title: Bool value of Tensor with more than one value is ambiguous in Pytorch
body: <p>I want to create a model in pytorch, but I can't 
  compute the loss. 
  It's always return <strong>Bool value of Tensor with more 
  than one value is ambiguous</strong>
  Actually, I run example code, it work.</p>
  
  <pre><code>loss = CrossEntropyLoss()
  input = torch.randn(8, 5)
  input
  target = torch.empty(8,dtype=torch.long).random_(5)
  target
  output = loss(input, target)
  </code></pre>
  
  <p>Here is my code,</p>
  
  <pre><code>################################################################################
  ##
  ##
  import torch
  from torch.nn import Conv2d, MaxPool2d, Linear, CrossEntropyLoss, MultiLabelSoftMarginLoss
  from torch.nn.functional import relu, conv2d, max_pool2d, linear, softmax
  from torch.optim import adadelta
  ##
  ##
  ##  Train
  Train = {}
  Train["Image"]    = torch.rand(2000, 3, 76, 76)
  Train["Variable"] = torch.rand(2000, 6)
  Train["Label"] = torch.empty(2000, dtype=torch.long).random_(2)
  ##
  ##
  ##  Valid
  Valid = {}
  Valid["Image"]    = torch.rand(150, 3, 76, 76)
  Valid["Variable"] = torch.rand(150, 6)
  Valid["Label"]    = torch.empty(150, dtype=torch.long).random_(2)
  ################################################################################
  ##
  ##
  ##  Model
  ImageTerm    = Train["Image"]
  VariableTerm = Train["Variable"]
  Pip = Conv2d(in_channels=3, out_channels=32, kernel_size=(3,3), stride=1, padding=0)(ImageTerm)
  Pip = MaxPool2d(kernel_size=(2,2), stride=None, padding=0)(Pip)
  Pip = Conv2d(in_channels=32, out_channels=64, kernel_size=(3,3), stride=1, padding=0)(Pip)
  Pip = MaxPool2d(kernel_size=(2,2), stride=None, padding=0)(Pip)
  Pip = Pip.view(2000, -1)
  Pip = torch.cat([Pip, VariableTerm], 1)
  Pip = Linear(in_features=18502, out_features=1000 , bias=True)(Pip)
  Pip = Linear(in_features=1000, out_features=2 , bias=True)(Pip)
  ##
  ##
  ##  Loss
  Loss = CrossEntropyLoss(Pip, Train["Label"])
  </code></pre>
  
  <p>The error is on  <strong>Loss = CrossEntropyLoss(Pip, Train["Label"])</strong>,
  thanks.</p>