An simple implementation of autograd / backpropagation.
All you need to run a simple neural network using autograd is the following code:
The code defines a data set X, expected output (or ground truth) y. It then trains the neural network by performing backward propagation (.backward()), then applies the calculated gradients through .optimise() along with a learning rate of 0.01.
from src.nn import MLP
from src.loss import mse
X = [
[ 0.0, 1.0, 2.0 ],
[ 2.0, 1.0, 0.0 ],
[ 2.0, 2.0, 2.0 ],
[ 3.0, 3.0, 3.0 ]
]
y = [ 1.0, -1.0, 1.0, -1.0 ]
n = MLP(3, [ 4, 4, 1 ])
for i in range(400):
pred = [ n(x) for x in X ]
loss = mse(y, pred)
loss.zero_grad()
loss.backward()
n.optimise(0.01)
print(pred)