I was trying to do some work with MNIST-dataset. But received the following error:
ValueError Traceback (most recent call last)
in ()
47
48
—> 49 optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
50 “”"
51 def train(epoch):
1 frames
/usr/local/lib/python3.6/dist-packages/torch/optim/sgd.py in init(self, params, lr, momentum, dampening, weight_decay, nesterov)
66 if nesterov and (momentum <= 0 or dampening != 0):
67 raise ValueError(“Nesterov momentum requires a momentum and zero dampening”)
—> 68 super(SGD, self).init(params, defaults)
69
70 def setstate(self, state):
/usr/local/lib/python3.6/dist-packages/torch/optim/optimizer.py in init(self, params, defaults)
44 param_groups = list(params)
45 if len(param_groups) == 0:
—> 46 raise ValueError(“optimizer got an empty parameter list”)
47 if not isinstance(param_groups[0], dict):
48 param_groups = [{‘params’: param_groups}]
ValueError: optimizer got an empty parameter list
Maybe you could help me with this problem. This is my code:
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
import numpy as np
import matplotlib.pyplot as plt
kwargs = {}
train_data = torch.utils.data.DataLoader(datasets.MNIST(‘data’, train=True, download=True,
transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.1307,),(0.3081,))])),
batch_size=64, shuffle=True, **kwargs)
test_data = torch.utils.data.DataLoader(datasets.MNIST(‘data’, train=False,
transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.1307,),(0.3081,))])),
batch_size=64, shuffle=True, **kwargs)
class Netz(nn.Module):
def _init_(self):
super(Netz, self)._init_()
self.convl = nn.Conv2d(1, 10,kernel_size=5)
self.conv2 = nn.Conv2d(10,20,kernel_size=5)
self.conv_dopout = nn.Dopout2d()
self.fc1 = nn.Linear(320, 60)
self.fc2 = nn.Linear(60, 10)
def forward(self, x):
x = self.conv1(x)
x = F.max_pool2d(x, 2)
x = F.relu(x)
x = self.conv2(x)
x = self.conv_dropout
x = F.max_pool2d(x, 2)
x = F.relu(x)
print(x.size())
exit()
model = Netz()
list(model.parameters())
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
def train(epoch):
model.train()
for batch_id, (data, target) in enumerate(train_data):
data = Variable(data)
target = Variable(target)
optimizer.zero_grad()
out = model(data)
criterion = F.nll_loss
loss = criterion(out, target)
loss.backward()
optimizer.step()
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_id * len(data), len(train_data.dataset),
100. * batch_id / len(train_data), loss.data[0]))
for epoch in range(1, 30):
train(epoch)