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        "author": "The saliency authors",
        "author_email": "tf-saliency-dev@google.com",
        "bugtrack_url": null,
        "classifiers": [
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            "Intended Audience :: Developers",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.3",
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        "description": "# Saliency Methods\n\n## Introduction\n\nThis repository contains code for the following saliency techniques:\n\n*   XRAI* ([paper](https://arxiv.org/abs/1906.02825))\n*   SmoothGrad* ([paper](https://arxiv.org/abs/1706.03825))\n*   Vanilla Gradients\n    ([paper](https://scholar.google.com/scholar?q=Visualizing+higher-layer+features+of+a+deep+network&btnG=&hl=en&as_sdt=0%2C22),\n    [paper](https://arxiv.org/abs/1312.6034))\n*   Guided Backpropogation ([paper](https://arxiv.org/abs/1412.6806))\n*   Integrated Gradients ([paper](https://arxiv.org/abs/1703.01365))\n*   Occlusion\n*   Grad-CAM ([paper](https://arxiv.org/abs/1610.02391))\n\n\\*Developed by PAIR.\n\nThis list is by no means comprehensive. We are accepting pull requests to add\nnew methods!\n\n## Download\n\n```\npip install saliency\n```\n\nor for the development version:\n```\ngit clone https://github.com/pair-code/saliency\ncd saliency\n```\n\n\n## Usage\n\nEach saliency mask class extends from the `SaliencyMask` base class. This class\ncontains the following methods:\n\n*   `__init__(graph, session, y, x)`: Constructor of the SaliencyMask. This can\n    modify the graph, or sometimes create a new graph. Often this will add nodes\n    to the graph, so this shouldn't be called continuously. `y` is the output\n    tensor to compute saliency masks with respect to, `x` is the input tensor\n    with the outer most dimension being batch size.\n*   `GetMask(x_value, feed_dict)`: Returns a mask of the shape of non-batched\n    `x_value` given by the saliency technique.\n*   `GetSmoothedMask(x_value, feed_dict)`: Returns a mask smoothed of the shape\n    of non-batched `x_value` with the SmoothGrad technique.\n\nThe visualization module contains two visualization methods:\n\n* ```VisualizeImageGrayscale(image_3d, percentile)```: Marginalizes across the\n  absolute value of each channel to create a 2D single channel image, and clips\n  the image at the given percentile of the distribution. This method returns a\n  2D tensor normalized between 0 to 1.\n* ```VisualizeImageDiverging(image_3d, percentile)```: Marginalizes across the\n  value of each channel to create a 2D single channel image, and clips the\n  image at the given percentile of the distribution. This method returns a\n  2D tensor normalized between -1 to 1 where zero remains unchanged.\n\nIf the sign of the value given by the saliency mask is not important, then use\n```VisualizeImageGrayscale```, otherwise use ```VisualizeImageDiverging```. See\nthe SmoothGrad paper for more details on which visualization method to use.\n\n## Examples\n\n[This example iPython notebook](http://github.com/pair-code/saliency/blob/master/Examples.ipynb) shows\nthese techniques is a good starting place.\n\nAnother example of using GuidedBackprop with SmoothGrad from TensorFlow:\n\n```\nfrom guided_backprop import GuidedBackprop\nimport visualization\n\n...\n# Tensorflow graph construction here.\ny = logits[5]\nx = tf.placeholder(...)\n...\n\n# Compute guided backprop.\n# NOTE: This creates another graph that gets cached, try to avoid creating many\n# of these.\nguided_backprop_saliency = GuidedBackprop(graph, session, y, x)\n\n...\n# Load data.\nimage = GetImagePNG(...)\n...\n\nsmoothgrad_guided_backprop =\n    guided_backprop_saliency.GetMask(image, feed_dict={...})\n\n# Compute a 2D tensor for visualization.\ngrayscale_visualization = visualization.VisualizeImageGrayscale(\n    smoothgrad_guided_backprop)\n```\n\nThis is not an official Google product.\n\n\n",
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