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basenet-api-1.3.0
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مشاهده بیشتر
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مشاهده بیشترتوضیحات
Basenet API: A simpler way to build ML models.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | OS Independent |
| نام فایل | basenet-api-1.3.0 |
| نام | basenet-api |
| نسخه کتابخانه | 1.3.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Palomo-Alonso, Alberto |
| ایمیل نویسنده | a.palomo@edu.uah |
| آدرس صفحه اصلی | https://github.com/iTzAlver/basenet_api.git |
| آدرس اینترنتی | https://pypi.org/project/basenet-api/ |
| مجوز | - |
# BaseNet: A simpler way to build AI models.
<p align="center">
<img src="https://raw.githubusercontent.com/iTzAlver/basenet_api/main/doc/multimedia/basenet_logo.png">
</p>
<p align="center">
<a href="https://github.com/iTzAlver/basenet_api/blob/main/LICENSE">
<img src="https://img.shields.io/github/license/iTzAlver/basenet_api?color=purple&style=plastic" /></a>
<a href="https://github.com/iTzAlver/basenet_api/tree/main/test">
<img src="https://img.shields.io/badge/tests-passed-green?color=green&style=plastic" /></a>
<a href="https://github.com/iTzAlver/basenet_api/blob/main/requirements.txt">
<img src="https://img.shields.io/badge/requirements-pypi-red?color=red&style=plastic" /></a>
<a href="https://htmlpreview.github.io/?https://github.com/iTzAlver/basenet_api/blob/main/doc/basenet.html">
<img src="https://img.shields.io/badge/doc-available-green?color=yellow&style=plastic" /></a>
<a href="https://github.com/iTzAlver/basenet_api.git">
<img src="https://img.shields.io/badge/release-1.3.0-white?color=white&style=plastic" /></a>
</p>
<p align="center">
<a href="https://www.tensorflow.org/">
<img src="https://img.shields.io/badge/dependencies-tensorflow-red?color=orange&style=for-the-badge" /></a>
<a href="https://keras.io/">
<img src="https://img.shields.io/badge/dependencies-keras-red?color=red&style=for-the-badge" /></a>
</p>
# Basenet API Package - 1.3.0
This package implements an API over Keras and Tensorflow to build Deep Learning models easily without losing the
framework flexibility. BaseNet API tries to implement almost everything from a few lines of code.
## About ##
Author: A.Palomo-Alonso (a.palomo@uah.es)
Universidad de Alcalá.
Escuela Politécnica Superior.
Departamento de Teoría De la Señal y Comunicaciones (TDSC).
ISDEFE Chair of Research.
## Features
* **Feature 1:** Real-time logging.
* **Feature 2:** Database train, validation and test automatic and random segmentation.
* **Feature 3:** Real multiprocessing training process.
* **Feature 4:** Automatic and custom GPU usage.
* **Feature 5:** Easy-to-use classes.
* **Feature 6:** Model merging.
* **Feature 7:** Multiple model inputs.
* **Feature 8:** API documentation.
* **Feature 9:** Python Packaging and PyPi indexing.
* **Feature 10:** Automatic GPU configuration and assignment.
## Basic and fast usage
### BaseNetDataset
BaseNetDatabase is an easy-to-use database wrapper for the API. You can build your
database with the BaseNetDatabase class.
### Example of building a BaseNetDataset.
from basenet_api import BaseNetDatabase
my_data_x, my_data_y = load_my_data()
print(my_data_y)
# > array([[0.], [1.], ...], dtype=float32)
print(my_data_x)
# > array([[255., 0., 255., ..., dtype=float32)
distribution = {'train': 60, 'test': 5, 'val': 35}
mydb = BaseNetDatabase(my_data_x, my_data_y,
distribution=distribution)
print(mydb)
# > BaseNetDatabase with 32000 instances.
mydb.save('./mydb.db')
### BaseNetCompiler
BaseNetCompiler takes the model architecture and builds a BaseNetModel with the given
parameters. You can build your BaseNetCompiler from Python code only or a .yaml file.
### Example of building a BaseNetCompiler from Python code only.
from basenet_api import BaseNetDatabase, BaseNetCompiler
mydb = BaseNetDatabase.load('./mydb.db')
print(mydb)
# > BaseNetDatabase with 32000 instances.
layers = [
{'Dense': ((255,), {})},
{'Dense': ((64,), {'activation': 'relu'})},
{'Dropout': ((0.5,), {})}
]
my_devs = BaseNetCompiler.show_devs()
print(my_devs)
# > {'/device:CPU:0': 'Idle',
# '/device:GPU:0': 'Train'}
my_first_model = BaseNetCompiler(
io_shape=((8,), 8),
compile_options={'loss': 'mean_squared_error', 'optimizer': 'adam'},
devices=my_devs,
layers=layers,
name='my_first_model'
).compile()
my_first_model.add_database(mydb)
You can also use the BaseNetModel.add() method to add layers.
my_first_compiler = BaseNetCompiler(
io_shape=((8,), 8),
compile_options={'loss': 'mean_squared_error', 'optimizer': 'adam'},
devices=my_devs,
name='my_first_model'
)
for layer in layers:
my_first_compiler.add(layer)
my_first_model = my_first_compiler.compile()
You can also load the database from the path.
my_fitst_model.add_database('./mydb.db')
### Example of building a BaseNetCompiler from .yaml file.
Suppose you have a ``.yaml`` file in the ``./my_model.yaml`` location with
the proper format you can load your compiler with the method ``BaseNetCompiler().build_from_yaml(yaml_path)``
and omit the process of loading the parameters into the compiler manually.
from basenet_api import BaseNetDatabase, BaseNetCompiler
mydb = BaseNetDatabase.load('./mydb.db')
print(mydb)
# > BaseNetDatabase with 32000 instances.
yaml_path = './my_model.yaml'
my_first_model = BaseNetCompiler.build_from_yaml(yaml_path).compile()
my_first_model.add_database(mydb)
An example of ``.yaml`` to replicate the same model as in the section
``Building a BaseNetCompiler from Python code only.``, the ``.yaml`` file will be the following:
compiler:
name: "my_first_model"
input_shape:
- 8
output_shape: 8
compile_options:
loss: "mean_squared_error"
optimizer: "adam"
devices:
- cpu:
name: "/device:CPU:0"
state: "Idle"
- gpu:
name: "/device:GPU:0"
state: "Train"
layers:
- layer:
name: "Dense"
shape:
- 128
options:
- layer:
name: "Dense"
shape:
- 128
options:
- option:
name: "activation"
value: "relu"
- layer:
name: "Dropout"
shape:
- 0.5
options:
If you want to learn more about building a model from a ```.yaml``` file, please, check the API
[documentation](https://htmlpreview.github.io/?https://github.com/iTzAlver/basenet_api/blob/main/doc/basenet.html).
### Example of usage of the BaseNetModel.
Once you build and compile a ``BaseNetModel`` with a ```BaseNetCompiler.compile()``` method, you can make use of all the
methods that the BaseNetModel provides:
* ```BaseNetModel.load()```: This method loads a tf.keras.model and the compiler from the given path.
* ```BaseNetModel.save()```: This method saves a tf.keras.model and the compiler into the given path.
* ```BaseNetModel.print()```: This method renders a ``.png`` image of the model into the given path.
* ```BaseNetModel.add_database()```: The ``BaseNetModel`` contains a breech of databases. It is a list with all the loaded
databases previously. This method adds a database from a path or from a ```BaseNetDatabase``` object.
* ```BaseNetModel.predict()```: Performs a prediction given an input.
* ```BaseNetModel.evaluate()```: Evaluates the model with the pointed database test subset.
* ```BaseNetModel.fit()```: Trains the model with the pointed database.
* ```BaseNetModel.call()```: Merges two models into one. It can be used as a function.
#### Printing and fitting a model.
from basenet_api import BaseNetDatabase, BaseNetCompiler
mydb = BaseNetDatabase.load('./mydb.db')
my_first_model = BaseNetCompiler.build_from_yaml('./my_model.yaml').compile()
my_first_model.add_database(mydb)
# Select database with index 0.
my_first_model.fit(0, epochs=6, tensorboard=False)
# > Tensorflow fitting info vomiting.
# Print the model.
my_first_model.print('./my_model.png')
<p align="center">
<img src="https://raw.githubusercontent.com/iTzAlver/basenet_api/main/doc/multimedia/example_model.png">
</p>
#### Fitting a model in other process.
**Important:** Debugging is not working properly when fitting a new process.
Imagine working on a GUI. The training process of your model implemented on your
GUI will block the parent process. The API implements a solution. Just activate
``avoid_lock=True`` in the ``BaseNetModel.fit()`` method and check the results whenever you want.
from basenet_api import BaseNetDatabase, BaseNetCompiler
mydb = BaseNetDatabase.load('./mydb.db')
my_first_model = BaseNetCompiler.build_from_yaml('./my_model.yaml').compile()
my_first_model.add_database(mydb)
# Select database with index 0.
my_results = my_first_model.fit(0, epochs=6, tensorboard=False, avoid_lock=True)
while my_results.is_training:
do_my_main_activity(update_gui, collect_data, run_server, or_whatever)
current_loss_curve = my_results.get()
my_first_model.recover()
keep_doing_my_main_activity(update_gui, collect_data, run_server, or_whatever)
Nota that if you don't make use of the method ``BaseNetModel.recover()`` the model will be empty as
the trained model is bypassed by the child process until the parent process is able to recover the trained model.
#### Using Tensorboard.
The API also implements Tensorboard automatic opening and initialization. You can see the training process and keras
app in real time while training.
my_first_model.fit(0, epochs=6, tensorboard=True)
#### Merging two models into one with several inputs.
You can merge two BaseNetModels by calling the object as a function:
from basenet_api import BaseNetDatabase, BaseNetCompiler
mydb = BaseNetDatabase.load('./mydb.db')
my_first_model = BaseNetCompiler.build_from_yaml('./my_model.yaml', verbose=True).compile()
my_second_model = BaseNetCompiler.build_from_yaml('./my_model_2.yaml', verbose=True).compile()
my_first_model.add_database(mydb)
my_first_model(my_second_model, parallel=True, name='merged_model')
my_first_model.print('./')
It will merge the two models into one single with two outputs if ``parallel=True``, else it will be added at the bottom.
<p align="center">
<img src="https://raw.githubusercontent.com/iTzAlver/basenet_api/main/doc/multimedia/example_model2.png">
</p>
#### Obtaining training results from the fitting process.
Once you train the model, you can get a ``BaseNetResults`` object with the training results. You can obtain the values from:
my_results = my_first_model.fit(0, epochs=6)
losses = my_results.get()
print(losses)
# > {'loss': [1., 0.7, 0.6, 0.5, 0.4, 0.3],
# 'val_loss': [1., 0.8, 0.7, 0.6, 0.5, 0.4]}
## What's new?
### < 0.1.0
1. BaseNetModel included.
2. BaseNetDatabase included.
3. BaseNetCompiler included.
4. Inheritance from CorNetAPI project.
5. Multi-processing fitting.
6. Tensorboard launching.
### 0.2.0
1. BaseNetResults included (working).
2. Now the model is callable.
3. Switched print to logging.
4. Project documentation.
### 1.0.0 - 1.0.3
1. Python packaging
3. 1.0.x: Upload bug solving.
### 1.1.0
1. Functional package.
2. PyPi indexing.
### 1.2.0:
1. Loss results included in the BaseNetResults while multiprocessing.
2. GPU auto set up to avoid TensorFlow memory errors.
3. Method ``BaseNetCompiler.set_up_devices()`` configures the GPUs according to the free RAM to be used in the API.
### 1.3.0
1. Included WindowDiff to the project scope.
### Cite as
Please, cite this library as:
@misc{basenetapi,
title={CorNet: Correlation clustering solving methods based on Deep Learning Models},
author={A. Palomo-Alonso},
booktitle={PhD in Telecommunication Engeneering},
year={2022}
}
نیازمندی
| مقدار | نام |
|---|---|
| ~=2.10.0 | tensorflow |
| ==2.10.* | tensorflow-gpu |
| >=0.20.1 | graphviz |
| ==3.6.1 | matplotlib |
| ~=0.18.2 | future |
| ~=9.2.0 | Pillow |
| ==1.23.4 | numpy |
| ~=0.12.1 | ttkwidgets |
| ~=1.4.2 | pydot |
| ~=2.9.0 | keras |
| ~=60.2.0 | setuptools |
| ~=6.0 | PyYAML |
| ~=2.9.1 | tensorboard |
| ~=5.9.3 | psutil |
| ~=11.4.1 | pynvml |
| - | check-manifest |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl basenet-api-1.3.0:
pip install basenet-api-1.3.0.whl
نصب پکیج tar.gz basenet-api-1.3.0:
pip install basenet-api-1.3.0.tar.gz