if '__main__' == __name__:
from torch import nn
from learn_pytorch.OptimizationAlgorithm.traner import train_concise
from learn_pytorch.OptimizationAlgorithm.net import LinearReg
from utility.load_airfoil_self_noise import load_airfoil_self_noise
lr = 0.01
batch_size = 10
num_epoch = 2
features, labels = load_airfoil_self_noise(
'../../data/airfoil_self_noise.dat')
net = LinearReg(5, 1)
train_concise('RMSprop', {
'lr': lr,
'alpha': 0.99
}, net, nn.MSELoss(), features, labels, batch_size, num_epoch)
s_hat = s / (1 - beta2 ** t)
p.data -= (v_hat * lr) / (torch.sqrt(s_hat) + err)
states['t'] = t + 1
if '__main__' == __name__:
from learn_pytorch.OptimizationAlgorithm.loss_func import sqrt_loss
from learn_pytorch.OptimizationAlgorithm.traner import train
from learn_pytorch.OptimizationAlgorithm.net import LinearReg
from utility.load_airfoil_self_noise import load_airfoil_self_noise
features, labels = load_airfoil_self_noise('../../data/airfoil_self_noise.dat')
batch_size = 10
lr = 0.01
num_epoch = 2
net = LinearReg(5, 1)
params = net.parameters()
states = init_states(params)
train(Adam,
states,
{'lr': lr},
net,
sqrt_loss,
features,
labels,
batch_size,
num_epoch
)
def sgd(params, states, hyperparams):
for param in params:
param.data -= hyperparams['lr'] * param.grad.data
if '__main__' == __name__:
from learn_pytorch.OptimizationAlgorithm.loss_func import sqrt_loss
from learn_pytorch.OptimizationAlgorithm.traner import train
from learn_pytorch.OptimizationAlgorithm.net import LinearReg
from utility.load_airfoil_self_noise import load_airfoil_self_noise
features, labels = load_airfoil_self_noise('../../data/airfoil_self_noise.dat')
batch_size = 1
lr = 0.005
num_epoch = 2
train(sgd,
None,
{'lr': lr},
LinearReg(5, 1),
sqrt_loss,
features,
labels,
batch_size,
num_epoch
)