Deep learning library
Project description
BlueBird
Simple deep learning library.
Usage
Install:
pip install bluebird-stoick01
Here is a simple implemetation of a model in bluebird.
from bluebird.nn import NeuralNet
from bluebird.activations import Relu, Softmax
from bluebird.layers import Input, Dense
from bluebird.loss import CategoricalCrossEntropy
from bluebird.optimizers import SGD
# create the neural net
net = NeuralNet([
Input(200), # input layer
Dense(100, activation=Relu()), # hidden layers with relu activation
Dense(50, activation=Relu()),
Dense(10, activation=Softmax()) # last hiddent layer with softmax activation
])
# define optimizer and loss function
net.build(optimizer=SGD(lr=0.003), loss=CategoricalCrossEntropy())
# train your model
net.fit(X_train, y_train, num_epochs=20)
For more info checkout the docs
Roadmap
There are a lot of updates planed, you will find comments throughout the library that define what features I'm planing to add in the future.
Contribution
Feel free to help, I know that there are many things that need to be optimized and implemented in the future, any help is welcome.
Project details
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