deepfree 0.2.4

keras-style deep network package for classification and prediction

# 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