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        "description": "# Keras Position Embedding\n\n[![Travis](https://travis-ci.org/CyberZHG/keras-pos-embd.svg)](https://travis-ci.org/CyberZHG/keras-pos-embd)\n[![Coverage](https://coveralls.io/repos/github/CyberZHG/keras-pos-embd/badge.svg?branch=master)](https://coveralls.io/github/CyberZHG/keras-pos-embd)\n[![Version](https://img.shields.io/pypi/v/keras-pos-embd.svg)](https://pypi.org/project/keras-pos-embd/)\n\n![](https://img.shields.io/badge/keras-tensorflow-blue.svg)\n![](https://img.shields.io/badge/keras-theano-blue.svg)\n![](https://img.shields.io/badge/keras-tf.keras-blue.svg)\n\n\\[[\u4e2d\u6587](https://github.com/CyberZHG/keras-pos-embd/blob/master/README.zh-CN.md)|[English](https://github.com/CyberZHG/keras-pos-embd/blob/master/README.md)\\]\n\nPosition embedding layers in Keras.\n\n## Install\n\n```bash\npip install keras-pos-embd\n```\n\n## Usage\n\n### Trainable Embedding\n\n```python\nimport keras\nfrom keras_pos_embd import PositionEmbedding\n\nmodel = keras.models.Sequential()\nmodel.add(PositionEmbedding(\n    input_shape=(None,),\n    input_dim=10,     # The maximum absolute value of positions.\n    output_dim=2,     # The dimension of embeddings.\n    mask_zero=10000,  # The index that presents padding (because `0` will be used in relative positioning).\n    mode=PositionEmbedding.MODE_EXPAND,\n))\nmodel.compile('adam', 'mse')\nmodel.summary()\n```\n\nNote that you don't need to enable `mask_zero` if you want to add/concatenate other layers like word embeddings with masks:\n\n```python\nimport keras\nfrom keras_pos_embd import PositionEmbedding\n\nmodel = keras.models.Sequential()\nmodel.add(keras.layers.Embedding(\n    input_shape=(None,),\n    input_dim=10,\n    output_dim=5,\n    mask_zero=True,\n))\nmodel.add(PositionEmbedding(\n    input_dim=100,\n    output_dim=5,\n    mode=PositionEmbedding.MODE_ADD,\n))\nmodel.compile('adam', 'mse')\nmodel.summary()\n```\n\n### Sin & Cos Embedding\n\nThe [sine and cosine embedding](https://arxiv.org/pdf/1706.03762) has no trainable weights. The layer has three modes, it works just like `PositionEmbedding` in `expand` mode:\n\n```python\nimport keras\nfrom keras_pos_embd import TrigPosEmbedding\n\nmodel = keras.models.Sequential()\nmodel.add(TrigPosEmbedding(\n    input_shape=(None,),\n    output_dim=30,                      # The dimension of embeddings.\n    mode=TrigPosEmbedding.MODE_EXPAND,  # Use `expand` mode\n))\nmodel.compile('adam', 'mse')\nmodel.summary()\n```\n\nIf you want to add this embedding to existed embedding, then there is no need to add a position input in `add` mode:\n\n```python\nimport keras\nfrom keras_pos_embd import TrigPosEmbedding\n\nmodel = keras.models.Sequential()\nmodel.add(keras.layers.Embedding(\n    input_shape=(None,),\n    input_dim=10,\n    output_dim=5,\n    mask_zero=True,\n))\nmodel.add(TrigPosEmbedding(\n    output_dim=5,\n    mode=TrigPosEmbedding.MODE_ADD,\n))\nmodel.compile('adam', 'mse')\nmodel.summary()\n```",
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