I trained a model with two class，and it works well in my dataload, but when I want to test it with an arbitrary data, the pred is wrong, and I find that the two score of log_softmax are always the same. I’m confused with this data.
my net is here :
class Mnet(nn.Module):
 def __init__(self, k = 2):
        super(Mnet, self).__init__()
        self.feat = Nnet()
        self.fc1 = nn.Linear(1024, 512)
        self.fc2 = nn.Linear(512, 256)
        self.fc3 = nn.Linear(256, k)
 def forward(self, x):
        x, trans = self.feat(x)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        fc3 = x
        print('f3: ',fc3)
        print('F_log: ',F.log_softmax(x, dim=0))
        return F.log_softmax(x, dim=0), trans,fc3

and there is my data with my test loader:
f3:  tensor([[-0.2002,  0.2312],
        [-0.5871,  2.8717],
        [-0.5332,  2.4883],
        [-0.1908,  0.1144],
        [-0.4099,  0.8726],
        [-0.4612,  1.9445],
        [-0.2196,  0.1526],
        [-0.5849,  2.8590],
        [-0.5060,  2.2855]], device='cuda:0', grad_fn=<ThAddmmBackward>)
F_log:  tensor([[-1.9994, -4.0161],
        [-2.3863, -1.3755],
        [-2.3324, -1.7589],
        [-1.9900, -4.1329],
        [-2.2091, -3.3746],
        [-2.2603, -2.3027],
        [-2.0188, -4.0947],
        [-2.3840, -1.3883],
        [-2.3052, -1.9617]], device='cuda:0', grad_fn=<LogSoftmaxBackward>)


the batch_size is 10
but when I test one data, thefc3 and F_log are:
f3:  tensor([[-0.0625, -0.1528]], device='cuda:0', grad_fn=<ThAddmmBackward>)
F_log:  tensor([[-0.6931, -0.6931]], device='cuda:0', grad_fn=<LogSoftmaxBackward>)

why the output of F.log_softmax is the same?