micrograd2023 0.0.2

micrograd with added documentations using nbdev

micrograd2023

Introduction

How to install

The micrograd2023 package was uploaded to PyPI and can be easily installed using the below command.

pip install micrograd2023

Developer install

If you want to develop micrograd2023 yourself, please use an editable installation.

git clone https://github.com/hdocmsu/micrograd2023.git

pip install -e "micrograd2023[dev]"

You also need to use an editable installation of nbdev, fastcore, and execnb.

Happy Coding!!!

How to use

Here are examples of using micrograd2023.

# import necessary objects and functions
from micrograd2023.engine import Value
from micrograd2023.nn import Neuron, Layer, MLP
from micrograd2023.utils import draw_dot
import random

# inputs xs, weights ws, and bias b
w1 = Value(1.1)
x1 = Value(0.5)
w2 = Value(0.12)
x2 = Value(1.7)
b = Value(0.34)

# pre-activation
s = w1*x1 + x2*w2 + b

# activation
y = s.tanh()

# automatic differentiation
y.backward()

# show the computation graph of the perceptron
draw_dot(y)

# added random seed for reproducibility
random.seed(1234)
n = Neuron(3)
x = [Value(0.15), Value(-0.21), Value(-0.91) ]
y = n(x)
y.backward()
draw_dot(y)

You can use micrograd2023 to train a MLP and learn fundamental concepts such as overfilling, optimal learning rate, etc.

Good training

Overfitting

Testings

To perform unit testing, using terminal to navigate to the directory, which contains tests folder, then simply type python -m pytest on the terminal. Note that, PyTorch is needed for the test to run since derivatives calculated using micrograd2023 are compared against those calculated using PyTorch as references.

python -m pytest