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# autograd
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`.
```py
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)
```
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