Loss value is not reducing(stuck at 1.5 approx), is there something wrong in this network?


(Veeru) #1
from __future__ import print_function
import argparse
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 pandas as pd


# Training settings
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument('--batch-size', type=int, default=64, metavar='N',
                    help='input batch size for training (default: 64)')
parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
                    help='input batch size for testing (default: 1000)')
parser.add_argument('--epochs', type=int, default=10, metavar='N',
                    help='number of epochs to train (default: 10)')
parser.add_argument('--lr', type=float, default=0.0001, metavar='LR',
                    help='learning rate (default: 0.001)')
parser.add_argument('--momentum', type=float, default=0, metavar='M',
                    help='SGD momentum (default: 0.5)')
parser.add_argument('--no-cuda', action='store_true', default=False,
                    help='disables CUDA training')
parser.add_argument('--seed', type=int, default=0, metavar='S',
                    help='random seed (default: 1)')
parser.add_argument('--log-interval', type=int, default=10, metavar='N',
                    help='how many batches to wait before logging training status')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()

torch.manual_seed(args.seed)
if args.cuda:
    torch.cuda.manual_seed(args.seed)


kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
train_loader = torch.utils.data.DataLoader(
    datasets.MNIST('../data', train=True, download=True,
                   transform=transforms.Compose([
                       transforms.ToTensor(),
                       transforms.Normalize((0.1307,), (0.3081,))
                   ])),
    batch_size=args.batch_size, shuffle=True, **kwargs)
test_loader = torch.utils.data.DataLoader(
    datasets.MNIST('../data', train=False, transform=transforms.Compose([
                       transforms.ToTensor(),
                       transforms.Normalize((0.1307,), (0.3081,))
                   ])),
    batch_size=args.test_batch_size, shuffle=True, **kwargs)

# Convolutional Neural Network creation
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 64, kernel_size=3, stride=2, padding = 0)
        self.conv2 = nn.Conv2d(64, 32, kernel_size=2, stride=1, padding = 1)
        self.conv2_drop = nn.Dropout2d(0.35)
        self.fc1 = nn.Linear(5408, 256)
        self.fc1_drop = nn.Dropout2d(0.50)
        self.fc2 = nn.Linear(256, 10)
     
    def forward(self, x):
        
        x = F.relu(self.conv1(x)) #x = F.relu(F.max_pool2d(self.conv1(x), 2))
        
        x = F.relu(self.conv2(x))
        
        x = F.max_pool2d(x, 2, 1)
        
        x = self.conv2_drop(x)
        
        x = x.view(-1, 5408)
        x = F.tanh(self.fc1(x))
        x = self.fc1_drop(x)
        
        #x = F.dropout(x, training=self.training)
        
        x = self.fc2(x)
        return F.softmax(x)

model = Net()

if args.cuda:
    model.cuda()

optimizer = optim.Adam(model.parameters())#, lr=args.lr, momentum=args.momentum)


def train(epoch):
    model.train()
    df = pd.DataFrame(columns=['loss','epoch'])
    index = 0
    for batch_idx, (data, target) in enumerate(train_loader):
        if args.cuda:
            data, target = data.cuda(), target.cuda()
        data, target = Variable(data), Variable(target)
        
        if batch_idx % args.log_interval == 0:
            optimizer.zero_grad()
            output = model(data)
            loss = F.cross_entropy(output, target)
            loss.backward()
            optimizer.step()
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.data[0]))
            
            df = df.append({'loss': loss.data[0], 'epoch' : epoch}, ignore_index=True)
        
    with open('log.csv', 'a') as f:
        df.to_csv(f, header=False, sep='\t', columns=['loss','epoch'])
        


def test():
    model.eval()
    test_loss = 0
    correct = 0
    for data, target in test_loader:
        if args.cuda:
            data, target = data.cuda(), target.cuda()
        data, target = Variable(data, volatile=True), Variable(target)
        output = model(data)
        test_loss += F.cross_entropy(output, target, size_average=False).data[0] # sum up batch loss
        pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability
        correct += pred.eq(target.data.view_as(pred)).cpu().sum()

    test_loss /= len(test_loader.dataset)
    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
        test_loss, correct, len(test_loader.dataset),
        100. * correct / len(test_loader.dataset)))


for epoch in range(1, args.epochs + 1):
    train(epoch)
test()

(Ashish Karel) #2

Have you found any solution yet?I’m also stuck in a similar situation!


(Veeru) #3

No. Still in the same page. I am waiting for the solution.


#4

CrossEntropyLoss expects scores for each class as its input.
You are using softmax in your model, so just remove it.
Alternatively you could change it to F.log_softmax and use NLLLoss.
Let me know, if your model is learning!


(Ashish Karel) #5

try using different initializations of weights, maybe your model is unable to break symmetry!