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deepex-0.0.9
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DeepEX is a universal convenient frame with keras and Tensorflow. You can get well-known Wide&Deep model such as DeepFM here. Or, you can define you custom model use this frame.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | deepex-0.0.9 |
| نام | deepex |
| نسخه کتابخانه | 0.0.9 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | zhangzehua |
| ایمیل نویسنده | zzh_0729@foxmail.com |
| آدرس صفحه اصلی | https://github.com/FlashSnail/DeepEX |
| آدرس اینترنتی | https://pypi.org/project/deepex/ |
| مجوز | - |
# DeepEX
[TOC]
## Overview
**DeepEX** is a universal convenient frame with keras and Tensorflow,
You can get well-known Wide&Deep model such as DeepFM here.
Or, you can define you custom model use this frame.
### How to Install
* For Linux:
```shell
pip install deepex
```
* For Windows:
You need active cmd first and,
```powershell
pip install deepex
```
Notice: This frame needs keras and tensorflow, maybe it has some problem on windows with python 2.x because of tensorflow.
## DeepEX Class API
In the functional API, given some parmeters, you can instantiate a `DeepEX` object, via:
```python
DeepEX(data = None, feature_dim=None, category_index=None,embedding_dict_size=1000,
embedding_size=64, depths_size = [1024,256,64],class_num=2,
aggregate_flag=False, metrics=None, optimizer='Adam', activation='relu',
embedding_way='dense')
```
Only data is necessary, other parmeters have default value.
**Arguments**
* **data**: np.array
* **feature_dim**: integer, feature dimension
* **category_index**:
1. Can be a 2D list, like [A,B], A and B also a list, if len(A)>1, that means the element of A belong to a field, and will be input embedding layer together.
2. Can be a integer, it specific feature_dim % integer == 0, that means split feature as equal intervals with category_index
3. If None, all of feature will be embedding
4. *NEW UPDATE: Can be a mix type, like [A,B,c], A and B is list but c is a integer. It can achive more flexible way of split field, A and B will be split as col.1 and the rest of element will be split as col.2 with parameter c. Notice: c must be the last element, and the count of remaining element can be evenly divisible by c*
* **embedding_dict_size**: embedding dict size of categroy feature
* **embedding_size**: embedding size, it make output size like (?, len(category_index), embedding_size)
* **depths_size**: network of deep part parameter, last dimension means fc7 shape
* **class_num**: multi class or binary class, if class_num < 2, you will get a regression model
* **aggregate_flag**:
* if True, first_order and second_order of FM part output as (?,1)
* if False, output as (?, len(category_index)) and (?, embedding_size)
* **metrics**: can recive custom metrics, if None, binary class use AUC, multi class use auccary
* **optimizer**: Network optimizer, default adam, see [optimizer](https://keras.io/optimizers/).
* **activation**: Deep part activation, default relu, see [activations](https://keras.io/activations/).
* **embedding_way**: How network to do embedding, Embedding layer or Dense layer
## Methods
### **get_embedding_layer**
embedding input data as class parameter.
**return**
* inputs: A list, which elements are Input layer, prepare to deep model
* numerics: A list, which elements are numeric feature tensor
* embeddings: A list, which elements are categroy feature embedding tensor
* embedding_layer: A tensor, which is concate numeric feature tensor and categroy feature embedding tensor
-----
### get_wide_model
get fm part
**return**
- A tensor, shape depends on class parameter **aggregate_flag**
-----
### get_deep_model
get deep part
**return**
* inputs: A list, which elements are Input layer, prepare to deep model
* model: A tensor, which is also a keras functional layer
-----
### deepfm
get deepfm model, fc7 which is the last layer before classifier, it will be a class variable
**return**
* model: A keras functional model
-----
### auc
```python
auc(y_true, y_pred)
```
A custom metrics, when class_num=2, use this metrics to evaluate model
-----
### fit
```python
fit(model, y, save_model_path = None, batch_size=None, epochs=1, verbose=1,
callbacks=None,validation_split=0.0, validation_data=None,
shuffle=True, class_weight=None,sample_weight=None,
initial_epoch=0, steps_per_epoch=None, validation_steps=None)
```
fit data to train model
**Arguments**
* **model**: a DeepEX model
* **save_model_path**: A string, where model to save, if None, model will not be saved
* **others**: see document [keras fit](https://keras.io/models/model/#fit)
### get_fc7_output
```python
get_fc7_output(self, model_path = None, layer_name = 'fc7', data = None)
```
Get model's fc7 layer output, it can use for other operation, such as model ensemble
**Arguments**
- **model_path**: A string, only model_path is necessary, tell function where model is, the model file should be saved use keras.models.Model.save() function.
- **layer_name**: A string, default 'fc7'
- **data**: numpy array, NOTICE: if you declare a DeepEX object use same parameter just like model will be load (actually just need [data] and [category_index] are same), this parametre can be None, data will read from self.data. HOWEVER, if you declare other way, you need split data format as model input
**return**
* intermediate_output: Numpy array(s) of intermediate outputs.
## How to Use
This class is very easy to use, three steps to go：
```python
# step 1, declare DeepEX object
deepEX = DeepEX(...)
# step 2, get model you want
model = deepEX.deepfm()
# step 3, train and save
deepEX.fit(...)
```
### example
```python
from deepex import *
import numpy as np
samples = 100000 # set samples num
feat_dim = 10 # set feat_dim
cate = np.random.randint(1,6,samples) # set a categroy feat randomly
x = np.random.random((samples,feat_dim)) # generate feat randomly
x[:,3] = cate # chose a column to be categroy feat
y = np.random.randint(0,2,samples) # generate label
# declare DeepEX objects
deepEX = DeepEX(data = x, feature_dim=feat_dim, category_index=[[0,1],4], embedding_dict_size=1000,
embedding_size=64, depths_size = [1024,256,64], class_num=2,
aggregate_flag=False, metrics=None, optimizer='Adam', activation='relu', embedding_way='emb')
model = deepEX.deepfm() # get DeepFM
plot_model(model,'deepFM.png',show_shapes=True) # show model graph
# train deepfm
path = None
deepEX.fit(model, y, save_model_path=path, batch_size=None, epochs=1, verbose=1, callbacks=None,validation_split=0.0, validation_data=None, shuffle=True, class_weight=None,sample_weight=None, initial_epoch=0, steps_per_epoch=None, validation_steps=None)
# get fc7 output tensor
fc7 = deepEX.get_fc7_output(model_path=path, layer_name='fc7', data=deepEX.data_split)
```
نیازمندی
| مقدار | نام |
|---|---|
| >=1.16.2 | numpy |
| >=2.2.4 | keras |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.* | Python |
نحوه نصب
نصب پکیج whl deepex-0.0.9:
pip install deepex-0.0.9.whl
نصب پکیج tar.gz deepex-0.0.9:
pip install deepex-0.0.9.tar.gz