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fullresattn-0.1.0
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مشاهده بیشترتوضیحات
Library for full residual deep network with attention layers
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | fullresattn-0.1.0 |
| نام | fullresattn |
| نسخه کتابخانه | 0.1.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Lianfa Li |
| ایمیل نویسنده | lspatial@gmail.com |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/fullresattn/ |
| مجوز | - |
# Library of Full Residual Deep Network with Attention Layers (fullresattn)
The python library of full residual deep network with attention layers (fullresattn).
Current version just supports the KERAS package of deep learning and
will extend to the others in the future.
## Major modules
**Model**
* fullresAttCoder: major class to obtain a full residual
deep network with optional attention layers by setting the arguments. See the class and its
member functions' help for details.
* pmetrics: functions for regression metrics like rsquared and RMSE.
**Data**
* data: function to access each of two datasets,
sim': simulated dataset in the format of Pandas's Data Frame,
'pm2.5':string, the name for a real dataset of the 2015 PM2.5
and the relevant covariates for the Beijing-Tianjin-Tangshan
area. It is sampled by the fraction of 0.8 from the
the original dataset (stratified by the julian day).
See this function's help for details.
* simdata: function to simulate the test dataset,
The simulated dataset generated according to the formula:
y=x1+x2*np.sqrt(x3)+x4+np.power((x5/500),0.3)-x6+
np.sqrt(x7)+x8+noise
See this function's help for details.
## Installation
You can directly install it using the following command for the latest version:
```
pip install fullresattn
```
## Note for installation and use
**Runtime requirements**
fullresattn requires installation of Keras with support of Tensorflow or other
backend system of deep learning (to support Keras). Also Pandas and Numpy should
be installed.
## Use case
The package provides two specific examples for use of full residual deep network with optional attention layers.
See the following example.
## License
The fullresattn is provided under a MIT license that can be found in the LICENSE
file. By using, distributing, or contributing to this project, you agree to the
terms and conditions of this license.
## Test call
**Load the packages**
```python
import fullresattn
from fullresattn.model import r2KAuto,r2K
from keras.callbacks import ModelCheckpoint
```
**Load the simulated sample dataset**
```python
simdata=fullresattn.data('sim')
```
**Preprocess the data**
```python
tcol=['x'+str(i) for i in range(1,9)]
X=simdata[tcol].values
y=simdata['y'].values
y=y.reshape((y.shape[0],1))
scX = preprocessing.StandardScaler().fit(X)
scy = preprocessing.StandardScaler().fit(y)
Xn=scX.transform(X)
yn=scy.transform(y)
```
**Sampling**
```python
x_train, x_test, y_train,y_test = train_test_split(Xn,yn,test_size=0.2)
x_train, x_valid, y_train,y_valid = train_test_split(x_train,y_train,test_size=0.2)
```
**Model --Set the check point to check the validation**
```python
wtPath='/tmp/res_sim_wei.hdf5'
checkpointw=ModelCheckpoint(wtPath, monitor="loss",verbose=0,
save_best_only=True, mode="min")
```
**Call the model class**
```python
modelCls = fullresattn.model.fullresAttCoder(x_train.shape[1], [32,16,8,4],'relu'
1,inresidual=True,reg=keras.regularizers.l1_l2(0),batchnorm=True,
outnres=None,defact='linear',outputtype=0,nAttlayers=4)
```
**Get the residual autoencoder network**
```python
resmodel = modelCls.resAutoNet()
```
**Show the network model's topology**
```python
resmodel.summary()
resmodel.compile(optimizer="adam", loss= 'mean_squared_error',metrics=['mean_squared_error',r2KAuto])
```
**Starting to train the model... ...**
```python
fhist_res=resmodel.fit(x_train, y_train, batch_size=128, epochs=200, verbose=1,
shuffle=True,validation_data=(x_valid, y_valid),callbacks=[checkpointw])
```
**Test performance**
Tests on the simulated dataset show that the full residual model with 4 attention
layers increased validation R2 by about 4% for the model with no attention layers.
## Collaboration
Welcome to contact Dr. Lianfa Li (Email: lspatial@gmail.com).
نحوه نصب
نصب پکیج whl fullresattn-0.1.0:
pip install fullresattn-0.1.0.whl
نصب پکیج tar.gz fullresattn-0.1.0:
pip install fullresattn-0.1.0.tar.gz