I want to create a model in pytorch, but I can't 
compute the loss. 
It's always return **Bool value of Tensor with more 
than one value is ambiguous**
Actually, I run example code, it work.

```
loss = CrossEntropyLoss()
input = torch.randn(8, 5)
input
target = torch.empty(8,dtype=torch.long).random_(5)
target
output = loss(input, target)

```

Here is my 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"])

```

The error is on **Loss = CrossEntropyLoss(Pip, Train["Label"])**,
thanks.