Hi, I’m a bit confused about an error i have.
[torch.FloatTensor of size 3x1x296]
Traceback (most recent call last):
File “pytorch.py 3”, line 168, in 
train(epoch)
File “pytorch.py 3”, line 139, in train
output = model(data)
File “/home/david/anaconda3/lib/python3.5/site-packages/torch/nn/modules/module.py”, line 206, in call
result = self.forward(*input, **kwargs)
File “pytorch.py 3”, line 118, in forward
x = F.relu(self.fc1(x))
File “/home/david/anaconda3/lib/python3.5/site-packages/torch/nn/modules/module.py”, line 206, in call
result = self.forward(*input, **kwargs)
File “/home/david/anaconda3/lib/python3.5/site-packages/torch/nn/modules/linear.py”, line 54, in forward
return self._backend.Linear()(input, self.weight, self.bias)
File "/home/david/anaconda3/lib/python3.5/site-packages/torch/nn/functions/linear.py", line 10, in forward
output.addmm(0, 1, input, weight.t())
RuntimeError: matrices expected, got 3D, 2D tensors at /b/wheel/pytorch-src/torch/lib/TH/generic/THTensorMath.c:1232
my tensor is as i would have hope 3x1x196 which is the expected size with 3 being the batch size.
i think it’s the batch of 3 which is causing the 3d, 2d tensor confusion but not sure how to get around this. is the input to NN the whole batch or just one record ?
full code below.
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 torch.utils.data as utils_data
import numpy
from random import randrange
# fix random seed for reproducibility
numpy.random.seed(7)

# 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=0.01, metavar='LR',
                help='learning rate (default: 0.01)')
parser.add_argument('--momentum', type=float, default=0.5, 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=1, 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 {}
class MyDataset():
    def __init__(self):
        dataset = numpy.genfromtxt("vectorFile.csv", delimiter=",")

        self.features = dataset[:,0:148]
        self.labels = dataset[:,149]
        self.size = len(dataset)

    def __getitem__(self, index):
    # choose random index in features
        firstPick = randrange(0,self.size)
        firstPlayer = self.features[firstPick][0]#dealer or not ?
        while True:
            secondPick = randrange(0,self.size)
            #print('secondPick')
            if firstPlayer==self.features[secondPick][0]:
                #print('match')
                break

        if self.labels[firstPick]>self.labels[secondPick]:
            #print('adding to outputAnswer')
            target = numpy.array([(1,0)])
        else:
            #print('adding to outputAnswer')
            target = numpy.array([(1,0)])

        target = torch.from_numpy(target).float()

        data = numpy.array([(self.features[firstPick], self.features[secondPick])])
        data = data.reshape((1, 296))
        data = torch.from_numpy(data).float()

        #print('data', data)
        #print('target', target)
        return data, target

    def __len__(self):
        return self.size

train_loader = torch.utils.data.DataLoader(MyDataset(), batch_size=3)

class Net(nn.Module):
def __init__(self):
    super(Net, self).__init__()
    #self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
    #self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
    #self.conv2_drop = nn.Dropout2d()
    self.fc1 = nn.Linear(296, 50)
    self.fc2 = nn.Linear(50, 2)

def forward(self, x):
    print('x')
    print(x)
    #x = F.relu(F.max_pool2d(self.conv1(x), 2))
    #x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
    #x = x.view(-1, 320)
    x = F.relu(self.fc1(x))
    #x = F.dropout(x, training=self.training)
    x = self.fc2(x)
    return F.log_softmax(x)

model = Net()
if args.cuda:
    model.cuda()

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

def train(epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        print(batch_idx)
        print(data)
        print(target)
        if args.cuda:
            data, target = data.cuda(), target.cuda()
            data, target = Variable(data), Variable(target)
            optimizer.zero_grad()
            output = model(data)
            loss = F.nll_loss(output, target)
            loss.backward()
            optimizer.step()
        if batch_idx % args.log_interval == 0:
            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]))

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.nll_loss(output, target, size_average=False).data[0] # sum up batch loss
            pred = output.data.max(1)[1] # get the index of the max log-probability
            correct += pred.eq(target.data).cpu().sum()

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


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