def training(m, inputs, targets, batch_size, nb_epochs, lr):
"""
Training function
:param m: model
:param inputs: input data
:param targets:
:param batch_size:
:param nb_epochs:
:param lr: learning rate
:return:
"""
criterion = MSELoss()
optimizer = SGD(m, lr, momentum=0.9)
for epoch in range(nb_epochs):
for batch in range(0, inputs.size(0), batch_size):
output = m.forward(inputs.narrow(0, batch, batch_size))
loss = criterion.forward(output,
targets.narrow(0, batch, batch_size))
dl = criterion.backward()
m.backward(dl)
optimizer.step()
if (epoch % 50 == 0) or (epoch == nb_epochs - 1):
print('Epoch: {} Loss: {:.04f}'.format(epoch, loss.item()))
import numpy as np
from tensor import Tensor
from layers import Sequential, Linear
from activations import Tanh, Sigmoid
from optimizers import SGD
from losses import MSELoss
np.random.seed(0)
data = Tensor(np.array([[0, 0], [0, 1], [1, 0], [1, 1]]), autograd=True)
target = Tensor(np.array([[0], [1], [0], [1]]), autograd=True)
model = Sequential([Linear(2, 3), Tanh(), Linear(3, 1), Sigmoid()])
criterion = MSELoss()
optim = SGD(parameters=model.get_parameters(), alpha=1)
for i in range(10):
pred = model.forward(data)
loss = criterion.forward(pred, target)
loss.backward()
optim.step()
print(loss)