After cloning the repository, run:
conda create -n deeplib python==3.10 pip
pip install -r requirements.txt
In this example we create a simple model the learns celsius to fahrenheit conversion.
from layer import Linear
from nn import Sequential
from optim import SGD
from loss import MSELoss
import numpy as np
X = np.arange(1, 101).reshape((100, 1)) #100 different celsius temperatures
y = X.copy()*1.8 + 32 #its conversions
model = Sequential(Linear(1, 10),
Linear(10, 1))
diff = np.mean(y - model(X), axis = 0)
print("Difference before training:", diff)
criterion = MSELoss()
optim = SGD(model, lr=0.0000001)
epochs = 100000
end = '\r'
for e in range(epochs):
output = model(X)
criterion.loss(y, output)
criterion.backward()
optim.step(criterion.grads["L"])
loss = criterion.param["loss"]
if e == epochs -1:
end = '\n'
print(f"LOSS: {loss}", end = end)
diff2 = np.mean(y - model(X), axis = 0)
print("Difference after training:", diff2)