├── src ├── cover.png └── teaser.gif ├── LICENSE ├── .gitignore ├── README.md └── exps ├── toy_regression.py └── notebooks └── mnist_classification.ipynb /src/cover.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/cvlab-epfl/iter_unc/HEAD/src/cover.png -------------------------------------------------------------------------------- /src/teaser.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/cvlab-epfl/iter_unc/HEAD/src/teaser.gif -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2024 CVLAB @ EPFL 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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For a more nuclear 159 | # option (not recommended) you can uncomment the following to ignore the entire idea folder. 160 | #.idea/ 161 | 162 | **/.DS_Store 163 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Enabling Uncertainty Estimation in Iterative Neural Networks 2 | 3 | ![Project Page](./src/teaser.gif) 4 | 5 | [![arXiv](https://img.shields.io/badge/cs.CV-arXiv%3A2403.16732-blue?logo=arxiv&color=red)](https://arxiv.org/abs/2403.16732) 6 | [![Python](https://img.shields.io/badge/Python-3.10-blue?logo=python&color=blue)](https://www.python.org/downloads/release/python-31014/) 7 | [![Pytorch](https://img.shields.io/badge/Pytorch-2.2.1-blue?logo=pytorch&color=blue)](https://pytorch.org/) 8 | [![License: MIT](https://img.shields.io/badge/License-MIT-yellow)](https://github.com/cvlab-epfl/iter_unc/blob/main/LICENSE) 9 | 10 | ### [Project Page](https://www.norange.io/projects/unc_iter/) | [ICML Paper]() | [Poster](https://icml.cc/virtual/2024/poster/34213) 11 | 12 | > **For a quick tryout of the method, check the Colab notebooks below!** 13 | 14 | > **Please also refer to the [Zigzag](https://github.com/cvlab-epfl/zigzag) paper, which served as the foundation for our work.** 15 | 16 | ## Abstract 17 | 18 | Turning pass-through network architectures into iterative ones, which use their own output as input, is a well-known approach for boosting performance. In this paper, we argue that such architectures offer an additional benefit: The convergence rate of their successive outputs is highly correlated with the accuracy of the value to which they converge. Thus, we can use the convergence rate as a useful proxy for uncertainty. This results in an approach to uncertainty estimation that provides state-of-the-art estimates at a much lower computational cost than techniques like Ensembles, and without requiring any modifications to the original iterative model. We demonstrate its practical value by embedding it in two application domains: road detection in aerial images and the estimation of aerodynamic properties of 2D and 3D shapes. 19 | 20 | ## TL;DR 21 | 22 |

23 | Project or Page Cover 24 |

25 | 26 | 27 | **Uncertainty in recursive models:** Such models use their initial predictions as inputs to produce subsequent predictions. We display the output of three consecutive iterations of a network trained to compute distance maps to road pixels. **(Top:)** All roads are clearly visible. The three maps are similar and the per pixel variance is low. **(Bottom:)** The road in the red square is tree-covered. It is eventually detected properly but the variance is high. 28 | 29 | ## Experiments 30 | 31 | ### 1D Regression 32 | 33 | **Uncertainty Estimation for Regression:** The task is to regress $y$-axis values for $x$-axis data points drawn from the range $x \in [-1, 1.3]$ using a third-degree polynomial with added Gaussian noise. The method displays higher uncertainty or iteration variance for out-of-distribution inputs and lower for in-distribution samples. 34 | 35 | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/cvlab-epfl/iter_unc/blob/main/exps/notebooks/toy_regression.ipynb) 36 | 37 | ### MNIST Classification 38 | 39 | **MNIST vs FashionMNIST:** We train the networks on MNIST and compute the accuracy and calibration metrics (rAULC). We then use the uncertainty measure they produce to classify images from the test sets of MNIST and FashionMNIST as being within the MNIST distribution or not to compute the OOD metrics, ROC- and PR-AUCs. We use a standard architecture with several convolution and pooling layers, followed by fully connected layers with LeakyReLU activations. 40 | 41 | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/cvlab-epfl/iter_unc/blob/main/exps/notebooks/mnist_classification.ipynb) 42 | 43 | ### Delineation Experiments (TBD) 44 | 45 | In these experiments, we focus on the delineation task, particularly for road detection in aerial imagery. This task requires precise identification and outlining of narrow, intricate features within varied image datasets. Our experiments utilized the U-Net architecture, known for its efficacy in image segmentation. We employed it to classify pixels within the images as belonging to the target structures (roads) or not, producing a binary map as the final output. The networks were trained and tested on two public datasets, "RoadTracer" and "Massachusetts," which encompass a diverse range of urban and rural landscapes, thereby providing a comprehensive benchmark for our methods. 46 | 47 | ## Citation 48 | 49 | If you find this code useful, please consider citing our paper: 50 | 51 | > Durasov, Nikita, et al. "Enabling Uncertainty Estimation in Iterative Neural Networks", ICML, 2024. 52 | 53 | ```bibtex 54 | @inproceedings{ 55 | durasov2024enabling, 56 | title={Enabling Uncertainty Estimation in Iterative Neural Networks}, 57 | author={Nikita Durasov and Doruk Oner and Jonathan Donier and Hieu Le and Pascal Fua}, 58 | booktitle={Forty-first International Conference on Machine Learning}, 59 | year={2024} 60 | } 61 | ``` 62 | 63 | > Durasov, Nikita, et al. "ZigZag: Universal Sampling-free Uncertainty Estimation Through Two-Step Inference." TMLR 20224. 64 | 65 | ```bibtex 66 | @article{durasov2024zigzag, 67 | title = {ZigZag: Universal Sampling-free Uncertainty Estimation Through Two-Step Inference}, 68 | author = {Nikita Durasov and Nik Dorndorf and Hieu Le and Pascal Fua}, 69 | journal = {Transactions on Machine Learning Research}, 70 | issn = {2835-8856}, 71 | year = {2024} 72 | } 73 | ``` 74 | -------------------------------------------------------------------------------- /exps/toy_regression.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """simple_regression.ipynb 3 | 4 | Automatically generated by Colab. 5 | 6 | Original file is located at 7 | https://colab.research.google.com/drive/1687Kj81qQYGJ3BBoWiVYbtfHYrlm0DA6 8 | 9 | # Training Dataset 10 | """ 11 | 12 | import numpy as np 13 | 14 | import matplotlib.cm as cm # Import cm for colormap 15 | import matplotlib.pyplot as plt 16 | 17 | seed = 42 18 | 19 | import torch 20 | torch.manual_seed(seed) 21 | 22 | import numpy as np 23 | np.random.seed(seed) 24 | 25 | import random 26 | random.seed(seed) 27 | 28 | ###### Generate Data ###### 29 | def polynom(X): 30 | return 0 + X + -5 * X**2 + 1.5*X ** 3 31 | 32 | def generate(N=1000): 33 | X = np.random.uniform(-1, 1.5, N) 34 | Y = 0 + X + -5 * X**2 + 1.5*X ** 3 35 | Y += np.random.randn(Y.shape[0]) / 5 36 | return torch.tensor(X).float()[:, None], torch.tensor(Y)[:, None].float() 37 | 38 | X_data, Y_data = generate(100) 39 | Y_data += 3.5 40 | Y_data /= 15 41 | Y_data -= 0.2 42 | 43 | ###### Data Visualization ###### 44 | fig, (ax) = plt.subplots(1, 1, figsize=(10, 6)) 45 | X_min, X_max = -5, 5 46 | 47 | ax.grid() 48 | ax.scatter(X_data, Y_data, color="seagreen", label="data") 49 | 50 | Y_min, Y_max = -0.9, 0.7 51 | ax.fill_between([-1, 1.5], Y_min, Y_max, color='seagreen', alpha=0.1, label="In-distibution Region") 52 | ax.fill_between([1.5, X_max], Y_min, Y_max, color='red', alpha=0.1, label="Out-of-distibution Region") 53 | ax.fill_between([X_min, -1], Y_min, Y_max, color='red', alpha=0.1) 54 | 55 | ax.legend(fontsize=15, ncol=2, loc='upper left', framealpha=0.7) 56 | ax.grid() 57 | ax.set_xlim(X_min, X_max) 58 | ax.set_ylim(Y_min, Y_max) 59 | ax.set_yticks(np.linspace(Y_min, Y_max, 8).round(2)) 60 | ax.set_yticklabels(np.linspace(Y_min, Y_max, 8).round(2), fontsize=15) 61 | ax.set_xticks(range(-5, 5, 1)) 62 | ax.set_xticklabels(range(-5, 5, 1), fontsize=15) 63 | ax.grid() 64 | 65 | ###### Model Architechture ###### 66 | 67 | import torch 68 | import numpy as np 69 | import matplotlib.pyplot as plt 70 | import torch.optim as optim 71 | 72 | A = -1 # initial value of the second argument 73 | class Net(torch.nn.Module): 74 | 75 | def __init__(self, hidden_layer=100): 76 | super().__init__() 77 | 78 | self.hidden_layer = hidden_layer 79 | self.fc1 = torch.nn.Linear(2, hidden_layer) 80 | self.fc2 = torch.nn.Linear(hidden_layer, 1) 81 | self.activation = torch.nn.LeakyReLU() 82 | 83 | def forward(self, x, y=None): 84 | 85 | if y is None: 86 | x = torch.concat([x, -A * torch.ones([x.shape[0], 1])], dim=1) 87 | 88 | else: 89 | x = torch.concat([x, y], dim=1) 90 | 91 | x = self.activation(self.fc1(x)) 92 | x = self.fc2(x) 93 | 94 | return x 95 | 96 | ###### Optimization Step ###### 97 | I = 3 # number of iterations 98 | def step(epoch, model, optimizer, criterion, X_data, Y_data, l_gradient_penalty): 99 | 100 | model.train() 101 | optimizer.zero_grad() 102 | 103 | Y_0 = network(X_data, -A*torch.ones_like(Y_data)) 104 | loss = criterion(Y_0, Y_data) 105 | 106 | Y_t = Y_0 107 | for _ in range(I): 108 | Y_t = network(X_data, Y_t) 109 | loss += criterion(Y_t, Y_data) 110 | 111 | loss.backward() 112 | optimizer.step() 113 | 114 | return loss.item() 115 | 116 | epochs = 150 117 | 118 | network = Net(hidden_layer=128) 119 | 120 | criterion = torch.nn.MSELoss() 121 | optimizer = optim.Adam(network.parameters(), lr=0.05) 122 | 123 | network.train() 124 | 125 | ###### Training ###### 126 | for epoch in range(epochs): 127 | network.train() 128 | step(epoch, network, optimizer, criterion, X_data, Y_data, 3e-4) 129 | 130 | ###### Visualize Final Result ###### 131 | network.eval() 132 | 133 | X_min, X_max = -5, 5 134 | Xt = torch.linspace(X_min, X_max, 100)[:, None] 135 | 136 | Y_1 = network(Xt) 137 | Y_t = Y_1.detach() 138 | 139 | preds = [Y_t] 140 | for _ in range(I): 141 | Y_t = network(Xt, Y_t) 142 | Y_t = Y_t.detach() 143 | Y_final = Y_t 144 | preds.append(Y_final) 145 | 146 | preds = torch.stack(preds) 147 | 148 | error = torch.std(preds, dim=0).detach() 149 | 150 | # Create figure and subplots 151 | fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 6)) 152 | 153 | ax = ax1 154 | ax.grid() 155 | 156 | cmap = cm.get_cmap('winter', len(preds)) # 'winter' colormap goes from blue to green 157 | 158 | ax.scatter(X_data, Y_data, color="seagreen", label="data") 159 | for i, p in enumerate(preds): 160 | color = cmap(i) 161 | ax.plot(Xt, p, c=color, label=f"$y_{i}$") 162 | 163 | Y_min, Y_max = -0.9, 0.7 164 | ax.fill_between([-1, 1.5], Y_min, Y_max, color='seagreen', alpha=0.1, label="In-distibution Region") 165 | ax.fill_between([1.5, X_max], Y_min, Y_max, color='red', alpha=0.1, label="Out-of-distibution Region") 166 | ax.fill_between([X_min, -1], Y_min, Y_max, color='red', alpha=0.1) 167 | ax.fill_between(Xt[:, 0], preds.min(dim=0)[0][:, 0], preds.max(dim=0)[0][:, 0], alpha=0.4, color="skyblue", label="$\sigma_{pred}$") 168 | 169 | ax.legend(fontsize=15, ncol=2, loc='upper left', framealpha=0.7) 170 | ax.grid() 171 | ax.set_xlim(X_min, X_max) 172 | ax.set_ylim(Y_min, Y_max) 173 | ax.set_yticks(np.linspace(Y_min, Y_max, 8).round(2)) 174 | ax.set_yticklabels(np.linspace(Y_min, Y_max, 8).round(2), fontsize=15) 175 | ax.set_xticks(range(-5, 5, 1)) 176 | ax.set_xticklabels(range(-5, 5, 1), fontsize=15) 177 | ax.grid() 178 | 179 | ax = ax2 180 | ax.set_title("Iteration Variance") 181 | plt.plot(Xt[:, 0], error, c="dodgerblue", label="Iteration Variance") 182 | ax.fill_between([-1, 1.5], 0, 1, color='seagreen', alpha=0.1, label="In-distibution Region") 183 | ax.fill_between([1.5, X_max], 0, 1, color='red', alpha=0.1, label="Out-of-distibution Region") 184 | ax.fill_between([X_min, -1], 0, 1, color='red', alpha=0.1) 185 | ax.legend(fontsize=15, loc='upper left', framealpha=0.7) 186 | ax.grid(True) 187 | ax.set_xlim(-5, 5) 188 | ax.set_ylim(0, 1) 189 | ax.set_xticks(range(-5, 5, 1)) 190 | ax.set_xticklabels(range(-5, 5, 1), fontsize=15) 191 | 192 | fig.suptitle(f"Epoch #{epoch}", fontsize=16) 193 | 194 | plt.tight_layout() 195 | plt.show() 196 | 197 | -------------------------------------------------------------------------------- /exps/notebooks/mnist_classification.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [], 7 | "gpuType": "T4" 8 | }, 9 | "kernelspec": { 10 | "name": "python3", 11 | "display_name": "Python 3" 12 | }, 13 | "language_info": { 14 | "name": "python" 15 | }, 16 | "accelerator": "GPU" 17 | }, 18 | "cells": [ 19 | { 20 | "cell_type": "markdown", 21 | "source": [ 22 | "# MNIST Classification with Iterative Uncertainty\n", 23 | "\n", 24 | "In this notebook, we will implement a simple neural network to classify handwritten digits from the MNIST dataset using PyTorch. On top of it, we will apply the iterative uncertainty approach and evaluate its uncertainty quality in terms of out-of-distribution detection." 25 | ], 26 | "metadata": { 27 | "id": "zhr7Isu5wkQj" 28 | } 29 | }, 30 | { 31 | "cell_type": "code", 32 | "source": [ 33 | "import torch\n", 34 | "import torchvision\n", 35 | "import numpy as np\n", 36 | "import random\n", 37 | "\n", 38 | "n_epochs = 3 # number of epochs for training\n", 39 | "batch_size_train = 64 # batch size for training\n", 40 | "batch_size_test = 1000 # batch size for testing\n", 41 | "learning_rate = 0.01 # learning rate for Adam\n", 42 | "momentum = 0.5 # optimizer momentum\n", 43 | "log_interval = 10 # logging interval for metrics\n", 44 | "device = \"cuda\"\n", 45 | "\n", 46 | "# fixing random seeds\n", 47 | "random_seed = 6\n", 48 | "torch.backends.cudnn.enabled = False\n", 49 | "torch.manual_seed(random_seed)\n", 50 | "np.random.seed(random_seed)\n", 51 | "random.seed(random_seed)" 52 | ], 53 | "metadata": { 54 | "id": "N17oLGkpHftI" 55 | }, 56 | "execution_count": 1, 57 | "outputs": [] 58 | }, 59 | { 60 | "cell_type": "markdown", 61 | "source": [ 62 | "# Train / Test data loading\n", 63 | "\n", 64 | "The MNIST dataset is a collection of handwritten digits commonly used for training and testing in the field of machine learning. It contains 70,000 grayscale images of handwritten digits, split into 60,000 training images and 10,000 test images. Each image is 28x28 pixels in size and represents a digit from 0 to 9." 65 | ], 66 | "metadata": { 67 | "id": "lx0CY2jPw_Fn" 68 | } 69 | }, 70 | { 71 | "cell_type": "code", 72 | "source": [ 73 | "train_loader = torch.utils.data.DataLoader(\n", 74 | " torchvision.datasets.MNIST('./MNIST/', train=True, download=True,\n", 75 | " transform=torchvision.transforms.Compose([\n", 76 | " torchvision.transforms.ToTensor(),\n", 77 | " torchvision.transforms.Normalize(\n", 78 | " (0.1307,), (0.3081,))\n", 79 | " ])),\n", 80 | " batch_size=batch_size_train, shuffle=True)\n", 81 | "\n", 82 | "test_loader = torch.utils.data.DataLoader(\n", 83 | " torchvision.datasets.MNIST('./MNIST/', train=False, download=True,\n", 84 | " transform=torchvision.transforms.Compose([\n", 85 | " torchvision.transforms.ToTensor(),\n", 86 | " torchvision.transforms.Normalize(\n", 87 | " (0.1307,), (0.3081,))\n", 88 | " ])),\n", 89 | " batch_size=batch_size_test, shuffle=True)" 90 | ], 91 | "metadata": { 92 | "id": "wIp68p_WHlVt" 93 | }, 94 | "execution_count": 2, 95 | "outputs": [] 96 | }, 97 | { 98 | "cell_type": "code", 99 | "source": [ 100 | "import matplotlib.pyplot as plt\n", 101 | "\n", 102 | "images, targets = next(iter(train_loader))\n", 103 | "\n", 104 | "# Number of images you want to display\n", 105 | "num_images = 10\n", 106 | "\n", 107 | "# Create a figure and a row of subplots\n", 108 | "fig, axes = plt.subplots(1, num_images, figsize=(15, 3))\n", 109 | "\n", 110 | "# Plot each image on a separate subplot\n", 111 | "for i in range(num_images):\n", 112 | " axes[i].imshow(images[i, 0], cmap='gray')\n", 113 | " axes[i].axis('off') # Hide axis\n", 114 | "\n", 115 | "plt.show()" 116 | ], 117 | "metadata": { 118 | "id": "90b10PHvxE7i", 119 | "outputId": "955d5cde-8ded-41b3-bc74-d979fc1b7e4e", 120 | "colab": { 121 | "base_uri": "https://localhost:8080/", 122 | "height": 155 123 | } 124 | }, 125 | "execution_count": 3, 126 | "outputs": [ 127 | { 128 | "output_type": "display_data", 129 | "data": { 130 | "text/plain": [ 131 | "
" 132 | ], 133 | "image/png": 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134 | }, 135 | "metadata": {} 136 | } 137 | ] 138 | }, 139 | { 140 | "cell_type": "markdown", 141 | "source": [ 142 | "## NN Architechture (Iterative schema)\n", 143 | "\n", 144 | "We define a simple neural network model with several Conv2D layers followed by pooling layers and some fully connected layers. The input layer takes 28x28 MNIST images. The hidden layers have (320, 50) and (50, 50) units respectively, and the output layer has 10 units (one for each digit).\n", 145 | "\n", 146 | "The only difference from the common architecture is that the forward function, in addition to taking an input image, can also take the output from the previous step. Therefore, our model can process inputs iteratively.\n" 147 | ], 148 | "metadata": { 149 | "id": "wpdY17tRxRTG" 150 | } 151 | }, 152 | { 153 | "cell_type": "code", 154 | "source": [ 155 | "import torch.nn as nn\n", 156 | "import torch.nn.functional as F\n", 157 | "import torch.optim as optim\n", 158 | "\n", 159 | "class Net(nn.Module):\n", 160 | " def __init__(self):\n", 161 | " super(Net, self).__init__()\n", 162 | " self.conv1 = nn.Conv2d(1, 10, kernel_size=5)\n", 163 | " self.conv2 = nn.Conv2d(10, 20, kernel_size=5)\n", 164 | " self.fc1 = nn.Linear(320, 50)\n", 165 | " self.fc2 = nn.Linear(50, 50)\n", 166 | " self.fc3 = nn.Linear(50, 10)\n", 167 | " self.activation = nn.ELU()\n", 168 | "\n", 169 | " self.cf = nn.Linear(10, 50)\n", 170 | "\n", 171 | " def forward(self, x, pred_prob=None):\n", 172 | " x = self.activation(F.max_pool2d(self.conv1(x), 2))\n", 173 | " x = self.activation(F.max_pool2d(self.conv2(x), 2))\n", 174 | " x = x.view(-1, 320)\n", 175 | " x = self.activation(self.fc1(x))\n", 176 | "\n", 177 | " # iterative part, if \"pred_prob\"\n", 178 | " # is not None, we use it for inference\n", 179 | " if pred_prob is not None:\n", 180 | " pred_prob = torch.nn.functional.softmax(pred_prob, 1)\n", 181 | " x += self.activation(self.cf(pred_prob))\n", 182 | "\n", 183 | " x = self.activation(self.fc2(x))\n", 184 | " x = self.fc3(x)\n", 185 | " return F.log_softmax(x)" 186 | ], 187 | "metadata": { 188 | "id": "t3CXPKqeHlzW" 189 | }, 190 | "execution_count": 4, 191 | "outputs": [] 192 | }, 193 | { 194 | "cell_type": "markdown", 195 | "source": [ 196 | "# Model Training" 197 | ], 198 | "metadata": { 199 | "id": "sGGLrajiykjK" 200 | } 201 | }, 202 | { 203 | "cell_type": "code", 204 | "source": [ 205 | "network = Net().to(device)\n", 206 | "optimizer = optim.Adam(network.parameters(), lr=learning_rate)\n", 207 | "\n", 208 | "train_losses = []\n", 209 | "train_counter = []\n", 210 | "test_losses = []\n", 211 | "test_counter = [i*len(train_loader.dataset) for i in range(n_epochs + 1)]" 212 | ], 213 | "metadata": { 214 | "id": "Av8yDtCaHwE1" 215 | }, 216 | "execution_count": 5, 217 | "outputs": [] 218 | }, 219 | { 220 | "cell_type": "code", 221 | "source": [ 222 | "# helper function for training inference\n", 223 | "def inference_train(x, N = 3):\n", 224 | " preds = []\n", 225 | " pred = None\n", 226 | " for _ in range(N):\n", 227 | " pred = network(x, pred)\n", 228 | " preds.append(pred)\n", 229 | " return torch.cat(preds)\n", 230 | "\n", 231 | "# helper function for testing inference\n", 232 | "def inference_test(x, N = 3):\n", 233 | " preds = []\n", 234 | " pred = None\n", 235 | " for _ in range(N):\n", 236 | " pred = torch.nn.functional.softmax(network(x, pred), 1)\n", 237 | " preds.append(pred)\n", 238 | "\n", 239 | " # similar to ensembles, final predictions\n", 240 | " # is an average of predictions\n", 241 | " return sum(preds) / N" 242 | ], 243 | "metadata": { 244 | "id": "xdh7CeqpH8Rs" 245 | }, 246 | "execution_count": 6, 247 | "outputs": [] 248 | }, 249 | { 250 | "cell_type": "code", 251 | "source": [ 252 | "def train(epoch):\n", 253 | " network.train()\n", 254 | " for batch_idx, (data, target) in enumerate(train_loader):\n", 255 | "\n", 256 | " data = data.to(device)\n", 257 | " target = target.to(device)\n", 258 | "\n", 259 | " optimizer.zero_grad()\n", 260 | " output = inference_train(data)\n", 261 | "\n", 262 | " # For further details, see Eq. 4 in https://arxiv.org/pdf/2403.16732\n", 263 | " # The first input with an additional \"blank\" channel, the first term in Eq. 1\n", 264 | " loss = F.nll_loss(output, target.tile(3))\n", 265 | "\n", 266 | " loss.backward()\n", 267 | " optimizer.step()\n", 268 | " if batch_idx % log_interval == 0:\n", 269 | " print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n", 270 | " epoch, batch_idx * len(data), len(train_loader.dataset),\n", 271 | " 100. * batch_idx / len(train_loader), loss.item()))\n", 272 | " train_losses.append(loss.item())\n", 273 | " train_counter.append(\n", 274 | " (batch_idx*64) + ((epoch-1)*len(train_loader.dataset)))\n", 275 | "\n", 276 | "def test():\n", 277 | " network.eval()\n", 278 | " test_loss = 0\n", 279 | " correct = 0\n", 280 | " with torch.no_grad():\n", 281 | " for data, target in test_loader:\n", 282 | " data = data.to(device)\n", 283 | " target = target.to(device)\n", 284 | " output = inference_test(data)\n", 285 | " test_loss += F.nll_loss(output, target, size_average=False).item()\n", 286 | " pred = output.data.max(1, keepdim=True)[1]\n", 287 | " correct += pred.eq(target.data.view_as(pred)).sum()\n", 288 | " test_loss /= len(test_loader.dataset)\n", 289 | " test_losses.append(test_loss)\n", 290 | " print('\\nTest set: Avg. loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\n", 291 | " test_loss, correct, len(test_loader.dataset),\n", 292 | " 100. * correct / len(test_loader.dataset)))" 293 | ], 294 | "metadata": { 295 | "id": "l8J7gph4IBK-" 296 | }, 297 | "execution_count": 7, 298 | "outputs": [] 299 | }, 300 | { 301 | "cell_type": "code", 302 | "source": [ 303 | "test()\n", 304 | "for epoch in range(1, n_epochs + 1):\n", 305 | " train(epoch)\n", 306 | " test()\n", 307 | "\n", 308 | "for param_group in optimizer.param_groups:\n", 309 | " param_group['lr'] = 1e-3\n", 310 | "\n", 311 | "for _ in range(2):\n", 312 | " train(epoch)\n", 313 | " test()" 314 | ], 315 | "metadata": { 316 | "colab": { 317 | "base_uri": "https://localhost:8080/" 318 | }, 319 | "id": "SQwPqCUtIELo", 320 | "outputId": "219030a6-b56a-4031-d1e0-9662e267e33f" 321 | }, 322 | "execution_count": 8, 323 | "outputs": [ 324 | { 325 | "output_type": "stream", 326 | "name": "stderr", 327 | "text": [ 328 | ":31: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n", 329 | " return F.log_softmax(x)\n", 330 | "/usr/local/lib/python3.10/dist-packages/torch/nn/_reduction.py:42: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.\n", 331 | " warnings.warn(warning.format(ret))\n" 332 | ] 333 | }, 334 | { 335 | "output_type": "stream", 336 | "name": "stdout", 337 | "text": [ 338 | "\n", 339 | "Test set: Avg. loss: -0.1003, Accuracy: 1781/10000 (18%)\n", 340 | "\n", 341 | "Train Epoch: 1 [0/60000 (0%)]\tLoss: 2.312376\n", 342 | "Train Epoch: 1 [640/60000 (1%)]\tLoss: 1.144794\n", 343 | "Train Epoch: 1 [1280/60000 (2%)]\tLoss: 0.467108\n", 344 | "Train Epoch: 1 [1920/60000 (3%)]\tLoss: 0.602299\n", 345 | "Train Epoch: 1 [2560/60000 (4%)]\tLoss: 0.497253\n", 346 | "Train Epoch: 1 [3200/60000 (5%)]\tLoss: 0.241655\n", 347 | "Train Epoch: 1 [3840/60000 (6%)]\tLoss: 0.283207\n", 348 | "Train Epoch: 1 [4480/60000 (7%)]\tLoss: 0.301319\n", 349 | "Train Epoch: 1 [5120/60000 (9%)]\tLoss: 0.293853\n", 350 | "Train Epoch: 1 [5760/60000 (10%)]\tLoss: 0.265691\n", 351 | "Train Epoch: 1 [6400/60000 (11%)]\tLoss: 0.175180\n", 352 | "Train Epoch: 1 [7040/60000 (12%)]\tLoss: 0.286978\n", 353 | "Train Epoch: 1 [7680/60000 (13%)]\tLoss: 0.053208\n", 354 | "Train Epoch: 1 [8320/60000 (14%)]\tLoss: 0.179960\n", 355 | "Train Epoch: 1 [8960/60000 (15%)]\tLoss: 0.142399\n", 356 | "Train Epoch: 1 [9600/60000 (16%)]\tLoss: 0.080546\n", 357 | "Train Epoch: 1 [10240/60000 (17%)]\tLoss: 0.230218\n", 358 | "Train Epoch: 1 [10880/60000 (18%)]\tLoss: 0.307157\n", 359 | "Train Epoch: 1 [11520/60000 (19%)]\tLoss: 0.316138\n", 360 | "Train Epoch: 1 [12160/60000 (20%)]\tLoss: 0.254721\n", 361 | "Train Epoch: 1 [12800/60000 (21%)]\tLoss: 0.171003\n", 362 | "Train Epoch: 1 [13440/60000 (22%)]\tLoss: 0.273300\n", 363 | "Train Epoch: 1 [14080/60000 (23%)]\tLoss: 0.064190\n", 364 | "Train Epoch: 1 [14720/60000 (25%)]\tLoss: 0.292505\n", 365 | "Train Epoch: 1 [15360/60000 (26%)]\tLoss: 0.099401\n", 366 | "Train Epoch: 1 [16000/60000 (27%)]\tLoss: 0.321094\n", 367 | "Train Epoch: 1 [16640/60000 (28%)]\tLoss: 0.156088\n", 368 | "Train Epoch: 1 [17280/60000 (29%)]\tLoss: 0.551294\n", 369 | "Train Epoch: 1 [17920/60000 (30%)]\tLoss: 0.109925\n", 370 | "Train Epoch: 1 [18560/60000 (31%)]\tLoss: 0.060247\n", 371 | "Train Epoch: 1 [19200/60000 (32%)]\tLoss: 0.017538\n", 372 | "Train Epoch: 1 [19840/60000 (33%)]\tLoss: 0.154289\n", 373 | "Train Epoch: 1 [20480/60000 (34%)]\tLoss: 0.164849\n", 374 | "Train Epoch: 1 [21120/60000 (35%)]\tLoss: 0.104086\n", 375 | "Train Epoch: 1 [21760/60000 (36%)]\tLoss: 0.261193\n", 376 | "Train Epoch: 1 [22400/60000 (37%)]\tLoss: 0.080790\n", 377 | "Train Epoch: 1 [23040/60000 (38%)]\tLoss: 0.309249\n", 378 | "Train Epoch: 1 [23680/60000 (39%)]\tLoss: 0.038709\n", 379 | "Train Epoch: 1 [24320/60000 (41%)]\tLoss: 0.208796\n", 380 | "Train Epoch: 1 [24960/60000 (42%)]\tLoss: 0.018224\n", 381 | "Train Epoch: 1 [25600/60000 (43%)]\tLoss: 0.165220\n", 382 | "Train Epoch: 1 [26240/60000 (44%)]\tLoss: 0.111875\n", 383 | "Train Epoch: 1 [26880/60000 (45%)]\tLoss: 0.081600\n", 384 | "Train Epoch: 1 [27520/60000 (46%)]\tLoss: 0.040166\n", 385 | "Train Epoch: 1 [28160/60000 (47%)]\tLoss: 0.168901\n", 386 | "Train Epoch: 1 [28800/60000 (48%)]\tLoss: 0.041811\n", 387 | "Train Epoch: 1 [29440/60000 (49%)]\tLoss: 0.152959\n", 388 | "Train Epoch: 1 [30080/60000 (50%)]\tLoss: 0.113740\n", 389 | "Train Epoch: 1 [30720/60000 (51%)]\tLoss: 0.089509\n", 390 | "Train Epoch: 1 [31360/60000 (52%)]\tLoss: 0.188480\n", 391 | "Train Epoch: 1 [32000/60000 (53%)]\tLoss: 0.253904\n", 392 | "Train Epoch: 1 [32640/60000 (54%)]\tLoss: 0.244142\n", 393 | "Train Epoch: 1 [33280/60000 (55%)]\tLoss: 0.286333\n", 394 | "Train Epoch: 1 [33920/60000 (57%)]\tLoss: 0.075342\n", 395 | "Train Epoch: 1 [34560/60000 (58%)]\tLoss: 0.195837\n", 396 | "Train Epoch: 1 [35200/60000 (59%)]\tLoss: 0.148247\n", 397 | "Train Epoch: 1 [35840/60000 (60%)]\tLoss: 0.240426\n", 398 | "Train Epoch: 1 [36480/60000 (61%)]\tLoss: 0.056933\n", 399 | "Train Epoch: 1 [37120/60000 (62%)]\tLoss: 0.191502\n", 400 | "Train Epoch: 1 [37760/60000 (63%)]\tLoss: 0.144343\n", 401 | "Train Epoch: 1 [38400/60000 (64%)]\tLoss: 0.178543\n", 402 | "Train Epoch: 1 [39040/60000 (65%)]\tLoss: 0.156982\n", 403 | "Train Epoch: 1 [39680/60000 (66%)]\tLoss: 0.231199\n", 404 | "Train Epoch: 1 [40320/60000 (67%)]\tLoss: 0.193805\n", 405 | "Train Epoch: 1 [40960/60000 (68%)]\tLoss: 0.430056\n", 406 | "Train Epoch: 1 [41600/60000 (69%)]\tLoss: 0.039209\n", 407 | "Train Epoch: 1 [42240/60000 (70%)]\tLoss: 0.271172\n", 408 | "Train Epoch: 1 [42880/60000 (71%)]\tLoss: 0.039059\n", 409 | "Train Epoch: 1 [43520/60000 (72%)]\tLoss: 0.083934\n", 410 | "Train Epoch: 1 [44160/60000 (74%)]\tLoss: 0.143705\n", 411 | "Train Epoch: 1 [44800/60000 (75%)]\tLoss: 0.014715\n", 412 | "Train Epoch: 1 [45440/60000 (76%)]\tLoss: 0.034582\n", 413 | "Train Epoch: 1 [46080/60000 (77%)]\tLoss: 0.046080\n", 414 | "Train Epoch: 1 [46720/60000 (78%)]\tLoss: 0.142156\n", 415 | "Train Epoch: 1 [47360/60000 (79%)]\tLoss: 0.485598\n", 416 | "Train Epoch: 1 [48000/60000 (80%)]\tLoss: 0.328156\n", 417 | "Train Epoch: 1 [48640/60000 (81%)]\tLoss: 0.087537\n", 418 | "Train Epoch: 1 [49280/60000 (82%)]\tLoss: 0.017084\n", 419 | "Train Epoch: 1 [49920/60000 (83%)]\tLoss: 0.011264\n", 420 | "Train Epoch: 1 [50560/60000 (84%)]\tLoss: 0.027894\n", 421 | "Train Epoch: 1 [51200/60000 (85%)]\tLoss: 0.208086\n", 422 | "Train Epoch: 1 [51840/60000 (86%)]\tLoss: 0.125015\n", 423 | "Train Epoch: 1 [52480/60000 (87%)]\tLoss: 0.127294\n", 424 | "Train Epoch: 1 [53120/60000 (88%)]\tLoss: 0.238284\n", 425 | "Train Epoch: 1 [53760/60000 (90%)]\tLoss: 0.030879\n", 426 | "Train Epoch: 1 [54400/60000 (91%)]\tLoss: 0.089591\n", 427 | "Train Epoch: 1 [55040/60000 (92%)]\tLoss: 0.049601\n", 428 | "Train Epoch: 1 [55680/60000 (93%)]\tLoss: 0.666180\n", 429 | "Train Epoch: 1 [56320/60000 (94%)]\tLoss: 0.067744\n", 430 | "Train Epoch: 1 [56960/60000 (95%)]\tLoss: 0.048680\n", 431 | "Train Epoch: 1 [57600/60000 (96%)]\tLoss: 0.164761\n", 432 | "Train Epoch: 1 [58240/60000 (97%)]\tLoss: 0.180428\n", 433 | "Train Epoch: 1 [58880/60000 (98%)]\tLoss: 0.137088\n", 434 | "Train Epoch: 1 [59520/60000 (99%)]\tLoss: 0.089983\n", 435 | "\n", 436 | "Test set: Avg. loss: -0.9662, Accuracy: 9717/10000 (97%)\n", 437 | "\n", 438 | "Train Epoch: 2 [0/60000 (0%)]\tLoss: 0.021369\n", 439 | "Train Epoch: 2 [640/60000 (1%)]\tLoss: 0.112997\n", 440 | "Train Epoch: 2 [1280/60000 (2%)]\tLoss: 0.182833\n", 441 | "Train Epoch: 2 [1920/60000 (3%)]\tLoss: 0.109863\n", 442 | "Train Epoch: 2 [2560/60000 (4%)]\tLoss: 0.066127\n", 443 | "Train Epoch: 2 [3200/60000 (5%)]\tLoss: 0.312570\n", 444 | "Train Epoch: 2 [3840/60000 (6%)]\tLoss: 0.107947\n", 445 | "Train Epoch: 2 [4480/60000 (7%)]\tLoss: 0.129223\n", 446 | "Train Epoch: 2 [5120/60000 (9%)]\tLoss: 0.006381\n", 447 | "Train Epoch: 2 [5760/60000 (10%)]\tLoss: 0.038859\n", 448 | "Train Epoch: 2 [6400/60000 (11%)]\tLoss: 0.013144\n", 449 | "Train Epoch: 2 [7040/60000 (12%)]\tLoss: 0.020549\n", 450 | "Train Epoch: 2 [7680/60000 (13%)]\tLoss: 0.011785\n", 451 | "Train Epoch: 2 [8320/60000 (14%)]\tLoss: 0.231971\n", 452 | "Train Epoch: 2 [8960/60000 (15%)]\tLoss: 0.147990\n", 453 | "Train Epoch: 2 [9600/60000 (16%)]\tLoss: 0.171210\n", 454 | "Train Epoch: 2 [10240/60000 (17%)]\tLoss: 0.147863\n", 455 | "Train Epoch: 2 [10880/60000 (18%)]\tLoss: 0.333462\n", 456 | "Train Epoch: 2 [11520/60000 (19%)]\tLoss: 0.023902\n", 457 | "Train Epoch: 2 [12160/60000 (20%)]\tLoss: 0.224719\n", 458 | "Train Epoch: 2 [12800/60000 (21%)]\tLoss: 0.025668\n", 459 | "Train Epoch: 2 [13440/60000 (22%)]\tLoss: 0.064675\n", 460 | "Train Epoch: 2 [14080/60000 (23%)]\tLoss: 0.037010\n", 461 | "Train Epoch: 2 [14720/60000 (25%)]\tLoss: 0.050318\n", 462 | "Train Epoch: 2 [15360/60000 (26%)]\tLoss: 0.083974\n", 463 | "Train Epoch: 2 [16000/60000 (27%)]\tLoss: 0.138085\n", 464 | "Train Epoch: 2 [16640/60000 (28%)]\tLoss: 0.217917\n", 465 | "Train Epoch: 2 [17280/60000 (29%)]\tLoss: 0.116676\n", 466 | "Train Epoch: 2 [17920/60000 (30%)]\tLoss: 0.034423\n", 467 | "Train Epoch: 2 [18560/60000 (31%)]\tLoss: 0.032855\n", 468 | "Train Epoch: 2 [19200/60000 (32%)]\tLoss: 0.225311\n", 469 | "Train Epoch: 2 [19840/60000 (33%)]\tLoss: 0.006941\n", 470 | "Train Epoch: 2 [20480/60000 (34%)]\tLoss: 0.072871\n", 471 | "Train Epoch: 2 [21120/60000 (35%)]\tLoss: 0.058232\n", 472 | "Train Epoch: 2 [21760/60000 (36%)]\tLoss: 0.015516\n", 473 | "Train Epoch: 2 [22400/60000 (37%)]\tLoss: 0.105818\n", 474 | "Train Epoch: 2 [23040/60000 (38%)]\tLoss: 0.127326\n", 475 | "Train Epoch: 2 [23680/60000 (39%)]\tLoss: 0.212847\n", 476 | "Train Epoch: 2 [24320/60000 (41%)]\tLoss: 0.173671\n", 477 | "Train Epoch: 2 [24960/60000 (42%)]\tLoss: 0.158303\n", 478 | "Train Epoch: 2 [25600/60000 (43%)]\tLoss: 0.514192\n", 479 | "Train Epoch: 2 [26240/60000 (44%)]\tLoss: 0.491562\n", 480 | "Train Epoch: 2 [26880/60000 (45%)]\tLoss: 0.124600\n", 481 | "Train Epoch: 2 [27520/60000 (46%)]\tLoss: 0.151669\n", 482 | "Train Epoch: 2 [28160/60000 (47%)]\tLoss: 0.042192\n", 483 | "Train Epoch: 2 [28800/60000 (48%)]\tLoss: 0.102364\n", 484 | "Train Epoch: 2 [29440/60000 (49%)]\tLoss: 0.048251\n", 485 | "Train Epoch: 2 [30080/60000 (50%)]\tLoss: 0.084469\n", 486 | "Train Epoch: 2 [30720/60000 (51%)]\tLoss: 0.548334\n", 487 | "Train Epoch: 2 [31360/60000 (52%)]\tLoss: 0.256530\n", 488 | "Train Epoch: 2 [32000/60000 (53%)]\tLoss: 0.018591\n", 489 | "Train Epoch: 2 [32640/60000 (54%)]\tLoss: 0.255403\n", 490 | "Train Epoch: 2 [33280/60000 (55%)]\tLoss: 0.034644\n", 491 | "Train Epoch: 2 [33920/60000 (57%)]\tLoss: 0.044591\n", 492 | "Train Epoch: 2 [34560/60000 (58%)]\tLoss: 0.040136\n", 493 | "Train Epoch: 2 [35200/60000 (59%)]\tLoss: 0.105666\n", 494 | "Train Epoch: 2 [35840/60000 (60%)]\tLoss: 0.225017\n", 495 | "Train Epoch: 2 [36480/60000 (61%)]\tLoss: 0.076942\n", 496 | "Train Epoch: 2 [37120/60000 (62%)]\tLoss: 0.142982\n", 497 | "Train Epoch: 2 [37760/60000 (63%)]\tLoss: 0.008805\n", 498 | "Train Epoch: 2 [38400/60000 (64%)]\tLoss: 0.030216\n", 499 | "Train Epoch: 2 [39040/60000 (65%)]\tLoss: 0.525962\n", 500 | "Train Epoch: 2 [39680/60000 (66%)]\tLoss: 0.081722\n", 501 | "Train Epoch: 2 [40320/60000 (67%)]\tLoss: 0.293573\n", 502 | "Train Epoch: 2 [40960/60000 (68%)]\tLoss: 0.278041\n", 503 | "Train Epoch: 2 [41600/60000 (69%)]\tLoss: 0.066600\n", 504 | "Train Epoch: 2 [42240/60000 (70%)]\tLoss: 0.083429\n", 505 | "Train Epoch: 2 [42880/60000 (71%)]\tLoss: 0.176400\n", 506 | "Train Epoch: 2 [43520/60000 (72%)]\tLoss: 0.070648\n", 507 | "Train Epoch: 2 [44160/60000 (74%)]\tLoss: 0.136096\n", 508 | "Train Epoch: 2 [44800/60000 (75%)]\tLoss: 0.072206\n", 509 | "Train Epoch: 2 [45440/60000 (76%)]\tLoss: 0.044717\n", 510 | "Train Epoch: 2 [46080/60000 (77%)]\tLoss: 0.054737\n", 511 | "Train Epoch: 2 [46720/60000 (78%)]\tLoss: 0.155012\n", 512 | "Train Epoch: 2 [47360/60000 (79%)]\tLoss: 0.033407\n", 513 | "Train Epoch: 2 [48000/60000 (80%)]\tLoss: 0.572021\n", 514 | "Train Epoch: 2 [48640/60000 (81%)]\tLoss: 0.045251\n", 515 | "Train Epoch: 2 [49280/60000 (82%)]\tLoss: 0.180520\n", 516 | "Train Epoch: 2 [49920/60000 (83%)]\tLoss: 0.084542\n", 517 | "Train Epoch: 2 [50560/60000 (84%)]\tLoss: 0.150355\n", 518 | "Train Epoch: 2 [51200/60000 (85%)]\tLoss: 0.001370\n", 519 | "Train Epoch: 2 [51840/60000 (86%)]\tLoss: 0.027981\n", 520 | "Train Epoch: 2 [52480/60000 (87%)]\tLoss: 0.023305\n", 521 | "Train Epoch: 2 [53120/60000 (88%)]\tLoss: 0.031542\n", 522 | "Train Epoch: 2 [53760/60000 (90%)]\tLoss: 0.161370\n", 523 | "Train Epoch: 2 [54400/60000 (91%)]\tLoss: 0.013536\n", 524 | "Train Epoch: 2 [55040/60000 (92%)]\tLoss: 0.026579\n", 525 | "Train Epoch: 2 [55680/60000 (93%)]\tLoss: 0.022276\n", 526 | "Train Epoch: 2 [56320/60000 (94%)]\tLoss: 0.105375\n", 527 | "Train Epoch: 2 [56960/60000 (95%)]\tLoss: 0.092841\n", 528 | "Train Epoch: 2 [57600/60000 (96%)]\tLoss: 0.091326\n", 529 | "Train Epoch: 2 [58240/60000 (97%)]\tLoss: 0.110826\n", 530 | "Train Epoch: 2 [58880/60000 (98%)]\tLoss: 0.081717\n", 531 | "Train Epoch: 2 [59520/60000 (99%)]\tLoss: 0.092696\n", 532 | "\n", 533 | "Test set: Avg. loss: -0.9776, Accuracy: 9831/10000 (98%)\n", 534 | "\n", 535 | "Train Epoch: 3 [0/60000 (0%)]\tLoss: 0.063463\n", 536 | "Train Epoch: 3 [640/60000 (1%)]\tLoss: 0.114469\n", 537 | "Train Epoch: 3 [1280/60000 (2%)]\tLoss: 0.006183\n", 538 | "Train Epoch: 3 [1920/60000 (3%)]\tLoss: 0.039701\n", 539 | "Train Epoch: 3 [2560/60000 (4%)]\tLoss: 0.106729\n", 540 | "Train Epoch: 3 [3200/60000 (5%)]\tLoss: 0.012483\n", 541 | "Train Epoch: 3 [3840/60000 (6%)]\tLoss: 0.236577\n", 542 | "Train Epoch: 3 [4480/60000 (7%)]\tLoss: 0.063634\n", 543 | "Train Epoch: 3 [5120/60000 (9%)]\tLoss: 0.061753\n", 544 | "Train Epoch: 3 [5760/60000 (10%)]\tLoss: 0.052922\n", 545 | "Train Epoch: 3 [6400/60000 (11%)]\tLoss: 0.190764\n", 546 | "Train Epoch: 3 [7040/60000 (12%)]\tLoss: 0.058996\n", 547 | "Train Epoch: 3 [7680/60000 (13%)]\tLoss: 0.022317\n", 548 | "Train Epoch: 3 [8320/60000 (14%)]\tLoss: 0.007046\n", 549 | "Train Epoch: 3 [8960/60000 (15%)]\tLoss: 0.080138\n", 550 | "Train Epoch: 3 [9600/60000 (16%)]\tLoss: 0.063802\n", 551 | "Train Epoch: 3 [10240/60000 (17%)]\tLoss: 0.041988\n", 552 | "Train Epoch: 3 [10880/60000 (18%)]\tLoss: 0.098359\n", 553 | "Train Epoch: 3 [11520/60000 (19%)]\tLoss: 0.153680\n", 554 | "Train Epoch: 3 [12160/60000 (20%)]\tLoss: 0.464352\n", 555 | "Train Epoch: 3 [12800/60000 (21%)]\tLoss: 0.070375\n", 556 | "Train Epoch: 3 [13440/60000 (22%)]\tLoss: 0.018513\n", 557 | "Train Epoch: 3 [14080/60000 (23%)]\tLoss: 0.022059\n", 558 | "Train Epoch: 3 [14720/60000 (25%)]\tLoss: 0.003471\n", 559 | "Train Epoch: 3 [15360/60000 (26%)]\tLoss: 0.019260\n", 560 | "Train Epoch: 3 [16000/60000 (27%)]\tLoss: 0.055360\n", 561 | "Train Epoch: 3 [16640/60000 (28%)]\tLoss: 0.185760\n", 562 | "Train Epoch: 3 [17280/60000 (29%)]\tLoss: 0.094002\n", 563 | "Train Epoch: 3 [17920/60000 (30%)]\tLoss: 0.088621\n", 564 | "Train Epoch: 3 [18560/60000 (31%)]\tLoss: 0.202922\n", 565 | "Train Epoch: 3 [19200/60000 (32%)]\tLoss: 0.217287\n", 566 | "Train Epoch: 3 [19840/60000 (33%)]\tLoss: 0.022548\n", 567 | "Train Epoch: 3 [20480/60000 (34%)]\tLoss: 0.064074\n", 568 | "Train Epoch: 3 [21120/60000 (35%)]\tLoss: 0.217457\n", 569 | "Train Epoch: 3 [21760/60000 (36%)]\tLoss: 0.041618\n", 570 | "Train Epoch: 3 [22400/60000 (37%)]\tLoss: 0.426640\n", 571 | "Train Epoch: 3 [23040/60000 (38%)]\tLoss: 0.064455\n", 572 | "Train Epoch: 3 [23680/60000 (39%)]\tLoss: 0.029709\n", 573 | "Train Epoch: 3 [24320/60000 (41%)]\tLoss: 0.374250\n", 574 | "Train Epoch: 3 [24960/60000 (42%)]\tLoss: 0.019916\n", 575 | "Train Epoch: 3 [25600/60000 (43%)]\tLoss: 0.077315\n", 576 | "Train Epoch: 3 [26240/60000 (44%)]\tLoss: 0.120163\n", 577 | "Train Epoch: 3 [26880/60000 (45%)]\tLoss: 0.007376\n", 578 | "Train Epoch: 3 [27520/60000 (46%)]\tLoss: 0.048888\n", 579 | "Train Epoch: 3 [28160/60000 (47%)]\tLoss: 0.148275\n", 580 | "Train Epoch: 3 [28800/60000 (48%)]\tLoss: 0.070750\n", 581 | "Train Epoch: 3 [29440/60000 (49%)]\tLoss: 0.353416\n", 582 | "Train Epoch: 3 [30080/60000 (50%)]\tLoss: 0.076076\n", 583 | "Train Epoch: 3 [30720/60000 (51%)]\tLoss: 0.074755\n", 584 | "Train Epoch: 3 [31360/60000 (52%)]\tLoss: 0.120992\n", 585 | "Train Epoch: 3 [32000/60000 (53%)]\tLoss: 0.245001\n", 586 | "Train Epoch: 3 [32640/60000 (54%)]\tLoss: 0.023723\n", 587 | "Train Epoch: 3 [33280/60000 (55%)]\tLoss: 0.081988\n", 588 | "Train Epoch: 3 [33920/60000 (57%)]\tLoss: 0.150652\n", 589 | "Train Epoch: 3 [34560/60000 (58%)]\tLoss: 0.028882\n", 590 | "Train Epoch: 3 [35200/60000 (59%)]\tLoss: 0.078787\n", 591 | "Train Epoch: 3 [35840/60000 (60%)]\tLoss: 0.063896\n", 592 | "Train Epoch: 3 [36480/60000 (61%)]\tLoss: 0.012244\n", 593 | "Train Epoch: 3 [37120/60000 (62%)]\tLoss: 0.090776\n", 594 | "Train Epoch: 3 [37760/60000 (63%)]\tLoss: 0.207105\n", 595 | "Train Epoch: 3 [38400/60000 (64%)]\tLoss: 0.006419\n", 596 | "Train Epoch: 3 [39040/60000 (65%)]\tLoss: 0.035537\n", 597 | "Train Epoch: 3 [39680/60000 (66%)]\tLoss: 0.145132\n", 598 | "Train Epoch: 3 [40320/60000 (67%)]\tLoss: 0.049233\n", 599 | "Train Epoch: 3 [40960/60000 (68%)]\tLoss: 0.056022\n", 600 | "Train Epoch: 3 [41600/60000 (69%)]\tLoss: 0.593132\n", 601 | "Train Epoch: 3 [42240/60000 (70%)]\tLoss: 0.180111\n", 602 | "Train Epoch: 3 [42880/60000 (71%)]\tLoss: 0.065261\n", 603 | "Train Epoch: 3 [43520/60000 (72%)]\tLoss: 0.024525\n", 604 | "Train Epoch: 3 [44160/60000 (74%)]\tLoss: 0.086528\n", 605 | "Train Epoch: 3 [44800/60000 (75%)]\tLoss: 0.120266\n", 606 | "Train Epoch: 3 [45440/60000 (76%)]\tLoss: 0.077596\n", 607 | "Train Epoch: 3 [46080/60000 (77%)]\tLoss: 0.043466\n", 608 | "Train Epoch: 3 [46720/60000 (78%)]\tLoss: 0.120706\n", 609 | "Train Epoch: 3 [47360/60000 (79%)]\tLoss: 0.015336\n", 610 | "Train Epoch: 3 [48000/60000 (80%)]\tLoss: 0.051106\n", 611 | "Train Epoch: 3 [48640/60000 (81%)]\tLoss: 0.118524\n", 612 | "Train Epoch: 3 [49280/60000 (82%)]\tLoss: 0.023619\n", 613 | "Train Epoch: 3 [49920/60000 (83%)]\tLoss: 0.203596\n", 614 | "Train Epoch: 3 [50560/60000 (84%)]\tLoss: 0.005664\n", 615 | "Train Epoch: 3 [51200/60000 (85%)]\tLoss: 0.076866\n", 616 | "Train Epoch: 3 [51840/60000 (86%)]\tLoss: 0.049961\n", 617 | "Train Epoch: 3 [52480/60000 (87%)]\tLoss: 0.099215\n", 618 | "Train Epoch: 3 [53120/60000 (88%)]\tLoss: 0.239370\n", 619 | "Train Epoch: 3 [53760/60000 (90%)]\tLoss: 0.044627\n", 620 | "Train Epoch: 3 [54400/60000 (91%)]\tLoss: 0.134020\n", 621 | "Train Epoch: 3 [55040/60000 (92%)]\tLoss: 0.125089\n", 622 | "Train Epoch: 3 [55680/60000 (93%)]\tLoss: 0.096078\n", 623 | "Train Epoch: 3 [56320/60000 (94%)]\tLoss: 0.087274\n", 624 | "Train Epoch: 3 [56960/60000 (95%)]\tLoss: 0.128849\n", 625 | "Train Epoch: 3 [57600/60000 (96%)]\tLoss: 0.022029\n", 626 | "Train Epoch: 3 [58240/60000 (97%)]\tLoss: 0.014636\n", 627 | "Train Epoch: 3 [58880/60000 (98%)]\tLoss: 0.124867\n", 628 | "Train Epoch: 3 [59520/60000 (99%)]\tLoss: 0.113813\n", 629 | "\n", 630 | "Test set: Avg. loss: -0.9710, Accuracy: 9783/10000 (98%)\n", 631 | "\n", 632 | "Train Epoch: 3 [0/60000 (0%)]\tLoss: 0.034476\n", 633 | "Train Epoch: 3 [640/60000 (1%)]\tLoss: 0.415755\n", 634 | "Train Epoch: 3 [1280/60000 (2%)]\tLoss: 0.181356\n", 635 | "Train Epoch: 3 [1920/60000 (3%)]\tLoss: 0.086929\n", 636 | "Train Epoch: 3 [2560/60000 (4%)]\tLoss: 0.029738\n", 637 | "Train Epoch: 3 [3200/60000 (5%)]\tLoss: 0.016178\n", 638 | "Train Epoch: 3 [3840/60000 (6%)]\tLoss: 0.001996\n", 639 | "Train Epoch: 3 [4480/60000 (7%)]\tLoss: 0.051265\n", 640 | "Train Epoch: 3 [5120/60000 (9%)]\tLoss: 0.060758\n", 641 | "Train Epoch: 3 [5760/60000 (10%)]\tLoss: 0.007387\n", 642 | "Train Epoch: 3 [6400/60000 (11%)]\tLoss: 0.016684\n", 643 | "Train Epoch: 3 [7040/60000 (12%)]\tLoss: 0.027652\n", 644 | "Train Epoch: 3 [7680/60000 (13%)]\tLoss: 0.083977\n", 645 | "Train Epoch: 3 [8320/60000 (14%)]\tLoss: 0.031926\n", 646 | "Train Epoch: 3 [8960/60000 (15%)]\tLoss: 0.071717\n", 647 | "Train Epoch: 3 [9600/60000 (16%)]\tLoss: 0.001477\n", 648 | "Train Epoch: 3 [10240/60000 (17%)]\tLoss: 0.023606\n", 649 | "Train Epoch: 3 [10880/60000 (18%)]\tLoss: 0.022856\n", 650 | "Train Epoch: 3 [11520/60000 (19%)]\tLoss: 0.067140\n", 651 | "Train Epoch: 3 [12160/60000 (20%)]\tLoss: 0.020441\n", 652 | "Train Epoch: 3 [12800/60000 (21%)]\tLoss: 0.000635\n", 653 | "Train Epoch: 3 [13440/60000 (22%)]\tLoss: 0.017227\n", 654 | "Train Epoch: 3 [14080/60000 (23%)]\tLoss: 0.022927\n", 655 | "Train Epoch: 3 [14720/60000 (25%)]\tLoss: 0.027828\n", 656 | "Train Epoch: 3 [15360/60000 (26%)]\tLoss: 0.032064\n", 657 | "Train Epoch: 3 [16000/60000 (27%)]\tLoss: 0.026003\n", 658 | "Train Epoch: 3 [16640/60000 (28%)]\tLoss: 0.029695\n", 659 | "Train Epoch: 3 [17280/60000 (29%)]\tLoss: 0.000279\n", 660 | "Train Epoch: 3 [17920/60000 (30%)]\tLoss: 0.000815\n", 661 | "Train Epoch: 3 [18560/60000 (31%)]\tLoss: 0.030595\n", 662 | "Train Epoch: 3 [19200/60000 (32%)]\tLoss: 0.004104\n", 663 | "Train Epoch: 3 [19840/60000 (33%)]\tLoss: 0.012980\n", 664 | "Train Epoch: 3 [20480/60000 (34%)]\tLoss: 0.140954\n", 665 | "Train Epoch: 3 [21120/60000 (35%)]\tLoss: 0.036284\n", 666 | "Train Epoch: 3 [21760/60000 (36%)]\tLoss: 0.056631\n", 667 | "Train Epoch: 3 [22400/60000 (37%)]\tLoss: 0.001214\n", 668 | "Train Epoch: 3 [23040/60000 (38%)]\tLoss: 0.229223\n", 669 | "Train Epoch: 3 [23680/60000 (39%)]\tLoss: 0.018490\n", 670 | "Train Epoch: 3 [24320/60000 (41%)]\tLoss: 0.122923\n", 671 | "Train Epoch: 3 [24960/60000 (42%)]\tLoss: 0.027173\n", 672 | "Train Epoch: 3 [25600/60000 (43%)]\tLoss: 0.006939\n", 673 | "Train Epoch: 3 [26240/60000 (44%)]\tLoss: 0.038346\n", 674 | "Train Epoch: 3 [26880/60000 (45%)]\tLoss: 0.083031\n", 675 | "Train Epoch: 3 [27520/60000 (46%)]\tLoss: 0.144066\n", 676 | "Train Epoch: 3 [28160/60000 (47%)]\tLoss: 0.002842\n", 677 | "Train Epoch: 3 [28800/60000 (48%)]\tLoss: 0.047957\n", 678 | "Train Epoch: 3 [29440/60000 (49%)]\tLoss: 0.032559\n", 679 | "Train Epoch: 3 [30080/60000 (50%)]\tLoss: 0.075398\n", 680 | "Train Epoch: 3 [30720/60000 (51%)]\tLoss: 0.000979\n", 681 | "Train Epoch: 3 [31360/60000 (52%)]\tLoss: 0.039745\n", 682 | "Train Epoch: 3 [32000/60000 (53%)]\tLoss: 0.087644\n", 683 | "Train Epoch: 3 [32640/60000 (54%)]\tLoss: 0.245594\n", 684 | "Train Epoch: 3 [33280/60000 (55%)]\tLoss: 0.263317\n", 685 | "Train Epoch: 3 [33920/60000 (57%)]\tLoss: 0.057245\n", 686 | "Train Epoch: 3 [34560/60000 (58%)]\tLoss: 0.024240\n", 687 | "Train Epoch: 3 [35200/60000 (59%)]\tLoss: 0.079506\n", 688 | "Train Epoch: 3 [35840/60000 (60%)]\tLoss: 0.001688\n", 689 | "Train Epoch: 3 [36480/60000 (61%)]\tLoss: 0.024482\n", 690 | "Train Epoch: 3 [37120/60000 (62%)]\tLoss: 0.090378\n", 691 | "Train Epoch: 3 [37760/60000 (63%)]\tLoss: 0.111796\n", 692 | "Train Epoch: 3 [38400/60000 (64%)]\tLoss: 0.034894\n", 693 | "Train Epoch: 3 [39040/60000 (65%)]\tLoss: 0.047220\n", 694 | "Train Epoch: 3 [39680/60000 (66%)]\tLoss: 0.135415\n", 695 | "Train Epoch: 3 [40320/60000 (67%)]\tLoss: 0.040737\n", 696 | "Train Epoch: 3 [40960/60000 (68%)]\tLoss: 0.015579\n", 697 | "Train Epoch: 3 [41600/60000 (69%)]\tLoss: 0.007837\n", 698 | "Train Epoch: 3 [42240/60000 (70%)]\tLoss: 0.000603\n", 699 | "Train Epoch: 3 [42880/60000 (71%)]\tLoss: 0.020224\n", 700 | "Train Epoch: 3 [43520/60000 (72%)]\tLoss: 0.026869\n", 701 | "Train Epoch: 3 [44160/60000 (74%)]\tLoss: 0.001488\n", 702 | "Train Epoch: 3 [44800/60000 (75%)]\tLoss: 0.007323\n", 703 | "Train Epoch: 3 [45440/60000 (76%)]\tLoss: 0.005850\n", 704 | "Train Epoch: 3 [46080/60000 (77%)]\tLoss: 0.011149\n", 705 | "Train Epoch: 3 [46720/60000 (78%)]\tLoss: 0.017864\n", 706 | "Train Epoch: 3 [47360/60000 (79%)]\tLoss: 0.034819\n", 707 | "Train Epoch: 3 [48000/60000 (80%)]\tLoss: 0.021358\n", 708 | "Train Epoch: 3 [48640/60000 (81%)]\tLoss: 0.006743\n", 709 | "Train Epoch: 3 [49280/60000 (82%)]\tLoss: 0.003964\n", 710 | "Train Epoch: 3 [49920/60000 (83%)]\tLoss: 0.011560\n", 711 | "Train Epoch: 3 [50560/60000 (84%)]\tLoss: 0.000351\n", 712 | "Train Epoch: 3 [51200/60000 (85%)]\tLoss: 0.016029\n", 713 | "Train Epoch: 3 [51840/60000 (86%)]\tLoss: 0.040337\n", 714 | "Train Epoch: 3 [52480/60000 (87%)]\tLoss: 0.021229\n", 715 | "Train Epoch: 3 [53120/60000 (88%)]\tLoss: 0.086175\n", 716 | "Train Epoch: 3 [53760/60000 (90%)]\tLoss: 0.008898\n", 717 | "Train Epoch: 3 [54400/60000 (91%)]\tLoss: 0.021425\n", 718 | "Train Epoch: 3 [55040/60000 (92%)]\tLoss: 0.002073\n", 719 | "Train Epoch: 3 [55680/60000 (93%)]\tLoss: 0.053018\n", 720 | "Train Epoch: 3 [56320/60000 (94%)]\tLoss: 0.001988\n", 721 | "Train Epoch: 3 [56960/60000 (95%)]\tLoss: 0.010265\n", 722 | "Train Epoch: 3 [57600/60000 (96%)]\tLoss: 0.001524\n", 723 | "Train Epoch: 3 [58240/60000 (97%)]\tLoss: 0.048560\n", 724 | "Train Epoch: 3 [58880/60000 (98%)]\tLoss: 0.017292\n", 725 | "Train Epoch: 3 [59520/60000 (99%)]\tLoss: 0.004543\n", 726 | "\n", 727 | "Test set: Avg. loss: -0.9860, Accuracy: 9886/10000 (99%)\n", 728 | "\n", 729 | "Train Epoch: 3 [0/60000 (0%)]\tLoss: 0.000774\n", 730 | "Train Epoch: 3 [640/60000 (1%)]\tLoss: 0.005242\n", 731 | "Train Epoch: 3 [1280/60000 (2%)]\tLoss: 0.087029\n", 732 | "Train Epoch: 3 [1920/60000 (3%)]\tLoss: 0.042792\n", 733 | "Train Epoch: 3 [2560/60000 (4%)]\tLoss: 0.035679\n", 734 | "Train Epoch: 3 [3200/60000 (5%)]\tLoss: 0.000884\n", 735 | "Train Epoch: 3 [3840/60000 (6%)]\tLoss: 0.008985\n", 736 | "Train Epoch: 3 [4480/60000 (7%)]\tLoss: 0.017822\n", 737 | "Train Epoch: 3 [5120/60000 (9%)]\tLoss: 0.000644\n", 738 | "Train Epoch: 3 [5760/60000 (10%)]\tLoss: 0.007079\n", 739 | "Train Epoch: 3 [6400/60000 (11%)]\tLoss: 0.003289\n", 740 | "Train Epoch: 3 [7040/60000 (12%)]\tLoss: 0.014545\n", 741 | "Train Epoch: 3 [7680/60000 (13%)]\tLoss: 0.037752\n", 742 | "Train Epoch: 3 [8320/60000 (14%)]\tLoss: 0.012087\n", 743 | "Train Epoch: 3 [8960/60000 (15%)]\tLoss: 0.036052\n", 744 | "Train Epoch: 3 [9600/60000 (16%)]\tLoss: 0.040073\n", 745 | "Train Epoch: 3 [10240/60000 (17%)]\tLoss: 0.049330\n", 746 | "Train Epoch: 3 [10880/60000 (18%)]\tLoss: 0.001090\n", 747 | "Train Epoch: 3 [11520/60000 (19%)]\tLoss: 0.013898\n", 748 | "Train Epoch: 3 [12160/60000 (20%)]\tLoss: 0.003239\n", 749 | "Train Epoch: 3 [12800/60000 (21%)]\tLoss: 0.167850\n", 750 | "Train Epoch: 3 [13440/60000 (22%)]\tLoss: 0.011250\n", 751 | "Train Epoch: 3 [14080/60000 (23%)]\tLoss: 0.026054\n", 752 | "Train Epoch: 3 [14720/60000 (25%)]\tLoss: 0.015770\n", 753 | "Train Epoch: 3 [15360/60000 (26%)]\tLoss: 0.003927\n", 754 | "Train Epoch: 3 [16000/60000 (27%)]\tLoss: 0.024283\n", 755 | "Train Epoch: 3 [16640/60000 (28%)]\tLoss: 0.002161\n", 756 | "Train Epoch: 3 [17280/60000 (29%)]\tLoss: 0.036340\n", 757 | "Train Epoch: 3 [17920/60000 (30%)]\tLoss: 0.000880\n", 758 | "Train Epoch: 3 [18560/60000 (31%)]\tLoss: 0.101127\n", 759 | "Train Epoch: 3 [19200/60000 (32%)]\tLoss: 0.004294\n", 760 | "Train Epoch: 3 [19840/60000 (33%)]\tLoss: 0.008890\n", 761 | "Train Epoch: 3 [20480/60000 (34%)]\tLoss: 0.005472\n", 762 | "Train Epoch: 3 [21120/60000 (35%)]\tLoss: 0.065323\n", 763 | "Train Epoch: 3 [21760/60000 (36%)]\tLoss: 0.069902\n", 764 | "Train Epoch: 3 [22400/60000 (37%)]\tLoss: 0.001641\n", 765 | "Train Epoch: 3 [23040/60000 (38%)]\tLoss: 0.076958\n", 766 | "Train Epoch: 3 [23680/60000 (39%)]\tLoss: 0.032411\n", 767 | "Train Epoch: 3 [24320/60000 (41%)]\tLoss: 0.034886\n", 768 | "Train Epoch: 3 [24960/60000 (42%)]\tLoss: 0.010985\n", 769 | "Train Epoch: 3 [25600/60000 (43%)]\tLoss: 0.005426\n", 770 | "Train Epoch: 3 [26240/60000 (44%)]\tLoss: 0.032913\n", 771 | "Train Epoch: 3 [26880/60000 (45%)]\tLoss: 0.126640\n", 772 | "Train Epoch: 3 [27520/60000 (46%)]\tLoss: 0.037310\n", 773 | "Train Epoch: 3 [28160/60000 (47%)]\tLoss: 0.008347\n", 774 | "Train Epoch: 3 [28800/60000 (48%)]\tLoss: 0.046984\n", 775 | "Train Epoch: 3 [29440/60000 (49%)]\tLoss: 0.009953\n", 776 | "Train Epoch: 3 [30080/60000 (50%)]\tLoss: 0.002314\n", 777 | "Train Epoch: 3 [30720/60000 (51%)]\tLoss: 0.085070\n", 778 | "Train Epoch: 3 [31360/60000 (52%)]\tLoss: 0.016453\n", 779 | "Train Epoch: 3 [32000/60000 (53%)]\tLoss: 0.045299\n", 780 | "Train Epoch: 3 [32640/60000 (54%)]\tLoss: 0.000546\n", 781 | "Train Epoch: 3 [33280/60000 (55%)]\tLoss: 0.241897\n", 782 | "Train Epoch: 3 [33920/60000 (57%)]\tLoss: 0.005824\n", 783 | "Train Epoch: 3 [34560/60000 (58%)]\tLoss: 0.060355\n", 784 | "Train Epoch: 3 [35200/60000 (59%)]\tLoss: 0.002446\n", 785 | "Train Epoch: 3 [35840/60000 (60%)]\tLoss: 0.006852\n", 786 | "Train Epoch: 3 [36480/60000 (61%)]\tLoss: 0.001183\n", 787 | "Train Epoch: 3 [37120/60000 (62%)]\tLoss: 0.004089\n", 788 | "Train Epoch: 3 [37760/60000 (63%)]\tLoss: 0.011785\n", 789 | "Train Epoch: 3 [38400/60000 (64%)]\tLoss: 0.102750\n", 790 | "Train Epoch: 3 [39040/60000 (65%)]\tLoss: 0.010851\n", 791 | "Train Epoch: 3 [39680/60000 (66%)]\tLoss: 0.005771\n", 792 | "Train Epoch: 3 [40320/60000 (67%)]\tLoss: 0.030049\n", 793 | "Train Epoch: 3 [40960/60000 (68%)]\tLoss: 0.005349\n", 794 | "Train Epoch: 3 [41600/60000 (69%)]\tLoss: 0.003538\n", 795 | "Train Epoch: 3 [42240/60000 (70%)]\tLoss: 0.063063\n", 796 | "Train Epoch: 3 [42880/60000 (71%)]\tLoss: 0.012078\n", 797 | "Train Epoch: 3 [43520/60000 (72%)]\tLoss: 0.000568\n", 798 | "Train Epoch: 3 [44160/60000 (74%)]\tLoss: 0.000667\n", 799 | "Train Epoch: 3 [44800/60000 (75%)]\tLoss: 0.095291\n", 800 | "Train Epoch: 3 [45440/60000 (76%)]\tLoss: 0.002779\n", 801 | "Train Epoch: 3 [46080/60000 (77%)]\tLoss: 0.005778\n", 802 | "Train Epoch: 3 [46720/60000 (78%)]\tLoss: 0.057257\n", 803 | "Train Epoch: 3 [47360/60000 (79%)]\tLoss: 0.007636\n", 804 | "Train Epoch: 3 [48000/60000 (80%)]\tLoss: 0.069810\n", 805 | "Train Epoch: 3 [48640/60000 (81%)]\tLoss: 0.002557\n", 806 | "Train Epoch: 3 [49280/60000 (82%)]\tLoss: 0.139816\n", 807 | "Train Epoch: 3 [49920/60000 (83%)]\tLoss: 0.060314\n", 808 | "Train Epoch: 3 [50560/60000 (84%)]\tLoss: 0.000701\n", 809 | "Train Epoch: 3 [51200/60000 (85%)]\tLoss: 0.001461\n", 810 | "Train Epoch: 3 [51840/60000 (86%)]\tLoss: 0.225825\n", 811 | "Train Epoch: 3 [52480/60000 (87%)]\tLoss: 0.014944\n", 812 | "Train Epoch: 3 [53120/60000 (88%)]\tLoss: 0.041378\n", 813 | "Train Epoch: 3 [53760/60000 (90%)]\tLoss: 0.042047\n", 814 | "Train Epoch: 3 [54400/60000 (91%)]\tLoss: 0.020290\n", 815 | "Train Epoch: 3 [55040/60000 (92%)]\tLoss: 0.010125\n", 816 | "Train Epoch: 3 [55680/60000 (93%)]\tLoss: 0.003389\n", 817 | "Train Epoch: 3 [56320/60000 (94%)]\tLoss: 0.131909\n", 818 | "Train Epoch: 3 [56960/60000 (95%)]\tLoss: 0.051108\n", 819 | "Train Epoch: 3 [57600/60000 (96%)]\tLoss: 0.006570\n", 820 | "Train Epoch: 3 [58240/60000 (97%)]\tLoss: 0.067857\n", 821 | "Train Epoch: 3 [58880/60000 (98%)]\tLoss: 0.006648\n", 822 | "Train Epoch: 3 [59520/60000 (99%)]\tLoss: 0.149868\n", 823 | "\n", 824 | "Test set: Avg. loss: -0.9873, Accuracy: 9892/10000 (99%)\n", 825 | "\n" 826 | ] 827 | } 828 | ] 829 | }, 830 | { 831 | "cell_type": "markdown", 832 | "source": [ 833 | "# Out-of-distribution Detection (FashionMNIST)\n", 834 | "\n", 835 | "The FashionMNIST dataset serves as an out-of-distribution (OOD) dataset for the MNIST dataset, providing a more challenging and diverse set of images. While MNIST consists of 70,000 grayscale images of handwritten digits (0-9), FashionMNIST contains 70,000 grayscale images of various clothing items, including t-shirts, trousers, pullovers, dresses, coats, sandals, shirts, sneakers, bags, and ankle boots. Both datasets have the same structure, with 28x28 pixel images and 10 classes, making FashionMNIST a suitable alternative for evaluating models trained on MNIST. By using FashionMNIST as OOD data, we can assess the robustness our model when exposed to visually different, yet structurally similar, data." 836 | ], 837 | "metadata": { 838 | "id": "EIXcgft4zcwM" 839 | } 840 | }, 841 | { 842 | "cell_type": "code", 843 | "source": [ 844 | "train_fashion_loader = torch.utils.data.DataLoader(\n", 845 | " torchvision.datasets.FashionMNIST('./FMNIST/', train=True, download=True,\n", 846 | " transform=torchvision.transforms.Compose([\n", 847 | " torchvision.transforms.ToTensor(),\n", 848 | " torchvision.transforms.Normalize(\n", 849 | " (0.1307,), (0.3081,))\n", 850 | " ])),\n", 851 | " batch_size=batch_size_train, shuffle=True)\n", 852 | "\n", 853 | "test_fashion_loader = torch.utils.data.DataLoader(\n", 854 | " torchvision.datasets.FashionMNIST('./FMNIST/', train=False, download=True,\n", 855 | " transform=torchvision.transforms.Compose([\n", 856 | " torchvision.transforms.ToTensor(),\n", 857 | " torchvision.transforms.Normalize(\n", 858 | " (0.1307,), (0.3081,))\n", 859 | " ])),\n", 860 | " batch_size=batch_size_test, shuffle=True)" 861 | ], 862 | "metadata": { 863 | "id": "LfO5qfyIISEk" 864 | }, 865 | "execution_count": 9, 866 | "outputs": [] 867 | }, 868 | { 869 | "cell_type": "code", 870 | "source": [ 871 | "import matplotlib.pyplot as plt\n", 872 | "\n", 873 | "images, targets = next(iter(test_fashion_loader))\n", 874 | "\n", 875 | "# Number of images you want to display\n", 876 | "num_images = 10\n", 877 | "\n", 878 | "# Create a figure and a row of subplots\n", 879 | "fig, axes = plt.subplots(1, num_images, figsize=(15, 3))\n", 880 | "\n", 881 | "# Plot each image on a separate subplot\n", 882 | "for i in range(num_images):\n", 883 | " axes[i].imshow(images[i, 0], cmap='gray')\n", 884 | " axes[i].axis('off') # Hide axis\n", 885 | "\n", 886 | "plt.show()" 887 | ], 888 | "metadata": { 889 | "id": "3i5gW6af0QXT", 890 | "outputId": "7b8279b6-bcd5-4f2e-8af1-07617fa77fd4", 891 | "colab": { 892 | "base_uri": "https://localhost:8080/", 893 | "height": 155 894 | } 895 | }, 896 | "execution_count": 10, 897 | "outputs": [ 898 | { 899 | "output_type": "display_data", 900 | "data": { 901 | "text/plain": [ 902 | "
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905 | }, 906 | "metadata": {} 907 | } 908 | ] 909 | }, 910 | { 911 | "cell_type": "code", 912 | "source": [ 913 | "# Computing OOD metrics\n", 914 | "\n", 915 | "network.eval()\n", 916 | "\n", 917 | "uncertainties = np.array([])\n", 918 | "labels = np.array([])\n", 919 | "eps = 1e-10\n", 920 | "\n", 921 | "with torch.no_grad():\n", 922 | " for data, target in test_loader:\n", 923 | "\n", 924 | " data = data.to(device)\n", 925 | " prob = inference_test(data)\n", 926 | " uncertainty = (-prob * torch.log(prob + eps)).sum(dim=1).cpu().detach().numpy()\n", 927 | " label = np.zeros_like(uncertainty)\n", 928 | "\n", 929 | " uncertainties = np.concatenate([uncertainties, uncertainty])\n", 930 | " labels = np.concatenate([labels, label])\n", 931 | "\n", 932 | "with torch.no_grad():\n", 933 | " for data, target in test_fashion_loader:\n", 934 | "\n", 935 | " data = data.to(device)\n", 936 | "\n", 937 | " prob = inference_test(data)\n", 938 | " uncertainty = (-prob * torch.log(prob + eps)).sum(dim=1).cpu().detach().numpy()\n", 939 | " label = np.ones_like(uncertainty)\n", 940 | "\n", 941 | " uncertainties = np.concatenate([uncertainties, uncertainty])\n", 942 | " labels = np.concatenate([labels, label])" 943 | ], 944 | "metadata": { 945 | "colab": { 946 | "base_uri": "https://localhost:8080/" 947 | }, 948 | "id": "siZj8y_8IVDg", 949 | "outputId": "0281b8fa-98ed-490e-c464-c5b806083a7a" 950 | }, 951 | "execution_count": 11, 952 | "outputs": [ 953 | { 954 | "output_type": "stream", 955 | "name": "stderr", 956 | "text": [ 957 | ":31: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n", 958 | " return F.log_softmax(x)\n" 959 | ] 960 | } 961 | ] 962 | }, 963 | { 964 | "cell_type": "code", 965 | "source": [ 966 | "import sklearn.metrics\n", 967 | "roc_auc = sklearn.metrics.roc_auc_score(labels, uncertainties)\n", 968 | "precision, recall, thresholds = sklearn.metrics.precision_recall_curve(labels, uncertainties)\n", 969 | "pr_auc = sklearn.metrics.auc(recall, precision)\n", 970 | "\n", 971 | "# evaluate ROC- and PR-AUC metrics, see https://arxiv.org/abs/1802.10501 for more details\n", 972 | "print(f\"ROC AUC: {roc_auc:.4f} \")\n", 973 | "print(f\"PR AUC: {pr_auc:.4f}\")" 974 | ], 975 | "metadata": { 976 | "id": "Lxy3IUwzMK_x", 977 | "outputId": "0051f24c-f1bd-49f0-8f5a-82443d59cea8", 978 | "colab": { 979 | "base_uri": "https://localhost:8080/" 980 | } 981 | }, 982 | "execution_count": 12, 983 | "outputs": [ 984 | { 985 | "output_type": "stream", 986 | "name": "stdout", 987 | "text": [ 988 | "ROC AUC: 0.9536 \n", 989 | "PR AUC: 0.9517\n" 990 | ] 991 | } 992 | ] 993 | }, 994 | { 995 | "cell_type": "code", 996 | "source": [ 997 | "# Plot ROC curve\n", 998 | "fpr, tpr, _ = sklearn.metrics.roc_curve(labels, uncertainties)\n", 999 | "plt.figure()\n", 1000 | "plt.plot(fpr, tpr, label=f'ROC curve (area = {roc_auc:.2f})')\n", 1001 | "plt.plot([0, 1], [0, 1], 'g--', label=\"Random classifier\")\n", 1002 | "plt.hlines(1, xmin=0, xmax=1, color='k', linestyle=\"--\")\n", 1003 | "plt.vlines(0, ymin=0, ymax=1, color='k', linestyle=\"--\")\n", 1004 | "plt.xlim([-0.01, 1.05])\n", 1005 | "plt.ylim([0.0, 1.05])\n", 1006 | "plt.xlabel('False Positive Rate')\n", 1007 | "plt.ylabel('True Positive Rate')\n", 1008 | "plt.title('Receiver Operating Characteristic')\n", 1009 | "plt.grid()\n", 1010 | "plt.legend(loc=\"lower right\")\n", 1011 | "plt.show()\n", 1012 | "\n", 1013 | "# Plot PR curve\n", 1014 | "plt.figure()\n", 1015 | "plt.plot(recall, precision, label=f'PR curve (area = {pr_auc:.2f})')\n", 1016 | "plt.hlines(1, xmin=0, xmax=1, color='k', linestyle=\"--\")\n", 1017 | "plt.vlines(1, ymin=0, ymax=1, color='k', linestyle=\"--\")\n", 1018 | "plt.xlabel('Recall')\n", 1019 | "plt.ylabel('Precision')\n", 1020 | "plt.title('Precision-Recall curve')\n", 1021 | "plt.xlim([-0.01, 1.05])\n", 1022 | "plt.ylim([0.0, 1.05])\n", 1023 | "plt.grid()\n", 1024 | "plt.legend(loc=\"lower left\")\n", 1025 | "plt.show()\n" 1026 | ], 1027 | "metadata": { 1028 | "id": "qGoMn6os0oZl", 1029 | "outputId": "e351b1aa-21ed-45f0-b8d3-bd336647afaa", 1030 | "colab": { 1031 | "base_uri": "https://localhost:8080/", 1032 | "height": 947 1033 | } 1034 | }, 1035 | "execution_count": 13, 1036 | "outputs": [ 1037 | { 1038 | "output_type": "display_data", 1039 | "data": { 1040 | "text/plain": [ 1041 | "
" 1042 | ], 1043 | "image/png": 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\n" 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\n" 1054 | }, 1055 | "metadata": {} 1056 | } 1057 | ] 1058 | }, 1059 | { 1060 | "cell_type": "code", 1061 | "source": [], 1062 | "metadata": { 1063 | "id": "tMHnbvfr0sdC" 1064 | }, 1065 | "execution_count": null, 1066 | "outputs": [] 1067 | } 1068 | ] 1069 | } --------------------------------------------------------------------------------