INSANELY SIMPLE AI/ML FRAMEWORK
Project description
froog ![unit test badge](https://pypi-camo.freetls.fastly.net/6b2e3c0e205673573eccf4f48b6a139c7630d85b/68747470733a2f2f6769746875622e636f6d2f6b65766275682f66726f6f672f616374696f6e732f776f726b666c6f77732f746573742e796d6c2f62616467652e737667)
FROOG: fast real-time optimization of gradients
a beautifully compact machine-learning library
homepage | documentation | pip
FROOG is a SUPER SIMPLE machine learning framework with the goal of creating tools with AI --> easily and efficiently.
Installation
pip install froog
Overview of Features
- Custom Tensors
- Backpropagation
- Automatic Differentiation (autograd)
- Forward and backward passes
- Comming ML Operations
- 2D Convolutions (im2col)
- Numerical gradient checking
- Acceleration methods (Adam)
- Avg & Max pooling
- Efficient-Net inference
- GPU Support
- and a bunch more
Sneak Peek
from froog.tensor import Tensor
from froog.utils import Linear
import froog.optim as optim
class mnistMLP:
def __init__(self):
self.l1 = Tensor(Linear(784, 128))
self.l2 = Tensor(Linear(128, 10))
def forward(self, x):
return x.dot(self.l1).relu().dot(self.l2).logsoftmax()
model = mnistMLP()
optim = optim.SGD([model.l1, model.l2], lr=0.001)
Bounties
THERES LOT OF STUFF TO WORK ON! VISIT THE BOUNTY SHOP
Pull requests will be merged if they:
- increase simplicity
- increase functionality
- increase efficiency
more info on contributing
Project details
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