我正在尝试训练一个简单的 MLP 来近似 y=f(a,b,c)。我的代码如下。
import torch
import torch.nn as nn
from torch.autograd import Variable
# hyper parameters
input_size = 3
output_size = 1
num_epochs = 50
learning_rate = 0.001
# Network definition
class FeedForwardNet(nn.Module):
def __init__(self, l1_size, l2_size):
super(FeedForwardNet, self).__init__()
self.fc1 = nn.Linear(input_size, l1_size)
self.relu1 = nn.ReLU()
self.fc2 = nn.Linear(l1_size, l2_size)
self.relu2 = nn.ReLU()
self.fc3 = nn.Linear(l2_size, output_size)
def forward(self, x):
out = self.fc1(x)
out = self.relu1(out)
out = self.fc2(out)
out = self.relu2(out)
out = self.fc3(out)
return out
model = FeedForwardNet(5 , 3)
# sgd optimizer
optimizer = torch.optim.SGD(model.parameters(), learning_rate, momentum=0.9)
for epoch in range(11):
print ('Epoch ', epoch)
for i in range(trainX_light.shape[0]):
X = Variable( torch.from_numpy(trainX_light[i]).view(-1, 3) )
Y = Variable( torch.from_numpy(trainY_light[i]).view(-1, 1) )
# forward
optimizer.zero_grad()
output = model(X)
loss = (Y - output).pow(2).sum()
print (output.data[0,0])
loss.backward()
optimizer.step()
totalnorm = 0
for p in model.parameters():
modulenorm = p.grad.data.norm()
totalnorm += modulenorm ** 2
totalnorm = math.sqrt(totalnorm)
print (totalnorm)
# validation code
if (epoch + 1) % 5 == 0:
print (' test points',testX_light.shape[0])
total_loss = 0
for t in range(testX_light.shape[0]):
X = Variable( torch.from_numpy(testX_light[t]).view(-1, 3) )
Y = Variable( torch.from_numpy(testY_light[t]).view(-1, 1) )
output = model(X)
loss = (Y - output).pow(2).sum()
print (output.data[0,0])
total_loss += loss
print ('epoch ', epoch, 'avg_loss ', total_loss.data[0] / testX_light.shape[0])
print ('Done')
我现在遇到的问题是验证码
输出 = 模型（X）
总是产生完全相同的输出值（我猜这个值是某种垃圾）。我不确定我在这部分犯了什么错误。有人可以帮我找出代码中的错误吗？