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        "author": "Mats L. Richter & Justin Shenk",
        "author_email": "shenk.justin@gmail.com",
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        "classifiers": [
            "Development Status :: 2 - Pre-Alpha",
            "Intended Audience :: Developers",
            "Intended Audience :: Education",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: 3.8",
            "Programming Language :: Python :: 3.9",
            "Programming Language :: Python :: 3.10",
            "Topic :: Scientific/Engineering :: Mathematics",
            "Topic :: Software Development :: Libraries",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "# Delve: Deep Live Visualization and Evaluation ![logo](https://github.com/delve-team/delve/blob/master/images/delve_logo.png)\n\n[![PyPI version](https://badge.fury.io/py/delve.svg)](https://badge.fury.io/py/delve) [![Tests](https://github.com/delve-team/delve/actions/workflows/tests.yaml/badge.svg)](https://github.com/delve-team/delve/actions/workflows/tests.yaml) [![codecov.io](https://codecov.io/github/delve-team/delve/coverage.svg?branch=master)](https://codecov.io/github/delve-team/delve/?branch=master) [![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT) [![DOI](https://zenodo.org/badge/136951823.svg)](https://zenodo.org/badge/latestdoi/136951823)\nDelve is a Python package for analyzing the inference dynamics of your model.\n\n![playground](https://github.com/justinshenk/playground/blob/master/saturation_demo.gif)\n\nUse Delve if you need a lightweight PyTorch extension that:\n- Gives you insight into the inference dynamics of your architecture\n- Allows you to optimize and adjust neural networks models to your dataset\n  without much trial and error\n- Allows you to analyze the eigenspaces your data at different stages of inference\n- Provides you basic tooling for experiment logging\n\n------------------\n\n## Motivation\n\nDesigning a deep neural network is a trial and error heavy process that mostly revolves around comparing\nperformance metrics of different runs.\nOne of the key issues with this development process is that the results of metrics not really propagate back easily\nto concrete design improvements.\nDelve provides you with spectral analysis tools that allow you to investigate the inference\ndynamic evolving in the model while training.\nThis allows you to spot underutilized and unused layers. Mismatches between\nobject size and neural architecture among other inefficiencies.\nThese observations can be propagated back directly to design changes in the architecture even before\nthe model has fully converged, allowing for a quicker and more guided design process.\n\n## Installation\n\n```bash\npip install delve\n```\n\n\n### Using Layer Saturation to improve model performance\nThe saturation metric is the core feature of delve. By default saturation is a value between 0 and 1.0 computed\nfor any convolutional, lstm or dense layer in the network.\nThe saturation describes the percentage of eigendirections required for explaining 99% of the variance.\nSimply speaking, it tells you how much your data is \"filling up\" the individual layers inside\nyour model.\n\nIn the image below you can see how saturation portraits inefficiencies in your neural network.\nThe depicted model is ResNet18 trained on 32 pixel images, which is way to small for\na model with a receptive field exceeding 400 pixels in the final layers.\n\n![resnet.PNG](./images/resnet.PNG)\n\nTo visualize what this poorly chosen input resolution does to the inference, we trained logistic regressions on the output of\nevery layer to solve the same task as the model.\nYou can clearly see that only the first half of the model (at best) is improving\nthe intermedia solutions of our logistic regression \"probes\".\nThe layers following this are contributing nothing to the quality of the prediction!\nYou also see that saturation is extremly low for this layers!\n\nWe call this a *tail* and it can be removed by either increasing the input resolution or\n(which is more economical) reducing the receptive field size to match the object size of your\ndataset.\n\n![resnetBetter.PNG](./images/resnetBetter.PNG)\n\nWe can do this by removing the first two downsampling layers, which quarters the growth\nof the receptive field of your network, which reduced not only the number of\nparameters but also makes more use of the available parameters, by making more layers\ncontribute effectivly!\n\n__For more details check our publication on this topics__\n- [Spectral Analysis of Latent Representations](https://arxiv.org/abs/1907.08589)\n- [Feature Space Saturation during Training](https://arxiv.org/abs/2006.08679)\n- [(Input) Size Matters for CNN Classifiers](https://link.springer.com/chapter/10.1007/978-3-030-86340-1_11)\n- [Should you go deeper? Optimizing Convolutional Neural Networks without training](https://arxiv.org/abs/2106.12307)\n- Go with the Flow: the distribution of information processing in multi-path networks (soon)\n\n\n## Demo\n\n````python\n\nimport torch\nfrom delve import SaturationTracker\nfrom torch.cuda import is_available\nfrom torch.nn import CrossEntropyLoss\nfrom torchvision.datasets import CIFAR10\nfrom torchvision.transforms import ToTensor, Compose\nfrom torch.utils.data.dataloader import DataLoader\nfrom torch.optim import Adam\nfrom torchvision.models.vgg import vgg16\n\n# setup compute device\nfrom tqdm import tqdm\n\nif __name__ == \"__main__\":\n\n  device = \"cuda:0\" if is_available() else \"cpu\"\n\n  # Get some data\n  train_data = CIFAR10(root=\"./tmp\", train=True,\n                       download=True, transform=Compose([ToTensor()]))\n  test_data = CIFAR10(root=\"./tmp\", train=False, download=True, transform=Compose([ToTensor()]))\n\n  train_loader = DataLoader(train_data, batch_size=1024,\n                            shuffle=True, num_workers=6,\n                            pin_memory=True)\n  test_loader = DataLoader(test_data, batch_size=1024,\n                           shuffle=False, num_workers=6,\n                           pin_memory=True)\n\n  # instantiate model\n  model = vgg16(num_classes=10).to(device)\n\n  # instantiate optimizer and loss\n  optimizer = Adam(params=model.parameters())\n  criterion = CrossEntropyLoss().to(device)\n\n  # initialize delve\n  tracker = SaturationTracker(\"my_experiment\", save_to=\"plotcsv\", modules=model, device=device)\n\n  # begin training\n  for epoch in range(10):\n    model.train()\n    for (images, labels) in tqdm(train_loader):\n      images, labels = images.to(device), labels.to(device)\n      prediction = model(images)\n      optimizer.zero_grad(set_to_none=True)\n      with torch.cuda.amp.autocast():\n        outputs = model(images)\n        _, predicted = torch.max(outputs.data, 1)\n\n        loss = criterion(outputs, labels)\n      loss.backward()\n      optimizer.step()\n\n    total = 0\n    test_loss = 0\n    correct = 0\n    model.eval()\n    for (images, labels) in tqdm(test_loader):\n      images, labels = images.to(device), labels.to(device)\n      outputs = model(images)\n      loss = criterion(outputs, labels)\n      _, predicted = torch.max(outputs.data, 1)\n\n      total += labels.size(0)\n      correct += torch.sum((predicted == labels)).item()\n      test_loss += loss.item()\n\n    # add some additional metrics we want to keep track of\n    tracker.add_scalar(\"accuracy\", correct / total)\n    tracker.add_scalar(\"loss\", test_loss / total)\n\n    # add saturation to the mix\n    tracker.add_saturations()\n\n  # close the tracker to finish training\n  tracker.close()\n\n````\n\n### Why this name, Delve?\n\n__delve__ (*verb*):\n\n   - reach inside a receptacle and search for something\n   - to carry on intensive and thorough research for data, information, or the like\n\n\n",
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