diff --git a/README.md b/README.md index 8b3a07d..70dfeac 100644 --- a/README.md +++ b/README.md @@ -87,27 +87,37 @@ ## Примеры и тьюториалы -Колабы, демонстрирующие, как работать с различными модулями нашего API: +Мы предоставляем примеры разного уровня сложности: +* [минимальные] минималистичные примеры, представляющие наш API +* [базовые] применение eXNN для простых задач, таких как классификация MNIST +* [сценарии использования] демонстрация использования eXplain-NN для решения различных проблем, возникающих в промышленных задачах. + +### Минимальные +Этот колаб содержит минималистическую демонстрацию нашего API на фиктивных объектах: + +[![minimal](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1lOiB50LppDiiRHTv184JMuQ2IvZ4I4rp?usp=sharing) + +### Базовые +Вот колабы, демонстрирующие, как работать с разными модулями нашего API на простых задачах: + | Colab Link | Module | | ------------- | ------------- | | [![bayes](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Ayd0IronxUIfnbAmWQLHiILG2qtBBpF4?usp=sharing)| bayes | | [![topology](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1T5ENfNaCIRI61LM2ZhtU8lfmvRmlfiEo?usp=sharing)| topology | | [![visualization](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1LJVdWTv-wcASSMX4is_E15TR7XJsT7W3?usp=sharing)| visualization | -Больше ноутбуков с примерами можно найти [здесь](/examples/) - -### Use Cases -TBD - -### Industrial Applications -В этом блоке приведены примеры использования eXplain-NN для решения промышленных задач. Для демонстрационных целей используются 2 задачи: -* [casting] выявление дефектов литейных материалов -* [CIFAR] классификация естественных изображений в наборе данных CIFAR. +### Сценарии использования +В этом блоке представлены примеры использования eXplain-NN для решения различных вариантов использования в промышленных задачах. Для демонстрационных целей используются 3 задачи: +* [спутник] классификация ландшафтов по спутниковым снимкам. +* [электроника] классификация электронных компонентов и устройств +* [ЭКГ] диагностика ЭКГ | Colab Link | Task | Use Case | | ------------- | ------------- | ------------- | -| [![casting_viz](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/16buElLs1hv0lBP2wglbiM7DLTwdsudcm?usp=sharing)| casting | Визуализация изменения многообразия данных от слоя к слою | -| [![CIFAR_viz](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/12ZJigH-0geGTefNXnCM5dQ71d4tqlf6L?usp=sharing)| CIFAR | Визуализация изменения многообразия данных от слоя к слою | +| [![CNN_viz](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/12ZJigH-0geGTefNXnCM5dQ71d4tqlf6L?usp=sharing)| спутник | Визуализация изменения многообразия данных от слоя к слою | +| TBD | спутник | Детекция adversarial данных | +| TBD | электроника | Оценка обобщающей способности нейронной сети | +| TBD | ЭКГ | Визуализация изменения многообразия данных от слоя к слою | ## Как помочь проекту [Инструкции](/docs/contribution.md). diff --git a/README_eng.md b/README_eng.md index e9b16c3..2a21246 100644 --- a/README_eng.md +++ b/README_eng.md @@ -87,33 +87,36 @@ eXplain-NNs Library API is available [here](https://med-ai-lab.github.io/eXplain ## Examples & Tutorials -We provides examples of several types: -* [API demonstration] minimalistic examples showing how to work with our API -* [use cases] use cases where eXplain-NNs can be used with detailed explanations -* [industrial applications] examples with application of our library to solving industrial tasks +We provides examples of different levels of complexity: +* [minimal] minimalistic examples presenting our API +* [basic] applying eXNN to simple tasks like MNIST classification +* [use cases] demonstation of eXplain-NN usage for solving different use cases in industrial tasks -### API Demostration -Here are colabs demonstrating how to work with different modules of our API: +### Minimal +This colab contains minimalistic demonstration of our API on dummy objects: + +[![minimal](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1lOiB50LppDiiRHTv184JMuQ2IvZ4I4rp?usp=sharing) + +### Basic +Here are colabs demonstrating how to work with different modules of our API on simple tasks: | Colab Link | Module | | ------------- | ------------- | | [![bayes](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Ayd0IronxUIfnbAmWQLHiILG2qtBBpF4?usp=sharing)| bayes | | [![topology](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1T5ENfNaCIRI61LM2ZhtU8lfmvRmlfiEo?usp=sharing)| topology | | [![visualization](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1LJVdWTv-wcASSMX4is_E15TR7XJsT7W3?usp=sharing)| visualization | -Also more notebooks with examples can be found [here](/examples/) - ### Use Cases -TBD - -### Industrial Applications -This block provides examples how eXplain-NNs can be used to solve industrial tasks. For demonstration purposed 2 tasks are used: -* [casting] detecting defects of casting materials -* [CIFAR] natural image classification on CIFAR dataset +This block provides examples how eXplain-NNs can be used to solve different use cases in industrial tasks. For demonstration purposed 3 tasks are used: +* [satellite] landscape classification from satellite imagery +* [electronics] electronic components and devices classification +* [ECG] ECG diagnostics | Colab Link | Task | Use Case | | ------------- | ------------- | ------------- | -| [![casting_viz](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/16buElLs1hv0lBP2wglbiM7DLTwdsudcm?usp=sharing)| casting | Visualization of data manifold evolution from layer to layer | -| [![CIFAR_viz](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/12ZJigH-0geGTefNXnCM5dQ71d4tqlf6L?usp=sharing)| CIFAR | Visualization of data manifold evolution from layer to layer | +| [![CNN_viz](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/12ZJigH-0geGTefNXnCM5dQ71d4tqlf6L?usp=sharing)| satellite | Visualization of data manifold evolution from layer to layer | +| TBD | satellite | Detecting adversarial examples | +| TBD | electronics | Estimating generalization of a NN | +| TBD | ECG | Visualization of data manifold evolution from layer to layer | ## Contribution Guide The contribution guide is available in the [repository](/docs/contribution.md). diff --git a/examples/CIFAR10/Bayesianization_for_pruning.ipynb b/examples/CIFAR10/Bayesianization_for_pruning.ipynb deleted file mode 100644 index 6901005..0000000 --- a/examples/CIFAR10/Bayesianization_for_pruning.ipynb +++ /dev/null @@ -1,272 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "generous-cursor", - "metadata": {}, - "source": [ - "Pruning is an important technique that allows to adapt the trained model to lower resources environment. [wikipedia](https://en.wikipedia.org/wiki/Pruning_(artificial_neural_network))" - ] - }, - { - "cell_type": "markdown", - "id": "democratic-adaptation", - "metadata": {}, - "source": [ - "In this notebook we'll show how to use bayesianization to determine what percentage of neural network weights is required for making prediction (and therefore what percentage can be pruned without decrease in performance)." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "unlimited-handle", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "os.chdir('../..')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "stainless-cleaners", - "metadata": {}, - "outputs": [], - "source": [ - "import copy\n", - "from pathlib import Path\n", - "from IPython.display import display\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import torch\n", - "from torchvision.datasets import CIFAR10\n", - "from torchvision.models import resnet18\n", - "import torchvision.transforms as TF\n", - "from torchmetrics.classification import MulticlassAccuracy\n", - "from examples.CIFAR10.models import *\n", - "from eXNN.NetBayesianization import BasicBayesianWrapper" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "operational-doctrine", - "metadata": {}, - "outputs": [], - "source": [ - "# prepare data\n", - "_normalize = TF.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n", - "tfm = TF.Compose([TF.ToTensor(), _normalize])\n", - "train_ds = CIFAR10(root='./.cache', train=True, download=False, transform=tfm)\n", - "test_ds = CIFAR10(root='./.cache', train=False, download=False, transform=tfm)\n", - "train_dl = torch.utils.data.DataLoader(train_ds, batch_size=128, shuffle=True)\n", - "test_dl = torch.utils.data.DataLoader(test_ds, batch_size=128, shuffle=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "quiet-holocaust", - "metadata": {}, - "outputs": [], - "source": [ - "train_batch = next(iter(train_dl))\n", - "test_batch = next(iter(test_dl))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "improved-cotton", - "metadata": {}, - "outputs": [], - "source": [ - "# download repository https://github.com/Med-AI-Lab/eXNN-task-CIFAR10\n", - "# change model_repo to the root of the downloaded repository\n", - "model_repo = Path('../eXNN-task-CIFAR10')" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "explicit-garlic", - "metadata": {}, - "outputs": [], - "source": [ - "# select cuda device\n", - "device = torch.device('cpu')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "lesbian-silence", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of parameters: 12668746\n", - "Number of parameters: 7206832\n" - ] - } - ], - "source": [ - "# load models\n", - "# full model\n", - "svd_model = resnet18(num_classes=10)\n", - "decompose_module(svd_model, \"channel\")\n", - "svd_model.load_state_dict(torch.load(model_repo / \"ResNet18_SVD_channel_O-100.0_H-0.000100.sd.pt\", map_location=device))\n", - "print(f\"Number of parameters: {number_of_params(svd_model)}\")\n", - "svd_model = svd_model.eval()\n", - "\n", - "# pruned model\n", - "pruned_model = copy.deepcopy(svd_model)\n", - "prune_model(model=pruned_model, energy_threshold=0.9)\n", - "print(f\"Number of parameters: {number_of_params(pruned_model)}\")\n", - "pruned_model = pruned_model.eval()" - ] - }, - { - "cell_type": "markdown", - "id": "local-mitchell", - "metadata": {}, - "source": [ - "First, let's look at the full model. We are going to make predictions with a Bayesian version of the model with different dropout probability `p`. The value of `p` corresponds to the percentage of weights dropped (pruned)." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "dietary-warren", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Accuracy')" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "acc_list = []\n", - "num_iters = 30\n", - "metric = MulticlassAccuracy(num_classes=10)\n", - "\n", - "for p in np.arange(0, 1, 0.05):\n", - " bayes_model = BasicBayesianWrapper(svd_model, 'basic', p, None, None)\n", - " pred = bayes_model.predict(test_batch[0], n_iter=num_iters)\n", - " acc_list.append(metric(pred[\"mean\"], test_batch[1]).item())\n", - "\n", - "plt.figure(figsize = (15, 5))\n", - "plt.plot(np.arange(0, 1, 0.05)*100, acc_list)\n", - "plt.xlabel(\"Sparsity (in percent)\")\n", - "plt.ylabel(\"Accuracy\")" - ] - }, - { - "cell_type": "markdown", - "id": "signal-competition", - "metadata": {}, - "source": [ - "As we can see from the plot, around 25% of the full model weights can be dropped without significant loss in performance." - ] - }, - { - "cell_type": "markdown", - "id": "wooden-saudi", - "metadata": {}, - "source": [ - "Now let's do the same with the pruned model." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "received-occupation", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Accuracy')" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "acc_list = []\n", - "num_iters = 30\n", - "metric = MulticlassAccuracy(num_classes=10)\n", - "\n", - "for p in np.arange(0, 1, 0.05):\n", - " bayes_model = BasicBayesianWrapper(pruned_model, 'basic', p, None, None)\n", - " pred = bayes_model.predict(test_batch[0], n_iter=num_iters)\n", - " acc_list.append(metric(pred[\"mean\"], test_batch[1]).item())\n", - "\n", - "plt.figure(figsize = (15, 5))\n", - "plt.plot(np.arange(0, 1, 0.05)*100, acc_list)\n", - "plt.xlabel(\"Sparsity (in percent)\")\n", - "plt.ylabel(\"Accuracy\")" - ] - }, - { - "cell_type": "markdown", - "id": "internal-authentication", - "metadata": {}, - "source": [ - "As we can see from the plot, around 20% of the pruned model weights can be dropped without significant loss in performance." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "eXNN", - "language": "python", - "name": "exnn" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/CIFAR10/Homologies.ipynb b/examples/CIFAR10/Homologies.ipynb deleted file mode 100644 index ac1ec57..0000000 --- a/examples/CIFAR10/Homologies.ipynb +++ /dev/null @@ -1,432 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "sublime-theorem", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "os.chdir('../..')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "injured-archives", - "metadata": {}, - "outputs": [], - "source": [ - "import copy\n", - "from pathlib import Path\n", - "from IPython.display import display\n", - "import torch\n", - "from torchvision.datasets import CIFAR10\n", - "from torchvision.models import resnet18\n", - "import torchvision.transforms as TF\n", - "from examples.CIFAR10.models import *\n", - "from eXNN.InnerNeuralTopology import NetworkHomologies" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "chicken-explorer", - "metadata": {}, - "outputs": [], - "source": [ - "# prepare data\n", - "_normalize = TF.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n", - "tfm = TF.Compose([TF.ToTensor(), _normalize])\n", - "train_ds = CIFAR10(root='./.cache', train=True, download=False, transform=tfm)\n", - "test_ds = CIFAR10(root='./.cache', train=False, download=False, transform=tfm)\n", - "train_dl = torch.utils.data.DataLoader(train_ds, batch_size=128, shuffle=True)\n", - "test_dl = torch.utils.data.DataLoader(test_ds, batch_size=128, shuffle=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "welsh-amino", - "metadata": {}, - "outputs": [], - "source": [ - "train_batch = next(iter(train_dl))[0]\n", - "test_batch = next(iter(test_dl))[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "worldwide-denver", - "metadata": {}, - "outputs": [], - "source": [ - "# download repository https://github.com/Med-AI-Lab/eXNN-task-CIFAR10\n", - "# change model_repo to the root of the downloaded repository\n", - "model_repo = Path('../eXNN-task-CIFAR10')" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "continuing-receptor", - "metadata": {}, - "outputs": [], - "source": [ - "# select cuda device\n", - "device = torch.device('cpu')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "marine-investigator", - "metadata": {}, - "outputs": [], - "source": [ - "# homologies computation settings\n", - "layers = [\"layer2\", \"layer4\", \"fc\"]\n", - "hom_type = \"sparse\"\n", - "coefs_type = \"2\"" - ] - }, - { - "cell_type": "markdown", - "id": "promotional-advantage", - "metadata": {}, - "source": [ - "# Baseline model" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "offshore-belle", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of parameters: 11181642\n" - ] - } - ], - "source": [ - "model = resnet18(num_classes=10)\n", - "model.load_state_dict(torch.load(model_repo / \"ResNet18.sd.pt\", map_location=device));\n", - "print(f\"Number of parameters: {number_of_params(model)}\")\n", - "model = model.eval()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "hawaiian-tuesday", - "metadata": {}, - "outputs": [], - "source": [ - "results_train = NetworkHomologies(model, train_batch, layers=layers, hom_type=hom_type, coefs_type=coefs_type)\n", - "results_test = NetworkHomologies(model, test_batch, layers=layers, hom_type=hom_type, coefs_type=coefs_type)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "laughing-arrest", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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lUZP19fWIiDh48GBE2EPY7KXr4wJ7CBERzz//fDz88MNRlmWcOHHC/sEmL10fF9g/uOeee+Knfuqn4sd//Mc3Pa9jD1kYy586Rf7v//4vnn/++SiKYtPzoijiq1/9ak1VMUl+6Id+KD784Q/HjTfeGJ1OJ9773vfGbbfdFk8//XQcOnSo7vKYIBf2jO32k//5n/+poyQmzMmTJ+Mtb3lLHD16NJ555pn47d/+7XjTm94UTz75ZDSbzbrLI6OUUtx///3xIz/yI3HTTTdFhD2E/2+79RFhDyHizJkzceLEifjGN74RS0tL8cgjj8RrXvOa4T8W7R/z7VLrI8L+QcTDDz8cn/vc5+Lxxx/f8l4dfweZ+zDqgkajsel1SmnLM+bTyZMnh7+/9rWvjRMnTsR3f/d3x1/8xV/E/fffX2NlTCr7CZfy1re+dfj7TTfdFMePH4+jR4/G3/7t38bdd99dY2Xk9q53vSv+4z/+I/7lX/5ly3v2EC61PuwhfO/3fm889dRT8fWvfz0+9rGPxalTp+L06dPD9+0f8+1S6+M1r3mN/WPOnT17Nu6999741Kc+Fa94xSsu+bmce8jcH9O75ppr4mUve9mWLqhz585tSQUhIqLdbsdrX/va+PKXv1x3KUyYC7cs2k8Y1bXXXhtHjx61n8yZd7/73fE3f/M38elPfzquu+664XN7CBGXXh/bsYfMn6uvvjq+53u+J44fPx5ra2txyy23xAc+8AH7BxFx6fWxHfvHfHnyySfj3Llzceutt8bCwkIsLCzE6dOn4/d///djYWFhuE/k3EPmPoy6+uqr49Zbb41HH3100/NHH300brvttpqqYpL1+/34whe+ENdee23dpTBhjh07FocPH960n3zzm9+M06dP20/Y1te+9rU4e/as/WROpJTiXe96V3z84x+Pf/zHf4xjx45tet8eMt92Wx/bsYeQUop+v2//YFsX1sd27B/z5Y477ogzZ87EU089Nfw5fvx4/OIv/mI89dRT8V3f9V3Z9xDH9CLi/vvvj7e//e1x/PjxOHHiRHzoQx+KZ599Nt75znfWXRoT4Nd//dfjrrvuiuuvvz7OnTsX733ve2NjYyNOnTpVd2nUoNvtxn/+538OXz/zzDPx1FNPxcGDB+P666+P++67L973vvfFDTfcEDfccEO8733vi8XFxXjb295WY9XkstP6OHjwYDz44IPxcz/3c3HttdfGf//3f8d73vOeuOaaa+Jnf/Zna6yaXO6555746Ec/Gp/4xCfiwIEDw/99XFlZiVarFY1Gwx4yx3ZbH91u1x4y597znvfEyZMnY3V1Nc6fPx8PP/xwfOYzn4m///u/t3+w4/qwf3DgwIFNMwgjXjzxc+jQoeHz7HvIWO7om0J/8Ad/kI4ePZquvvrq9AM/8AObrtFlvr31rW9N1157bXr5y1+ejhw5ku6+++709NNP110WNfn0pz+dImLLz6lTp1JKL16L+sADD6TDhw+nZrOZfuzHfiydOXOm3qLJZqf1UVVVuvPOO9MrX/nK9PKXvzxdf/316dSpU+nZZ5+tu2wy2W5tRET6sz/7s+Fn7CHza7f1YQ/hl3/5l4f/XnnlK1+Z7rjjjvSpT31q+L79Y77ttD7sH2zn9ttvT/fee+/wde49pJFSSuOJuQAAAABgs7mfGQUAAABAPsIoAAAAALIRRgEAAACQjTAKAAAAgGyEUQAAAABkI4wCAAAAIBthFAAAAADZCKMAAAAAyEYYBQAAAEA2wigAAAAAshFGAQAAAJCNMAoAAACAbP4f2bwjBoyr+B4AAAAASUVORK5CYII=\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(results_train[\"fc\"], results_test[\"fc\"])" - ] - }, - { - "cell_type": "markdown", - "id": "honest-smile", - "metadata": {}, - "source": [ - "# SVD decomposed model" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "silent-kansas", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of parameters: 12668746\n" - ] - } - ], - "source": [ - "svd_model = resnet18(num_classes=10)\n", - "decompose_module(svd_model, \"channel\")\n", - "svd_model.load_state_dict(torch.load(model_repo / \"ResNet18_SVD_channel_O-100.0_H-0.000100.sd.pt\", map_location=device))\n", - "print(f\"Number of parameters: {number_of_params(svd_model)}\")\n", - "svd_model = svd_model.eval()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "emerging-integration", - "metadata": {}, - "outputs": [], - "source": [ - "results_train = NetworkHomologies(svd_model, train_batch, layers=layers, hom_type=hom_type, coefs_type=coefs_type)\n", - "results_test = NetworkHomologies(svd_model, test_batch, layers=layers, hom_type=hom_type, coefs_type=coefs_type)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "random-tobacco", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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vF10KAADQYwRXTU7EAgAAAEiLUwWbKpVKVKvVossA6FqVSqXoEgAAgB4juGpqNBpzjngHoHWTk5MxODhYdBkAAEAPsVWwyVBhgONjHQUAANpNcAUAAABAkmwVBKAt6vX6rIMuBgYGHHwBAAAcF8EVAG1x9JxAM68AAIDjZasgAB1Rq9XMvQIAAI6L4AqAjsiyLOr1etFlAAAAXcxWQQA6plarRYR5VwAAwPLouAKgY7Isi6GhIZ1XAADAsui4AqDjpjuvInRfAQAArRNcAdBxR5446LRBAACgVbYKNvn0HwAAACAtOq6aKpVKVKvVossA6Hl5nhvaDgAAtERw1dRoNGZtZQGg82wbBAAAFmOrYFOe50WXANB3rL0AAMBiBFcAAAAAJElwBUBh6vW6risAAGBBgisACpNlWdTr9aLLAAAAEmU4OwCFcsIgAACwEB1XABQqy7IYGhrSeQUAAMwhuAIgCbVazbwrAABgFsEVAEkw7woAADia4AqAZOi4AgAAjiS4AgAAACBJgqsmJ1kBFM9aDAAAHGlV0QWkolKpRLVaLboMgL6W53nUarUYGBgQYgEAAIKraY1GI7IsK7oMACJicnIyBgcHiy4DAAAomK2CTQYCA6TDmgwAAEQIrgAAAABIlOAKgOTU6/Wo1Wo6rwAAoM+1PONqamoqpqamZh5PTEx0pCAAmJ45aNYVAAD0t5Y7rsbGxmJkZGTma3R0tJN1AUDUarWZL91XAADQf0p5i/9PYL6Oq9HR0RgfH4/h4eGOFbhSJicnY+3atUWXAcACdF8BAEC6JiYmYmRkpO05UctbBcvlcpTL5bb9wwCwFDquAACg/xjODgAAAECSWu64AoAi1ev1KJVKERExMDAw8z0AANC7BFcAdIXpkwYjzLsCAIB+Yatgk0/uAQAAANKi46qpUqlEtVotugwAWlCpVIouAQAAWAGCq6ZGozFrGwoA6bJVEAAA+oOtgk2OWQfoHrVazboNAAB9QHAFQNfJsizq9XrRZQAAAB0muAKgK+m6AgCA3ie4AqAr6boCAIDeZzg7AF2rVqvNejwwMBClUqmgagAAgHYTXAHQtY4+DdZpgwAA0FsEVwD0jKM7sCJ0YQEAQDcTXAHQM47uwIrQhQUAAN3McHYAeprTBwEAoHsJrgDoaU4fBACA7iW4AqDn6boCAIDuJLgCoOfpugIAgO4kuGpy4hQAAABAWpwq2FSpVKJarRZdBgAdUqlUii4BAABYIsFVU6PRmPcYdQB6w+TkZAwODhZdBgAAsASCqyZDewF6W61Wi4iIgYEB28MBAKBLmHEFQF/IsiyGhoYMaQcAgC6i4wqAvqLzCgAAuoeOKwD6is4rAADoHoIrAPqS2YYAAJA+wRUAAAAASRJcAQAAAJAkwRUAAAAASRJcAdCX6vW6OVcAAJA4wRUAfSnLMicLAgBA4gRXTaVSqegSAAAAADjCqqILSEWlUolqtVp0GQCsoDzPo1arRUTEwMCADzEAACAxgqumRqMRWZYVXQYABZmcnIzBwcGiywAAAI4guGoyoBegv+m8AgCA9JhxBQDxg2HtQ0NDBrYDAEBCdFwBwBGmO6+OpAsLAACKIbgCgCPMN+/Q/CsAACiG4AoAjsH8KwAAKIYZVwBwDOZfAQBAMXRcAUCLdF4BAMDK0nEFAC3SeQUAACtLxxUALJHOKwAAWBk6rgBgiXReAQDAyhBcNfnEHAAAACAttgo2VSqVqFarRZcBQBfJ8zzyPPfhBwAAdIjgqqnRaESWZUWXAUCXmZycjMHBwaLLAACAniS4asrzvOgSAOhCBrUDAEDnmHEFAMfBoHYAAOgcHVcA0AY6rwAAoP10XAFAG+i8AgCA9mu542pqaiqmpqZmHk9MTHSkIADoZrVaTdcVAAC0ScsdV2NjYzEyMjLzNTo62sm6AKArZVmm6woAANqklLd4nN58HVejo6MxPj4ew8PDHStwpUxOTsbatWuLLgOAHlCtVmNwcHDBn+vIAgCg10xMTMTIyEjbc6KWtwqWy+Uol8tt+4cBoFdlWbbozycnJxcNtgAAgB9wqiAArLDpEwgjdF8BAMBiBFcAsMKO7MjSfQUAAAtreTh7r/NpNwAAAEBadFw1VSqVqFarRZcBQJ+pVCpFlwAAAMkSXDU1Go1jDtMFgHazVRAAABYmuGrK87zoEgDoQ9OD2g1pBwCAucy4AoACZVkWQ0NDUa/Xiy4FAACSI7gCgATo/AUAgLkEVwAAAAAkyYwrAEhAvV6fmXFl3hUAAPyA4AoAEnDkybZOGgQAgB+wVRAAElOr1cy8AgCAEFwBQHKyLHPKIAAAhK2CAJCkWq0267G5VwAA9CPBFQAk6MiZVxHmXgEA0J9sFWzyKTYAAABAWnRcNVUqlahWq0WXAQDzyvN8ZvugbYMAAPQLwVVTo9GYsy0DAFJk2yAAAP3CVsEmx44D0C1qtZr/bgEA0BcEVwDQZbIsi3q9XnQZAADQcbYKAkAXmp53FWHmFQAAvUtwBQBd6Mi5jGZeAQDQq2wVBIAuZ94VAAC9SnAFAAAAQJJsFQSALlev12dmXJl3BQBALxFcAUCXM+8KAIBeJbgCgB7itEEAAHqJ4AoAeojuKwAAeonh7E0+kQYAAABIi46rpkqlEtVqtegyAKBt8jyftXWwVbYYAgCQCsFVU6PRmLW9AgD6lS2GAACkQnDVlOd50SUAQBKW06UVoVMLAID2E1wBALMstwNZpxYAAO0muAIA2mKhTi2dWAAALJfgCgBoi4U6tXRiAQCwXIIrAKCjljszqx/oRgMAWJzgCgDoKKf2Lkw3GgDA4gRXAAAF6dduNJ1mAECrBFcAAAXp1240nWYAQKsEVwAArKhWO810ZgEAgisAAFZUq51mOrMAgBOKLiAVPs0DAAAASIuOq6ZKpRLVarXoMgAAaMrz/LgH2NtuCADdTXDV1Gg0+nZAKgBAr7LdEAC6m+CqKc/zoksAAKDNltuxpVMLANIguAIAoGctt6NepxYApEFwBQAARzne2VrdQFcZAN1AcAUAAEfph9mnusoA6AaCKwAA6EP90FUGqdHpCEsnuAIAgD7UD11lkBqdjrB0gisAAABYATodmY9OvMW1HFxNTU3F1NTUzOOJiYmOFAQAAAC9SKcj89GJt7gTWr1wbGwsRkZGZr5GR0c7WdeKk24CAAAApKWU53neyoXzdVyNjo7G+Ph4DA8Pd6zAlZLnedTr9aLLAAAAAPpIr2wVnJiYiJGRkbbnRC1vFSyXy1Eul9v2D6emVCppzQMAAABISMtbBQEAAABgJQmuAAAAAEiS4AoAAACAJAmuAAAAAEiS4AoAAACAJAmuAAAAAEiS4AoAAACAJAmuAAAAAEiS4AoAAACAJAmuAAAAAEiS4AoAAACAJAmuAAAAAEiS4AoAAACAJAmuAAAAAEiS4AoAAACAJAmuAAAAAEiS4AoAAACAJAmuAAAAAEiS4AoAAACAJAmuAAAAAEiS4AoAAACAJAmuAAAAAEiS4AoAAACAJAmuAAAAAEiS4AoAAACAJAmuAAAAAEiS4AoAAACAJAmuAAAAAEiS4AoAAACAJAmuAAAAAEiS4AoAAACAJAmuAAAAAEiS4AoAAACAJAmuAAAAAEiS4AoAAACAJAmuAAAAAEiS4AoAAACAJAmuAAAAAEiS4AoAAACAJAmuAAAAAEiS4AoAAACAJAmuAAAAAEiS4AoAAACAJAmuAAAAAEiS4AoAAACAJAmuAAAAAEiS4AoAAACAJAmuAAAAAEiS4AoAAACAJAmuAAAAAEiS4AoAAACAJK1q9cKpqamYmpqaeTw+Ph4RERMTE+2vCgAAAICuMZ0P5Xne1vdtObgaGxuLXbt2zXl+dHS0rQUBAAAA0J2ee+65GBkZadv7lfIWo7CjO64OHz4czzzzTJx00klRKpXaVlCRJiYmYnR0NA4ePBjDw8NFl0OfcN9RBPcdRXDfsdLccxTBfUcR3HcU4ej7Ls/zeO6552Ljxo1xwgntm0zVcsdVuVyOcrk867lXvOIVbSskJcPDw/7Hzopz31EE9x1FcN+x0txzFMF9RxHcdxThyPuunZ1W0wxnBwAAACBJgisAAAAAkiS4OkK5XI6bbrppzpZI6CT3HUVw31EE9x0rzT1HEdx3FMF9RxFW6r5reTg7AAAAAKwkHVcAAAAAJElwBQAAAECSBFcAAAAAJElwBQAAAECS+i64+uQnPxlbtmyJNWvWxPnnnx9f//rXF71+3759cf7558eaNWviR37kR+JTn/rUClVKLxgbG4s3vvGNsXbt2jjllFPip3/6p2P//v2LvuaBBx6IUqk05+s73/nOClVNt7v55pvn3D/r169f9DXWOo7XaaedNu/adf311897vbWOpfra174Wl112WWzcuDFKpVJ88YtfnPXzPM/j5ptvjo0bN0alUom3vvWt8e1vf/uY77t79+54zWteE+VyOV7zmtfEPffc06HfgG602H334osvxm/91m/FueeeG4ODg7Fx48Z473vfG//zP/+z6Hveeeed865/zz//fId/G7rFsda7a6+9ds79s3Xr1mO+r/WOxRzrvptv3SqVSvEHf/AHC75nu9a7vgqu/uqv/ipuuOGG+O3f/u145JFH4i1veUvs2LEjnnjiiXmvP3DgQPzET/xEvOUtb4lHHnkkPvKRj8Sv/uqvxu7du1e4crrVvn374vrrr49vfvObcf/998dLL70U27dvj1qtdszX7t+/P5588smZrzPOOGMFKqZXvPa1r511/zz22GMLXmutox0eeuihWffc/fffHxER7373uxd9nbWOVtVqtTjvvPPi1ltvnffnv//7vx9/9Ed/FLfeems89NBDsX79+vjxH//xeO655xZ8zwcffDCuuuqquOaaa+Jf/uVf4pprrokrr7wy/vEf/7FTvwZdZrH7rl6vx8MPPxy/8zu/Ew8//HDcfffd8R//8R/xUz/1U8d83+Hh4Vlr35NPPhlr1qzpxK9AFzrWehcR8Y53vGPW/fPlL3950fe03nEsx7rvjl6zbr/99iiVSvGud71r0fdty3qX95E3velN+XXXXTfrubPPPjv/0Ic+NO/1v/mbv5mfffbZs5775V/+5Xzr1q0dq5HedujQoTwi8n379i14zd69e/OIyP/3f/935Qqjp9x00035eeed1/L11jo64YMf/GB++umn54cPH57359Y6jkdE5Pfcc8/M48OHD+fr16/Pf+/3fm/mueeffz4fGRnJP/WpTy34PldeeWX+jne8Y9Zzb3/72/Orr7667TXT/Y6+7+bzT//0T3lE5N/97ncXvOaOO+7IR0ZG2lscPWu++27nzp355ZdfvqT3sd6xFK2sd5dffnn+tre9bdFr2rXe9U3H1QsvvBD//M//HNu3b5/1/Pbt2+Mb3/jGvK958MEH51z/9re/Pb71rW/Fiy++2LFa6V3j4+MREbFu3bpjXvv6178+NmzYEJdeemns3bu306XRYx5//PHYuHFjbNmyJa6++ur4r//6rwWvtdbRbi+88EJ89rOfjV/4hV+IUqm06LXWOtrhwIED8dRTT81ay8rlclxyySUL/p0XsfD6t9hrYDHj4+NRKpXiFa94xaLXTU5OxubNm+PUU0+Nd77znfHII4+sTIH0jAceeCBOOeWUOPPMM+N973tfHDp0aNHrrXe0U7Vajfvuuy9+8Rd/8ZjXtmO965vg6vvf/368/PLLkWXZrOezLIunnnpq3tc89dRT817/0ksvxfe///2O1UpvyvM8brzxxnjzm98c55xzzoLXbdiwIT796U/H7t274+67746zzjorLr300vja1762gtXSzX70R3807rrrrvjKV74Sf/EXfxFPPfVUXHjhhfH000/Pe721jnb74he/GM8++2xce+21C15jraOdpv+WW8rfedOvW+prYCHPP/98fOhDH4r3vOc9MTw8vOB1Z599dtx5553xpS99KT7/+c/HmjVr4qKLLorHH398Baulm+3YsSP+8i//Mr761a/Gxz72sXjooYfibW97W0xNTS34Gusd7fSZz3wm1q5dG1dcccWi17VrvVt1PMV2o6M/+c3zfNFPg+e7fr7n4Vje//73x7/+67/G3//93y963VlnnRVnnXXWzONt27bFwYMH4w//8A/j4osv7nSZ9IAdO3bMfH/uuefGtm3b4vTTT4/PfOYzceONN877Gmsd7XTbbbfFjh07YuPGjQteY62jE5b6d95yXwNHe/HFF+Pqq6+Ow4cPxyc/+clFr926deusQdoXXXRRvOENb4g//dM/jY9//OOdLpUecNVVV818f84558QFF1wQmzdvjvvuu2/RIMF6R7vcfvvt8XM/93PHnFXVrvWubzquTj755PihH/qhOYnyoUOH5iTP09avXz/v9atWrYqTTjqpY7XSez7wgQ/El770pdi7d2+ceuqpS3791q1bfQrHsg0ODsa555674D1kraOdvvvd78aePXvil37pl5b8WmsdyzV9cupS/s6bft1SXwNHe/HFF+PKK6+MAwcOxP33379ot9V8TjjhhHjjG99o/WPZNmzYEJs3b170HrLe0S5f//rXY//+/cv6W2+5613fBFerV6+O888/f+aUo2n3339/XHjhhfO+Ztu2bXOu/9u//du44IIL4sQTT+xYrfSOPM/j/e9/f9x9993x1a9+NbZs2bKs93nkkUdiw4YNba6OfjE1NRX//u//vuA9ZK2jne6444445ZRT4id/8ieX/FprHcu1ZcuWWL9+/ay17IUXXoh9+/Yt+HdexMLr32KvgSNNh1aPP/547NmzZ1kf+OR5Ho8++qj1j2V7+umn4+DBg4veQ9Y72uW2226L888/P84777wlv3a5611fbRW88cYb45prrokLLrggtm3bFp/+9KfjiSeeiOuuuy4iIj784Q/H9773vbjrrrsiIuK6666LW2+9NW688cZ43/veFw8++GDcdttt8fnPf77IX4Mucv3118fnPve5uPfee2Pt2rUzn3KMjIxEpVKJiLn33R//8R/HaaedFq997WtnBhzv3r07du/eXdjvQXf59V//9bjsssti06ZNcejQofjd3/3dmJiYiJ07d0aEtY7OOXz4cNxxxx2xc+fOWLVq9p8Y1jqO1+TkZPznf/7nzOMDBw7Eo48+GuvWrYtNmzbFDTfcEB/96EfjjDPOiDPOOCM++tGPxsDAQLznPe+Zec173/veeNWrXhVjY2MREfHBD34wLr744rjlllvi8ssvj3vvvTf27NlzzG399I/F7ruNGzfGz/7sz8bDDz8cf/M3fxMvv/zyzN9669ati9WrV0fE3Ptu165dsXXr1jjjjDNiYmIiPv7xj8ejjz4an/jEJ1b+FyRJi91369ati5tvvjne9a53xYYNG+K///u/4yMf+UicfPLJ8TM/8zMzr7HesVTH+u9sRMTExER84QtfiI997GPzvkfH1rvjPpewy3ziE5/IN2/enK9evTp/wxvekO/bt2/mZzt37swvueSSWdc/8MAD+etf//p89erV+WmnnZb/2Z/92QpXTDeLiHm/7rjjjplrjr7vbrnllvz000/P16xZk//wD/9w/uY3vzm/7777Vr54utZVV12Vb9iwIT/xxBPzjRs35ldccUX+7W9/e+bn1jo65Stf+UoeEfn+/fvn/Mxax/Hau3fvvP9N3blzZ57neX748OH8pptuytevX5+Xy+X84osvzh977LFZ73HJJZfMXD/tC1/4Qn7WWWflJ554Yn722Wfnu3fvXqHfiG6w2H134MCBBf/W27t378x7HH3f3XDDDfmmTZvy1atX56985Svz7du359/4xjdW/pcjWYvdd/V6Pd++fXv+yle+Mj/xxBPzTZs25Tt37syfeOKJWe9hvWOpjvXf2TzP8z//8z/PK5VK/uyzz877Hp1a70p53pzACwAAAAAJ6ZsZVwAAAAB0F8EVAAAAAEkSXAEAAACQJMEVAAAAAEkSXAEAAACQJMEVAAAAAEkSXAEAAACQJMEVAAAAAEkSXAEAAACQJMEVAAAAAEkSXAEAAACQJMEVAAAAAEn6PyZHxXwsJhTnAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(results_train[\"fc\"], results_test[\"fc\"])" - ] - }, - { - "cell_type": "markdown", - "id": "fundamental-classic", - "metadata": {}, - "source": [ - "# Pruned model" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "adaptive-damages", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of parameters: 7206832\n" - ] - } - ], - "source": [ - "pruned_model = copy.deepcopy(svd_model)\n", - "prune_model(model=pruned_model, energy_threshold=0.9)\n", - "print(f\"Number of parameters: {number_of_params(pruned_model)}\")\n", - "pruned_model = pruned_model.eval()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "aboriginal-float", - "metadata": {}, - "outputs": [], - "source": [ - "results_train = NetworkHomologies(pruned_model, train_batch, layers=layers, hom_type=hom_type, coefs_type=coefs_type)\n", - "results_test = NetworkHomologies(pruned_model, test_batch, layers=layers, hom_type=hom_type, coefs_type=coefs_type)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "respective-position", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(results_train[\"fc\"], results_test[\"fc\"])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "talented-ranch", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/CIFAR10/Homologies_analysis.ipynb b/examples/CIFAR10/Homologies_analysis.ipynb deleted file mode 100644 index 068a8ca..0000000 --- a/examples/CIFAR10/Homologies_analysis.ipynb +++ /dev/null @@ -1,316 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "bound-shannon", - "metadata": {}, - "source": [ - "In this notebook we'll show how homologies can be used to analyze neural networks." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "assisted-stephen", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "os.chdir('../..')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "studied-sponsorship", - "metadata": {}, - "outputs": [], - "source": [ - "import copy\n", - "from pathlib import Path\n", - "from IPython.display import display\n", - "import torch\n", - "from torchvision.datasets import CIFAR10\n", - "from torchvision.models import resnet18\n", - "import torchvision.transforms as TF\n", - "from examples.CIFAR10.models import *\n", - "from eXNN.InnerNeuralTopology import NetworkHomologies" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "becoming-rugby", - "metadata": {}, - "outputs": [], - "source": [ - "# prepare data\n", - "_normalize = TF.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n", - "tfm = TF.Compose([TF.ToTensor(), _normalize])\n", - "train_ds = CIFAR10(root='./.cache', train=True, download=False, transform=tfm)\n", - "test_ds = CIFAR10(root='./.cache', train=False, download=False, transform=tfm)\n", - "train_dl = torch.utils.data.DataLoader(train_ds, batch_size=128, shuffle=True)\n", - "test_dl = torch.utils.data.DataLoader(test_ds, batch_size=128, shuffle=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "positive-stockholm", - "metadata": {}, - "outputs": [], - "source": [ - "train_batch = next(iter(train_dl))[0]\n", - "test_batch = next(iter(test_dl))[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "enabling-gallery", - "metadata": {}, - "outputs": [], - "source": [ - "# download repository https://github.com/Med-AI-Lab/eXNN-task-CIFAR10\n", - "# change model_repo to the root of the downloaded repository\n", - "model_repo = Path('../eXNN-task-CIFAR10')" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "thick-exchange", - "metadata": {}, - "outputs": [], - "source": [ - "# select cuda device\n", - "device = torch.device('cpu')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "serial-integer", - "metadata": {}, - "outputs": [], - "source": [ - "# homologies computation settings\n", - "layers = [\"layer2\", \"layer4\", \"fc\"]\n", - "hom_type = \"sparse\"\n", - "coefs_type = \"2\"" - ] - }, - { - "cell_type": "markdown", - "id": "coupled-confidence", - "metadata": {}, - "source": [ - "# Homology barcodes visualization" - ] - }, - { - "cell_type": "markdown", - "id": "married-spectrum", - "metadata": { - "heading_collapsed": true - }, - "source": [ - "## Full model" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "animated-substance", - "metadata": { - "hidden": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of parameters: 12668746\n" - ] - } - ], - "source": [ - "svd_model = resnet18(num_classes=10)\n", - "decompose_module(svd_model, \"channel\")\n", - "svd_model.load_state_dict(torch.load(model_repo / \"ResNet18_SVD_channel_O-100.0_H-0.000100.sd.pt\", map_location=device))\n", - "print(f\"Number of parameters: {number_of_params(svd_model)}\")\n", - "svd_model = svd_model.eval()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "distant-decimal", - "metadata": { - "hidden": true - }, - "outputs": [], - "source": [ - "results_train = NetworkHomologies(svd_model, train_batch, layers=layers, hom_type=hom_type, coefs_type=coefs_type)\n", - "results_test = NetworkHomologies(svd_model, test_batch, layers=layers, hom_type=hom_type, coefs_type=coefs_type)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "primary-laptop", - "metadata": { - "hidden": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(results_train[\"fc\"], results_test[\"fc\"])" - ] - }, - { - "cell_type": "markdown", - "id": "important-subsection", - "metadata": { - "heading_collapsed": true - }, - "source": [ - "## Pruned model" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "mysterious-luther", - "metadata": { - "hidden": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of parameters: 7206832\n" - ] - } - ], - "source": [ - "pruned_model = copy.deepcopy(svd_model)\n", - "prune_model(model=pruned_model, energy_threshold=0.9)\n", - "print(f\"Number of parameters: {number_of_params(pruned_model)}\")\n", - "pruned_model = pruned_model.eval()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "processed-writer", - "metadata": { - "hidden": true - }, - "outputs": [], - "source": [ - "results_train = NetworkHomologies(pruned_model, train_batch, layers=layers, hom_type=hom_type, coefs_type=coefs_type)\n", - "results_test = NetworkHomologies(pruned_model, test_batch, layers=layers, hom_type=hom_type, coefs_type=coefs_type)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "drawn-manitoba", - "metadata": { - "hidden": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(results_train[\"fc\"], results_test[\"fc\"])" - ] - }, - { - "cell_type": "markdown", - "id": "assisted-walnut", - "metadata": {}, - "source": [ - "# Analysis" - ] - }, - { - "cell_type": "markdown", - "id": "ambient-seattle", - "metadata": {}, - "source": [ - "So, what do the above plots show us?\n", - "\n", - "Observation: full model separates the test data even better than the training data.
\n", - "Why: the test data barcode is 28% longer that the training data barcode.
\n", - "Implications: the model is robust, however it contains uninformative features.
\n", - "\n", - "Observation: pruned model separates the test data and the training data similarly well.
\n", - "Why: the training data barcode is 2% longer that the test data barcode.
\n", - "Implications: the model is almost as robust as the full model and it does not contain uninformative features.
" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "eXNN", - "language": "python", - "name": "exnn" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/CIFAR10/Resnet_Homologies_analysis.ipynb b/examples/CIFAR10/Resnet_Homologies_analysis.ipynb deleted file mode 100644 index 2147fdb..0000000 --- a/examples/CIFAR10/Resnet_Homologies_analysis.ipynb +++ /dev/null @@ -1,1003 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Вспомогательные функции" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# путь к папке с моделями\n", - "model_path = \"./\"" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# класс и методы необходимые для SVD разложения\n", - "from typing import Union\n", - "\n", - "import torch\n", - "from torch import Tensor\n", - "from torch.nn import Conv2d, Parameter, Module\n", - "from torch.nn.common_types import _size_2_t\n", - "import os\n", - "\n", - "class DecomposedConv2d(Conv2d):\n", - " \"\"\"Extends the Conv2d layer by implementing the singular value decomposition of\n", - " the weight matrix.\n", - " \"\"\"\n", - "\n", - " def __init__(\n", - " self,\n", - " in_channels: int,\n", - " out_channels: int,\n", - " kernel_size: _size_2_t,\n", - " stride: _size_2_t = 1,\n", - " padding: Union[str, _size_2_t] = 0,\n", - " dilation: _size_2_t = 1,\n", - " groups: int = 1,\n", - " bias: bool = True,\n", - " padding_mode: str = \"zeros\",\n", - " decomposing: bool = True,\n", - " decomposing_mode: str = \"channel\",\n", - " device=None,\n", - " dtype=None,\n", - " ) -> None:\n", - "\n", - " super().__init__(\n", - " in_channels,\n", - " out_channels,\n", - " kernel_size,\n", - " stride,\n", - " padding,\n", - " dilation,\n", - " groups,\n", - " bias,\n", - " padding_mode,\n", - " device,\n", - " dtype,\n", - " )\n", - "\n", - " n, c, w, h = self.weight.size()\n", - " self.decomposing_modes_dict = {\n", - " \"channel\": (n, c * w * h),\n", - " \"spatial\": (n * w, c * h),\n", - " }\n", - "\n", - " if decomposing:\n", - " self.decompose(decomposing_mode)\n", - " else:\n", - " self.U = None\n", - " self.S = None\n", - " self.Vh = None\n", - " self.decomposing = False\n", - "\n", - " def decompose(self, decomposing_mode: str) -> None:\n", - " \"\"\"Decompose the weight matrix in singular value decomposition.\"\"\"\n", - "\n", - " if decomposing_mode not in self.decomposing_modes_dict.keys():\n", - " raise ValueError(\n", - " \"decomposing_mode must be one of {}, but got decomposing_mode='{}'\".format(\n", - " self.decomposing_modes_dict.keys(), decomposing_mode\n", - " )\n", - " )\n", - " W = self.weight.view(self.decomposing_modes_dict[decomposing_mode])\n", - " U, S, Vh = torch.linalg.svd(W, full_matrices=False)\n", - "\n", - " self.U = Parameter(U)\n", - " self.S = Parameter(S)\n", - " self.Vh = Parameter(Vh)\n", - " self.register_parameter(\"weight\", None)\n", - " self.decomposing = True\n", - "\n", - " def compose(self) -> None:\n", - " \"\"\"Compose the weight matrix from singular value decomposition.\"\"\"\n", - "\n", - " W = self.U @ torch.diag(self.S) @ self.Vh\n", - " self.weight = Parameter(\n", - " W.view(\n", - " self.out_channels, self.in_channels // self.groups, *self.kernel_size\n", - " )\n", - " )\n", - "\n", - " self.register_parameter(\"U\", None)\n", - " self.register_parameter(\"S\", None)\n", - " self.register_parameter(\"Vh\", None)\n", - " self.decomposing = False\n", - "\n", - " def forward(self, input: Tensor) -> Tensor:\n", - "\n", - " if self.decomposing:\n", - " W = self.U @ torch.diag(self.S) @ self.Vh\n", - " return self._conv_forward(\n", - " input,\n", - " W.view(\n", - " self.out_channels,\n", - " self.in_channels // self.groups,\n", - " *self.kernel_size\n", - " ),\n", - " self.bias,\n", - " )\n", - " else:\n", - " return self._conv_forward(input, self.weight, self.bias)\n", - "\n", - " def set_U_S_Vh(self, u: Tensor, s: Tensor, vh: Tensor) -> None:\n", - " \"\"\"Update U, S, Vh matrices.\"\"\"\n", - "\n", - " assert self.decomposing, \"for setting U, S and Vh, the model must be decomposed\"\n", - " self.U = Parameter(u)\n", - " self.S = Parameter(s)\n", - " self.Vh = Parameter(vh)\n", - "\n", - "def energy_threshold_pruning(conv: DecomposedConv2d, energy_threshold: float) -> None:\n", - " \"\"\"Prune the weight matrices to the energy_threshold (in-place).\"\"\"\n", - " assert conv.decomposing, \"for pruning, the model must be decomposed\"\n", - " S, indices = conv.S.sort()\n", - " U = conv.U[:, indices]\n", - " Vh = conv.Vh[indices, :]\n", - " sum = (S ** 2).sum()\n", - " threshold = energy_threshold * sum\n", - " for i, s in enumerate(S):\n", - " sum -= s ** 2\n", - " if sum < threshold:\n", - " conv.set_U_S_Vh(U[:, i:].clone(), S[i:].clone(), Vh[i:, :].clone())\n", - " break\n", - "\n", - "\n", - "def decompose_module(model: Module, decomposing_mode: str = \"channel\") -> None:\n", - " \"\"\"Replace Conv2d layers with DecomposedConv2d layers in module (in-place).\"\"\"\n", - " for name, module in model.named_children():\n", - " if len(list(module.children())) > 0:\n", - " decompose_module(module, decomposing_mode=decomposing_mode)\n", - "\n", - " if isinstance(module, Conv2d):\n", - " new_module = DecomposedConv2d(\n", - " in_channels=module.in_channels,\n", - " out_channels=module.out_channels,\n", - " kernel_size=module.kernel_size,\n", - " stride=module.stride,\n", - " padding=module.padding,\n", - " dilation=module.dilation,\n", - " groups=module.groups,\n", - " bias=(module.bias is not None),\n", - " padding_mode=module.padding_mode,\n", - " decomposing=False,\n", - " )\n", - " new_module.load_state_dict(module.state_dict())\n", - " new_module.decompose(decomposing_mode=decomposing_mode)\n", - " setattr(model, name, new_module)\n", - "\n", - "def prune_model(model, energy_threshold) -> None:\n", - " \"\"\"Prune the model weights to the energy_threshold.\"\"\"\n", - " for module in model.modules():\n", - " if isinstance(module, DecomposedConv2d):\n", - " energy_threshold_pruning(conv=module, energy_threshold=energy_threshold)\n", - "\n", - "def number_of_params(model) -> int:\n", - " \"\"\"Return number of model parameters.\"\"\"\n", - " return sum(p.numel() for p in model.parameters())" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from eXNN.InnerNeuralTopology import homologies, api\n", - "from torchvision.datasets import CIFAR10\n", - "import torchvision.transforms as TF" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from torchmetrics.classification import MulticlassAccuracy\n", - "metric = MulticlassAccuracy(num_classes=10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Загрузка и предобработка датасетов" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Files already downloaded and verified\n" - ] - } - ], - "source": [ - "import torchvision.transforms as TF\n", - "\n", - "tfm = TF.Compose([\n", - " TF.ToTensor(),\n", - " TF.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n", - "])\n", - "\n", - "train_ds = CIFAR10(root='./.cache', train=True, download=True, \n", - " transform=tfm) #TF.ToTensor()) \n", - "test_ds = CIFAR10(root='./.cache', train=False, download=False, \n", - " transform=tfm)#TF.ToTensor())" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "train_dl = torch.utils.data.DataLoader(train_ds, batch_size=500, shuffle=True)\n", - "test_dl = torch.utils.data.DataLoader(test_ds, batch_size=500, shuffle=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Базовая модель (ResNet18_SVD)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Ниже производится оценка модели ResNet18_SVD по двум пунктам:\n", - "\n", - "1) Разница топологий образов тренировочных и тестовых данных\n", - "\n", - "2) Робастность модели к шумам через оценку разницы топологии зашумлённых и не зашумлённых данных" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of parameters: 12668746\n" - ] - }, - { - "data": { - "text/plain": [ - "ResNet(\n", - " (conv1): DecomposedConv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n", - " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (relu): ReLU(inplace=True)\n", - " (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n", - " (layer1): Sequential(\n", - " (0): BasicBlock(\n", - " (conv1): DecomposedConv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", - " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (relu): ReLU(inplace=True)\n", - " (conv2): DecomposedConv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", - " (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " )\n", - " (1): BasicBlock(\n", - " (conv1): DecomposedConv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", - " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (relu): ReLU(inplace=True)\n", - " (conv2): DecomposedConv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", - " (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " )\n", - " )\n", - " (layer2): Sequential(\n", - " (0): BasicBlock(\n", - " (conv1): DecomposedConv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", - " (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (relu): ReLU(inplace=True)\n", - " (conv2): DecomposedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", - " (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (downsample): Sequential(\n", - " (0): DecomposedConv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", - " (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " )\n", - " )\n", - " (1): BasicBlock(\n", - " (conv1): DecomposedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", - " (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (relu): ReLU(inplace=True)\n", - " (conv2): DecomposedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", - " (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " )\n", - " )\n", - " (layer3): Sequential(\n", - " (0): BasicBlock(\n", - " (conv1): DecomposedConv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", - " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (relu): ReLU(inplace=True)\n", - " (conv2): DecomposedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", - " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (downsample): Sequential(\n", - " (0): DecomposedConv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", - " (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " )\n", - " )\n", - " (1): BasicBlock(\n", - " (conv1): DecomposedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", - " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (relu): ReLU(inplace=True)\n", - " (conv2): DecomposedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", - " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " )\n", - " )\n", - " (layer4): Sequential(\n", - " (0): BasicBlock(\n", - " (conv1): DecomposedConv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", - " (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (relu): ReLU(inplace=True)\n", - " (conv2): DecomposedConv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", - " (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (downsample): Sequential(\n", - " (0): DecomposedConv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", - " (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " )\n", - " )\n", - " (1): BasicBlock(\n", - " (conv1): DecomposedConv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", - " (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (relu): ReLU(inplace=True)\n", - " (conv2): DecomposedConv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", - " (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " )\n", - " )\n", - " (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n", - " (fc): Linear(in_features=512, out_features=10, bias=True)\n", - ")" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# загрузка обученной разложенной модели\n", - "svd_model_name = 'ResNet18_SVD_channel_O-100.0_H-0.000100.sd.pt'\n", - "svd_model = resnet18(num_classes=10)\n", - "decompose_module(svd_model, \"channel\")\n", - "svd_model.load_state_dict(torch.load(model_path + svd_model_name, map_location=device))\n", - "print(f\"Number of parameters: {number_of_params(svd_model)}\")\n", - " \n", - "svd_model.eval()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "results_train = api.NetworkHomologies(svd_model, train_batch, layers = [\"layer2\", \"layer4\", \"fc\"],\n", - " hom_type = \"sparse\", coefs_type = \"2\")\n", - "results_test = api.NetworkHomologies(svd_model, test_batch, layers = [\"layer2\", \"layer4\", \"fc\"],\n", - " hom_type = \"sparse\", coefs_type = \"2\")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(results_train[\"fc\"], results_test[\"fc\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "По графику видно, что топология образов тестовых данных и тренировочных данных существенно отличается - на более, чем 100% - что говорит о том, что модель, скорее всего, выявила существенно неинформативные признаки. С другой стороны, разделенность только растёт, что говорит об устойчивости классификации" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Оценка робастности" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "results_cleaned = api.NetworkHomologies(svd_model, train_batch, layers = [\"layer2\", \"layer4\", \"fc\"],\n", - " hom_type = \"sparse\", coefs_type = \"2\")\n", - "noise = torch.randn(train_batch.shape)*(train_batch.max() - train_batch.min())*0.1\n", - "results_noised = api.NetworkHomologies(svd_model, train_batch + noise, \n", - " layers = [\"layer2\", \"layer4\", \"fc\"],\n", - " hom_type = \"sparse\", coefs_type = \"2\")" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(results_cleaned[\"fc\"], results_noised[\"fc\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "По графику видно, что изменение протяжённости баркода при добавлении шума составляет примерно 25%, что говорит о том, что модель уязвима к шумам" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Прореженная модель (ResNet18_SVD_pruned)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Ниже производится оценка модели ResNet18_SVD_pruned по двум пунктам:\n", - "\n", - "1) Разница топологий образов тренировочных и тестовых данных\n", - "\n", - "2) Робастность модели к шумам через оценку разницы топологии зашумлённых и не зашумлённых данных" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of parameters: 7206832\n" - ] - }, - { - "data": { - "text/plain": [ - "ResNet(\n", - " (conv1): DecomposedConv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n", - " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (relu): ReLU(inplace=True)\n", - " (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n", - " (layer1): Sequential(\n", - " (0): BasicBlock(\n", - " (conv1): DecomposedConv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", - " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (relu): ReLU(inplace=True)\n", - " (conv2): DecomposedConv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", - " (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " )\n", - " (1): BasicBlock(\n", - " (conv1): DecomposedConv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", - " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (relu): ReLU(inplace=True)\n", - " (conv2): DecomposedConv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", - " (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " )\n", - " )\n", - " (layer2): Sequential(\n", - " (0): BasicBlock(\n", - " (conv1): DecomposedConv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", - " (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (relu): ReLU(inplace=True)\n", - " (conv2): DecomposedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", - " (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (downsample): Sequential(\n", - " (0): DecomposedConv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", - " (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " )\n", - " )\n", - " (1): BasicBlock(\n", - " (conv1): DecomposedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", - " (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (relu): ReLU(inplace=True)\n", - " (conv2): DecomposedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", - " (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " )\n", - " )\n", - " (layer3): Sequential(\n", - " (0): BasicBlock(\n", - " (conv1): DecomposedConv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", - " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (relu): ReLU(inplace=True)\n", - " (conv2): DecomposedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", - " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (downsample): Sequential(\n", - " (0): DecomposedConv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", - " (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " )\n", - " )\n", - " (1): BasicBlock(\n", - " (conv1): DecomposedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", - " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (relu): ReLU(inplace=True)\n", - " (conv2): DecomposedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", - " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " )\n", - " )\n", - " (layer4): Sequential(\n", - " (0): BasicBlock(\n", - " (conv1): DecomposedConv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", - " (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (relu): ReLU(inplace=True)\n", - " (conv2): DecomposedConv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", - " (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (downsample): Sequential(\n", - " (0): DecomposedConv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", - " (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " )\n", - " )\n", - " (1): BasicBlock(\n", - " (conv1): DecomposedConv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", - " (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (relu): ReLU(inplace=True)\n", - " (conv2): DecomposedConv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", - " (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " )\n", - " )\n", - " (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n", - " (fc): Linear(in_features=512, out_features=10, bias=True)\n", - ")" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import copy\n", - "\n", - "#обрезка модели, значения energy_threshold из промежутка (0, 1)\n", - "pruned_model = copy.deepcopy(svd_model)\n", - "prune_model(model=pruned_model, energy_threshold=0.9)\n", - "print(f\"Number of parameters: {number_of_params(pruned_model)}\")\n", - "pruned_model.eval()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "results_train = api.NetworkHomologies(pruned_model, train_batch, layers = [\"layer2\", \"layer4\", \"fc\"],\n", - " hom_type = \"sparse\", coefs_type = \"2\")\n", - "results_test = api.NetworkHomologies(pruned_model, test_batch, layers = [\"layer2\", \"layer4\", \"fc\"],\n", - " hom_type = \"sparse\", coefs_type = \"2\")" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(results_train[\"fc\"], results_test[\"fc\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Из графиков видно, что изменение разделённости между тренировочными и тестовыми данными составляет не более 15%, что говорит о том, что прореживание эффективно убрало из модели неинформативные признаки. С другой стороны, сохранилась закономерность, что разделённость только растёт, что говорит о том, что устойчивость классификации сохранилась" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Оценка робастности" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "results_cleaned = api.NetworkHomologies(pruned_model, train_batch, layers = [\"layer2\", \"layer4\", \"fc\"],\n", - " hom_type = \"sparse\", coefs_type = \"2\")\n", - "noise = torch.randn(train_batch.shape)*(train_batch.max() - train_batch.min())*0.1\n", - "results_noised = api.NetworkHomologies(pruned_model, train_batch + noise, \n", - " layers = [\"layer2\", \"layer4\", \"fc\"],\n", - " hom_type = \"sparse\", coefs_type = \"2\")" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(results_cleaned[\"fc\"], results_noised[\"fc\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "По графику видно, что изменение протяженности баркода близко к 0%, что говорит о высоком уровне робастности модели." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Проверка" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Проведём проверку того, что изменение в протяжённости баркодов коррелирует с уровнем шума в данных.\n", - "\n", - "Замерять связь будем с помощью коэффициента корелляции Спирмена - коэффициента, измеряющего монотонную взаимосвязь между переменными. Также дополнительно сопроводим измерение графиком" - ] - }, - { - "cell_type": "code", - "execution_count": 120, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "def noise_batch(batch, std = 0.1):\n", - " return batch + torch.randn(batch.shape)*std\n", - "\n", - "vals = np.arange(0, 10, 0.25)\n", - "\n", - "min_list = []\n", - "max_list = []\n", - "\n", - "diff_list = []\n", - "\n", - "results_cleaned = api.NetworkHomologies(svd_model, train_batch, layers = [\"fc\"],\n", - " hom_type = \"sparse\", coefs_type = \"2\")\n", - "res0 = np.array([i.get_xdata()[1] - i.get_xdata()[0] for i in results_cleaned[\"fc\"].get_axes()[0].lines])\n", - "\n", - "for i in vals:\n", - " results_noised = api.NetworkHomologies(svd_model, noise_batch(train_batch, i), layers = [\"fc\"],\n", - " hom_type = \"sparse\", coefs_type = \"2\")\n", - " \n", - " res = np.array([i.get_xdata()[1] - i.get_xdata()[0] for i in results_noised[\"fc\"].get_axes()[0].lines])\n", - " max_list.append(np.percentile(res, 90))\n", - " min_list.append(np.percentile(res, 10))\n", - " \n", - " diff_list.append(np.mean(np.abs(res - res0)))" - ] - }, - { - "cell_type": "code", - "execution_count": 123, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'barcode mean abs difference')" - ] - }, - "execution_count": 123, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "from scipy.stats import spearmanr\n", - "\n", - "corr = spearmanr(vals, diff_list)[0]\n", - "plt.plot(vals, diff_list)\n", - "plt.title(\"Spearman correlation is {}\".format(round(corr, 2)))\n", - "plt.xlabel(\"noise_std\")\n", - "plt.ylabel(\"barcode mean abs difference\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "По графику и величине коэффициента корелляции Спирмена видна сильная монотонная взаимосвязь между уровнем шума в данных и изменением в протяженности баркода" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Влияние коэффициента гомологий на итоговый баркод" - ] - }, - { - "cell_type": "code", - "execution_count": 134, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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jY6PvUQCYM8IKgJlSa82tW7ecWgEwUcIKgJnTtq1TKwAmSlgBMHNGp1arq6tOrgCYCGEFwExq2zabm5tOrgCYCGEFwEyqtaZtW89bATARwgqAmWYFOwCTIKwAmGlWsAMwCYMPq3v37vU9AgBTrtaad999N0tLS32PAsCMGnxYAcCzevfdd3PlypW+xwBgBg0+rC5cuND3CAAMyO7ubt8jADCDBh9WAHAS9+/ft8gCgFMnrACYK23bWmQBwKkTVgDMlVprbt68mcXFRc9bAXBqhBUAc6fruuzv72d7e1tcAXAqhBUAc6vruuzs7HjmCoCxPTWsSimvllK2SilbN27cmMRMADAxbdtmfX1dXAEwlqeGVa31tVrrSq11xYsVAZg1tdYcHByIKwDG4iogAHNvFFdra2ueuQLguZzvewAAmAa11jRNk52dnayuriZJLl++nKtXr/Y7GACDIKwA4Ii2bbO5uZkkeeONN/L666/n2rVrPU8FwLRzFRAAjqi1pm3btG2bpmmsZAfgmQgrAHgCK9kBeBbCCgCewkp2AJ5GWAHAU9gaCMDTCCsAeAajrYG7u7t9jwLAFBJWAHAC9+/fdyUQgA8QVgBwAm3bZm1tLYuLi64FAvCQsAKAExhdCdzf38/29naWl5edYAEgrADgeXVdl729PRsDARBWADCOoxsDl5eXs7q6KrIA5lCptT7zN6+srNStra0zHOfkFhYWcvfu3b7HAICUUvLCCy/k3LlzWVpayqVLl3L58uVcvXq179EAOCWllDdrrSuPft2JFQCcklpr2rZN0zTZ29vL5ubmw5Msp1gAs01YAcAZeDSyRpsERRbAbHIVEAAmqJSSCxcuZGFhIRcvXsylS5fe9/ddHQSYbo+7Cni+j2EAYF6N1rU3TZPbt2/n+vXr7/v7b7zxRl5//fVcu3atpwkBeB6uAgJAT0bXBY/+apom29vbWVxc9BJigAERVgAwZbquy/7+/sOXEIsrgOknrABginVdl52dHQsvAKacsAKAKde2bdbX1718GGCKCSsAmHK11hwcHLzvvVgiC2C6CCsAGAAvHwaYbt5jBQADNnov1tLS0sN3YnkXFsDZedx7rIQVAMyAUkpeeOHBRZRz585lYWEhSXLx4sW88sorQgvglAgrAJhDoxOthYWFXLx40akWwJgeF1bn+xgGAJiMWmuapknTNLl9+3auX7+eJBn9oFRcAZwOyysAYE6MFmC0bZuDgwMr3AFOkRMrAJhDR1e4O70CGJ8TKwCYU6MTrNHplZMrgOdneQUA8L4lF8/q6DKMEUsxgFlnKyAAcKqOrngfObrqPUk+8YlP5Nq1a5MeDeDM2AoIAJyq0VXCo9q2TdM0D3+/vb2dxcXFJMefcCVOuYDZ4MQKAJiI4064kg+ecj0uwI4jyoBJcxUQABiExwXYcUZR9miMCS7grAgrAGBmPRpjo+DyjBdw2h4XVtatAwCDd/Tlx6PnvPb39x8+43XlypW+RwRmnOUVAMDM6rrufYGVPPkZLlcIgeclrACAmTcKrCS5fft2rl+/fuz3jR55EFfASXnGCgDg0KMvSrYiHnjUzC6vOH/+/AfeoQEAcJYeDbDk9yJMdMFsE1YAAGdotJnw0fdyPc1xp2LiDKbXzIaVq4AAwJAd996uJ8XZxYsX88orrwgv6ImwAgCYAUevITrtgskTVgAAM+ZZT7uetGJ+SEQj00BYAQDMqeMCbIgejcZZCUaON60h/biweup7rEopryZ5NUleeumlMxhtPJ/85Cfz9ttv9z0GAAAwxwZ/YgUAADApjzuxGv6ZMAAAQM+EFQAAwJiEFQAAwJiEFQAAwJiEFQAAwJiEFQAAwJiEFQAAwJiEFQAAwJiEFQAAwJiEFQAAwJiEFQAAwJhKrfXZv7mUG0m+cXbjPLePJnm37yHmkM+9Hz73fvjc++Fz74fPvR8+93743Psx5M/9D9Ralx794onCalqVUrZqrSt9zzFvfO798Ln3w+feD597P3zu/fC598Pn3o9Z/NxdBQQAABiTsAIAABjTrITVa30PMKd87v3wuffD594Pn3s/fO798Ln3w+fej5n73GfiGSsAAIA+zcqJFQAAQG8GHVallB8spfzvUsqvl1L+Tt/zzItSys+UUr5ZStnpe5Z5UUr5rlLKfymlfLWU8pVSyk/3PdM8KKV8eynlf5ZSvnz4uf/DvmeaJ6WUc6WUa6WUf9f3LPOklPL1UsqvlVLeKqVs9T3PvCilLJZSPl9K2T38d/2f7HumWVdK+Z7Df85Hv26XUj7T91zzoJTyNw//u7pTSvlcKeXb+57pNAz2KmAp5VySryX5M0neSfKrSX6i1vq/eh1sDpRSvj/JnST/otb6R/ueZx6UUj6W5GO11i+VUr4jyZtJfsw/72erlFKSvFhrvVNKuZBkM8lP11r/R8+jzYVSyt9KspLkw7XWH+57nnlRSvl6kpVa61DfLzNIpZSfTfLfa63rpZRvS/KhWuutvueaF4d/rvztJH+81jqN72ydGaWUj+fBf0//cK31/5VSfj7JL9Va/3m/k41vyCdW35fk12utv1FrbZL8XJIf7XmmuVBr/W9JbvY9xzyptf5OrfVLh3/9u0m+muTj/U41++oDdw5/e+Hw1zB/GjUwpZTlJH8uyXrfs8BZK6V8OMn3J/lsktRaG1E1cZ9O8n9E1cScT7JQSjmf5ENJ9nqe51QMOaw+nuS3jvz+nfiDJnOglPJykitJfqXfSebD4XW0t5J8M8l/qrX63CfjapK/naTre5A5VJP8cinlzVLKq30PMyf+YJIbSf7Z4fXX9VLKi30PNWd+PMnn+h5iHtRafzvJP07ym0l+J8l+rfWX+53qdAw5rMoxX/OTZGZaKeVikl9I8pla6+2+55kHtda21no5yXKS7yuluP56xkopP5zkm7XWN/ueZU59qtb6vUl+KMlfO7z+zdk6n+R7k6zVWq8keS+JZ8cn5PDq5Y8k+Td9zzIPSikfyYNbZp9I8vuTvFhK+Yv9TnU6hhxW7yT5riO/X86MHCPCcQ6f8fmFJBu11i/0Pc+8ObyW81+T/GDPo8yDTyX5kcNnfX4uyZ8upfyrfkeaH7XWvcP//WaSL+bB1XvO1jtJ3jlyIv75PAgtJuOHknyp1vp/+x5kTvxAkrdrrTdqrfeSfCHJn+p5plMx5LD61STfXUr5xOFPGn48yS/2PBOcicMlCp9N8tVa6z/pe555UUpZKqUsHv71Qh78x2C336lmX63179Zal2utL+fBv9v/c611Jn6aOe1KKS8eLsjJ4VW0P5vEBtgzVmu9nuS3Sinfc/ilTyexnGhyfiKuAU7Sbyb5E6WUDx3++ebTefDs+OCd73uA51VrvV9K+etJ/mOSc0l+ptb6lZ7HmgullM8lWU3y0VLKO0n+Qa31s/1ONfM+leQnk/za4fM+SfL3aq2/1ONM8+BjSX72cFvUC0l+vtZq9Tez7DuTfPHBn3VyPsm/rrX+h35Hmht/I8nG4Q+LfyPJX+55nrlQSvlQHmyY/it9zzIvaq2/Ukr5fJIvJbmf5FqS1/qd6nQMdt06AADAtBjyVUAAAICpIKwAAADGJKwAAADGJKwAAADGJKwAAADGJKwAAADGJKwAAADGJKwAAADG9P8BW5jqvrVa3bYAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "execution_count": 134, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "results_cleaned1 = api.NetworkHomologies(svd_model, train_batch, layers = [\"fc\"],\n", - " hom_type = \"sparse\", coefs_type = \"7\")\n", - "\n", - "results_cleaned1[\"fc\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 133, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "execution_count": 133, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "results_cleaned[\"fc\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "По графикам видно, что различий между результатами с коэффициентами из поля характеристики 2 и коэффициентами из поля характеристики 7 не наблюдается" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - }, - "vscode": { - "interpreter": { - "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49" - } - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/examples/CIFAR10/Visualization.ipynb b/examples/CIFAR10/Visualization.ipynb deleted file mode 100644 index ff889de..0000000 --- a/examples/CIFAR10/Visualization.ipynb +++ /dev/null @@ -1,230 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "italian-sight", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "os.chdir('../..')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "loved-thomson", - "metadata": {}, - "outputs": [], - "source": [ - "from pathlib import Path\n", - "import torch\n", - "from torchvision.models import resnet18\n", - "from pathlib import Path\n", - "from torchvision.datasets import CIFAR10\n", - "import torchvision.transforms as TF\n", - "from examples.CIFAR10.models import *\n", - "from eXNN.InnerNeuralViz import VisualizeNetSpace" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "plastic-inspector", - "metadata": {}, - "outputs": [], - "source": [ - "# prepare data\n", - "_normalize = TF.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n", - "tfm = TF.Compose([TF.ToTensor(), _normalize])\n", - "test_ds = CIFAR10(root='./.cache', train=False, download=False, transform=tfm)\n", - "test_dl = torch.utils.data.DataLoader(test_ds, batch_size=128, shuffle=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "upset-apparatus", - "metadata": {}, - "outputs": [], - "source": [ - "data, labels = [], []\n", - "itr = iter(test_dl)\n", - "for i in range(10):\n", - " batch = next(itr)\n", - " data.append(batch[0])\n", - " labels.append(batch[1])\n", - "data = torch.cat(data, dim=0)\n", - "labels = torch.cat(labels, dim=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "imported-latex", - "metadata": {}, - "outputs": [], - "source": [ - "# download repository https://github.com/Med-AI-Lab/eXNN-task-CIFAR10\n", - "# change model_repo to the root of the downloaded repository\n", - "model_repo = Path('../eXNN-task-CIFAR10')" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "green-paste", - "metadata": {}, - "outputs": [], - "source": [ - "# load pretrained model\n", - "device = torch.device('cuda:0')\n", - "simple_model = resnet18(num_classes=10)\n", - "simple_model.load_state_dict(torch.load(model_repo / \"ResNet18.sd.pt\", map_location=device));\n", - "simple_model = simple_model.eval()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "similar-treaty", - "metadata": {}, - "outputs": [], - "source": [ - "layers = ['layer1', 'layer2', 'layer3', 'layer4', 'avgpool', 'fc']" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "stock-grill", - "metadata": {}, - "outputs": [], - "source": [ - "res = VisualizeNetSpace(simple_model, 'umap', data, layers, labels=labels, chunk_size=128)" - ] - }, - { - "cell_type": "markdown", - "id": "initial-notion", - "metadata": {}, - "source": [ - "Let's look at how well the trained model separated classes" - ] - }, - { - "cell_type": "markdown", - "id": "miniature-brief", - "metadata": {}, - "source": [ - "Input data is not split at all" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "ongoing-discharge", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "res['input']" - ] - }, - { - "cell_type": "markdown", - "id": "trying-senior", - "metadata": {}, - "source": [ - "After half of a network some structure emerges" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "fitting-favor", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "res['layer2']" - ] - }, - { - "cell_type": "markdown", - "id": "wired-spring", - "metadata": {}, - "source": [ - "Finally, after the last layer classes are well split" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "impressive-shame", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "res['fc']" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "eXNN", - "language": "python", - "name": "exnn" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/CIFAR10/models.py b/examples/CIFAR10/models.py deleted file mode 100644 index 5db37f9..0000000 --- a/examples/CIFAR10/models.py +++ /dev/null @@ -1,168 +0,0 @@ -from typing import Union - -import torch -from torch import Tensor -from torch.nn import Conv2d, Module, Parameter -from torch.nn.common_types import _size_2_t - - -class DecomposedConv2d(Conv2d): - """Extends the Conv2d layer by implementing the singular value decomposition of - the weight matrix. - """ - - def __init__( - self, - in_channels: int, - out_channels: int, - kernel_size: _size_2_t, - stride: _size_2_t = 1, - padding: Union[str, _size_2_t] = 0, - dilation: _size_2_t = 1, - groups: int = 1, - bias: bool = True, - padding_mode: str = "zeros", - decomposing: bool = True, - decomposing_mode: str = "channel", - device=None, - dtype=None, - ) -> None: - - super().__init__( - in_channels, - out_channels, - kernel_size, - stride, - padding, - dilation, - groups, - bias, - padding_mode, - device, - dtype, - ) - - n, c, w, h = self.weight.size() - self.decomposing_modes_dict = { - "channel": (n, c * w * h), - "spatial": (n * w, c * h), - } - - if decomposing: - self.decompose(decomposing_mode) - else: - self.U = None - self.S = None - self.Vh = None - self.decomposing = False - - def decompose(self, decomposing_mode: str) -> None: - """Decompose the weight matrix in singular value decomposition.""" - - if decomposing_mode not in self.decomposing_modes_dict.keys(): - raise ValueError( - "decomposing_mode must be one of {}, but got decomposing_mode='{}'".format( - self.decomposing_modes_dict.keys(), - decomposing_mode, - ), - ) - W = self.weight.view(self.decomposing_modes_dict[decomposing_mode]) - U, S, Vh = torch.linalg.svd(W, full_matrices=False) - - self.U = Parameter(U) - self.S = Parameter(S) - self.Vh = Parameter(Vh) - self.register_parameter("weight", None) - self.decomposing = True - - def compose(self) -> None: - """Compose the weight matrix from singular value decomposition.""" - - W = self.U @ torch.diag(self.S) @ self.Vh - self.weight = Parameter( - W.view( - self.out_channels, - self.in_channels // self.groups, - *self.kernel_size, - ), - ) - - self.register_parameter("U", None) - self.register_parameter("S", None) - self.register_parameter("Vh", None) - self.decomposing = False - - def forward(self, input: Tensor) -> Tensor: - - if self.decomposing: - W = self.U @ torch.diag(self.S) @ self.Vh - return self._conv_forward( - input, - W.view( - self.out_channels, - self.in_channels // self.groups, - *self.kernel_size, - ), - self.bias, - ) - else: - return self._conv_forward(input, self.weight, self.bias) - - def set_decomposition_matrices(self, u: Tensor, s: Tensor, vh: Tensor) -> None: - """Update U, S, Vh matrices.""" - - assert self.decomposing, "for setting U, S and Vh, the model must be decomposed" - self.U = Parameter(u) - self.S = Parameter(s) - self.Vh = Parameter(vh) - - -def energy_threshold_pruning(conv: DecomposedConv2d, energy_threshold: float) -> None: - """Prune the weight matrices to the energy_threshold (in-place).""" - assert conv.decomposing, "for pruning, the model must be decomposed" - S, indices = conv.S.sort() - U = conv.U[:, indices] - Vh = conv.Vh[indices, :] - summ = (S ** 2).sum() - threshold = energy_threshold * summ - for i, s in enumerate(S): - summ -= s ** 2 - if summ < threshold: - conv.set_decomposition_matrices(U[:, i:].clone(), S[i:].clone(), Vh[i:, :].clone()) - break - - -def decompose_module(model: Module, decomposing_mode: str = "channel") -> None: - """Replace Conv2d layers with DecomposedConv2d layers in module (in-place).""" - for name, module in model.named_children(): - if len(list(module.children())) > 0: - decompose_module(module, decomposing_mode=decomposing_mode) - - if isinstance(module, Conv2d): - new_module = DecomposedConv2d( - in_channels=module.in_channels, - out_channels=module.out_channels, - kernel_size=module.kernel_size, - stride=module.stride, - padding=module.padding, - dilation=module.dilation, - groups=module.groups, - bias=(module.bias is not None), - padding_mode=module.padding_mode, - decomposing=False, - ) - new_module.load_state_dict(module.state_dict()) - new_module.decompose(decomposing_mode=decomposing_mode) - setattr(model, name, new_module) - - -def prune_model(model, energy_threshold) -> None: - """Prune the model weights to the energy_threshold.""" - for module in model.modules(): - if isinstance(module, DecomposedConv2d): - energy_threshold_pruning(conv=module, energy_threshold=energy_threshold) - - -def number_of_params(model) -> int: - """Return number of model parameters.""" - return sum(p.numel() for p in model.parameters()) diff --git a/examples/CIFAR10/quantitative_indices_example.ipynb b/examples/CIFAR10/quantitative_indices_example.ipynb deleted file mode 100644 index 8bb2a2b..0000000 --- a/examples/CIFAR10/quantitative_indices_example.ipynb +++ /dev/null @@ -1,1013 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this notebook we'll show how we can compute the quantitative indices for CIFAR10 dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "os.chdir('../..')" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "import torchvision\n", - "from torchvision.datasets import CIFAR10\n", - "import torchvision.transforms as transforms\n", - "from eXNN.InnerNeuralTopology import quantitative_indices as qi\n", - "from gtda.homology import VietorisRipsPersistence" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the transformation to apply to the data\n", - "# prepare data\n", - "_normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n", - "tfm = transforms.Compose([transforms.ToTensor(), _normalize])" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./.cache/cifar-10-python.tar.gz\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "82f2833dc54240ad8712de2228ea5c46", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/170498071 [00:00 max_std:\n", - " max_std = corrupted_pred_std.mean().item()\n", - " example_error = [img.cpu().detach(), corrupted_img.cpu().detach(), \n", - " {i: j.cpu().detach() for i, j in pred.items()},\n", - " {i: j.cpu().detach() for i, j in corrupted_pred.items()}]\n", - " \n", - " if (example_error is not None) and (i > 100):\n", - " break" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's look at the results" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Firstly, let's compare uncertainty statistics on original and corrupted data" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "simple_data = np.array([np.mean(i) for i in simple_res[\"uncert\"]])\n", - "corrupted_data = np.array([np.mean(i) for i in corrupted_res[\"uncert\"]])\n", - "\n", - "plt.boxplot([simple_data[simple_data < np.percentile(simple_data, 98)],\n", - " corrupted_data[corrupted_data < np.percentile(corrupted_data, 98)]])\n", - "plt.xticks([1, 2], [\"original\", \"corrupted\"])\n", - "plt.plot();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Predictions on corrupted data have higher uncertainty and thus can be detected." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's look at the sample on which the model made an erroneous prediction with largest uncertainty" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "def viz_preproc(img):\n", - " img = (img - img.min())/(img.max() - img.min() + 1e-8)\n", - " img = img.cpu().detach().numpy()\n", - " img = np.moveaxis(img, 0, -1)\n", - " return img\n", - "\n", - "fig, ax = plt.subplots(1, 2)\n", - "ax[0].imshow(viz_preproc(example_error[0].squeeze()))\n", - "\n", - "pred = example_error[2]\n", - "mean, std = pred[\"mean\"].argmax().tolist(), [round(i, 2) for i in pred[\"std\"].squeeze().cpu().detach().tolist()]\n", - "ax[0].set_title(\"Pred {} | Std {}\".format(mean, std))\n", - "\n", - "ax[1].imshow(viz_preproc(example_error[1].squeeze()))\n", - "\n", - "corrupted_pred = example_error[3]\n", - "mean, std = corrupted_pred[\"mean\"].argmax().tolist(), [round(i, 2) for i in corrupted_pred[\"std\"].squeeze().cpu().detach().tolist()]\n", - "ax[1].set_title(\"Pred {} | Std {}\".format(mean, std))\n", - "plt.plot();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This example shows that adversarially corrupted example has highter uncertainty." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.12" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/casting/Visualization.ipynb b/examples/casting/Visualization.ipynb deleted file mode 100644 index 777f099..0000000 --- a/examples/casting/Visualization.ipynb +++ /dev/null @@ -1,206 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "pacific-tunisia", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "os.chdir('../..')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "dietary-building", - "metadata": {}, - "outputs": [], - "source": [ - "from pathlib import Path\n", - "import torch\n", - "from torch.utils.data import DataLoader\n", - "from examples.casting.data import create_datasets\n", - "from eXNN.InnerNeuralViz import VisualizeNetSpace" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "roman-tanzania", - "metadata": {}, - "outputs": [], - "source": [ - "# download repository https://github.com/Med-AI-Lab/eXNN-task-casting-defects\n", - "# change ind_repo to the root of the downloaded repository\n", - "ind_repo = Path('../eXNN-task-casting-defects')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "environmental-violin", - "metadata": {}, - "outputs": [], - "source": [ - "# prepare data\n", - "_, test_ds = create_datasets(ind_repo / 'casting_512x512')\n", - "test_dl = DataLoader(test_ds, batch_size=32, shuffle=False)\n", - "\n", - "data, labels = [], []\n", - "for batch in test_dl:\n", - " data.append(batch[0])\n", - " labels.append(batch[1])\n", - "data = torch.cat(data, dim=0)\n", - "labels = torch.cat(labels, dim=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "honey-engineer", - "metadata": {}, - "outputs": [], - "source": [ - "# prepare model\n", - "device = torch.device('cuda:0')\n", - "model = torch.load(ind_repo / 'trained_model.pt', map_location=device)\n", - "model = model.eval()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "political-election", - "metadata": {}, - "outputs": [], - "source": [ - "# do visualization\n", - "layers = ['layer1', 'layer2', 'layer3', 'layer4', 'avgpool', 'fc']\n", - "res = VisualizeNetSpace(model, 'umap', data.to(device), layers, labels=labels, chunk_size=128)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "rocky-stanley", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# in input data classes are mixed and can't be separated\n", - "res['input']" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "bibliographic-virginia", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# after first two layers not really better\n", - "res['layer2']" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "global-directory", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# next layers doesn't really change much\n", - "res['layer3']" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "enclosed-review", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# final conv block separates the data\n", - "res['layer4']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "arabic-track", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "eXNN", - "language": "python", - "name": "exnn" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/casting/data.py b/examples/casting/data.py deleted file mode 100644 index 8ef7202..0000000 --- a/examples/casting/data.py +++ /dev/null @@ -1,46 +0,0 @@ -import os - -import numpy as np -import torchvision.transforms as TF -from PIL import Image -from torch.utils.data import Dataset - - -class MyDs(Dataset): - def __init__(self, data_path, pos_files, neg_files, tfm=None): - self.data_path = data_path - self.pos_files = pos_files - self.neg_files = neg_files - self.tfm = tfm - - def __len__(self): - return len(self.pos_files) + len(self.neg_files) - - def __getitem__(self, i): - if i < len(self.pos_files): - pf = self.data_path / "def_front" / self.pos_files[i] - lbl = 1 - else: - pf = self.data_path / "ok_front" / self.neg_files[i - len(self.pos_files)] - lbl = 0 - image = Image.open(pf) - if self.tfm is not None: - image = self.tfm(image) - return image, lbl - - -def create_datasets(data_path): - pos_files = sorted(os.listdir(data_path / "def_front")) - neg_files = sorted(os.listdir(data_path / "ok_front")) - np.random.seed(0) - np.random.shuffle(pos_files) - np.random.shuffle(neg_files) - _N = int(len(pos_files) * 0.8) - trn_pos_files, val_pos_files = pos_files[:_N], pos_files[_N:] - _N = int(len(neg_files) * 0.8) - trn_neg_files, val_neg_files = neg_files[:_N], neg_files[_N:] - _normalize = TF.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) - tfm = TF.Compose([TF.Resize((256, 256)), TF.ToTensor(), _normalize]) - trn_ds = MyDs(data_path, trn_pos_files, trn_neg_files, tfm=tfm) - val_ds = MyDs(data_path, val_pos_files, val_neg_files, tfm=tfm) - return trn_ds, val_ds diff --git a/examples/casting/impellers.png b/examples/casting/impellers.png deleted file mode 100644 index 523129d..0000000 Binary files a/examples/casting/impellers.png and /dev/null differ diff --git a/examples/casting/industrial_task.ipynb b/examples/casting/industrial_task.ipynb deleted file mode 100644 index acbd94a..0000000 --- a/examples/casting/industrial_task.ipynb +++ /dev/null @@ -1,272 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Существенной проблемой при применении моделей машинного обучения в промышленности является опасность сдвига распределения между данными, на которых строилась модель, и реальными данными. Поэтому типичным требованием к моделям в промышленности является способность детектировать данные, с которыми модель не сможет справиться. Покажем, как можно использовать библиотеку eXpain-NNs для решения данной задачи.\n", - "\n", - "Рассмотрим на примере задачи распознавания дефектов при изготовлении импеллеров для погружных насосов.\n", - "На картинке приведены изображения нормального импеллера (слева) и импеллера с дефектом (справа):\n", - "![impellers](impellers.png)\n", - "\n", - "Обучение модели можно посмотреть в [репозитории](https://github.com/Med-AI-Lab/eXNN-task-casting-defects). Посмотрим, как с помощью построения нейробайесовского аналога построенной модели и оценки неопределенности предсказаний мы сможем отличить чистые данные (на которых модель может сделать предсказание) от испорченных (не пригодных для предсказания). Испорченные данные построим с помощью состязательной FGSM-атаки." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "heading_collapsed": true - }, - "source": [ - "## Определения функций" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "heading_collapsed": true, - "hidden": true - }, - "source": [ - "### Загрузка зависимостей" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "hidden": true - }, - "outputs": [], - "source": [ - "import os\n", - "os.chdir('../..')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "hidden": true - }, - "outputs": [], - "source": [ - "from pathlib import Path\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import torch\n", - "import torch.nn as nn\n", - "from torch.utils.data import DataLoader\n", - "from examples.casting.data import create_datasets\n", - "from eXNN.NetBayesianization import BasicBayesianWrapper" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "heading_collapsed": true, - "hidden": true - }, - "source": [ - "### Состязательная атака" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "hidden": true - }, - "outputs": [], - "source": [ - "def fgsm_attack(model, loss, images, labels, eps, device):\n", - " \n", - " images = images\n", - " labels = labels\n", - " images.requires_grad = True\n", - " \n", - " outputs = model.forward(images)\n", - " \n", - " model.zero_grad()\n", - " cost = loss(outputs, labels).to(device)\n", - " cost.backward()\n", - " \n", - " attack_images = images + eps*images.grad.sign()\n", - " attack_images = torch.clamp(attack_images, 0, 1)\n", - " \n", - " return attack_images" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "heading_collapsed": true, - "hidden": true - }, - "source": [ - "### Сбор предсказаний модели" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "hidden": true - }, - "outputs": [], - "source": [ - "def _d(t: torch.Tensor): \n", - " return t.detach().cpu()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "hidden": true - }, - "outputs": [], - "source": [ - "def collect_predictions(determ_model, bayes_model, dataset, device):\n", - " simple_res = {\"acc\": [], \"uncert\": []}\n", - " corrupted_res = {\"acc\": [], \"uncert\": []}\n", - "\n", - " example_error = None\n", - " max_std = 0\n", - "\n", - " # collect predictions\n", - " for i, img_data in enumerate(dataset):\n", - " img, cls = img_data[0].to(device).unsqueeze(0), img_data[1]\n", - "\n", - " # make prediction on original data\n", - " pred = bayes_model.predict(img, n_iter = 10)\n", - " pred_mean, pred_std = _d(pred[\"mean\"]).argmax().item(), _d(pred[\"std\"])\n", - " simple_res[\"acc\"].append(pred_mean == cls)\n", - " simple_res[\"uncert\"].append(pred_std.numpy())\n", - "\n", - " # make prediction on corrupted data\n", - " corrupted_img = fgsm_attack(determ_model, nn.NLLLoss(), img, \n", - " torch.LongTensor([cls]).to(device), eps=0.01, device=device)\n", - " corrupted_pred = bayes_model.predict(corrupted_img, n_iter = 10)\n", - " corrupted_pred_mean, corrupted_pred_std = _d(corrupted_pred[\"mean\"]).argmax().item(), _d(corrupted_pred[\"std\"])\n", - " corrupted_res[\"acc\"].append(corrupted_pred_mean == cls)\n", - " corrupted_res[\"uncert\"].append(corrupted_pred_std.numpy())\n", - "\n", - " # select example of the erroneous prediction with largest uncertainty for visual analysis\n", - " if corrupted_pred_mean != pred_mean:\n", - " if corrupted_pred_std.mean().item() > max_std:\n", - " max_std = corrupted_pred_std.mean().item()\n", - " example_error = [img.cpu().detach(), corrupted_img.cpu().detach(), \n", - " {i: j.cpu().detach() for i, j in pred.items()},\n", - " {i: j.cpu().detach() for i, j in corrupted_pred.items()}]\n", - "\n", - " if (example_error is not None) and (i > 100):\n", - " break\n", - " \n", - " simple_data = np.array([np.mean(i) for i in simple_res[\"uncert\"]])\n", - " corrupted_data = np.array([np.mean(i) for i in corrupted_res[\"uncert\"]])\n", - " simple_data = simple_data[simple_data < np.percentile(simple_data, 98)]\n", - " corrupted_data = corrupted_data[corrupted_data < np.percentile(corrupted_data, 98)]\n", - " return simple_data, corrupted_data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Решение задачи" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# скачайте repository https://github.com/Med-AI-Lab/eXNN-task-casting-defects\n", - "# переопределите ind_repo так чтобы переменная указывала путь к загруженному репозиторию\n", - "ind_repo = Path('../eXNN-task-casting-defects')" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# подготовка данных\n", - "_, test_ds = create_datasets(ind_repo / 'casting_512x512')" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# подготовка модели\n", - "device = torch.device('cuda:0')\n", - "model = torch.load(ind_repo / 'trained_model.pt', map_location=device).eval()\n", - "# построим нейробайесовский аналог модели\n", - "wrapper_model = BasicBayesianWrapper(model, \"beta\", p = None, a = 0.6, b = 12.0)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# оценим неопределенность предсказаний на тестовой выборке на чистых и испорченных данных\n", - "simple_data, corrupted_data = collect_predictions(model, wrapper_model, test_ds, device)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# отобразим статистики неопределенности предсказаний на чистых и испорченных данных\n", - "plt.boxplot([simple_data, corrupted_data])\n", - "plt.xticks([1, 2], [\"чистые\", \"зашумленные\"])\n", - "plt.plot();" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.12" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/minimal/Bayesianization.ipynb b/examples/minimal/Bayesianization.ipynb deleted file mode 100755 index 36c89ab..0000000 --- a/examples/minimal/Bayesianization.ipynb +++ /dev/null @@ -1,493 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 3, - "id": "chinese-maine", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "os.chdir('../..')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "collected-embassy", - "metadata": {}, - "outputs": [], - "source": [ - "from torchvision.datasets import MNIST\n", - "import torch\n", - "import torch.nn as nn\n", - "import torchvision.transforms as TF\n", - "from tqdm.auto import tqdm\n", - "from eXNN.NetBayesianization import wrap, api" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "golden-florence", - "metadata": {}, - "outputs": [], - "source": [ - "train_ds = MNIST(root='./.cache', train=True, download=True, \n", - " transform=TF.ToTensor()) \n", - "test_ds = MNIST(root='./.cache', train=False, download=False, \n", - " transform=TF.ToTensor())" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "hundred-bottle", - "metadata": {}, - "outputs": [], - "source": [ - "train_dl = torch.utils.data.DataLoader(train_ds, batch_size=36, shuffle=True)\n", - "test_dl = torch.utils.data.DataLoader(test_ds, batch_size=36, shuffle=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "coral-consequence", - "metadata": {}, - "outputs": [], - "source": [ - "num_classes = 10" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "continuing-operations", - "metadata": {}, - "outputs": [], - "source": [ - "model = nn.Sequential(nn.Flatten() ,nn.Linear(28*28, 128), \n", - " nn.ReLU(), nn.Linear(128, 64), \n", - " nn.ReLU(), nn.Linear(64, num_classes), nn.Softmax(dim=1))\n", - "optimizer = torch.optim.SGD(model.parameters(), lr=0.003, momentum=0.9)\n", - "criterion = nn.CrossEntropyLoss()\n", - "images, labels = next(iter(train_dl))\n", - "images = images.view(images.shape[0], -1)\n", - "logps = model(images)\n", - "loss = criterion(logps, labels)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "corresponding-layout", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 0 - Training loss: 2.261323278294971\n", - "Epoch 1 - Training loss: 1.805009106139473\n", - "Epoch 2 - Training loss: 1.6562042623919215\n", - "Epoch 3 - Training loss: 1.6340304391428462\n", - "Epoch 4 - Training loss: 1.6253673680613838\n", - "Epoch 5 - Training loss: 1.6201491944433761\n", - "Epoch 6 - Training loss: 1.6161638898054282\n", - "Epoch 7 - Training loss: 1.6131943888341014\n", - "Epoch 8 - Training loss: 1.610495502461054\n", - "Epoch 9 - Training loss: 1.6082047563723338\n", - "Epoch 10 - Training loss: 1.6061707546987\n", - "Epoch 11 - Training loss: 1.6044750517545951\n", - "Epoch 12 - Training loss: 1.6026566071501733\n", - "Epoch 13 - Training loss: 1.6009767129168084\n", - "Epoch 14 - Training loss: 1.5995301769104415\n", - "Epoch 15 - Training loss: 1.598026476223882\n", - "Epoch 16 - Training loss: 1.596490390156298\n", - "Epoch 17 - Training loss: 1.5951921355602765\n", - "Epoch 18 - Training loss: 1.5939387459917989\n", - "Epoch 19 - Training loss: 1.592780917435974\n" - ] - } - ], - "source": [ - "# train\n", - "n_epochs = 20\n", - "for e in range(n_epochs):\n", - " running_loss = 0\n", - " for images, labels in train_dl:\n", - " optimizer.zero_grad()\n", - " output = model(images)\n", - " loss = criterion(output, labels)\n", - " loss.backward()\n", - " optimizer.step()\n", - " running_loss += loss.item()\n", - " else:\n", - " print(\"Epoch {} - Training loss: {}\".format(e, running_loss/len(train_dl)))" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "casual-shower", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number Of Images Tested = 10000\n", - "\n", - "Model Accuracy = 0.8676\n" - ] - } - ], - "source": [ - "correct_count, all_count = 0, 0\n", - "for images,labels in test_dl:\n", - " for i in range(len(labels)):\n", - " img = images[i].view(1, 784)\n", - " with torch.no_grad():\n", - " logps = model(img)\n", - "\n", - " ps = torch.exp(logps)\n", - " probab = list(ps.numpy()[0])\n", - " pred_label = probab.index(max(probab))\n", - " true_label = labels.numpy()[i]\n", - " if(true_label == pred_label):\n", - " correct_count += 1\n", - " all_count += 1\n", - "\n", - "print(\"Number Of Images Tested =\", all_count)\n", - "print(\"\\nModel Accuracy =\", (correct_count/all_count))" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "blond-concentration", - "metadata": {}, - "outputs": [], - "source": [ - "# build classical bayesian model\n", - "bayes_model = api.BasicBayesianWrapper(model, 'basic', 0.1, None, None)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "headed-gazette", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'mean': tensor([[2.9532e-11, 5.2978e-16, 8.1444e-11, 1.7176e-14, 9.9914e-01, 1.0547e-10,\n", - " 4.2251e-07, 3.6712e-07, 1.2748e-06, 8.5830e-04],\n", - " [1.1691e-05, 4.5427e-19, 1.2401e-11, 2.8679e-11, 6.7911e-02, 8.0002e-10,\n", - " 8.9902e-11, 6.6425e-03, 2.2742e-02, 9.0269e-01],\n", - " [3.4553e-11, 2.5543e-14, 1.0935e-09, 5.7184e-15, 9.9760e-01, 4.0230e-10,\n", - " 1.0476e-06, 2.0018e-06, 4.5529e-07, 2.3946e-03],\n", - " [1.1240e-07, 1.3264e-07, 4.9493e-02, 9.4469e-01, 2.1763e-11, 7.1732e-09,\n", - " 5.9953e-12, 1.9604e-07, 5.8205e-03, 6.3144e-09],\n", - " [6.4917e-07, 1.7414e-11, 6.5679e-06, 3.0981e-14, 3.3005e-05, 3.6413e-10,\n", - " 9.9993e-01, 4.1751e-09, 2.9818e-05, 8.0287e-07],\n", - " [6.5410e-11, 6.6342e-12, 1.3535e-09, 2.4292e-14, 9.9905e-01, 1.4828e-09,\n", - " 8.2082e-06, 1.3887e-04, 6.4032e-06, 7.9413e-04],\n", - " [3.4536e-11, 9.9948e-01, 4.6569e-05, 2.0753e-05, 1.9267e-09, 3.0979e-08,\n", - " 1.0521e-10, 1.5474e-04, 2.9737e-04, 8.0818e-08],\n", - " [7.6249e-08, 2.1923e-21, 1.2633e-12, 5.8596e-11, 3.3252e-11, 1.5483e-11,\n", - " 1.8945e-14, 9.9996e-01, 1.1970e-06, 3.6505e-05],\n", - " [2.4746e-08, 2.5182e-20, 3.7466e-01, 6.2241e-01, 1.0300e-22, 7.1300e-15,\n", - " 7.8195e-20, 2.9997e-06, 2.9281e-03, 4.5721e-16],\n", - " [4.4059e-06, 3.3438e-10, 2.1919e-03, 1.0622e-08, 2.4677e-07, 2.4794e-09,\n", - " 9.9780e-01, 1.8539e-09, 9.9301e-08, 1.0529e-11],\n", - " [3.8775e-07, 4.5346e-09, 3.2732e-01, 1.9245e-07, 1.0176e-10, 6.6516e-10,\n", - " 6.7267e-01, 1.9395e-06, 4.1945e-06, 9.6492e-08],\n", - " [9.9698e-01, 2.1078e-25, 4.9260e-09, 8.6904e-08, 2.2775e-20, 2.0775e-14,\n", - " 6.3757e-16, 3.5064e-10, 3.0176e-03, 4.6168e-13],\n", - " [7.1520e-11, 9.9198e-01, 6.9043e-06, 4.0900e-03, 2.6829e-09, 4.6651e-08,\n", - " 6.5929e-08, 1.4372e-03, 2.2930e-03, 1.9325e-04],\n", - " [5.5255e-12, 5.9469e-11, 9.9917e-01, 2.7469e-04, 1.4915e-16, 4.7527e-13,\n", - " 8.6415e-14, 1.2295e-05, 5.4089e-04, 4.8728e-12],\n", - " [7.5882e-12, 3.1069e-07, 1.0082e-06, 6.7307e-01, 5.6121e-14, 2.4525e-12,\n", - " 1.3250e-14, 3.4160e-10, 3.2693e-01, 4.9761e-10],\n", - " [1.5300e-12, 7.8408e-19, 8.0296e-12, 2.2149e-12, 9.9957e-01, 2.8388e-11,\n", - " 2.0583e-09, 2.8162e-09, 1.0111e-05, 4.2022e-04],\n", - " [1.0636e-04, 5.3497e-11, 4.1289e-08, 6.9251e-01, 8.5493e-09, 8.0031e-09,\n", - " 5.8158e-13, 1.5342e-09, 3.0738e-01, 1.4467e-08],\n", - " [5.7821e-13, 1.5704e-21, 2.8036e-08, 1.0877e-19, 1.5467e-13, 7.6538e-19,\n", - " 1.0000e+00, 4.6054e-23, 1.4071e-10, 6.7137e-19],\n", - " [1.7867e-14, 1.1333e-17, 6.0350e-08, 3.9653e-07, 2.8885e-20, 2.2173e-15,\n", - " 1.6792e-23, 1.0000e+00, 1.2793e-08, 4.4273e-09],\n", - " [2.2531e-07, 2.0562e-16, 3.1036e-06, 1.2432e-01, 1.7421e-16, 8.4244e-13,\n", - " 7.1240e-18, 1.4506e-11, 8.7567e-01, 1.7567e-08],\n", - " [1.7934e-06, 5.6103e-23, 6.5966e-12, 1.1016e-13, 1.3281e-01, 3.6958e-11,\n", - " 4.3662e-10, 4.8125e-04, 1.1116e-03, 8.6560e-01],\n", - " [8.0582e-01, 1.0909e-19, 2.0707e-10, 6.6656e-08, 1.9221e-15, 2.1462e-14,\n", - " 9.4625e-08, 5.3875e-17, 1.9418e-01, 1.4121e-14],\n", - " [1.2741e-12, 9.9996e-01, 5.2313e-06, 1.2778e-06, 2.7131e-12, 1.7222e-10,\n", - " 1.1467e-06, 3.5611e-09, 3.3198e-05, 1.0357e-09],\n", - " [1.1162e-16, 1.0103e-17, 1.0000e+00, 1.8743e-06, 1.3710e-27, 1.8964e-20,\n", - " 3.2808e-18, 1.2958e-15, 4.9383e-09, 4.9819e-25],\n", - " [6.3087e-13, 2.3703e-16, 7.0743e-08, 1.0000e+00, 4.8865e-25, 4.2783e-17,\n", - " 1.5532e-20, 1.4318e-11, 1.0737e-09, 3.8811e-18],\n", - " [4.2325e-13, 6.2211e-16, 2.2513e-12, 6.0268e-12, 9.9894e-01, 1.1815e-10,\n", - " 2.1535e-09, 1.9690e-07, 2.1638e-04, 8.4165e-04],\n", - " [8.0499e-03, 3.5356e-04, 1.4036e-04, 2.7409e-05, 2.0006e-02, 7.3161e-06,\n", - " 5.4193e-02, 4.8783e-06, 9.1722e-01, 8.5895e-07],\n", - " [3.9207e-11, 1.5274e-26, 3.8151e-08, 1.0181e-21, 3.5139e-11, 3.2051e-19,\n", - " 1.0000e+00, 6.7910e-22, 8.7406e-12, 7.1344e-18]],\n", - " grad_fn=),\n", - " 'std': tensor([[4.7925e-11, 1.1722e-15, 1.4181e-10, 3.4199e-14, 1.7284e-03, 1.3500e-10,\n", - " 8.8716e-07, 3.9786e-07, 2.4465e-06, 1.7287e-03],\n", - " [1.7169e-05, 9.9804e-19, 1.1622e-11, 5.7100e-11, 1.4875e-01, 1.4220e-09,\n", - " 1.9612e-10, 1.4485e-02, 5.0496e-02, 1.4791e-01],\n", - " [4.3818e-11, 5.6730e-14, 1.3513e-09, 1.2557e-14, 5.0983e-03, 7.4148e-10,\n", - " 2.1789e-06, 3.1245e-06, 6.4612e-07, 5.0951e-03],\n", - " [2.0008e-07, 2.9414e-07, 1.0632e-01, 1.0954e-01, 4.8663e-11, 1.1523e-08,\n", - " 8.3925e-12, 3.5558e-07, 8.3777e-03, 1.3800e-08],\n", - " [1.0861e-06, 3.1384e-11, 1.4095e-05, 3.5292e-14, 3.3999e-05, 4.9820e-10,\n", - " 9.6448e-05, 7.0105e-09, 6.5681e-05, 1.1212e-06],\n", - " [6.5112e-11, 1.3023e-11, 1.6513e-09, 5.0884e-14, 1.7283e-03, 1.7609e-09,\n", - " 1.7632e-05, 1.5617e-04, 1.2652e-05, 1.6612e-03],\n", - " [4.2326e-11, 6.2184e-04, 9.0331e-05, 2.4987e-05, 3.1510e-09, 3.9686e-08,\n", - " 1.1450e-10, 1.5214e-04, 5.2036e-04, 1.7650e-07],\n", - " [1.2575e-07, 3.6428e-21, 2.6019e-12, 1.2171e-10, 4.4922e-11, 3.4149e-11,\n", - " 4.1989e-14, 7.3088e-05, 2.6642e-06, 7.0291e-05],\n", - " [5.2352e-08, 5.6310e-20, 4.6282e-01, 4.6573e-01, 2.3033e-22, 1.5927e-14,\n", - " 1.7477e-19, 6.7035e-06, 6.5472e-03, 1.0150e-15],\n", - " [5.8167e-06, 3.5647e-10, 3.6039e-03, 1.0500e-08, 4.8324e-07, 3.8403e-09,\n", - " 3.6089e-03, 2.5534e-09, 2.1755e-07, 9.6248e-12],\n", - " [7.4842e-07, 6.9095e-09, 4.2577e-01, 3.1649e-07, 2.0835e-10, 9.7648e-10,\n", - " 4.2577e-01, 3.2136e-06, 9.1822e-06, 1.8669e-07],\n", - " [6.7430e-03, 4.7132e-25, 1.0522e-08, 1.4448e-07, 5.0553e-20, 2.7837e-14,\n", - " 1.3654e-15, 7.3977e-10, 6.7431e-03, 8.3217e-13],\n", - " [5.8630e-11, 8.1378e-03, 1.1172e-05, 7.2165e-03, 5.8489e-09, 4.8659e-08,\n", - " 1.1402e-07, 1.0817e-03, 2.8049e-03, 4.1093e-04],\n", - " [1.1167e-11, 1.3298e-10, 1.0722e-03, 4.1221e-04, 3.3350e-16, 9.1999e-13,\n", - " 1.8367e-13, 2.4293e-05, 1.1090e-03, 8.8584e-12],\n", - " [1.4543e-11, 6.9453e-07, 1.2641e-06, 4.6243e-01, 1.2549e-13, 4.2679e-12,\n", - " 2.6758e-14, 4.2605e-10, 4.6243e-01, 7.6616e-10],\n", - " [1.5618e-12, 1.7208e-18, 1.3017e-11, 3.6381e-12, 9.1010e-04, 3.6254e-11,\n", - " 4.5470e-09, 4.2433e-09, 1.9078e-05, 9.1356e-04],\n", - " [1.7740e-04, 1.1625e-10, 6.2768e-08, 4.2865e-01, 1.9070e-08, 1.1822e-08,\n", - " 1.0361e-12, 1.9665e-09, 4.2852e-01, 2.9499e-08],\n", - " [8.7616e-13, 2.4444e-21, 5.9493e-08, 1.5868e-19, 2.0463e-13, 1.3917e-18,\n", - " 5.3312e-08, 8.5570e-23, 3.1455e-10, 1.0424e-18],\n", - " [3.0328e-14, 2.5217e-17, 1.2540e-07, 6.9571e-07, 6.4578e-20, 4.0572e-15,\n", - " 2.3519e-23, 8.2763e-07, 2.8305e-08, 8.4444e-09],\n", - " [4.5363e-07, 4.5544e-16, 6.9381e-06, 2.5616e-01, 3.1772e-16, 1.8771e-12,\n", - " 1.5927e-17, 3.2298e-11, 2.5617e-01, 3.9281e-08],\n", - " [3.8750e-06, 1.2510e-22, 9.9205e-12, 1.4337e-13, 2.5096e-01, 8.0319e-11,\n", - " 9.5395e-10, 1.0463e-03, 2.4851e-03, 2.5061e-01],\n", - " [4.2089e-01, 2.4393e-19, 4.2671e-10, 1.0233e-07, 4.2787e-15, 2.5957e-14,\n", - " 1.6387e-07, 1.0790e-16, 4.2089e-01, 3.1468e-14],\n", - " [1.0217e-12, 3.9416e-05, 9.3934e-06, 2.2460e-06, 5.8968e-12, 1.5237e-10,\n", - " 1.6955e-06, 2.7872e-09, 3.8172e-05, 1.4270e-09],\n", - " [1.9022e-16, 2.2591e-17, 2.4912e-06, 2.4697e-06, 3.0656e-27, 3.4979e-20,\n", - " 6.2412e-18, 2.6981e-15, 1.1018e-08, 1.0853e-24],\n", - " [1.4065e-12, 4.8949e-16, 1.5814e-07, 1.5994e-07, 1.0927e-24, 7.5065e-17,\n", - " 2.5006e-20, 2.6360e-11, 1.6734e-09, 8.6304e-18],\n", - " [8.3632e-13, 1.2440e-15, 3.8195e-12, 8.5341e-12, 1.1175e-03, 2.3071e-10,\n", - " 4.7824e-09, 1.9505e-07, 4.1092e-04, 1.1906e-03],\n", - " [1.7891e-02, 4.7673e-04, 3.1139e-04, 5.1004e-05, 4.3868e-02, 1.5333e-05,\n", - " 4.7196e-02, 1.0831e-05, 9.5765e-02, 1.9041e-06],\n", - " [7.5856e-11, 3.3296e-26, 6.4628e-08, 2.2363e-21, 7.6273e-11, 6.3039e-19,\n", - " 5.3312e-08, 9.9086e-22, 1.9528e-11, 1.4239e-17]],\n", - " grad_fn=)}" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# predict\n", - "n_iter = 5\n", - "bayes_model.predict(images, n_iter)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "shaped-iceland", - "metadata": {}, - "outputs": [], - "source": [ - "# build bayesian model with beta distibution\n", - "bayes_model = api.BasicBayesianWrapper(model, 'beta', None, 0.6, 0.3)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "durable-approach", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'mean': tensor([[0.0441, 0.0519, 0.0385, 0.0234, 0.6511, 0.0391, 0.0440, 0.0502, 0.0187,\n", - " 0.0391],\n", - 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" 0.0391],\n", - " [0.0441, 0.0519, 0.6353, 0.0265, 0.0511, 0.0391, 0.0440, 0.0502, 0.0187,\n", - " 0.0391],\n", - " [0.0441, 0.0520, 0.0385, 0.6234, 0.0512, 0.0391, 0.0441, 0.0503, 0.0182,\n", - " 0.0391],\n", - " [0.0441, 0.0520, 0.0385, 0.0234, 0.6511, 0.0391, 0.0441, 0.0503, 0.0183,\n", - " 0.0391],\n", - " [0.0440, 0.0514, 0.0381, 0.0233, 0.1766, 0.0387, 0.0490, 0.0497, 0.4906,\n", - " 0.0387],\n", - " [0.0445, 0.0524, 0.0388, 0.0235, 0.0516, 0.0394, 0.6444, 0.0507, 0.0152,\n", - " 0.0394]], grad_fn=),\n", - " 'std': tensor([[0.0606, 0.0714, 0.0529, 0.0387, 0.4778, 0.0537, 0.0605, 0.0690, 0.0329,\n", - " 0.0537],\n", - " [0.0607, 0.0715, 0.0530, 0.0387, 0.2507, 0.0538, 0.0606, 0.0691, 0.0329,\n", - " 0.4563],\n", - " [0.0599, 0.0706, 0.0523, 0.0387, 0.4785, 0.0531, 0.0598, 0.0682, 0.0338,\n", - " 0.0531],\n", - " [0.0606, 0.0714, 0.0453, 0.5049, 0.0702, 0.0537, 0.0605, 0.0690, 0.0328,\n", - " 0.0537],\n", - " [0.0598, 0.0706, 0.0523, 0.0387, 0.0643, 0.0531, 0.4800, 0.0682, 0.0338,\n", - " 0.0519],\n", - " [0.0599, 0.0706, 0.0523, 0.0387, 0.4785, 0.0531, 0.0598, 0.0682, 0.0338,\n", - " 0.0531],\n", - " [0.0606, 0.4732, 0.0504, 0.0384, 0.0702, 0.0537, 0.0605, 0.0689, 0.0327,\n", - " 0.0537],\n", - " [0.0607, 0.0715, 0.0530, 0.0387, 0.0703, 0.0538, 0.0606, 0.4927, 0.0329,\n", - " 0.4049],\n", - " [0.0607, 0.0715, 0.4774, 0.4206, 0.0703, 0.0538, 0.0606, 0.0691, 0.0329,\n", - " 0.0538],\n", - " [0.0599, 0.0706, 0.0499, 0.0386, 0.0694, 0.0531, 0.4855, 0.0682, 0.0338,\n", - " 0.0531],\n", - " [0.0612, 0.0721, 0.4479, 0.0366, 0.0710, 0.0543, 0.0575, 0.0697, 0.0339,\n", - " 0.1628],\n", - " [0.4873, 0.0715, 0.0530, 0.0387, 0.0703, 0.0538, 0.0606, 0.0691, 0.0329,\n", - " 0.0538],\n", - " [0.0606, 0.4466, 0.0527, 0.0723, 0.0702, 0.0537, 0.0605, 0.0689, 0.0326,\n", - " 0.0495],\n", - " [0.0606, 0.0714, 0.4862, 0.0376, 0.0702, 0.0537, 0.0605, 0.0690, 0.0329,\n", - " 0.0537],\n", - " [0.0606, 0.0714, 0.0529, 0.5141, 0.0702, 0.0537, 0.0605, 0.0690, 0.0314,\n", - " 0.0537],\n", - " [0.0599, 0.0706, 0.0523, 0.0387, 0.4785, 0.0531, 0.0598, 0.0682, 0.0338,\n", - " 0.0531],\n", - " [0.0599, 0.0706, 0.0523, 0.4568, 0.0694, 0.0531, 0.0598, 0.0682, 0.0923,\n", - " 0.0531],\n", - " [0.0599, 0.0706, 0.0523, 0.0387, 0.0694, 0.0531, 0.4881, 0.0682, 0.0338,\n", - " 0.0531],\n", - " [0.0607, 0.0715, 0.2346, 0.1843, 0.0703, 0.0538, 0.0606, 0.5043, 0.0329,\n", - " 0.0530],\n", - " [0.0599, 0.0706, 0.0523, 0.4319, 0.0694, 0.0531, 0.0598, 0.0682, 0.5262,\n", - " 0.0531],\n", - " [0.0607, 0.0715, 0.0530, 0.0387, 0.3448, 0.0538, 0.0606, 0.0691, 0.0329,\n", - " 0.4793],\n", - " [0.4869, 0.0706, 0.0523, 0.0387, 0.0694, 0.0531, 0.0598, 0.0682, 0.0329,\n", - " 0.0531],\n", - " [0.0606, 0.4748, 0.0513, 0.0386, 0.0702, 0.0537, 0.0605, 0.0690, 0.0328,\n", - " 0.0537],\n", - " [0.0606, 0.0714, 0.4922, 0.0369, 0.0702, 0.0537, 0.0605, 0.0690, 0.0329,\n", - " 0.0537],\n", - " [0.0607, 0.0715, 0.0530, 0.5162, 0.0703, 0.0538, 0.0606, 0.0691, 0.0329,\n", - " 0.0538],\n", - " [0.0607, 0.0715, 0.0530, 0.0387, 0.4777, 0.0538, 0.0606, 0.0691, 0.0329,\n", - " 0.0538],\n", - " [0.0596, 0.0706, 0.0523, 0.0387, 0.2602, 0.0531, 0.0559, 0.0682, 0.4779,\n", - " 0.0531],\n", - " [0.0612, 0.0721, 0.0534, 0.0387, 0.0709, 0.0543, 0.4869, 0.0697, 0.0339,\n", - " 0.0543]], grad_fn=)}" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# predict\n", - "n_iter = 5\n", - "bayes_model.predict(images, n_iter)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dress-cocktail", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.12" - }, - "vscode": { - "interpreter": { - "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/minimal/Homologies.ipynb b/examples/minimal/Homologies.ipynb deleted file mode 100644 index 5a34f24..0000000 --- a/examples/minimal/Homologies.ipynb +++ /dev/null @@ -1,402 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "os.chdir('../..')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from torchvision.datasets import MNIST\n", - "import torch\n", - "import torch.nn as nn\n", - "import torchvision.transforms as TF\n", - "from eXNN.InnerNeuralTopology import api" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\torchvision\\datasets\\mnist.py:498: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ..\\torch\\csrc\\utils\\tensor_numpy.cpp:180.)\n", - " return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n" - ] - } - ], - "source": [ - "train_ds = MNIST(root='./.cache', train=True, download=True, transform=TF.ToTensor()) \n", - "test_ds = MNIST(root='./.cache', train=False, download=False, transform=TF.ToTensor())" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "train_dl = torch.utils.data.DataLoader(train_ds, batch_size=36, shuffle=True)\n", - "test_dl = torch.utils.data.DataLoader(test_ds, batch_size=36, shuffle=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "import torch.nn as nn\n", - "class SimpleNN(nn.Module):\n", - " def __init__(self, input_dim, output_dim, hidden_dim, leaky_coef=0.1):\n", - " super(SimpleNN, self).__init__()\n", - " \n", - " self.layer1 = nn.Sequential(\n", - " nn.Linear(input_dim, hidden_dim),\n", - " nn.LeakyReLU(leaky_coef)\n", - " )\n", - " self.layer2 = nn.Sequential(\n", - " nn.Linear(hidden_dim, hidden_dim),\n", - " nn.LeakyReLU(leaky_coef)\n", - " )\n", - " self.layer3 = nn.Sequential(\n", - " nn.Linear(hidden_dim, output_dim)#,\n", - " #nn.Sigmoid()\n", - " )\n", - " \n", - " def forward(self, x):\n", - " x = nn.Flatten()(x)\n", - " x = self.layer1(x)\n", - " x = self.layer2(x)\n", - " x = self.layer3(x)\n", - " return x" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "num_classes = 10\n", - "model = SimpleNN(28*28, num_classes, 64)\n", - "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "data = torch.stack([test_ds[i][0] for i in range(100)])\n", - "res_unnorm_before = api.NetworkHomologies(model, data, layers = ['layer2'], hom_type = \"standard\", coefs_type = \"2\")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 0 loss: 0.1818467527627945\n", - "Epoch 1 loss: 0.0348515659570694\n", - "Epoch 2 loss: 0.055760979652404785\n", - "Epoch 3 loss: 0.19302596151828766\n", - "Epoch 4 loss: 0.06088097393512726\n", - "Epoch 5 loss: 0.011163261719048023\n", - "Epoch 6 loss: 0.016706837341189384\n", - "Epoch 7 loss: 0.12026318907737732\n", - "Epoch 8 loss: 0.0041767000220716\n", - "Epoch 9 loss: 0.00704461382701993\n", - "Epoch 10 loss: 0.0001738993014441803\n", - "Epoch 11 loss: 0.003325033700093627\n", - "Epoch 12 loss: 0.00017342368664685637\n", - "Epoch 13 loss: 0.011458609253168106\n", - "Epoch 14 loss: 0.061536386609077454\n", - "Epoch 15 loss: 0.0010659132385626435\n", - "Epoch 16 loss: 6.29111600574106e-05\n", - "Epoch 17 loss: 0.0023900249507278204\n", - "Epoch 18 loss: 0.044998157769441605\n", - "Epoch 19 loss: 0.0016281820135191083\n" - ] - } - ], - "source": [ - "n_epochs = 20\n", - "loss_fn = nn.CrossEntropyLoss()\n", - "for epoch in list(range(n_epochs)):\n", - " for imgs, lbls in train_dl:\n", - " optimizer.zero_grad()\n", - " out = model(imgs)\n", - " loss = loss_fn(out, lbls)\n", - " loss.backward()\n", - " optimizer.step()\n", - " print(\"Epoch {} loss: {}\".format(epoch, loss.item()))" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "data = torch.stack([test_ds[i][0] for i in range(100)])\n", - "res_unnorm_after = api.NetworkHomologies(model, data, layers = ['layer2'], hom_type = \"standard\", coefs_type = \"2\")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 0 loss: 0.38132670521736145\n", - "Epoch 1 loss: 0.23823799192905426\n", - "Epoch 2 loss: 0.14199964702129364\n", - "Epoch 3 loss: 0.2566467523574829\n", - "Epoch 4 loss: 0.34684517979621887\n", - "Epoch 5 loss: 0.09718009829521179\n", - "Epoch 6 loss: 0.21725988388061523\n", - "Epoch 7 loss: 0.15713442862033844\n", - "Epoch 8 loss: 0.1687999963760376\n", - "Epoch 9 loss: 0.2254984825849533\n", - "Epoch 10 loss: 0.27371761202812195\n", - "Epoch 11 loss: 0.11111251264810562\n", - "Epoch 12 loss: 0.2517441213130951\n", - "Epoch 13 loss: 0.20030929148197174\n", - "Epoch 14 loss: 0.11384321004152298\n", - "Epoch 15 loss: 0.14429588615894318\n", - "Epoch 16 loss: 0.43001464009284973\n", - "Epoch 17 loss: 0.10627931356430054\n", - "Epoch 18 loss: 0.14384765923023224\n", - "Epoch 19 loss: 0.12838228046894073\n" - ] - } - ], - "source": [ - "num_classes = 20\n", - "model = SimpleNN(28*28, num_classes, 64)\n", - "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3, weight_decay = 0.01)\n", - "\n", - "n_epochs = 20\n", - "loss_fn = nn.CrossEntropyLoss()\n", - "for epoch in list(range(n_epochs)):\n", - " for imgs, lbls in train_dl:\n", - " optimizer.zero_grad()\n", - " out = model(imgs)\n", - " loss = loss_fn(out, lbls)\n", - " loss.backward()\n", - " optimizer.step()\n", - " print(\"Epoch {} loss: {}\".format(epoch, loss.item()))" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "data = torch.stack([test_ds[i][0] for i in range(100)])\n", - "res_norm_after = api.NetworkHomologies(model, data, layers = ['layer2'], hom_type = \"standard\", coefs_type = \"2\")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 0 loss: 2.9907264709472656\n", - "Epoch 1 loss: 2.9906322956085205\n", - "Epoch 2 loss: 2.990679979324341\n", - "Epoch 3 loss: 2.9907820224761963\n", - "Epoch 4 loss: 2.9908132553100586\n", - "Epoch 5 loss: 2.991191864013672\n", - "Epoch 6 loss: 2.9906914234161377\n", - "Epoch 7 loss: 2.9917633533477783\n", - "Epoch 8 loss: 2.990560531616211\n", - "Epoch 9 loss: 2.9909372329711914\n", - "Epoch 10 loss: 2.9908273220062256\n", - "Epoch 11 loss: 2.9900853633880615\n", - "Epoch 12 loss: 2.9901514053344727\n", - "Epoch 13 loss: 2.9906680583953857\n", - "Epoch 14 loss: 2.9907190799713135\n", - "Epoch 15 loss: 2.9911515712738037\n", - "Epoch 16 loss: 2.9904730319976807\n", - "Epoch 17 loss: 2.9907705783843994\n", - "Epoch 18 loss: 2.990163803100586\n", - "Epoch 19 loss: 2.9909746646881104\n" - ] - } - ], - "source": [ - "num_classes = 20\n", - "model = SimpleNN(28*28, num_classes, 64)\n", - "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3, weight_decay = 10)\n", - "\n", - "n_epochs = 20\n", - "loss_fn = nn.CrossEntropyLoss()\n", - "for epoch in list(range(n_epochs)):\n", - " for imgs, lbls in train_dl:\n", - " optimizer.zero_grad()\n", - " out = model(imgs)\n", - " loss = loss_fn(out, lbls)\n", - " loss.backward()\n", - " optimizer.step()\n", - " print(\"Epoch {} loss: {}\".format(epoch, loss.item()))" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "data = torch.stack([test_ds[i][0] for i in range(100)])\n", - "res_norm_destructive = api.NetworkHomologies(model, data, layers = ['layer2'], hom_type = \"standard\", coefs_type = \"2\")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "res_unnorm_before[\"layer2\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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2xvjMybcv8Vd8p26hLsHqwSQX7+o/K8m/X+6aqjqX5CtihID1O/VeHWN8aozxP8d//N0kL1jT2mBVq3znwqLGGJ8ZYzx8/Pu7kzyhqm5YeFnsoKp6Qo5C1R+OMe66xCW+U3dEl2D1d0luqqqvqaonJrk1yd0nrrk7yY8d//6qJH85/E+6WL9T79UTc9Uvz9E8NmySu5P86PFJVi9K8tAY4+NLLwouVlVf/eiz1FX1whz9N82nll0Vu+b4Hvz9JB8ZY/zaZS7znbojzi29gFWMMR6pqp9K8t4kj0/y5jHG/VV1e5KDMcbdObqp31ZVD+Soqbp1uRWzq1a8V3+mql6eo5OEPp3ktYstmJ1UVXcmeUmSG6rqwSS/kuQJSTLG+J0k707y/UkeSPL5JD++zErZZSvcp69K8vqqeiTJfyW51YYqC3hxktck+Yequvf4tV9McmPiO3XXlO8gAACAOV1GAQEAADaWYAUAADBJsAIAAJgkWAEAAEwSrAAAgPaq6s1V9Ymq+vB1+Kzvrqp7L/r576p6xRX/jlMBAQCA7qrqu5I8nOStY4xvvo6f+/QcHZf/rDHG5y93ncYKAABob4zxvhz9P0K/qKq+tqr+vKruqar3V9U3XsNHvyrJe64UqhLBCgAA2F53JPnpMcYLkvx8kt+6hs+4Ncmdp1107ho+GAAAYKNV1VOSfGeSP66qR19+0vF7P5Dk9kv8tX8bY3zfRZ/xjCTfkuS9p/3zBCsAAGAbPS7Jf44xbj75xhjjriR3rfAZP5TknWOM/13lHwYAALBVxhifSfLPVfWDSVJHnn+VH/PqrDAGmAhWAADAFqiqO5P8TZJvqKoHq+p1SX4kyeuq6r4k9ye55So+73ySZyf565Wud9w6AADAHI0VAADAJMEKAABgkmAFAAAwSbACAACYJFgBAABMEqwAAAAmCVYAAACTBCsAAIBJ/w9ThSx+6F++FgAAAABJRU5ErkJggg==\n", 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" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "res_norm_destructive[\"layer2\"]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.12" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/minimal/Visualization.ipynb b/examples/minimal/Visualization.ipynb deleted file mode 100644 index 95dcb7b..0000000 --- a/examples/minimal/Visualization.ipynb +++ /dev/null @@ -1,526 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "female-elder", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "os.chdir('../..')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "becoming-bracelet", - "metadata": {}, - "outputs": [], - "source": [ - "from torchvision.datasets import MNIST\n", - "import torch\n", - "import torch.nn as nn\n", - "import torchvision.transforms as TF\n", - "from eXNN.InnerNeuralViz import api" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "acute-registrar", - "metadata": {}, - "outputs": [], - "source": [ - "train_ds = MNIST(root='./.cache', train=True, download=True, transform=TF.ToTensor()) \n", - "test_ds = MNIST(root='./.cache', train=False, download=False, transform=TF.ToTensor())" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "stunning-property", - "metadata": {}, - "outputs": [], - "source": [ - "train_dl = torch.utils.data.DataLoader(train_ds, batch_size=36, shuffle=True)\n", - "test_dl = torch.utils.data.DataLoader(test_ds, batch_size=36, shuffle=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "suspected-block", - "metadata": {}, - "outputs": [], - "source": [ - "num_classes = 10" - ] - }, - { - "cell_type": "markdown", - "id": "bibliographic-variety", - "metadata": {}, - "source": [ - "# Fully-connected NN" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "acknowledged-beatles", - "metadata": {}, - "outputs": [], - "source": [ - "model = nn.Sequential(nn.Linear(28*28, 256), \n", - " nn.ReLU(), nn.Linear(256, 64), \n", - " nn.ReLU(), nn.Linear(64, num_classes))" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "blessed-sarah", - "metadata": {}, - "outputs": [], - "source": [ - "layers = ['1', '3']\n", - "mode = 'pca'" - ] - }, - { - "cell_type": "markdown", - "id": "chronic-cowboy", - "metadata": {}, - "source": [ - "## real data" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "alike-saudi", - "metadata": {}, - "outputs": [], - "source": [ - "data, labels = next(iter(train_dl))\n", - "data = torch.flatten(data, start_dim=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "abandoned-character", - "metadata": {}, - "outputs": [], - "source": [ - "visualizations = api.VisualizeNetSpace(model, mode, data, layers, labels=labels)" - ] - }, - { - "cell_type": "markdown", - "id": "bulgarian-stockholm", - "metadata": {}, - "source": [ - "### display" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "standing-unknown", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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/owWvLpUX9zTojP4656ozlZUT7g9/gZSBrTUbjzlmR12p4jbu21QBApA2sAmDjBThroKTTVHmaG2unJZkhKP89P5yHaZ/4J8Nyzfpu5N+prLNu+RGGhYsv/T3GfrLdx7Sj5/+jk6ZMDjoiIEJZDI8YtKO+YnA2f8ftF4mbbAUGaKGuwga40rpZ8lEuvgZC82gY84FSneKlPhXjKdeBdf5GQkhV1VepW+d+2PtLm3YNyYeiysea5imrtlXox988ucqXbs9yIiBCuTddkjByKS7/jlyNLhgJLcdhYApvFMyuTq6ELiSUyxT8NMgYuEEuU6mTu1wjyJOjg79NWP2/z33KrhOHXMuDCgdwujlB99U+c598uJHv/d4nlU0GtO03/8ngGSpIZAyMKxwtNqmt0/4yd/K6rz2l/icCkEwkT4ybadJ2VMl7b9kbHKl7Gtkip+WcTsFGQ8noDBjiCZ0eU79Cr+mvPQByo50V4ec8zW244MaUPRNyj58NevpeUnXqnkxT2/9620fE6WWwG4t3FW3U/es+ZnK6rbLkXPwL8kxjj7b7QaNKmL1eNg0vBRrJWXyRgGgWX1t9K1a9d7apGPyinL177IHfEqUWgLbdKg4o52+P/BXWla+QMvKFyjm1atLdg+dVnS2ctPyg4qFADUUABaUAWh+vUf01JrF6w+uEziS4zrqNay7z6lSR+iPMAYAtH6rF6zVV0+9NemY2578psZNGetTotTCcv0UsqSsVD9/d4a+O3u67l06TztrqoKOBACtQt+RvXTNj66UJDnOR9OQB2YkJ35uvM66/LQgoqUErgykgOpovb4641m9sWWtIvu3PvZkZST975hzde2gUcEGBIBW4q1/va1/3vmMVs1fI0nq0r+TpnzjYn3yS+fJccL7+ZgykAKuf/1pvbLxQ3kJ/ir+ePaluqjnAJ9TAUDrVVNVKy/uKTsviwXLYpogcB/u3aWXNqxOWASMpLsXzRGdDQCaT1ZOpnLysykC+1EGAvbyxtVykrwYraQP9pZpU2W5f6EAAKFCGQhYTSyatAwcOg4AgJZAGQhY38K2inmJt2aWpAzXVedc9l4AALQMykDALujeVwXpmQmPbXKN0eW9hyg3LcPXXACA8KAMBCzDjei3Ey6Waxy5R0wXuMaoa26Bvj1qXEDpAABhwK2FKWLRzlL9fvEcvb5pjayk3LR0fabfMN14yukqzGCLXgBAy6EMpJjqaL2qY1EVZmQpEuINMAAA/qEMAAAQcnz0BAAg5CgDAACEHGUAAICQowwAABBylAEAAEKOMgAAQMhRBgAACDnKAAAAIUcZAAAg5CgDAACEXCToAH7YUV2pDRV7lZOWrgFt2skxiQ4MBgAgfFp1GdhUsVe3z3vt4EmAktQtt0C3jDxLk3sPDjQbAACpotUeVLS5slyXPvuQyutrFW/kX/HHp03U5weNDCAZAACppdWuGfjNglkJi4Ak/b93Z2hvXY3PqQAASD2tsgxURuv07LoVCYuAJMW8uJ5du8LHVAAApKZWWQZ2VFcp5nlJx0QcR5sqyn1KBABA6mqVZaAgI/OYY+LWqjAzy4c0AACktlZZBoozs3Vmx+5JbyG01uqSngN8TAUAQGpqlWVAkm4ZeZYcGTVWB4ykz/Q7Rd3yCn1OBQBA6mm1ZWBUSWfdf/4Utc3KkSS5pqEYuMbomoEj9ZPTJwYbEACAFNFq9xk4IOZ5emPzWq0t363c9HRN7NpHJdm5QccCACBltPoyAAAAkmu10wQAAKBpKAMAAIQcZQAAgJCjDAAAEHKUAQAAQo4yAABAyFEGAAAIuUjQAYBDxby4Zu5cqUW718sYo1FFPXVGu/5yDb0VAFoKmw4hZayuKNUt7z2k7bXliux/849ZT52zinTXqGvUI7ck4IQA0DpRBpASdtdV6spZd6kyWitPh78kXeOoIC1bT4y7WflpHDsNAM2Na69ICU9veqfRIiBJcetpT32Vnts8P4BkAND6UQaQEl4uXdxoETjAyurl0iU+JgKA8KAMICVUxeuPPSZW50MSAAgfygBSQq/cEjnGJHzeNY5657X3MREAhAdl4Bis9VQZXa+K+jWKe3wybSmf7jZWXpK1rHHraUrX03xMBADhwT4DCVhrtbHin1q7937VxLdKkiImV93yr1Tfwq/KdTIDTtj8dpRV6K3Zq1RdXacunYt01ul9lJ7mz0tkXLsBmtRpuKZvXXTY40aSlTS5y6kaXdzblywAEDbcWpjAil2/1Lp9DzbyjKM2GcM1puN9ck2677laQiwW1+/+9Kqem75YkpHjGMXjnvLzMvXdWz6pM0/r40sOz3p6YsPbenT9LG2vLZckdcpqo6t7jtOUrqfJJJlGQMuoi0f1+vZlmlu2WjEvrsGFXXVRp5EqSM8OOhqAZkQZaER53QrN3npFkhFGg4q/rx75V/mWqSX9313/0X9eXaojXwnGSMYY3XXHVA0f2s23PJ71tLOuQkZSu4x8SkBA1lRs103z79fOuoqG9Ry24SpNuuPq58Ov0riSgUFHBNBMWDPQiE0VT8rITTpm477HfUrTsraU7tGLrxxdBCQdfOz+h2f5mskxjtpnFqgks4AiEJCqWJ2+9u7ftKuuUpLkWStPVlZW9V5M31n4qD6s2BZwSgDNhTLQiMroelnFk4ywqo5t8i1PS5rx1gdynMRvuJ5ntXjZZu3eU+VjKgRt+tZF2l1f2ejeDwceeXz9bH9DAWgxlIFGpDl5Ota3JmJy/AnTwioqa5OWgQMqq2p9SINU8daOFUr2qohbT2/sWO5LFmutauqiisaTFXQAJ4K7CRrRKedCba9+LeHzRq465V7kY6KW06lDgeJxL+mYSMRRcVGuT4mQCuq8aJL9IBvUe7EWzVAfjekfMxbpn28s0rY9FTJGOmtIT113wWgN7925Rb82EDZcGWhE+5zzlJvWN8G6AUeuyVSP/M/5nqslnDthoNIiiddHuI7RueMHKic7w8dUCNqA/M5Jj412ZNQvr2OLff36aExf+/3TunvaTG3bUyGpYQ3LnPfX64u/flIvv/dBi31tIIwoA41wTJpO6/hXFWQMldRwJcDsv4iS6ZbotI73KzutdXwyycvN1NdvmCip4e6BQ7mOUX5+lr70+XEBJEOQPtV1jDyb+IqRJ6sru5/eYl//0dcXaMGHW45a2Br3rKy1uu3vL6mcqSug2XBr4THsrVuqndWzZRVTQcYQlWSNkzHJ7zQ4Gb01e5Xue2imNmzaJUlyHKMJZ/bTDV84R+1L8gNOhyD8c/0c/Xrl83KMObg7pJGRldUnO43QbUOnyEly9eB4WWs16fv3aefeyoRjjJFumTJBnz1vZLN/fSCMWDNwDIUZQ1W4/wpBazb+zH4ad0ZfbdqyW1XV9erYvkCFBWwsE2ZTe5yh7rnt9Mi6mXp314eyknrnlugzPc7UxZ1HtkgRkKSK6rqkRUCSHGO0ektZi3x9IIwoAzjIGKNuXYqDjoEUMrZtX41t21dx68mzntKclv+VkZbWlCtvRhlNGgegKSgDAI7JNU7SBYXNKSs9TWMGdNP8DzYlPLwq7nmacApnVaS65XNX6Zk//EdL3loux3U0+oIRmvw/F6rH4K5BR8MRWDPQClnrKW5r5ZosdvDDSWneyo264XdPNfqc6xj16lisf3z/6ibtkYFgPHXX8/rzNx+UG3EUjzUsRnUjjqyVvvfITTp76pkBJ8ShKAOtSHV0s9aU/01bKp+VZ+sUMTnqkjdFvQv/Wxlu26DjAR/LM3Pe188effWjxYvGKO556tOpWH/8n8vVrpC9L1LVstkrdfO4/034vBtx9MAHd6tjz/Y+pkIylIFWoqJ+td4uvUZxr/qwrZSNXKW7xTqj06PKirTcfeFAS9i1r0rPvv2+Pty6S5lpEZ0zvI9OH9RdrsNd0ansp1f+WrOnvXPwisCRHNfRp2++WF+6s3Xs19IaUAZaAWutZm2ZosromkbPVDBy1TbrTI3ucE8A6QCEzRUdvqi9O8qTjuk/urf+MO8XPiXCsVCvW4Hy+mWqiK5KeLiSVVw7a2aqJlbqczIAYdSUpUqsZ0ot3E3QCuyra8rWrFYV9auZKkCorK/cocc3zNHr295XvRdVn7wOurL76ZrYYWiL7ZMAaeT5w/TG47OTThOMOv8Un1MhGcpAK+A6TTs3wDGcL4DwmFe2WrcseEietYrv31p52d5NWrJ3o2buWKkfD7uCQtBCPnXTRXr90VmNPmdMwwLCi64/3+dUSIYy0Aq0zTpDRhFZJT5FLuLkqU3GcP9C7bezrELPTV+st99Zo3jc05BBnXXZJ0eod892vmdBeFTGanXrwkcV8zzZQ85f9Pb/+aXSxRrRpocu73ZaUBFbtf6n9tbNf7led335XjmuOXiFwHEduRFHP3rq22rHBmcphQWErcSysp9qY8UTUoKDZ/u1uUl9Cr/sa6YFizfou7c/pWg0Ls9ryOW6RvG41TdumKhPXcK+8mgZ/9o4V79c/mzCY5iNpG45bfXkuFv8jBU6G1Zs1nP3vKTFb74v13U1etJwXfyVT6h9dz4MpBrKQCvh2agW7bhV26pflpErK7v/UJm4uuddpUHF35Px8ZLo3vJqTb32XtXVx5ToJXb3nVfplCHsRIbmd/uSJ/VS6SLFj/Hr7c2Jtysrku5TquDsLKvQps27lZmZpn59OyjiMj2CwwU6TVBbXa/yPVXKLchSTm5mkFFOeo5J08j2v9HeumXaWvmc6uK7lRXpoC65k5Wb7v+2rS++vDRpEXBdoyefnk8ZQIto2DrZKNGVsgOcVr6iffuOffrtn17R2++sOXgcdFGbHF1z1RmafNFwVvTjoEDKwNaNu/ToH1/VWy8uUSzmyThGp583SFffOFE9+3UIIlKrUZgxRIUZQ4KOofkL1ycsApIUj1vNX7TBx0QIk7Ft++q5Le8lfN6R0ZDCbspw03xM5a+dZRX6ys0Pq7y8Wof+KO7eU6Xf3vOKysurde1n2RIYDXy/VrRp7Q59/Yo/6I0XGoqAJFnPau7rK3Tz1Hu0cvFGvyOhBTRp8okZKrSQs9sPUvvMArkJPvl6srqm13ifU/nrocffVnl5teJe4z9nf39stnaUVficCqnK9zLw+9unqbqqXl788PtPvbinaH1Mv/7ev5J+osTJ4ZQhXZIeIuM4RsOGdPExEcIkzYno96depzbpDecXHHglHjh58cZ+kzS+ZGBA6VpeNBrX9FeXJSwCUsOmPy+9uszHVEhlvk4TbFlfpqXvrkv4vOdZbV63U8sXbtDgkT38C4Zmd/GkYXrkn3PleY3viuh5VldMPtXnVAiTHrklenLcLZq+dZHe3P6+auJR9cvvqMu7nqbeea37gJx9FTWqr098q7HUUAa2HWPLYISHr2Vg87qdTRq3ac1OysBJrm1xnm7/3qW67efPSNYe/IRy4NbC/776LI0e2TPglGjtciIZmtLtNE0J2X4COdkZchxz8JbexlkV5Gf7lgmpzdcykJXTtB3wmjoOqe2s0/vqgXuu09PPLdDsefs3HRrYSVMuHaVThnIXAdBSMjPTNP6Mfpo5Z1XCqYJ43Or8cwb5nAypytd9BqL1MV094Q7t21udcExaekT/mPUD5eRxqyEAHK+163fq+m88rFgsftQVAmOMJp49UD/89sUBpUOq8XUBYVp6RFfdcG7SMZdfexZFAABOUK8e7XTXHVPVviRf0kcnCbqO0aWfPEW3fuPCANMh1fi+A6G1Vo/+8TU99qfXZdSwqtyzVp5ndclVY3X99y+Ry+5YANAsPM9q4ZKNWr+xTJkZaRo7upeKi3KDjoUUE9h2xLt27NPrzy7Uzm3lalOcq3MuGa4OXYqCiAIAQKhxNkErZa3Vxh17VR+Lq0vbAmVltN6d1gAAJ4YjjFsZa62efXu5/vafedpc1nAPcWZ6RJeePlhfu+xM5WVxpwYA4HBcGWhl7n3+bd37wtyjHncco14di/XAt6YqJ7P1n9IGAGg6Vuq1Iht37G20CEgNi4jWbt2lx15f4HMqAECqowy0Is/MWSY3yXkAnrV68q0lPiYCAJwMKAOtyKade+UdY9anrLxK0Vjj5wUAAMKJMtCK5GVlyElwZOsB6RFXEfZxAAAcgneFVuQTo/olPbLUdYwmje4vc4zCAAAIF8pAKzK6fzed0rtTo+sGHGMUcR1dcz7HBgMADkcZaEUcx+jur16m0wZ0O/jPEafhr7hNXpbuuWmKenUsDjIiACAFsc9AK7V6S5lmLl2rumhM/buWaPzQXqwVAAA0ijIAAEDI8VERAICQowwAABByHFSEQCxes1WPv7FIi9duVcRxNG5oL009+xR1K2kTdDQACB3WDMB3909/R394ZrZcxxzcF8F1jIwx+tX1l2j80F4BJwSAcGGaAL6at2KD/vDMbEk6bIOkuGcVj3v6zl+eV1l5VVDxACCUKAPw1aOvL0x4mJKVFIt7enr2Un9DAUDIUQbgq/dWb066ZbJnrd5btdnHRAAAygB81ZRTETg7AQD8RRmAr8YM6JZwmkBqKAKj+3f1MREAgDIAX3323JEJpwmMkTIiriafMcTnVAAQbpQB+GpUvy761hUTJOmwKwSOY5QecXXXDZeqKD87qHgAEErsM4BArNy0Q0+8uViL12yV6zoaP7SXpowbqo5F+UFHA4DQoQwAABByTBMAABBylAEAAEKOMtBMYl616uJl8mws6CgAAHwsnFp4gvbULtTqvfeqrGa2JKuIyVXX/E+rT8GXlOYWBB0PAIBjYgFhErU19VqxcKOi0Zh6Deiotu0Pf3PfXjVD7+34uoyMrOKHPOMoJ62bTu/4qNIpBACAFEcZaEQ87unRP76maQ/OUk11vaSGnfHGnjtQN/5osora5Snu1ei1jWcrZqvVcMTO4YxcdcufqsHF3/c5PQAAHw9loBG/+u4Teu3ZhUe9xzuuo3YdC/T7J29UufuKlpT9IOn/j2syNbHbTLlOVgumBQDgxLCA8AgfLN2k1545ughIkhf3tHPrXk17eLYqox/KHGPJRdzWqia2rYWSAgDQPCgDR3j5qffkuom/LZ5n9Z8n3pFrstRoYzhCxGFrXQBAaqMMHKFse7nicS/pmD27KlWSfe4RiwaP5Cg/fZAyI+2bN+BJylqraCzZ9wsAEBRuLTxCYVGuXNdJWgjy8rNUkDFA7bImaGfNTEmNjfXUt/CGFst5sli/bbcefHm+ps//QHXRmIrysvXp8cP02fNGKi8rI+h4AABxZeAo5102ImkRcFxHn5hyqiRpRMmdaps5VlLD3QMNawiMjCIaUnyb2uec40fklLVkbak+e8djen7ectVFGzZj2l1RrftenKdr73xc+6pqA04IAJC4m+Ao1lr96CsPav6sVbLe4d8ax3WUV5ClPz59k4pL8g+OL69fptKqlxTzqpST1l2dcy9VhlsURPyUEfc8XfzDv2nn3ip5jbzEHMfostMH63+vPj+AdACAQ1EGGlFXG9U9P31Gr05bIO+QQtBvaBd9+/+uVJee7QJMd3KYtWydbvrjtKRj0iKuXr3zeqYLACBgrBloREZmmm7+2af1+W9coIVzVitaH1efwZ3VZ1CnoKOdND7YtFOuYxT3EnfNaCyuTTv2aFD3Dj4mAwAciTKQRFG7PJ132cigY5yU0tNcNeWaU3qElyAABI0FhGgR44b0bHStwKHat8lVr47FPiUCACRCGUCL6NGhSBOG9ZLjmIRjvjBpTNLnAQD+oAygxfz02kka2aezJMl1HBkjufvf/P970hhNGTcsyHgAgP24mwAtylqr+as2a/r8laqorlPntgWafMYQdW/fJuhoAID9KAMAAIQc0wQAAIQcZQAAgJCjDAAAEHKUAQAAQo4yAABAyFEGAAAIOcoAAAAhxykxPqirj2nGzJVauHijrLUaOriLJp49UFmZ6UFHAwCATYda2qoPt+k7t/1Le/ZWy3WMrCTPs8rNydAdt0/RsMFdgo4IAAg5ykAL2lterau/dJ+qquvkeYd/mx3HKD09oof+/AW1L8kPKCEAAKwZaFEvvrxUlVVHFwGp4epAfX1M015YGEAyAAA+QhloQW/O/kDJLrx4ntUbsz7wMREAAEejDLSg2trYMcfU1R17DAAALYky0IL69Wkv1zUJn3cdo769S3xMBADA0SgDLWjyxSMUjyeeJoh7VpdfMtLHRAAAHI0y0IIGD+ikaz5zuiTJMR9dITjwx8kXj9CYUT2DiAYAwEHcWuiDGTNX6vGn3tXKVaWSpN492+nKT43WBecNljGJpxEAAPADZcBHdfUxyVplZKQFHQUAgIMoAwAAhBxrBgAACDnKAAAAIUcZAAAg5CgDAACEHGUAAICQowwAABBylAEAAEKOMgAAQMhRBgAACDnKAAAAIUcZAAAg5CgDAACEHGUAAICQowwAABBylAEAAEKOMgAAQMhRBgAACDnKAAAAIUcZAAAg5CgDAACEXCToAEBrsLVyn/6+4j09vWa5Kuvr1C2/UFcPGKEr+w5VhsuP2QGetZq9dYPW7dutnLR0ndult9pkZgUdCwg9Y621QYcATmbv79quq6Y/rqpoveL7f5zM/udGlXTWwxdcqaxIWnABU8TbpRv1zZkvamvVPhlJVlKa4+i6QaN066gJch0uVAJB4acPOAGetfrK69MOKwJSwxudlbRg51bdtXBWYPlSxeKdpbrm5Se0rapCUsP3RpKinqe/LntXP573WnDhAFAGgBPx1pZ12lRZflgROJRnrR77YLFqY1Gfk6WW3yycJc9aeTr6+2QlPbxyoTZXlvsfDIAkygBwQhbtLFXEJP8xqozWa92+PT4lSj17amv01pZ1CQuTJBlj9OzaFT6mAnAoygBwAiKOI9vIp93GxoXV3rqaY36HXGO0u7balzwAjhbe31BAMxjfuUfST7yS1CE7V73yi3xKlHraZuXINSbpmJjnqXNugU+JAByJMgCcgGFtO+rUks5J3+y+PGRMqFfK56Vn6KIeA5J+jyKOo8t6DfQxFYBDhfc3FNBM/nTuZPUuKJYkOftvKnT3ryP43IARum7QqMCypYpvjRqn3LSMhIXg2yPHqygz2+dUAA5gnwGgGdTH43ppwyo9u3aF9tbXqndBkT7T7xQNb9cx6GgpY235bv1o7quauXX9wcc6ZOfq5hFnaWq/YcEFO04xz9OinVtVHYuqV0GRujDNgZMYZQCArzZVlGv9vj3KTU/XsOIOJ90UirVWD69cqN8tmqNd+xc9GknjO/fUT08/X93yCgPNBxwPygAAfAy/WzRbdy2cfdTjrjEqSM/Uc5d+Xp1z8wNIBhy/k6uSA0CASqsq9LtFcxp9Lm6tyutrdXeC54FURhkAgCb695r3kz4ft1ZPr3k/9DtO4uRDGQCAJiqt2nfwjpFE6r249tTV+pQIaB6UAQBooqLM7GPuOOkao/z0DJ8SAc2DMgAATXRZr4FJd5x0jdEnuvVVTlq6j6mAE0cZAIAm6l1QrKl9hzU6UeAYo4jj6uvDz/Q9F3CiKAMA8DH87IxP6LpBo446fKprboEemzRVA4raBZQMOH7sMwAAx2F3bbVmbF6r6mhUfQqLNbZDV5ljHMgEpCrKAAAAIcc0AQAAIUcZAAAg5CgDAACEHGUAAICQiwQdAADWlu/Wk6uXakvVPhVlZOmy3oM0vG1HVucDPuFuAgCBsdbqjvlv6C/L3pV78I3fKG49faJbX9094RJlRvjMArQ0pgkABOZvy+frL8veldRw4l/Dfz1J0qsbP9T/zn0lyHhAaFAGAAQi6sV1z+K5CZ/3ZPXU6mXaXl3hYyognCgDAAKxpGybdtfVJB3jyWrG5rU+JQLCizIAIBB1sdgxxxhJtU0YB+DEUAYABKJPYbGcY9wtYCUNLCrxJxAQYpQBAIEoyc7VBd36HnIXweFcY9Qrv0hj2nfxORkQPpQBAIH58diJap+dd1QhcI1RZiRNd0+4mL0GAB+wzwCAQO2qrdZflr6jf6xarH31dUp3XE3uPUg3DD1NPQuKgo4HhAJlAEBKsNaqOhZVphuR63DREvATZQAAgJCjfgMAEHKUAQAAQo4yAABAyFEGAAAIOcoAAAAhRxkAACDkKAMAAIQcZQAAgJCjDAAAEHKUAQAAQo4yAABAyFEGAAAIOcoAAAAhRxkAACDkKAMAAIQcZQAAgJCjDAAAEHKUAQAAQo4yAABAyFEGAAAIuf8PAqWaaTzAkr4AAAAASUVORK5CYII=\n", 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\n", - "text/plain": [ - "
" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "visualizations['3']" - ] - }, - { - "cell_type": "markdown", - "id": "civil-collection", - "metadata": {}, - "source": [ - "## syntetic data" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "impressive-northern", - "metadata": {}, - "outputs": [], - "source": [ - "data = api.get_random_input([36, 784])" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "rational-heritage", - "metadata": {}, - "outputs": [], - "source": [ - "visualizations = api.VisualizeNetSpace(model, mode, data, layers)" - ] - }, - { - "cell_type": "markdown", - "id": "allied-audience", - "metadata": {}, - "source": [ - "### display" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "young-brooks", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "visualizations['input']" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "boxed-memorial", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "visualizations['3']" - ] - }, - { - "cell_type": "markdown", - "id": "manual-bradford", - "metadata": {}, - "source": [ - "# CNN" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "transparent-business", - "metadata": {}, - "outputs": [], - "source": [ - "model = nn.Sequential(nn.Conv2d(in_channels=1, out_channels=32, kernel_size=3),\n", - " nn.ReLU(),\n", - " nn.MaxPool2d(kernel_size=2, stride=2),\n", - " nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3),\n", - " nn.ReLU(),\n", - " nn.MaxPool2d(kernel_size=2, stride=2),\n", - " nn.AdaptiveAvgPool2d(output_size=1),\n", - " nn.Flatten(),\n", - " nn.Linear(64, num_classes))" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "clinical-subscriber", - "metadata": {}, - "outputs": [], - "source": [ - "mode = 'umap'" - ] - }, - { - "cell_type": "markdown", - "id": "generous-distributor", - "metadata": {}, - "source": [ - "## real data" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "unlike-sympathy", - "metadata": {}, - "outputs": [], - "source": [ - "data, labels = next(iter(train_dl))" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "flying-timber", - "metadata": {}, - "outputs": [], - "source": [ - "visualizations = api.VisualizeNetSpace(model, mode, data, labels=labels)" - ] - }, - { - "cell_type": "markdown", - "id": "solar-schema", - "metadata": {}, - "source": [ - "### display" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "invalid-degree", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "visualizations['6']" - ] - }, - { - "cell_type": "markdown", - "id": "duplicate-thriller", - "metadata": {}, - "source": [ - "## synthetic data" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "funky-lease", - "metadata": {}, - "outputs": [], - "source": [ - "data = api.get_random_input([36, 1, 28, 28])" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "bridal-cedar", - "metadata": {}, - "outputs": [], - "source": [ - "visualizations = api.VisualizeNetSpace(model, mode, data)" - ] - }, - { - "cell_type": "markdown", - "id": "thick-pacific", - "metadata": {}, - "source": [ - "### display" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "complicated-contents", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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