainty scores\n",
+ "sorted_epistemic_preds = predictions[epistemic_indices] # order the prediction values\n",
+ "\n",
+ "# Visualize the 20 images with the LEAST epistemic uncertainty\n",
+ "plt.imshow(mdl.util.create_grid_of_images(epistemic_images[:20], (4, 5)))\n",
+ "\n",
+ "# Visualize the 20 images with the MOST epistemic uncertainty\n",
+ "plt.imshow(mdl.util.create_grid_of_images(epistemic_images[:20], (4, 5)))"
]
},
{
@@ -640,7 +584,7 @@
"id": "L0dA8EyX71Eh"
},
"source": [
- "Let's run the same analysis: check how the accuracy varies with epistemic uncertainty!"
+ "We quantify how the epistemic uncertainty relates to the classification accuracy by plotting the two against each other:"
]
},
{
@@ -687,7 +631,13 @@
"id": "iyn0IE6x71Eh"
},
"source": [
- "How do these compare to the bias plots? Was this expected or unexpected?"
+ "#### **TODO: Estimating epistemic uncertainties with Capsa**\n",
+ "\n",
+ "Write short answers to the questions below to complete the `TODO`s:\n",
+ "\n",
+ "1. How does accuracy relate to the epistemic uncertainty?\n",
+ "2. How do the results for epistemic uncertainty compare to the results for representation bias? Was this expected or unexpted? Why?\n",
+ "3. What may be instances in the facial detection task that could have high representation density but also high uncertainty? "
]
},
{
From ddea9969cdee9aca28842379916ee5b1c9e91d90 Mon Sep 17 00:00:00 2001
From: Ava Amini <apsoleimany@gmail.com>
Date: Wed, 11 Jan 2023 03:34:17 -0500
Subject: [PATCH 19/22] fixing links
---
lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb b/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb
index 62139c3b..00d6065b 100644
--- a/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb
+++ b/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb
@@ -7,9 +7,9 @@
" <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/2023/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb\">\n",
+ " <td align=\"center\"><a target=\"_blank\" href=\"https://colab.research.google.com/github/aamini/introtodeeplearning/blob/2023/lab3/solutions/Lab3_Bias_And_Uncertainty.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/2023/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb\">\n",
+ " <td align=\"center\"><a target=\"_blank\" href=\"https://github.com/aamini/introtodeeplearning/blob/2023/lab3/solutions/Lab3_Bias_And_Uncertainty.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",
From b2ba98253b7de2b81eedc75cb1eb0c1ccc7a859a Mon Sep 17 00:00:00 2001
From: Alexander Amini <xan.amini@gmail.com>
Date: Wed, 11 Jan 2023 03:49:30 -0500
Subject: [PATCH 20/22] clearing outputs
---
.../solutions/Lab3_Bias_And_Uncertainty.ipynb | 369 +++++-------------
1 file changed, 101 insertions(+), 268 deletions(-)
diff --git a/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb b/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb
index 00d6065b..ea538ee0 100644
--- a/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb
+++ b/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb
@@ -7,9 +7,9 @@
" <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/2023/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb\">\n",
+ " <td align=\"center\"><a target=\"_blank\" href=\"https://colab.research.google.com/github/aamini/introtodeeplearning/blob/2023/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.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/2023/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb\">\n",
+ " <td align=\"center\"><a target=\"_blank\" href=\"https://github.com/aamini/introtodeeplearning/blob/2023/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.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",
@@ -66,72 +66,13 @@
"Let's get started by installing the necessary dependencies:"
]
},
- {
- "cell_type": "code",
- "source": [
- "!git clone -b 2023 https://github.com/aamini/introtodeeplearning.git\n",
- "%cd introtodeeplearning/\n",
- "%pip install -e ."
- ],
- "metadata": {
- "id": "3pzGVPrh-4LQ",
- "outputId": "2049b9c3-1816-43a3-acda-fbd18df44e7c",
- "colab": {
- "base_uri": "https://localhost:8080/"
- }
- },
- "execution_count": null,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Cloning into 'introtodeeplearning'...\n",
- "remote: Enumerating objects: 2354, done.\u001b[K\n",
- "remote: Counting objects: 100% (337/337), done.\u001b[K\n",
- "remote: Compressing objects: 100% (162/162), done.\u001b[K\n",
- "remote: Total 2354 (delta 207), reused 284 (delta 172), pack-reused 2017\u001b[K\n",
- "Receiving objects: 100% (2354/2354), 136.06 MiB | 27.14 MiB/s, done.\n",
- "Resolving deltas: 100% (1334/1334), done.\n",
- "/content/introtodeeplearning\n",
- "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
- "Obtaining file:///content/introtodeeplearning\n",
- " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
- "Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from mitdeeplearning==0.3.0) (1.21.6)\n",
- "Requirement already satisfied: regex in /usr/local/lib/python3.8/dist-packages (from mitdeeplearning==0.3.0) (2022.6.2)\n",
- "Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from mitdeeplearning==0.3.0) (4.64.1)\n",
- "Requirement already satisfied: gym in /usr/local/lib/python3.8/dist-packages (from mitdeeplearning==0.3.0) (0.25.2)\n",
- "Requirement already satisfied: gym-notices>=0.0.4 in /usr/local/lib/python3.8/dist-packages (from gym->mitdeeplearning==0.3.0) (0.0.8)\n",
- "Requirement already satisfied: cloudpickle>=1.2.0 in /usr/local/lib/python3.8/dist-packages (from gym->mitdeeplearning==0.3.0) (2.2.0)\n",
- "Requirement already satisfied: importlib-metadata>=4.8.0 in /usr/local/lib/python3.8/dist-packages (from gym->mitdeeplearning==0.3.0) (6.0.0)\n",
- "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.8/dist-packages (from importlib-metadata>=4.8.0->gym->mitdeeplearning==0.3.0) (3.11.0)\n",
- "Installing collected packages: mitdeeplearning\n",
- " Running setup.py develop for mitdeeplearning\n",
- "Successfully installed mitdeeplearning-0.3.0\n"
- ]
- }
- ]
- },
{
"cell_type": "code",
"execution_count": null,
"metadata": {
- "id": "2PdAhs1371EU",
- "outputId": "dd327495-e85d-4849-9487-4f71535b6cae",
- "colab": {
- "base_uri": "https://localhost:8080/"
- }
- },
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
- "Requirement already satisfied: capsa in /usr/local/lib/python3.8/dist-packages (0.1.3)\n"
- ]
- }
- ],
+ "id": "2PdAhs1371EU"
+ },
+ "outputs": [],
"source": [
"# Import Tensorflow 2.0\n",
"#%tensorflow_version 2.x\n",
@@ -142,9 +83,9 @@
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from tqdm import tqdm\n",
- "from capsa import *\n",
"\n",
"# Download and import the MIT 6.S191 package\n",
+ "!pip install git+https://github.com/aamini/introtodeeplearning.git@2023\n",
"import mitdeeplearning as mdl\n",
"\n",
"# Download and import capsa\n",
@@ -174,26 +115,9 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "id": "HIA6EA1D71EW",
- "outputId": "162f7d36-81aa-4fb9-b265-af9a8fa9395d",
- "colab": {
- "base_uri": "https://localhost:8080/"
- }
- },
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Downloading data from https://www.dropbox.com/s/b5z1cd317y5u1tr/train_face_2023_v2.h5?dl=1\n",
- "1350735194/1350735194 [==============================] - 32s 0us/step\n",
- "Opening /root/.keras/datasets/train_face_2023_v2.h5\n",
- "Loading data into memory...\n",
- "Opening /root/.keras/datasets/train_face_2023_v2.h5\n",
- "Loading data into memory...\n"
- ]
- }
- ],
+ "id": "HIA6EA1D71EW"
+ },
+ "outputs": [],
"source": [
"batch_size = 32\n",
"\n",
@@ -409,11 +333,14 @@
"sorted_biases = bias[indices] # order the representation bias scores\n",
"sorted_preds = predictions[indices] # order the prediction values\n",
"\n",
- "# Visualize the 20 images with the lowest representation in the test dataset\n",
- "plt.imshow(mdl.util.create_grid_of_images(sorted_images[:20], (4, 5)))\n",
"\n",
- "# Visualize the 20 images with the highest representation in the test dataset\n",
- "plt.imshow(mdl.util.create_grid_of_images(sorted_images[-20:0], (4, 5)))"
+ "# Visualize the 20 images with the lowest and highest representation in the test dataset\n",
+ "fig, ax = plt.subplots(1, 2, figsize=(16, 8))\n",
+ "ax[0].imshow(mdl.util.create_grid_of_images(sorted_images[-20:], (4, 5)))\n",
+ "ax[0].set_title(\"Over-represented\")\n",
+ "\n",
+ "ax[1].imshow(mdl.util.create_grid_of_images(sorted_images[:20], (4, 5)))\n",
+ "ax[1].set_title(\"Under-represented\");"
]
},
{
@@ -429,37 +356,12 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "id": "DfzlOhWi71Ef",
- "outputId": "65e20e7a-5e3e-4452-ae49-559a33a063ba",
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 299
- }
- },
- "outputs": [
- {
- "output_type": "display_data",
- "data": {
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ],
- "image/png": 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\n"
- },
- "metadata": {
- "needs_background": "light"
- }
- },
- {
- "output_type": "display_data",
- "data": {
- "text/plain": [
- "<Figure size 432x288 with 0 Axes>"
- ]
- },
- "metadata": {}
- }
- ],
+ "id": "DfzlOhWi71Ef"
+ },
+ "outputs": [],
"source": [
+ "plt.xlabel(\"Density (Representation)\")\n",
+ "plt.ylabel(\"Accuracy\")\n",
"averaged_imgs = mdl.lab3.plot_accuracy_vs_risk(sorted_images, sorted_biases, sorted_preds, \"Bias vs. Accuracy\")"
]
},
@@ -479,38 +381,10 @@
"ax.imshow(mdl.util.create_grid_of_images(averaged_imgs, (1,10)))"
],
"metadata": {
- "id": "kn9IpPKYSECg",
- "outputId": "1685fde2-8f66-4ed6-95c4-2158a0358fbc",
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 153
- }
+ "id": "kn9IpPKYSECg"
},
"execution_count": null,
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- "<matplotlib.image.AxesImage at 0x7f8be1776280>"
- ]
- },
- "metadata": {},
- "execution_count": 33
- },
- {
- "output_type": "display_data",
- "data": {
- "text/plain": [
- "<Figure size 1080x360 with 1 Axes>"
- ],
- "image/png": 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\n"
- },
- "metadata": {
- "needs_background": "light"
- }
- }
- ]
+ "outputs": []
},
{
"cell_type": "markdown",
@@ -571,11 +445,14 @@
"sorted_epistemic = uncertainty[epistemic_indices] # order the uncertainty scores\n",
"sorted_epistemic_preds = predictions[epistemic_indices] # order the prediction values\n",
"\n",
- "# Visualize the 20 images with the LEAST epistemic uncertainty\n",
- "plt.imshow(mdl.util.create_grid_of_images(epistemic_images[:20], (4, 5)))\n",
"\n",
- "# Visualize the 20 images with the MOST epistemic uncertainty\n",
- "plt.imshow(mdl.util.create_grid_of_images(epistemic_images[:20], (4, 5)))"
+ "# Visualize the 20 images with the LEAST and MOST epistemic uncertainty\n",
+ "fig, ax = plt.subplots(1, 2, figsize=(16, 8))\n",
+ "ax[0].imshow(mdl.util.create_grid_of_images(epistemic_images[:20], (4, 5)))\n",
+ "ax[0].set_title(\"Least Uncertain\");\n",
+ "\n",
+ "ax[1].imshow(mdl.util.create_grid_of_images(epistemic_images[-20:], (4, 5)))\n",
+ "ax[1].set_title(\"Most Uncertain\");"
]
},
{
@@ -591,38 +468,13 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "id": "rzQwvSvA71Eh",
- "outputId": "4299761d-22b3-4f44-b9b3-d9194d04a6f2",
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 299
- }
- },
- "outputs": [
- {
- "output_type": "display_data",
- "data": {
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ],
- "image/png": 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\n"
- },
- "metadata": {
- "needs_background": "light"
- }
- },
- {
- "output_type": "display_data",
- "data": {
- "text/plain": [
- "<Figure size 432x288 with 0 Axes>"
- ]
- },
- "metadata": {}
- }
- ],
+ "id": "rzQwvSvA71Eh"
+ },
+ "outputs": [],
"source": [
- "_ = lab3.plot_accuracy_vs_risk(epistemic_images, sorted_epistemic, sorted_epistemic_preds, \"Epistemic Uncertainty vs. Accuracy\")"
+ "plt.xlabel(\"Epistemic Uncertainty\")\n",
+ "plt.ylabel(\"Accuracy\")\n",
+ "_ = mdl.lab3.plot_accuracy_vs_risk(epistemic_images, sorted_epistemic, sorted_epistemic_preds, \"Epistemic Uncertainty vs. Accuracy\")"
]
},
{
@@ -668,44 +520,6 @@
"First, let's do this for the bias. We have a smoothing parameter `alpha` that we can tune: as `alpha` increases, the probabilities will tend towards a uniform distribution, and as `alpha` decreases, the probabilities will correlate more directly with the bias. "
]
},
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "0wR2bMw571Ei"
- },
- "outputs": [],
- "source": [
- "def score_to_probability_bias(score, alpha):\n",
- " score = score + alpha\n",
- " probabilities = 1/score\n",
- " probabilities = probabilities/sum(probabilities)\n",
- " return probabilities"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "TUs-0O_v71Ei"
- },
- "source": [
- "Let's now define a similar function for the epistemic probabilities: note that in this case, we want high epistemic uncertainty to correlate with a higher probability!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "hLWGKvc971Ei"
- },
- "outputs": [],
- "source": [
- "def score_to_probability_epistemic(score, beta):\n",
- " score = score + beta\n",
- " probabilities = score/sum(score)\n",
- " return probabilities"
- ]
- },
{
"cell_type": "markdown",
"metadata": {
@@ -723,11 +537,11 @@
},
"outputs": [],
"source": [
- "standard_classifier = make_standard_classifier()\n",
- "dbvae = HistogramVAEWrapper(standard_classifier, latent_dim=100, num_bins=5, queue_size=2000, decoder=make_face_decoder_network())\n",
- "dbvae.compile(optimizer=tf.keras.optimizers.Adam(1e-4),\n",
- " loss=tf.keras.losses.BinaryCrossentropy(),\n",
- " metrics=[tf.keras.metrics.BinaryAccuracy()])\n",
+ "classifier = make_standard_classifier()\n",
+ "wrapper = capsa.HistogramVAEWrapper(classifier, latent_dim=32, num_bins=5, queue_size=2000, decoder=make_face_decoder_network())\n",
+ "wrapper.compile(optimizer=tf.keras.optimizers.Adam(5e-4),\n",
+ " loss=tf.keras.losses.BinaryCrossentropy(),\n",
+ " metrics=[tf.keras.metrics.BinaryAccuracy()])\n",
"train_imgs = train_loader.get_all_faces()"
]
},
@@ -740,25 +554,29 @@
"outputs": [],
"source": [
"# The training loop -- outer loop iterates over the number of epochs\n",
- "for i in range(6):\n",
- "\n",
- " print(\"Starting epoch {}/{}\".format(i+1, 6))\n",
+ "num_epochs = 6\n",
+ "for i in range(num_epochs):\n",
+ " print(\"Starting epoch {}/{}\".format(i+1, num_epochs))\n",
" \n",
- " # get a batch of training data and compute the training step\n",
+ " # Get a batch of training data and compute the training step\n",
" for step, data in enumerate(train_loader):\n",
- " metrics = dbvae.train_step(data)\n",
+ " metrics = wrapper.train_step(data)\n",
" if step % 100 == 0:\n",
" print(step)\n",
- " _, recon_loss, bias_scores = dbvae(train_imgs)\n",
- " recon_loss = np.squeeze(recon_loss)\n",
- "\n",
- " # Recompute data sampling proabilities\n",
- " p_faces = score_to_probability_bias(bias_scores.numpy(), 1e-7)\n",
- " p_recon = score_to_probability_epistemic(recon_loss, 1e-7)\n",
- " p_final = (p_faces + p_recon)/2\n",
- " p_final /= sum(p_final)\n",
- " \n",
- " train_loader.p_pos = p_final"
+ "\n",
+ " # After the epoch is done, recompute data sampling proabilities \n",
+ " # according to the inverse of the bias\n",
+ " pred, unc, bias = wrapper(train_imgs)\n",
+ "\n",
+ " # Increase the probability of sampling under-represented datapoints by setting \n",
+ " # the probability to the **inverse** of the biases\n",
+ " inverse_bias = 1.0 / (bias.numpy() + 1e-7)\n",
+ "\n",
+ " # Normalize the inverse biases in order to convert them to probabilities\n",
+ " p_faces = inverse_bias / np.sum(inverse_bias)\n",
+ "\n",
+ " # Update the training data loader to sample according to this new distribution\n",
+ " train_loader.p_pos = p_faces"
]
},
{
@@ -789,7 +607,7 @@
},
"outputs": [],
"source": [
- "predictions, reconstruction_loss, bias = dbvae.predict(test_imgs)"
+ "pred, unc, bias = wrapper.predict(test_imgs)"
]
},
{
@@ -803,23 +621,8 @@
"indices = np.argsort(bias, axis=None)\n",
"bias_images = test_imgs[indices]\n",
"sorted_bias = bias[indices]\n",
- "sorted_bias_preds = predictions[indices]\n",
- "_ = lab3.plot_accuracy_vs_risk(bias_images, sorted_bias, sorted_bias_preds, \"Bias vs. Accuracy\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "P6p2j_xa71Ej"
- },
- "outputs": [],
- "source": [
- "indices = np.argsort(reconstruction_loss, axis=None)\n",
- "epistemic_images = test_imgs[indices]\n",
- "sorted_epistemic = bias[indices]\n",
- "sorted_epistemic_preds = predictions[indices]\n",
- "_ = lab3.plot_accuracy_vs_risk(epistemic_images, sorted_epistemic, sorted_epistemic_preds, \"Epistemic Uncertainty vs. Accuracy\")"
+ "sorted_bias_preds = pred[indices]\n",
+ "_ = mdl.lab3.plot_accuracy_vs_risk(bias_images, sorted_bias, sorted_bias_preds, \"Bias vs. Accuracy\")"
]
},
{
@@ -830,25 +633,55 @@
"source": [
"# 3.6 Conclusion\n",
"\n",
- "We encourage you to think about and maybe even address some questions raised by the approach and results outlined here:\n",
+ "### How to enter the competition?\n",
+ "\n",
+ "We encourage you to think about and maybe even address some questions raised by this lab and dig into any questions that you may have about the risks inherrent to neural networks and their data. \n",
+ "\n",
+ "Now, you are well equiped to join a compeition to dig in deeper into deep learning models, uncover their deficiencies with Capsa, and submit your findings!\n",
+ "\n",
+ "**Below are some potential areas to start investigating -- but please keep in mind, you don't need to solve any of these to enter the competition.** In fact, we encourage you to identify other questions that could be solved with Capsa and use those as the basis of your submission. But, to help get you started, here are some interesting questions that you might look into solving with these new tools and knowledge that you've built up! \n",
+ "\n",
+ "1. In this lab, you've learned how to build a wrapper that can estimate the bias within the training data, and take the results from this wrapper to adaptively re-sample during training to encourage learning on under-represented data. \n",
+ " * Can we apply a similar approach to mitigate epistemic uncertainty in the model? \n",
+ " * Can this approach be combined with your original bias mitigation approach achieve robustness across both bias *and* uncertainty? \n",
+ "\n",
+ "\n",
+ "2. In this lab, you've focused on the `HistogramVAEWrapper`. \n",
+ " * How can you use other methods of uncertainty in Capsa to strengthen your uncertainty estimates? Checkout [Capsa documentation](https://themisai.io/capsa/api_documentation/index.html) for a list of all wrappers, and ask for help if you run into trouble applying them to your model!\n",
+ " * Can you combine uncertainty estimates from different wrappers to achieve greater robustness in your estimates? \n",
"\n",
- "* We did not analyze the *aleatoric* uncertainty of the above dataset. Try to develop a similar approach (assigning probabilities based on aleatoric uncertainty) and incorporate this as well! You may find some surprising results :)\n",
"\n",
- "* How can the performance of the classifier above be improved even further? We purposely did not optimize hyperparameters to leave this up to you!\n",
+ "3. So far in this part of the lab, we have focused only on bias and epistemic uncertainty; but what about aleatoric uncetainty? \n",
+ " * We've curated a dataset (available at [this URL](https://www.dropbox.com/s/wsdyma8a340k8lw/train_face_2023_perturbed_large.h5?dl=0)) of faces with greater amounts of aleatoric uncertainty -- can you use Capsa to wrap your model, estimate aleatoric uncertainty, and remove it from the dataset? \n",
+ " * Does removing aleatoric uncertainty help improve your training accuracy on this new dataset? \n",
+ " * Can you develop an approach to incorporate this aleatoric uncertainty estimation into the predictive training pipeline in order to improve accuracy? You may find some surprising results!!\n",
"\n",
- "* How can you use other methods of uncertainty in CAPSA to strengthen your uncertainty estimates?\n",
"\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",
+ "4. How can the performance of the classifier above be improved even further? We purposely did not optimize hyperparameters to leave this up to you!\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",
- "* Jupyter notebook with the code you used to generate your results;\n",
- "copy of the line plots from section 3.5 showing the performance of your model;\n",
+ "5. Are there other applications that you think Capsa and bias/uncertainty estimation would be helpful in? \n",
+ " * Try integrating Capsa into another domain or dataset and submit your findings!\n",
+ " * Are there applications where you may not want to debias your model? \n",
+ "\n",
+ "\n",
+ "To enter the competition, please upload the following to the lab submission site:\n",
+ "\n",
+ "* Jupyter notebook with the code you used to generate your results (along with all plots/visuals generated);\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 ;)."
]
+ },
+ {
+ "cell_type": "code",
+ "source": [],
+ "metadata": {
+ "id": "xzzPbKd8-ai6"
+ },
+ "execution_count": null,
+ "outputs": []
}
],
"metadata": {
From c750ee74355a605035595ae3391f7238f29f8bcb Mon Sep 17 00:00:00 2001
From: Ava Amini <apsoleimany@gmail.com>
Date: Wed, 11 Jan 2023 03:52:14 -0500
Subject: [PATCH 21/22] updating links again
---
lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb b/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb
index ea538ee0..61019a67 100644
--- a/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb
+++ b/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb
@@ -7,9 +7,9 @@
" <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/2023/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb\">\n",
+ " <td align=\"center\"><a target=\"_blank\" href=\"https://colab.research.google.com/github/aamini/introtodeeplearning/blob/2023/lab3/solutions/Lab3_Bias_And_Uncertainty.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/2023/lab3/solutions/Lab3_Part_1_Introduction_to_CAPSA.ipynb\">\n",
+ " <td align=\"center\"><a target=\"_blank\" href=\"https://github.com/aamini/introtodeeplearning/blob/2023/lab3/solutions/Lab3_Bias_and_Uncertainty.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",
From ed7501323f5b3f0f865db898b1ce6a0528df1586 Mon Sep 17 00:00:00 2001
From: Ava Amini <apsoleimany@gmail.com>
Date: Wed, 11 Jan 2023 04:21:34 -0500
Subject: [PATCH 22/22] finalizing lab3 part2
---
.../solutions/Lab3_Bias_And_Uncertainty.ipynb | 135 +++++++-----------
1 file changed, 55 insertions(+), 80 deletions(-)
diff --git a/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb b/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb
index 61019a67..a210f0f0 100644
--- a/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb
+++ b/lab3/solutions/Lab3_Bias_And_Uncertainty.ipynb
@@ -9,7 +9,7 @@
" Visit MIT Deep Learning</a></td>\n",
" <td align=\"center\"><a target=\"_blank\" href=\"https://colab.research.google.com/github/aamini/introtodeeplearning/blob/2023/lab3/solutions/Lab3_Bias_And_Uncertainty.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/2023/lab3/solutions/Lab3_Bias_and_Uncertainty.ipynb\">\n",
+ " <td align=\"center\"><a target=\"_blank\" href=\"https://github.com/aamini/introtodeeplearning/blob/2023/lab3/solutions/Lab3_Bias_And_Uncertainty.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",
@@ -360,6 +360,7 @@
},
"outputs": [],
"source": [
+ "# Plot the representation density vs. the accuracy\n",
"plt.xlabel(\"Density (Representation)\")\n",
"plt.ylabel(\"Accuracy\")\n",
"averaged_imgs = mdl.lab3.plot_accuracy_vs_risk(sorted_images, sorted_biases, sorted_preds, \"Bias vs. Accuracy\")"
@@ -371,7 +372,7 @@
"id": "i8ERzg2-71Ef"
},
"source": [
- "These representations scores relate back to data examples, so we can visualize what the average face looks like for a gien *percentile* of representation density:"
+ "These representations scores relate back to data examples, so we can visualize what the average face looks like for a given *percentile* of representation density:"
]
},
{
@@ -472,6 +473,7 @@
},
"outputs": [],
"source": [
+ "# Plot epistemic uncertainty vs. classification accuracy\n",
"plt.xlabel(\"Epistemic Uncertainty\")\n",
"plt.ylabel(\"Accuracy\")\n",
"_ = mdl.lab3.plot_accuracy_vs_risk(epistemic_images, sorted_epistemic, sorted_epistemic_preds, \"Epistemic Uncertainty vs. Accuracy\")"
@@ -499,34 +501,16 @@
},
"source": [
"# 3.4 Resampling based on risk metrics\n",
- " Then, our goal will be to continue our implementation of the DB-VAE and 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",
- " and we want to oversample these. Conversely, we want to undersample the areas of the dataset with the highest bias.\n",
+ "Finally, we will use the risk metrics just computed to actually *mitigate* the issues of bias and uncertainty in the facial detection classifier.\n",
"\n",
+ "Specifically, we will use the latent variables learned via the VAE to adaptively re-sample the face (CelebA) data during training, following the approach of [recent work](http://introtodeeplearning.com/AAAI_MitigatingAlgorithmicBias.pdf). 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",
- "Finally, let's use both the bias score and the reconstruction loss to adaptively resample from our dataset. Since we can calculate this score on-the-fly *during training*, we can adjust the probability of samples being chosen. \n",
+ "Note that we want to debias and amplify only the *positive* samples in the dataset -- the faces -- so we are going to only adjust probabilities and calculate scores for these samples. We focus on using the representation bias scores to implement this adaptive resampling to achieve model debiasing.\n",
"\n",
- "Note that we want to debias and amplify only the *positive* samples in the dataset, so we're going to only adjust probabilities and calculate scores for these samples. \n",
+ "We re-define the wrapped model with `HistogramVAEWrapper`, and then define the adaptive resampling operation for training. At each training epoch, we compute the predictions, uncertainties, and representation bias scores, then recompute the data sampling probabilities according to the *inverse* of the representation bias score. That is, samples with higher representation densities will end up with lower re-sampling probabilities; samples with lower representations will end up with higher re-sampling probabilities.\n",
"\n",
- "We want to *amplify*, or increase the probability of sampling, of images with high epistemic uncertainty, since these data points come from areas of the latent distribution that the model hasn't learned very well yet. We also want to amplify images with very low representation bias, since otherwise, the model won't see enough of these samples during training. Let's define two functions below to do this:"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "hRL5nUBs71Ei"
- },
- "source": [
- "First, let's do this for the bias. We have a smoothing parameter `alpha` that we can tune: as `alpha` increases, the probabilities will tend towards a uniform distribution, and as `alpha` decreases, the probabilities will correlate more directly with the bias. "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "meZxtxFS71Ei"
- },
- "source": [
- "Now, let's redefine and re-train our debiasing model!"
+ "Let's do all this below!"
]
},
{
@@ -537,11 +521,19 @@
},
"outputs": [],
"source": [
+ "### Define the standard CNN classifier and wrap with HistogramVAE ###\n",
+ "\n",
"classifier = make_standard_classifier()\n",
- "wrapper = capsa.HistogramVAEWrapper(classifier, latent_dim=32, num_bins=5, queue_size=2000, decoder=make_face_decoder_network())\n",
+ "# Wrap with HistogramVAE\n",
+ "wrapper = capsa.HistogramVAEWrapper(classifier, latent_dim=32, num_bins=5, \n",
+ " queue_size=2000, decoder=make_face_decoder_network())\n",
+ "\n",
+ "# Build the wrapped model for the classification task\n",
"wrapper.compile(optimizer=tf.keras.optimizers.Adam(5e-4),\n",
" loss=tf.keras.losses.BinaryCrossentropy(),\n",
" metrics=[tf.keras.metrics.BinaryAccuracy()])\n",
+ "\n",
+ "# Load training data\n",
"train_imgs = train_loader.get_all_faces()"
]
},
@@ -553,6 +545,8 @@
},
"outputs": [],
"source": [
+ "### Debiasing via resampling based on risk metrics ###\n",
+ "\n",
"# The training loop -- outer loop iterates over the number of epochs\n",
"num_epochs = 6\n",
"for i in range(num_epochs):\n",
@@ -585,18 +579,11 @@
"id": "SwXrAeBo71Ej"
},
"source": [
- "Now, we should have a debiased model that also mitigates some forms of uncertainty! Let's see how well our model does:"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "MXiB-DMH71Ej"
- },
- "source": [
- "# 3.5 Evaluation\n",
+ "That's it! We should have a debiased model (we hope!). Let's see how the model does.\n",
+ "\n",
+ "### Evaluation\n",
"\n",
- "Let's run the same analyses as before, and plot the accuracy vs. the bias and accuracy vs. epistemic uncertainty. We want the model to do better on less biased and more uncertain samples than it did previously\n"
+ "Let's run the same analyses as before, and plot the classification accuracy vs. the representation bias and classification accuracy vs. epistemic uncertainty. We want the model to do better across the data samples, achieving higher accuracies on the under-represented and more uncertain samples compared to previously.\n"
]
},
{
@@ -607,21 +594,20 @@
},
"outputs": [],
"source": [
- "pred, unc, bias = wrapper.predict(test_imgs)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "zCXVIsaJ71Ej"
- },
- "outputs": [],
- "source": [
+ "### Evaluation of debiased model ###\n",
+ "\n",
+ "# Get classification predictions, uncertainties, and representation bias scores\n",
+ "pred, unc, bias = wrapper.predict(test_imgs)\n",
+ "\n",
+ "# Sort according to lowest to highest representation scores\n",
"indices = np.argsort(bias, axis=None)\n",
- "bias_images = test_imgs[indices]\n",
- "sorted_bias = bias[indices]\n",
- "sorted_bias_preds = pred[indices]\n",
+ "bias_images = test_imgs[indices] # sort the images\n",
+ "sorted_bias = bias[indices] # sort the representation bias scores\n",
+ "sorted_bias_preds = pred[indices] # sort the predictions\n",
+ "\n",
+ "# Plot the representation bias vs. the accuracy\n",
+ "plt.xlabel(\"Density (Representation)\")\n",
+ "plt.ylabel(\"Accuracy\")\n",
"_ = mdl.lab3.plot_accuracy_vs_risk(bias_images, sorted_bias, sorted_bias_preds, \"Bias vs. Accuracy\")"
]
},
@@ -631,57 +617,46 @@
"id": "d1cEEnII71Ej"
},
"source": [
- "# 3.6 Conclusion\n",
- "\n",
- "### How to enter the competition?\n",
+ "# 3.5 Competition!\n",
"\n",
- "We encourage you to think about and maybe even address some questions raised by this lab and dig into any questions that you may have about the risks inherrent to neural networks and their data. \n",
+ "Now, you are well equipped to submit to the competition to dig in deeper into deep learning models, uncover their deficiencies with Capsa, address those deficiencies, and submit your findings!\n",
"\n",
- "Now, you are well equiped to join a compeition to dig in deeper into deep learning models, uncover their deficiencies with Capsa, and submit your findings!\n",
+ "**Below are some potential areas to start investigating -- the goal of the competition is to develop creative and innovative solutions to address bias and uncertainty, and to improve the overall performance of deep learning models.**\n",
"\n",
- "**Below are some potential areas to start investigating -- but please keep in mind, you don't need to solve any of these to enter the competition.** In fact, we encourage you to identify other questions that could be solved with Capsa and use those as the basis of your submission. But, to help get you started, here are some interesting questions that you might look into solving with these new tools and knowledge that you've built up! \n",
+ "We encourage you to identify other questions that could be solved with Capsa and use those as the basis of your submission. But, to help get you started, here are some interesting questions that you might look into solving with these new tools and knowledge that you've built up: \n",
"\n",
- "1. In this lab, you've learned how to build a wrapper that can estimate the bias within the training data, and take the results from this wrapper to adaptively re-sample during training to encourage learning on under-represented data. \n",
+ "1. In this lab, you learned how to build a wrapper that can estimate the bias within the training data, and take the results from this wrapper to adaptively re-sample during training to encourage learning on under-represented data. \n",
" * Can we apply a similar approach to mitigate epistemic uncertainty in the model? \n",
- " * Can this approach be combined with your original bias mitigation approach achieve robustness across both bias *and* uncertainty? \n",
+ " * Can this approach be combined with your original bias mitigation approach to achieve robustness across both bias *and* uncertainty? \n",
"\n",
- "\n",
- "2. In this lab, you've focused on the `HistogramVAEWrapper`. \n",
+ "2. In this lab, you focused on the `HistogramVAEWrapper`. \n",
" * How can you use other methods of uncertainty in Capsa to strengthen your uncertainty estimates? Checkout [Capsa documentation](https://themisai.io/capsa/api_documentation/index.html) for a list of all wrappers, and ask for help if you run into trouble applying them to your model!\n",
" * Can you combine uncertainty estimates from different wrappers to achieve greater robustness in your estimates? \n",
"\n",
- "\n",
- "3. So far in this part of the lab, we have focused only on bias and epistemic uncertainty; but what about aleatoric uncetainty? \n",
+ "3. So far in this part of the lab, we focused only on bias and epistemic uncertainty. What about aleatoric uncetainty? \n",
" * We've curated a dataset (available at [this URL](https://www.dropbox.com/s/wsdyma8a340k8lw/train_face_2023_perturbed_large.h5?dl=0)) of faces with greater amounts of aleatoric uncertainty -- can you use Capsa to wrap your model, estimate aleatoric uncertainty, and remove it from the dataset? \n",
" * Does removing aleatoric uncertainty help improve your training accuracy on this new dataset? \n",
" * Can you develop an approach to incorporate this aleatoric uncertainty estimation into the predictive training pipeline in order to improve accuracy? You may find some surprising results!!\n",
"\n",
- "\n",
"4. How can the performance of the classifier above be improved even further? We purposely did not optimize hyperparameters to leave this up to you!\n",
"\n",
- "\n",
"5. Are there other applications that you think Capsa and bias/uncertainty estimation would be helpful in? \n",
" * Try integrating Capsa into another domain or dataset and submit your findings!\n",
- " * Are there applications where you may not want to debias your model? \n",
+ " * Are there applications where you may *not* want to debias your model? \n",
"\n",
"\n",
- "To enter the competition, please upload the following to the lab submission site:\n",
+ "**To enter the competition, please upload the following to the [lab submission site](https://www.dropbox.com/request/TTYz3Ikx5wIgOITmm5i2):**\n",
"\n",
- "* Jupyter notebook with the code you used to generate your results (along with all plots/visuals generated);\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",
+ "* Written short-answer responses to `TODO`s from Lab 2, Part 2 on Facial Detection.\n",
+ "* Description of the wrappers, algorithms, and approach you used. What was your strategy? What wrappers did you implement? What debiasing or mitigation strategies did you try? How and why did these modifications affect performance? Describe *any* modifications or implementations you made to the template code, and what their effects were. Written text, visual diagram, and plots welcome!\n",
+ "* Jupyter notebook with the code you used to generate your results (along with all plots/visuals generated).\n",
+ "\n",
+ "**Name your file in the following format: `[FirstName]_[LastName]_Face`, followed by the file format (.zip, .ipynb, .pdf, etc).** ZIP files are preferred over individual files. If you submit individual files, you must name the individual files according to the above nomenclature (e.g., `[FirstName]_[LastName]_Face_TODO.pdf`, `[FirstName]_[LastName]_Face_Report.pdf`, etc.). **Submit your files [here](https://www.dropbox.com/request/TTYz3Ikx5wIgOITmm5i2).**\n",
+ "\n",
+ "We encourage you to think about and maybe even address some questions raised by this lab and dig into any questions that you may have about the risks inherrent to neural networks and their 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 ;)."
+ "<img src=\"https://i.ibb.co/BjLSRMM/ezgif-2-253dfd3f9097.gif\" />"
]
- },
- {
- "cell_type": "code",
- "source": [],
- "metadata": {
- "id": "xzzPbKd8-ai6"
- },
- "execution_count": null,
- "outputs": []
}
],
"metadata": {