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        "description": "# deepfree\nKeras-style deep network package for classification and prediction.\n# install\n``` python \npip install --upgrade numpy h5py\npip install --upgrade deepfree\n```\n# feature\n## fast learning\nThe main framework of the program relies on `Model` in `core._model` and `Layer` in `core._layer`, which can import directly through `'from deepfree import Model, Layer'`. You can quickly build and train the model by using them flexibly. In addition, the constructed `DBN` and `SAE` can be employed directly, which are inherited from `Model`.\n## stacking blocks\nBy calling `Model.add_layer(['a Layer of a list of Layer'])`, you can build the model like stack the blocks. There are a set of `Layer` can be selected, such as `phvariable`, `maxpooling2d`,`flatten`,`concatenate`, `Dense`, `Conv2D`.\n## flexible setting\nYou can set the model's parameters listed in `base._attribute` when first building model (`DBN(para=...)`, `SAE(para=...)`, `Model(para=...)`) or training it (`Model.training(para=...)`). If you do not set a value, the default value in `base._attribute` will be applied.\n## results display\n`'loss & test accuracy - epoch'` curve and `'prediction - epoch'` curve will be generated automatically. Furthermore, `real label -> predicted label` count result and `t-SNE visualization` image can be obtained by calling `Model.plot_label_cnt` and `Model.plot_tSNE`, respectively.\n# example\nA simple DNN can be constructed and trained as:\n```python\nfrom deepfree import Model\nfrom deepfree import phvariable,Dense\nmodel = Model()\nmodel.struct = [784, 100 ,10]\nmodel.input = phvariable(model.struct[0])('input')\nmodel.label = phvariable(model.struct[-1])('label')\n\nfor i in range(len(model.struct)-2):\n    model.add_layer(Dense(model.struct[i+1], \n                         activation = model.next_hidden_activation(), \n                         is_dropout = True))\nmodel.add_layer(Dense(model.struct[-1], activation = model.output_func))\nmodel.training(dataset = ...,data_path = ...)\n```\n# plot\nThe running result can be find in `'result'` folder.</br>\n\n- **loss & test accuracy - epoch curve:** </br>\n<div align=center><img width=\"682\" src=/deepfree/images/epoch_accuracy.png></div>\n\n- **prediction - epoch curve:** </br>\n<div align=center><img width=\"688\" src=/deepfree/images/pred_result.png></div>\n\n- **real label -> predicted label count result:** </br>\n<div align=center><img width=\"642\" src=/deepfree/images/label_cnt.png></div>\n\n- **t-SNE visualization:** </br>\n<div align=center><img width=\"648\" src=/deepfree/images/tSNE.png></div>\n\n# blog\n[Github](https://github.com/fuzimaoxinan/deepfree),\n[zhihu](https://www.zhihu.com/people/fu-zi-36-41/posts),\n[CSDN](https://blog.csdn.net/fuzimango/article/list/), \n[PyPI](https://pypi.org/project/deepfree/)</br>\nQQ Group:640571839 \n\n\n",
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        "maintainer": "Zhuofu Pan",
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