import os
import sys
module_path = os.path.abspath(os.path.join('..'))
if module_path not in sys.path:
    sys.path.append(module_path)

import numpy as np
from lightdlf_old.cpu.core import Tensor
from lightdlf_old.cpu.layers import Sequential, Embedding, Linear, Tanh, Sigmoid, MSELoss
from lightdlf_old.cpu.optimizers import SGD

np.random.seed(0)

data = Tensor(np.array([1,2,1,2]), autograd=True)
target = Tensor(np.array([[0],[1],[0],[1]]), autograd=True)

embed = Embedding(5,3)
model = Sequential([embed, Tanh(), Linear(3,1), Sigmoid()])
criterion = MSELoss()
optim = SGD(parameters=model.get_parameters(), alpha=0.5)

for i in range(10):
    # Prediccion
    pred = model.forward(data)
    
    # Comparar
    loss = criterion.forward(pred, target)
    
    # Aprender
    loss.backward(grad=None)
    optim.step()
    print(loss)

[0.98874126]
[0.6658868]
[0.45639889]
[0.31608168]
[0.2260925]
[0.16877423]
[0.13120515]
[0.10555487]
[0.08731868]
[0.07387834]