Metadata-Version: 2.1
Name: deepfree
Version: 0.2.6
Summary: keras-style deep network package for classification and prediction
Home-page: http://github.com/fuzimaoxinan/deepfree
Author: Zhuofu Pan
Author-email: 475366898@qq.com
Maintainer: Zhuofu Pan
Maintainer-email: 475366898@qq.com
License: UNKNOWN
Description: # deepfree
        Keras-style deep network package for classification and prediction.
        # install
        ``` python 
        pip install --upgrade numpy h5py
        pip install --upgrade deepfree
        ```
        # feature
        ## fast learning
        The 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`.
        ## stacking blocks
        By 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`.
        ## flexible setting
        You 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.
        ## results display
        `'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.
        # example
        A simple DNN can be constructed and trained as:
        ```python
        from deepfree import Model
        from deepfree import phvariable,Dense
        model = Model()
        model.struct = [784, 100 ,10]
        model.input = phvariable(model.struct[0])('input')
        model.label = phvariable(model.struct[-1])('label')
                
        for i in range(len(model.struct)-2):
            model.add_layer(Dense(model.struct[i+1], 
                                 activation = model.next_hidden_activation(), 
                                 is_dropout = True))
        model.add_layer(Dense(model.struct[-1], activation = model.output_func))
        model.training(dataset = ...,data_path = ...)
        ```
        # plot
        The running result can be find in `'result'` folder.</br>
        
        - **loss & test accuracy - epoch curve:** </br>
        <div align=center><img width="682" src=/deepfree/images/epoch_accuracy.png></div>
        
        - **prediction - epoch curve:** </br>
        <div align=center><img width="688" src=/deepfree/images/pred_result.png></div>
        
        - **real label -> predicted label count result:** </br>
        <div align=center><img width="642" src=/deepfree/images/label_cnt.png></div>
        
        - **t-SNE visualization:** </br>
        <div align=center><img width="648" src=/deepfree/images/tSNE.png></div>
        
        # blog
        [Github](https://github.com/fuzimaoxinan/deepfree),
        [zhihu](https://www.zhihu.com/people/fu-zi-36-41/posts),
        [CSDN](https://blog.csdn.net/fuzimango/article/list/), 
        [PyPI](https://pypi.org/project/deepfree/)</br>
        QQ Group:640571839 
        
Keywords: deep network,deep learning,DBN,SAE
Platform: any
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
