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"author": "keisen (Yasuhiro Kubota)",
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"Topic :: Education",
"Topic :: Scientific/Engineering",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
"Topic :: Scientific/Engineering :: Image Recognition",
"Topic :: Scientific/Engineering :: Visualization",
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"Topic :: Software Development :: Libraries",
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"description": "# [tf-keras-vis](https://keisen.github.io/tf-keras-vis-docs/)\n\n\n\n[](https://pepy.tech/project/tf-keras-vis)\n[](https://badge.fury.io/py/tf-keras-vis)\n[](https://github.com/keisen/tf-keras-vis/actions/workflows/python-package.yml)\n[](https://opensource.org/licenses/MIT)\n\n\n\n## Note!\n\n \n\nWe've released `v0.7.0`! In this release, the gradient calculation of ActivationMaximization is changed for the sake of fixing a critical problem. Although the calculation result are now a bit different compared to the past versions, you could avoid it by using legacy implementation as follows:\n\n```python\n# from tf_keras_vis.activation_maximization import ActivationMaximization\nfrom tf_keras_vis.activation_maximization.legacy import ActivationMaximization\n```\n\nIn addition to above, we've also fixed some problems related Regularizers. Although we newly provide `tf_keras_vis.activation_maximization.regularizers` module that includes the regularizers whose bugs are fixed, like ActivationMaximization, you could also use legacy implementation as follows:\n\n```python\n# from tf_keras_vis.activation_maximization.regularizers import Norm, TotalVariation2D \nfrom tf_keras_vis.utils.regularizers import Norm, TotalVariation2D\n```\n\nPlease see [the release note](https://github.com/keisen/tf-keras-vis/releases/tag/v0.7.0) for details. If you face any problem related to this release, please feel free to ask us in [Issues page](https://github.com/keisen/tf-keras-vis/issues)!\n\n\n\n## Web documents\n\nhttps://keisen.github.io/tf-keras-vis-docs/\n\n\n## Overview\n\n\n\ntf-keras-vis is a visualization toolkit for debugging `tf.keras.Model` in Tensorflow2.0+.\nCurrently supported methods for visualization include:\n\n* Feature Visualization\n - ActivationMaximization ([web](https://distill.pub/2017/feature-visualization/), [github](https://github.com/raghakot/keras-vis))\n* Class Activation Maps\n - GradCAM ([paper](https://arxiv.org/pdf/1610.02391v1.pdf))\n - GradCAM++ ([paper](https://arxiv.org/pdf/1710.11063.pdf))\n - ScoreCAM ([paper](https://arxiv.org/pdf/1910.01279.pdf), [github](https://github.com/haofanwang/Score-CAM))\n - Faster-ScoreCAM ([github](https://github.com/tabayashi0117/Score-CAM/blob/master/README.md#faster-score-cam))\n - LayerCAM ([paper](http://mftp.mmcheng.net/Papers/21TIP_LayerCAM.pdf), [github](https://github.com/PengtaoJiang/LayerCAM)) :new::zap:\n* Saliency Maps\n - Vanilla Saliency ([paper](https://arxiv.org/pdf/1312.6034.pdf))\n - SmoothGrad ([paper](https://arxiv.org/pdf/1706.03825.pdf))\n\ntf-keras-vis is designed to be light-weight, flexible and ease of use.\nAll visualizations have the features as follows:\n\n* Support **N-dim image inputs**, that's, not only support pictures but also such as 3D images.\n* Support **batch wise** processing, so, be able to efficiently process multiple input images.\n* Support the model that have either **multiple inputs** or **multiple outputs**, or both.\n* Support the **mixed-precision** model.\n\nAnd in ActivationMaximization,\n\n* Support Optimizers that are built to tf.keras.\n\n\n\n### Visualizations\n\n\n\n#### Dense Unit\n\n
\n\n#### Convolutional Filter\n\n
\n\n#### Class Activation Map\n\n
\n\nThe images above are generated by `GradCAM++`.\n\n#### Saliency Map\n\n
\n\nThe images above are generated by `SmoothGrad`.\n\n\n\n## Usage\n\n### ActivationMaximization (Visualizing Convolutional Filter)\n\n\n\n```python\nimport tensorflow as tf\nfrom tensorflow.keras.applications import VGG16\nfrom matplotlib import pyplot as plt\nfrom tf_keras_vis.activation_maximization import ActivationMaximization\nfrom tf_keras_vis.activation_maximization.callbacks import Progress\nfrom tf_keras_vis.activation_maximization.input_modifiers import Jitter, Rotate2D\nfrom tf_keras_vis.activation_maximization.regularizers import TotalVariation2D, Norm\nfrom tf_keras_vis.utils.model_modifiers import ExtractIntermediateLayer, ReplaceToLinear\nfrom tf_keras_vis.utils.scores import CategoricalScore\n\n# Create the visualization instance.\n# All visualization classes accept a model and model-modifier, which, for example,\n# replaces the activation of last layer to linear function so on, in constructor.\nactivation_maximization = \\\n ActivationMaximization(VGG16(),\n model_modifier=[ExtractIntermediateLayer('block5_conv3'),\n ReplaceToLinear()],\n clone=False)\n\n# You can use Score class to specify visualizing target you want.\n# And add regularizers or input-modifiers as needed.\nactivations = \\\n activation_maximization(CategoricalScore(FILTER_INDEX),\n steps=200,\n input_modifiers=[Jitter(jitter=16), Rotate2D(degree=1)],\n regularizers=[TotalVariation2D(weight=1.0),\n Norm(weight=0.3, p=1)],\n optimizer=tf.keras.optimizers.RMSprop(1.0, 0.999),\n callbacks=[Progress()])\n\n## Since v0.6.0, calling `astype()` is NOT necessary.\n# activations = activations[0].astype(np.uint8)\n\n# Render\nplt.imshow(activations[0])\n```\n\n\n\n### Gradcam++\n\n\n\n```python\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom matplotlib import cm\nfrom tf_keras_vis.gradcam_plus_plus import GradcamPlusPlus\nfrom tf_keras_vis.utils.model_modifiers import ReplaceToLinear\nfrom tf_keras_vis.utils.scores import CategoricalScore\n\n# Create GradCAM++ object\ngradcam = GradcamPlusPlus(YOUR_MODEL_INSTANCE,\n model_modifier=ReplaceToLinear(),\n clone=True)\n\n# Generate cam with GradCAM++\ncam = gradcam(CategoricalScore(CATEGORICAL_INDEX),\n SEED_INPUT)\n\n## Since v0.6.0, calling `normalize()` is NOT necessary.\n# cam = normalize(cam)\n\nplt.imshow(SEED_INPUT_IMAGE)\nheatmap = np.uint8(cm.jet(cam[0])[..., :3] * 255)\nplt.imshow(heatmap, cmap='jet', alpha=0.5) # overlay\n```\n\n\n\nPlease see the guides below for more details:\n\n### Getting Started Guides\n\n\n\n* [Saliency and CAMs](https://keisen.github.io/tf-keras-vis-docs/examples/attentions.html)\n* [Visualize Dense Layer](https://keisen.github.io/tf-keras-vis-docs/examples/visualize_dense_layer.html)\n* [Visualize Convolutional Filer](https://keisen.github.io/tf-keras-vis-docs/examples/visualize_conv_filters.html)\n\n\n\n**[NOTES]**\nIf you have ever used [keras-vis](https://github.com/raghakot/keras-vis), you may feel that tf-keras-vis is similar with keras-vis.\nActually tf-keras-vis derived from keras-vis, and both provided visualization methods are almost the same.\nBut please notice that tf-keras-vis APIs does NOT have compatibility with keras-vis.\n\n\n## Requirements\n\n\n\n* Python 3.7-3.10\n* tensorflow>=2.0.4\n\n\n\n\n## Installation\n\n\n\n* PyPI\n\n```bash\n$ pip install tf-keras-vis tensorflow\n```\n\n* Source (for development)\n\n```bash\n$ git clone https://github.com/keisen/tf-keras-vis.git\n$ cd tf-keras-vis\n$ pip install -e .[develop] tensorflow\n```\n\n\n\n## Use Cases\n\n\n\n* [chitra](https://github.com/aniketmaurya/chitra)\n * A Deep Learning Computer Vision library for easy data loading, model building and model interpretation with GradCAM/GradCAM++.\n\n\n## Known Issues\n\n* With InceptionV3, ActivationMaximization doesn't work well, that's, it might generate meaninglessly blur image.\n* With cascading model, Gradcam and Gradcam++ don't work well, that's, it might occur some error. So we recommend to use FasterScoreCAM in this case.\n* `channels-first` models and data is unsupported.\n\n\n## ToDo\n\n* Guides\n * Visualizing multiple attention or activation images at once utilizing batch-system of model\n * Define various score functions\n * Visualizing attentions with multiple inputs models\n * Visualizing attentions with multiple outputs models\n * Advanced score functions\n * Tuning Activation Maximization\n * Visualizing attentions for N-dim image inputs\n* We're going to add some methods such as below\n - Deep Dream\n - Style transfer\n\n\n",
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