MNIST-dataset: optimizer got an empty parameter list

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)

Could you change the _init_ to __init__ here.

def _init_(self):
  super(Netz, self)._init_()

Thank you very much for your quick response. I obviously wasn’t looking good enough on this line, now it is running.