{
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            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3",
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        "description": "# Keras NALU (Neural Arithmetic Logic Units)\n\n[![CircleCI](https://circleci.com/gh/genesant/keras-nalu/tree/master.svg?style=shield)](https://circleci.com/gh/genesant/keras-nalu/tree/master)\n\nKeras implementation of a NALU layer (Neural Arithmetic Logic Units).\nSee: https://arxiv.org/pdf/1808.00508.pdf.\n\n## Installation\n\n```\npip install keras-nalu\n```\n\n## Usage\n\n```py\nfrom keras.layers import Input\nfrom keras.models import Model\nfrom keras.optimizers import RMSprop\nfrom keras_nalu.nalu import NALU\n\n# Your dataset\nX_test = ... # Interpolation data\nY_test = ... # Interpolation data\n\nX_validation = ... # Extrapolation data (validation)\nY_validation = ... # Extrapolation data (validation)\n\nX_test = ... # Extrapolation data (test)\nY_test = ... # Extrapolation data (test)\n\n# Hyper parameters\nepoch_count = 1000\nlearning_rate = 0.05\nsequence_len = 100\n\ninputs = Input(shape=(sequence_len, ))\nhidden = NALU(units=2)(inputs)\nhidden = NALU(units=2)(hidden)\noutputs = NALU(units=1)(hidden)\n\nmodel = Model(inputs=inputs, outputs=outputs)\nmodel.summary()\nmodel.compile(loss='mse', optimizer=RMSprop(lr=learning_rate))\n\nmodel.fit(\n    batch_size=256,\n    epochs=epoch_count,\n    validation_data=(X_validation, Y_validation),\n    x=X_train,\n    y=Y_train,\n)\n\nextrapolation_loss = model.evaluate(\n    batch_size=256,\n    x=X_test,\n    y=Y_test,\n)\n```\n\n## Options\n\n### cell\n\nCell to use in the NALU layer. May be 'a' (addition/subtraction), 'm' (multiplication/division/power), or None which, will apply a gating function to toggle between 'a' or 'm'.\n\n-   Default: `None`\n-   Type: `?('a' | 'm' | None)`\n\n### e\n\nEpsilon value added to inputs in order to prevent calculating the log of zero.\n\n-   Default: `1e-7`\n-   Type: `?float`\n",
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