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ainshamsflow-0.1.0
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مشاهده بیشترتوضیحات
A keras inspired deep learning framework.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | ainshamsflow-0.1.0 |
| نام | ainshamsflow |
| نسخه کتابخانه | 0.1.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Pierre Nabil |
| ایمیل نویسنده | pierre.nabil.attya@gmail.com |
| آدرس صفحه اصلی | https://github.com/PierreNabil/AinShamsFlow |
| آدرس اینترنتی | https://pypi.org/project/ainshamsflow/ |
| مجوز | MIT |
# AinShamsFlow
4th CSE Neural Networks Project.
## Contents:
* [Project Description](#Project-Description)
* [Project Structure](#Project-Structure)
* [Install](#Install)
* [Usage](#Usage)
* [Team Members](#Team-Members)
* [Todo](#Todo)
## Project Description:
AinShamsFlow (asf) is a Deep Learning Framework built by our [Team](#Team-Members)
from Ain Shams University during December 2020 - January 2021.
The Design and Interface is inspired heavily from Keras and TensorFlow.
However, we only implement everything from scratch using only simple libraries
such as numpy and matplotlib.
## Project Structure:
The Design of all parts can be seen in the following UML Diagram.
This is how the Design Structure should work in our Framework.
## Install:
You can install the latest available version as follows:
```shell
$ pip install ainshamsflow
```
## Usage:
you start using this project by importing the package as follows:
```python
>>> import ainshamsflow as asf
```
then you can start creating a model:
```python
>>> model = asf.models.Sequential([
... asf.layers.Dense(300, activation="relu"),
... asf.layers.Dense(100, activation="relu"),
... asf.layers.Dense( 10, activation="softmax")
... ], input_shape=(784,), name="my_model")
>>> model.print_summary()
```
then compile and train the model:
```python
>>> model.compile(
... optimizer=asf.optimizers.SGD(lr=0.01),
... loss='sparsecategoricalcrossentropy',
... metrics=['accuracy']
... )
>>> history = model.fit(X_train, y_train, epochs=100)
```
finally you can evaluate, predict and show training statistics:
```python
>>> history.show()
>>> model.evaluate(X_valid, y_valid)
>>> y_pred = model.predict(X_test)
```
A more elaborate example usage can be found in [main.py](/main.py) or check out this
[demo notebook](https://colab.research.google.com/drive/1sqEeUUkG3bTplhlLb73QCGUbaubX2aXi?usp=sharing).
[
](https://colab.research.google.com/drive/1sqEeUUkG3bTplhlLb73QCGUbaubX2aXi?usp=sharing)
## Team Members:
* [Pierre Nabil](https://github.com/PierreNabil)
* [Ahmed Taha](https://github.com/atf01)
* [Girgis Micheal](https://github.com/girgismicheal)
* [Hazzem Mohammed](https://github.com/hazzum)
* [Ibrahim Shoukry](https://github.com/IbrahimShoukry512)
* [John Bahaa](https://github.com/John-Bahaa)
* [Michael Magdy](https://github.com/Michael-M-Mike)
* [Ziad Tarek](https://github.com/ziadtarekk)
## Todo:
### Framework Design:
- [x] A Data Module to read and process datasets.
- [x] Dataset
- [x] \_\_init\_\_()
- [x] \_\_bool\_\_()
- [x] \_\_len\_\_()
- [x] \_\_iter\_\_()
- [x] \_\_next\_\_()
- [x] apply()
- [x] numpy()
- [x] batch()
- [x] cardinality()
- [x] concatenate()
- [x] copy()
- [x] enumerate()
- [x] filter()
- [x] map()
- [x] range()
- [x] shuffle()
- [x] skip()
- [x] split()
- [x] take()
- [x] unbatch()
- [x] add_data()
- [x] add_targets()
- [x] normalize()
- [x] ImageDataGenerator
- [x] \_\_init\_\_()
- [x] flow_from_directory()
- [x] A NN Module to design different architectures.
- [x] Activation Functions
- [x] Linear
- [x] Sigmoid
- [x] Hard Sigmoid
- [x] Tanh
- [x] Hard Tanh
- [x] ReLU
- [x] LeakyReLU
- [x] ELU
- [x] SELU
- [x] Softmax
- [x] Softplus
- [x] Softsign
- [x] Swish
- [x] Layers
- DNN Layers:
- [x] Dense
- [x] BatchNorm
- [x] Dropout
- CNN Layers 1D: (optional)
- [x] Conv
- [x] Pool (Avg and Max)
- [x] GlobalPool (Avg and Max)
- [x] Upsample
- CNN Layers 2D:
- [x] Conv
- [x] Pool (Avg and Max)
- [x] FastConv
- [x] FastPool (Avg and Max)
- [x] GlobalPool (Avg and Max)
- [x] Upsample
- CNN Layers 3D: (optional)
- [x] Conv
- [x] Pool (Avg and Max)
- [x] GlobalPool (Avg and Max)
- [x] Upsample
- Other Extra Functionality:
- [x] Flatten
- [x] Activation
- [x] Reshape
- [x] Initializers
- [x] Constant
- [x] Uniform
- [x] Normal
- [x] Identity
- [x] Losses
- [x] MSE (Mean Squared Error)
- [x] MAE (Mean Absolute Error)
- [x] MAPE (Mean Absolute Percentage Error)
- [x] BinaryCrossentropy
- [x] CategoricalCrossentropy
- [x] SparseCategoricalCrossentropy
- [x] HuberLoss
- [x] LogLossLinearActivation
- [x] LogLossSigmoidActivation
- [x] PerceptronCriterionLoss
- [x] SvmHingeLoss
- [x] Evaluation Metrics
- [x] Accuracy
- [x] TP (True Positives)
- [x] TN (True Negatives)
- [x] FP (False Positives)
- [x] FN (False Negatives)
- [x] Precision
- [x] Recall
- [x] F1Score
- [x] Regularizers
- [x] L1
- [x] L2
- [x] L1_L2
- [x] Optimization Modules for training
- [x] SGD
- [x] Momentum
- [x] AdaGrad
- [x] RMSProp
- [x] AdaDelta
- [x] Adam
- [x] A Visualization Modules to track the training and testing processes
- [x] History Class for showing training statistics
- [x] ```verbose``` parameter in training
- [x] live plotting of training statistics
- [x] A utils module for reading and saving models
- [ ] Adding CUDA support
- [x] Publish to PyPI
- [x] Creating a Documentation for the Project
### Example Usage:
This part can be found in the
[demo notebook](https://colab.research.google.com/drive/1sqEeUUkG3bTplhlLb73QCGUbaubX2aXi?usp=sharing)
mentioned above.
- [x] Download and Split a dataset (MNIST or CIFAR-10) to training, validation and testing
- [x] Construct an Architecture ([LeNet](https://engmrk.com/lenet-5-a-classic-cnn-architecture/)
or [AlexNet](https://dl.acm.org/doi/abs/10.1145/3065386)) and make sure all of its components are
provided in your framework.
- [x] Train and test the model until a good accuracy is reached (Evaluation Metrics will need to be implemented in the framework also)
- [x] Save the model into a compressed format
Change Log
==========
0.1.0 (29/1/2021)
-------------------
- First Release
نیازمندی
| مقدار | نام |
|---|---|
| - | numpy |
| - | matplotlib |
| - | varname |
نحوه نصب
نصب پکیج whl ainshamsflow-0.1.0:
pip install ainshamsflow-0.1.0.whl
نصب پکیج tar.gz ainshamsflow-0.1.0:
pip install ainshamsflow-0.1.0.tar.gz