A beautiful library for DeeplLearning, with the help of numpy
This project is the occasion to reimplement my theoric learning as it grows.
Activation | |
---|---|
leakyReLU | ✔️ |
ReLU | ✔️ |
sigmoid | ✔️ |
tanh | ✔️ |
Layers | |
---|---|
Dense | ✔️ |
Output | ✔️ |
Dropout | ⬜ |
Batch Normalization | ⬜ |
Convolution | ⬜ |
RNN | ⬜ |
Long Short Term Memory | ⬜ |
Optimizers | |
---|---|
Gradient Descent | ✔️ |
MiniBatch Gradient Descent | ✔️ |
Stochastic Gradient Descent | ✔️ |
Momentum | ✔️ |
RMSprop | ✔️ |
Adam | ✔️ |
Validation | |
---|---|
Confusion Matrix | ⬜ |
Accuracy | ✔️ |
Precision | ⬜ |
Recall | ⬜ |
F1_score | ⬜ |
Cost | |
---|---|
Binary Cross Entropy | ✔️ |
Mean Square Error | ⬜ |
Soft Max | ⬜ |
Regularization | |
---|---|
L1 | ⬜ |
L2 | ⬜ |
Learning Rate Decay | |
---|---|
time based decay | ✔️ |
exponential decay | ⬜ |
staircase decay | ⬜ |
PreProcessing | |
---|---|
Standardization | ✔️ |
Nomalization | ⬜ |
pip install NoYetSelfAware
or
python3 -m pip install NoYetSelfAware
It's currently my first python package.
It was done following this nice tutorial: https://packaging.python.org/tutorials/packaging-projects/
Make sure you have the latest versions of PyPA’s build installed:
python3 -m pip install --upgrade build
Now run this command in the root of the project:
python3 -m build
This command should output a lot of text and once completed should generate two files in the dist directory:
dist/
NoYetSelfAware-$VERSION-py3-none-any.whl
NoYetSelfAware-$VERSION.tar.gz
Now that you are registered, you can use twine to upload the distribution packages.
You’ll need to install Twine:
python3 -m pip install --user --upgrade twine
Once installed, run Twine to upload all of the archives under dist
:
twine upload dist/*
You will be prompted for a username and password.
- For the username, use
__token__
. - For the password, use the token value (including the pypi- prefix).