From 598d2de50d22379da161173a5dcc379c7ee19d01 Mon Sep 17 00:00:00 2001
From: Sayak Paul <spsayakpaul@gmail.com>
Date: Thu, 1 Apr 2021 17:12:20 +0530
Subject: [PATCH] Added all results and consistency training works!

---
 CIFAR_10C_Evaluation.ipynb | 963 ++++++++++++++++++++++++++++++++-----
 1 file changed, 854 insertions(+), 109 deletions(-)

diff --git a/CIFAR_10C_Evaluation.ipynb b/CIFAR_10C_Evaluation.ipynb
index b2a1148..8920f1f 100644
--- a/CIFAR_10C_Evaluation.ipynb
+++ b/CIFAR_10C_Evaluation.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "japanese-airfare",
+   "id": "filled-jurisdiction",
    "metadata": {},
    "source": [
     "## Setup"
@@ -11,7 +11,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "instrumental-viewer",
+   "id": "liberal-edmonton",
    "metadata": {},
    "outputs": [
     {
@@ -19,21 +19,13 @@
      "output_type": "stream",
      "text": [
       "Downloading...\n",
-      "From: https://drive.google.com/uc?id=1-0mtggsTMLDmbT7cmxdIBH25eAieTyGb\n",
-      "To: /home/jupyter/teacher_model_swa.h5\n",
-      "94.6MB [00:01, 83.5MB/s]\n",
+      "From: https://drive.google.com/uc?id=10zxKXCPLrm8g423Rzjs3i3NVfS6rRtip\n",
+      "To: /home/jupyter/teacher_model.h5\n",
+      "94.6MB [00:01, 70.1MB/s]\n",
       "Downloading...\n",
-      "From: https://drive.google.com/uc?id=1--EvBdSAnNaBtKozkMuja--lq8erN0uC\n",
-      "To: /home/jupyter/student_noisy_swa.h5\n",
-      "94.6MB [00:01, 85.7MB/s]\n",
-      "Downloading...\n",
-      "From: https://drive.google.com/uc?id=1M0JwphtV6W1NM6V2Qv_VMTdjWdeAz6Sx\n",
-      "To: /home/jupyter/teacher_model_ma.h5\n",
-      "94.6MB [00:01, 71.0MB/s]\n",
-      "Downloading...\n",
-      "From: https://drive.google.com/uc?id=1pW2hx6fkQGH8fXFfh8Fn8xPADuYBy5mG\n",
-      "To: /home/jupyter/student_noisy_ma.h5\n",
-      "94.6MB [00:01, 63.9MB/s]\n"
+      "From: https://drive.google.com/uc?id=1e8WB4zaUzzxJRoVD4p3zVtPUBvMFF-34\n",
+      "To: /home/jupyter/student_noisy.h5\n",
+      "94.6MB [00:01, 65.4MB/s]\n"
      ]
     }
    ],
@@ -44,13 +36,17 @@
     "\n",
     "# MA weights\n",
     "!/home/jupyter/.local/bin/gdown --id 1M0JwphtV6W1NM6V2Qv_VMTdjWdeAz6Sx\n",
-    "!/home/jupyter/.local/bin/gdown --id 1pW2hx6fkQGH8fXFfh8Fn8xPADuYBy5mG"
+    "!/home/jupyter/.local/bin/gdown --id 1pW2hx6fkQGH8fXFfh8Fn8xPADuYBy5mG\n",
+    "\n",
+    "# Regular weights\n",
+    "!/home/jupyter/.local/bin/gdown --id 10zxKXCPLrm8g423Rzjs3i3NVfS6rRtip\n",
+    "!/home/jupyter/.local/bin/gdown --id 1e8WB4zaUzzxJRoVD4p3zVtPUBvMFF-34"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "weird-declaration",
+   "id": "three-niger",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -66,7 +62,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "hidden-institute",
+   "id": "residential-gossip",
    "metadata": {},
    "source": [
     "## Define Hyperparameters"
@@ -75,20 +71,20 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "complimentary-trust",
+   "id": "loose-devil",
    "metadata": {},
    "outputs": [],
    "source": [
     "AUTO = tf.data.AUTOTUNE\n",
     "DATASET_NAME = \"cifar10_corrupted\"\n",
-    "BATCH_SIZE = 64\n",
+    "BATCH_SIZE = 128\n",
     "IMAGE_SIZE = 72"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "aggressive-headquarters",
+   "id": "related-yahoo",
    "metadata": {},
    "outputs": [
     {
@@ -127,7 +123,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dried-ladder",
+   "id": "responsible-techno",
    "metadata": {},
    "source": [
     "## Utilities"
@@ -136,7 +132,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "informational-david",
+   "id": "dedicated-typing",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -153,7 +149,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "duplicate-concert",
+   "id": "designing-chancellor",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -178,7 +174,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "posted-crazy",
+   "id": "dangerous-processing",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -202,7 +198,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ignored-plant",
+   "id": "monthly-mount",
    "metadata": {},
    "source": [
     "## Evaluation"
@@ -210,7 +206,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "spectacular-prefix",
+   "id": "rural-attitude",
    "metadata": {},
    "source": [
     "### SWA"
@@ -219,7 +215,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "standard-shareware",
+   "id": "essential-height",
    "metadata": {},
    "outputs": [
     {
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@@ -542,7 +538,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "suffering-plaintiff",
+   "id": "instant-insurance",
    "metadata": {},
    "outputs": [
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   },
   {
    "cell_type": "markdown",
-   "id": "separated-rider",
+   "id": "stainless-prescription",
    "metadata": {},
    "source": [
     "### MA"
@@ -873,7 +869,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "departmental-generation",
+   "id": "limiting-scanning",
    "metadata": {},
    "outputs": [
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      "text": [
-      " 53%|█████▎    | 10/19 [00:22<00:19,  2.19s/it]"
+      " 53%|█████▎    | 10/19 [00:18<00:15,  1.76s/it]"
      ]
     },
     {
@@ -1044,7 +1040,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      " 58%|█████▊    | 11/19 [00:25<00:17,  2.19s/it]"
+      " 58%|█████▊    | 11/19 [00:20<00:14,  1.76s/it]"
      ]
     },
     {
@@ -1059,7 +1055,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      " 63%|██████▎   | 12/19 [00:27<00:15,  2.19s/it]"
+      " 63%|██████▎   | 12/19 [00:22<00:12,  1.76s/it]"
      ]
     },
     {
@@ -1074,7 +1070,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      " 68%|██████▊   | 13/19 [00:29<00:13,  2.19s/it]"
+      " 68%|██████▊   | 13/19 [00:23<00:10,  1.75s/it]"
      ]
     },
     {
@@ -1089,7 +1085,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      " 74%|███████▎  | 14/19 [00:31<00:10,  2.19s/it]"
+      " 74%|███████▎  | 14/19 [00:25<00:08,  1.75s/it]"
      ]
     },
     {
@@ -1104,7 +1100,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      " 79%|███████▉  | 15/19 [00:33<00:08,  2.18s/it]"
+      " 79%|███████▉  | 15/19 [00:27<00:07,  1.75s/it]"
      ]
     },
     {
@@ -1119,7 +1115,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      " 84%|████████▍ | 16/19 [00:35<00:06,  2.18s/it]"
+      " 84%|████████▍ | 16/19 [00:29<00:05,  1.75s/it]"
      ]
     },
     {
@@ -1134,7 +1130,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      " 89%|████████▉ | 17/19 [00:38<00:04,  2.18s/it]"
+      " 89%|████████▉ | 17/19 [00:30<00:03,  1.75s/it]"
      ]
     },
     {
@@ -1149,7 +1145,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      " 95%|█████████▍| 18/19 [00:40<00:02,  2.18s/it]"
+      " 95%|█████████▍| 18/19 [00:32<00:01,  1.75s/it]"
      ]
     },
     {
@@ -1164,7 +1160,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 19/19 [00:42<00:00,  2.24s/it]"
+      "100%|██████████| 19/19 [00:34<00:00,  1.81s/it]"
      ]
     },
     {
@@ -1196,7 +1192,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "virgin-history",
+   "id": "legendary-berry",
    "metadata": {},
    "outputs": [
     {
@@ -1217,7 +1213,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "  5%|▌         | 1/19 [00:03<00:59,  3.32s/it]"
+      "  5%|▌         | 1/19 [00:02<00:51,  2.84s/it]"
      ]
     },
     {
@@ -1232,7 +1228,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      " 11%|█         | 2/19 [00:05<00:44,  2.63s/it]"
+      " 11%|█         | 2/19 [00:04<00:37,  2.20s/it]"
      ]
     },
     {
@@ -1247,7 +1243,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      " 16%|█▌        | 3/19 [00:07<00:38,  2.42s/it]"
+      " 16%|█▌        | 3/19 [00:06<00:31,  1.99s/it]"
      ]
     },
     {
@@ -1262,7 +1258,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      " 21%|██        | 4/19 [00:09<00:34,  2.32s/it]"
+      " 21%|██        | 4/19 [00:08<00:28,  1.90s/it]"
      ]
     },
     {
@@ -1277,7 +1273,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      " 26%|██▋       | 5/19 [00:11<00:31,  2.26s/it]"
+      " 26%|██▋       | 5/19 [00:09<00:25,  1.85s/it]"
      ]
     },
     {
@@ -1292,7 +1288,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      " 32%|███▏      | 6/19 [00:14<00:28,  2.23s/it]"
+      " 32%|███▏      | 6/19 [00:11<00:23,  1.81s/it]"
      ]
     },
     {
@@ -1307,7 +1303,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      " 37%|███▋      | 7/19 [00:16<00:26,  2.20s/it]"
+      " 37%|███▋      | 7/19 [00:13<00:21,  1.79s/it]"
      ]
     },
     {
@@ -1322,7 +1318,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      " 42%|████▏     | 8/19 [00:18<00:24,  2.19s/it]"
+      " 42%|████▏     | 8/19 [00:15<00:19,  1.78s/it]"
      ]
     },
     {
@@ -1337,7 +1333,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      " 47%|████▋     | 9/19 [00:20<00:21,  2.18s/it]"
+      " 47%|████▋     | 9/19 [00:16<00:17,  1.77s/it]"
      ]
     },
     {
@@ -1352,7 +1348,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      " 53%|█████▎    | 10/19 [00:22<00:19,  2.17s/it]"
+      " 53%|█████▎    | 10/19 [00:18<00:15,  1.76s/it]"
      ]
     },
     {
@@ -1367,7 +1363,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      " 58%|█████▊    | 11/19 [00:24<00:17,  2.17s/it]"
+      " 58%|█████▊    | 11/19 [00:20<00:14,  1.76s/it]"
      ]
     },
     {
@@ -1382,7 +1378,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      " 63%|██████▎   | 12/19 [00:27<00:15,  2.17s/it]"
+      " 63%|██████▎   | 12/19 [00:22<00:12,  1.76s/it]"
      ]
     },
     {
@@ -1397,7 +1393,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      " 68%|██████▊   | 13/19 [00:29<00:13,  2.17s/it]"
+      " 68%|██████▊   | 13/19 [00:23<00:10,  1.75s/it]"
      ]
     },
     {
@@ -1412,7 +1408,7 @@
      "name": "stderr",
      "output_type": "stream",
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-      " 74%|███████▎  | 14/19 [00:31<00:10,  2.17s/it]"
+      " 74%|███████▎  | 14/19 [00:25<00:08,  1.75s/it]"
      ]
     },
     {
@@ -1427,7 +1423,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      " 79%|███████▉  | 15/19 [00:33<00:08,  2.17s/it]"
+      " 79%|███████▉  | 15/19 [00:27<00:06,  1.75s/it]"
      ]
     },
     {
@@ -1442,7 +1438,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      " 84%|████████▍ | 16/19 [00:35<00:06,  2.16s/it]"
+      " 84%|████████▍ | 16/19 [00:29<00:05,  1.74s/it]"
      ]
     },
     {
@@ -1457,7 +1453,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      " 89%|████████▉ | 17/19 [00:37<00:04,  2.17s/it]"
+      " 89%|████████▉ | 17/19 [00:30<00:03,  1.75s/it]"
      ]
     },
     {
@@ -1472,7 +1468,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      " 95%|█████████▍| 18/19 [00:40<00:02,  2.17s/it]"
+      " 95%|█████████▍| 18/19 [00:32<00:01,  1.75s/it]"
      ]
     },
     {
@@ -1487,7 +1483,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 19/19 [00:42<00:00,  2.22s/it]"
+      "100%|██████████| 19/19 [00:34<00:00,  1.81s/it]"
      ]
     },
     {
@@ -1515,6 +1511,755 @@
     "acc_dict, mean_top_1 = evaluate_model(student_noisy_ma)\n",
     "print(f\"Mean Top-1 Accuracy: {mean_top_1}%\")"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "regional-composer",
+   "metadata": {},
+   "source": [
+    "### Regular"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "valued-dominican",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|          | 0/19 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processing brightness_5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  5%|▌         | 1/19 [00:03<00:55,  3.08s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy on brightness_5: 75.52000284194946%\n",
+      "Processing contrast_5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 11%|█         | 2/19 [00:04<00:38,  2.29s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy on contrast_5: 24.3599995970726%\n",
+      "Processing defocus_blur_5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 16%|█▌        | 3/19 [00:06<00:32,  2.04s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy on defocus_blur_5: 71.95000052452087%\n",
+      "Processing elastic_5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 21%|██        | 4/19 [00:08<00:28,  1.93s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy on elastic_5: 74.55999851226807%\n",
+      "Processing fog_5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 26%|██▋       | 5/19 [00:10<00:26,  1.86s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy on fog_5: 45.71999907493591%\n",
+      "Processing frost_5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 32%|███▏      | 6/19 [00:11<00:23,  1.82s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy on frost_5: 62.48999834060669%\n",
+      "Processing frosted_glass_blur_5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 37%|███▋      | 7/19 [00:13<00:21,  1.80s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy on frosted_glass_blur_5: 63.33000063896179%\n",
+      "Processing gaussian_blur_5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 42%|████▏     | 8/19 [00:15<00:19,  1.78s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy on gaussian_blur_5: 66.90000295639038%\n",
+      "Processing gaussian_noise_5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 47%|████▋     | 9/19 [00:17<00:17,  1.78s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy on gaussian_noise_5: 44.62999999523163%\n",
+      "Processing impulse_noise_5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 53%|█████▎    | 10/19 [00:18<00:15,  1.77s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy on impulse_noise_5: 27.889999747276306%\n",
+      "Processing jpeg_compression_5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 58%|█████▊    | 11/19 [00:20<00:14,  1.76s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy on jpeg_compression_5: 78.46999764442444%\n",
+      "Processing motion_blur_5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 63%|██████▎   | 12/19 [00:22<00:12,  1.76s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy on motion_blur_5: 66.21999740600586%\n",
+      "Processing pixelate_5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 68%|██████▊   | 13/19 [00:24<00:10,  1.75s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy on pixelate_5: 75.98999738693237%\n",
+      "Processing saturate_5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 74%|███████▎  | 14/19 [00:25<00:08,  1.75s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy on saturate_5: 67.47999787330627%\n",
+      "Processing shot_noise_5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 79%|███████▉  | 15/19 [00:27<00:06,  1.75s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy on shot_noise_5: 48.44000041484833%\n",
+      "Processing snow_5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 84%|████████▍ | 16/19 [00:29<00:05,  1.75s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy on snow_5: 65.85999727249146%\n",
+      "Processing spatter_5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 89%|████████▉ | 17/19 [00:31<00:03,  1.74s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy on spatter_5: 63.81999850273132%\n",
+      "Processing speckle_noise_5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 95%|█████████▍| 18/19 [00:32<00:01,  1.74s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy on speckle_noise_5: 49.059998989105225%\n",
+      "Processing zoom_blur_5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 19/19 [00:34<00:00,  1.82s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy on zoom_blur_5: 77.02000141143799%\n",
+      "Mean Top-1 Accuracy: 60.51105205949984%\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Evaluate teacher model \n",
+    "teacher_model = get_training_model()\n",
+    "teacher_model.load_weights(\"teacher_model.h5\")\n",
+    "teacher_model.compile(loss=\"sparse_categorical_crossentropy\",\n",
+    "                         metrics=[\"accuracy\"])\n",
+    "acc_dict, mean_top_1 = evaluate_model(teacher_model)\n",
+    "print(f\"Mean Top-1 Accuracy: {mean_top_1}%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "hydraulic-finding",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|          | 0/19 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processing brightness_5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  5%|▌         | 1/19 [00:02<00:52,  2.92s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy on brightness_5: 79.80999946594238%\n",
+      "Processing contrast_5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 11%|█         | 2/19 [00:04<00:37,  2.23s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy on contrast_5: 20.76999992132187%\n",
+      "Processing defocus_blur_5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 16%|█▌        | 3/19 [00:06<00:32,  2.01s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy on defocus_blur_5: 68.98000240325928%\n",
+      "Processing elastic_5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 21%|██        | 4/19 [00:08<00:28,  1.90s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy on elastic_5: 71.53000235557556%\n",
+      "Processing fog_5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 26%|██▋       | 5/19 [00:09<00:25,  1.84s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy on fog_5: 46.140000224113464%\n",
+      "Processing frost_5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 32%|███▏      | 6/19 [00:11<00:23,  1.81s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy on frost_5: 57.56999850273132%\n",
+      "Processing frosted_glass_blur_5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 37%|███▋      | 7/19 [00:13<00:21,  1.79s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy on frosted_glass_blur_5: 57.03999996185303%\n",
+      "Processing gaussian_blur_5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 42%|████▏     | 8/19 [00:15<00:19,  1.78s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy on gaussian_blur_5: 63.429999351501465%\n",
+      "Processing gaussian_noise_5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 47%|████▋     | 9/19 [00:16<00:17,  1.77s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy on gaussian_noise_5: 44.06000077724457%\n",
+      "Processing impulse_noise_5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 53%|█████▎    | 10/19 [00:18<00:15,  1.76s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy on impulse_noise_5: 24.68000054359436%\n",
+      "Processing jpeg_compression_5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 58%|█████▊    | 11/19 [00:20<00:14,  1.76s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy on jpeg_compression_5: 74.55000281333923%\n",
+      "Processing motion_blur_5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 63%|██████▎   | 12/19 [00:22<00:12,  1.75s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy on motion_blur_5: 62.459999322891235%\n",
+      "Processing pixelate_5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 68%|██████▊   | 13/19 [00:23<00:10,  1.75s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy on pixelate_5: 70.16000151634216%\n",
+      "Processing saturate_5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 74%|███████▎  | 14/19 [00:25<00:08,  1.75s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy on saturate_5: 77.31000185012817%\n",
+      "Processing shot_noise_5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 79%|███████▉  | 15/19 [00:27<00:07,  1.75s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy on shot_noise_5: 48.60000014305115%\n",
+      "Processing snow_5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 84%|████████▍ | 16/19 [00:29<00:05,  1.75s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy on snow_5: 63.2099986076355%\n",
+      "Processing spatter_5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 89%|████████▉ | 17/19 [00:30<00:03,  1.75s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy on spatter_5: 65.49000144004822%\n",
+      "Processing speckle_noise_5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 95%|█████████▍| 18/19 [00:32<00:01,  1.75s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy on speckle_noise_5: 48.96999895572662%\n",
+      "Processing zoom_blur_5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 19/19 [00:34<00:00,  1.81s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy on zoom_blur_5: 75.85999965667725%\n",
+      "Mean Top-1 Accuracy: 58.98000041120931%\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Evaluate the corresponding student model\n",
+    "student_noisy = get_training_model()\n",
+    "student_noisy.load_weights(\"student_noisy.h5\")\n",
+    "student_noisy.compile(loss=\"sparse_categorical_crossentropy\",\n",
+    "                         metrics=[\"accuracy\"])\n",
+    "acc_dict, mean_top_1 = evaluate_model(student_noisy)\n",
+    "print(f\"Mean Top-1 Accuracy: {mean_top_1}%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "urban-sapphire",
+   "metadata": {},
+   "source": [
+    "## Evaluate on CIFAR-10 Test Set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "precious-release",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(_, _), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()\n",
+    "test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test))\n",
+    "test_ds = prepare_dataset(test_ds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "broadband-dream",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy with SWA teacher: 84.82%\n",
+      "Test accuracy with noisy SWA student: 85.24%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Evaluate teacher model trained with SWA\n",
+    "_, test_acc = teacher_model_swa.evaluate(test_ds, verbose=0)\n",
+    "print(\"Test accuracy with SWA teacher: {:.2f}%\".format(test_acc * 100))\n",
+    "\n",
+    "# Evaluate the corresponding student\n",
+    "_, test_acc = student_noisy_swa.evaluate(test_ds, verbose=0)\n",
+    "print(\"Test accuracy with noisy SWA student: {:.2f}%\".format(test_acc * 100))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "enormous-repository",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy with MA teacher: 83.88%\n",
+      "Test accuracy with noisy MA student: 84.42%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Evaluate teacher model trained with MA\n",
+    "_, test_acc = teacher_model_ma.evaluate(test_ds, verbose=0)\n",
+    "print(\"Test accuracy with MA teacher: {:.2f}%\".format(test_acc * 100))\n",
+    "\n",
+    "# Evaluate the corresponding student\n",
+    "_, test_acc = student_noisy_ma.evaluate(test_ds, verbose=0)\n",
+    "print(\"Test accuracy with noisy MA student: {:.2f}%\".format(test_acc * 100))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "dental-quest",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy with regular teacher: 83.20%\n",
+      "Test accuracy with regular noisy student: 82.16%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Evaluate regular teacher model\n",
+    "_, test_acc = teacher_model.evaluate(test_ds, verbose=0)\n",
+    "print(\"Test accuracy with regular teacher: {:.2f}%\".format(test_acc * 100))\n",
+    "\n",
+    "# Evaluate the corresponding student\n",
+    "_, test_acc = student_noisy.evaluate(test_ds, verbose=0)\n",
+    "print(\"Test accuracy with regular noisy student: {:.2f}%\".format(test_acc * 100))"
+   ]
   }
  ],
  "metadata": {