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        "description": "# text-autoencoder\n\n[![travis][travis-image]][travis-url]\n[![pypi][pypi-image]][pypi-url]\n\n[travis-image]: https://img.shields.io/travis/Yoctol/text-autoencoder.svg?style=flat\n[travis-url]: https://travis-ci.org/Yoctol/text-autoencoder\n[pypi-image]: https://img.shields.io/pypi/v/text-autoencoder.svg?style=flat\n[pypi-url]: https://pypi.python.org/pypi/text-autoencoder\n\nVarious autoencoder for text data.\n\n## Usage\n\n### Grab one autoencoder first\n```python\nfrom text_autoencoder.variational_autoencoders import VAEXXX\nmodel = VAEXXX(n_steps=..., latent_size=..., state_size=..., ...)\n```\n\n### How to train\n- Warning: please preprocess your data to be a numpy array with shape (data_size, maxlen, embedding_size)\n```python\nmodel.fit(x=..., mask=..., epochs=10)\n```\n\n### How to save model\n```python\nmodel.save(output_path)\n```\n\n### How to get latent vector `z`\n```python\nmodel.get_latent_vector(x=..., mask=..., batch_size=1)\n```\n\n### How to get output of encoder\n```python\nmodel.encode(x=..., mask=..., batch_size=1)\n```\n\n### How to load a trained model\n```python\nmodel.load(path)\n```\n\n### How to monitor the training process\n- get the output_dir you input when calling `model.fit`\n- monitor training loss\n```shell\n> tensorboard --logdir=\"<output_dir>/summary/subtrain/\"\n```\n\n- monitor validation loss\n```shell\n> tensorboard --logdir=\"<output_dir>/summary/valid/\"\n```\n\n\n",
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