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        "author": "Roland Zimmermann",
        "author_email": "rzrolandzimmermann@gmail.com",
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            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: Implementation :: CPython",
            "Programming Language :: Python :: Implementation :: PyPy"
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        "description": "\n# Keras Utility & Layer Collection [WIP]\nCollection of custom layers for Keras which are missing in the main framework. These layers might be useful to reproduce current state-of-the-art deep learning papers using Keras.\n\n## Applications\n\nUsing this library the following research papers have been reimplemented in Keras:\n\n- [Attention is all you need](https://github.com/FlashTek/attention-is-all-you-need-keras)\n- [Show, attend and tell](https://github.com/FlashTek/show-attend-and-tell-keras)\n\n## Overview of implemented Layers\n\nAt the moment the `Keras Layer Collection` offers the following layers/features:\n\n- [Scaled Dot-Product Attention](#sdpattention)\n- [Multi-Head Attention](#mhatn)\n- [Layer Normalization](#layernorm)\n- [Sequencewise Attention](#seqatn)\n- [Attention Wrapper](#atnwrapper)\n\n### Scaled Dot-Product Attention <a name=\"sdpattention\"></a>\n\nImplementation as described in [Attention Is All You Need](https://arxiv.org/abs/1706.03762). Performs a non-linear transformation on the values `V` by comparing the queries `Q` with the keys `K`. The illustration below is taken from the paper cited above.\n\n<img src=\"https://i.imgur.com/7zDGedN.jpg\" height=250>\n\n### Multi-Head Attention <a name=\"mhatn\"></a>\nImplementation as described in [Attention Is All You Need](https://arxiv.org/abs/1706.03762). This is basically just a bunch a [Scaled Dot-Product Attention](#sdpattention) blocks whose output is combined with a linear transformation. The illustration below is taken from the paper cited above.\n\n<img src=\"https://i.imgur.com/c0xLAfS.jpg\" height=250>\n\n### Layer Normalization <a name=\"layernorm\"></a>\n\n\n### Sequencewise Attention <a name=\"seqatn\"></a>\nThis layer applies various attention transformations on data. It needs a time-series of queries and a time-series of values to calculate the attention and the final linear transformation to obtain the output. This is a faster version of the general attention technique. It is similar to the `global attention` method described in [Effective Approaches to Attention-based Neural Machine Translation](https://arxiv.org/abs/1508.04025)\n\n### Attention Wrapper <a name=\"atnwrapper\"></a>\nThe idea of the implementation is based on the paper [Effective Approaches to Attention-based Neural Machine Translation](https://arxiv.org/abs/1508.04025). This layer can be wrapped around any `RNN` in `Keras`. It calculates for each time step of the `RNN` the attention vector between the previous output and all input steps. This way, a new attention-based input for the `RNN` is constructed. This input is finally fed into the `RNN`. This technique is similar to the `input-feeding` method described in the paper cited. The illustration below is taken from the paper cited above.\n\n<img src=\"https://i.imgur.com/AZKWSd2.png\" height=300>\n\n\n",
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