Micrograd

Micrograd Overview

from micrograd.engine import Value

a = Value(-4.0)
b = Value(2.0)
c = a + b
d = a * b + b**3
c += c + 1
c += 1 + c + (-a)
d += d * 2 + (b + a).relu()
d += 3 * d + (b - a).relu()
e = c - d
f = e**2
g = f / 2.0
g += 10.0 / f
print(f'{g.data:.4f}') # prints 24.7041, the outcome of this forward pass
g.backward()
print(f'{a.grad:.4f}') # prints 138.8338, i.e. the numerical value of dg/da
print(f'{b.grad:.4f}') # prints 645.5773, i.e. the numerical value of dg/db

We want to create a library to make a graph of all these calculations so that we can compute things like the change in the value of $g$ with a slight change in the value of $a$ in this particular case shown in print(f'{a.grad:.4f}').

This version of micrograd is predominantly for pedagogical reasons - where these operations would be done on tensors - here we work with integers.

The code is very simple: Code Repo

Micrograd is all you need for neural networks and the rest is just efficiency.

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