loss is nan

[w5688414]

I try to use my dataset, my data is a table with many discrete data, such as 0,1,2. I found the loss is nan

Train Epoch: 1 [0/49626 (0%)]	Loss: nan, Accuracy: 28/64 (43.0000%)
Train Epoch: 1 [640/49626 (1%)]	Loss: nan, Accuracy: 30/64 (46.0000%)
Train Epoch: 1 [1280/49626 (3%)]	Loss: nan, Accuracy: 36/64 (56.0000%)
Train Epoch: 1 [1920/49626 (4%)]	Loss: nan, Accuracy: 35/64 (54.0000%)
Train Epoch: 1 [2560/49626 (5%)]	Loss: nan, Accuracy: 40/64 (62.0000%)
Train Epoch: 1 [3200/49626 (6%)]	Loss: nan, Accuracy: 29/64 (45.0000%)
Train Epoch: 1 [3840/49626 (8%)]	Loss: nan, Accuracy: 33/64 (51.0000%)
Train Epoch: 1 [4480/49626 (9%)]	Loss: nan, Accuracy: 28/64 (43.0000%)
Train Epoch: 1 [5120/49626 (10%)]	Loss: nan, Accuracy: 30/64 (46.0000%)
Train Epoch: 1 [5760/49626 (12%)]	Loss: nan, Accuracy: 28/64 (43.0000%)
Train Epoch: 1 [6400/49626 (13%)]	Loss: nan, Accuracy: 38/64 (59.0000%)
Train Epoch: 1 [7040/49626 (14%)]	Loss: nan, Accuracy: 36/64 (56.0000%)
Train Epoch: 1 [7680/49626 (15%)]	Loss: nan, Accuracy: 27/64 (42.0000%)
Train Epoch: 1 [8320/49626 (17%)]	Loss: nan, Accuracy: 29/64 (45.0000%)
Train Epoch: 1 [8960/49626 (18%)]	Loss: nan, Accuracy: 31/64 (48.0000%)
Train Epoch: 1 [9600/49626 (19%)]	Loss: nan, Accuracy: 32/64 (50.0000%)
Train Epoch: 1 [10240/49626 (21%)]	Loss: nan, Accuracy: 34/64 (53.0000%)
Train Epoch: 1 [10880/49626 (22%)]	Loss: nan, Accuracy: 34/64 (53.0000%)
Train Epoch: 1 [11520/49626 (23%)]	Loss: nan, Accuracy: 29/64 (45.0000%)
Train Epoch: 1 [12160/49626 (24%)]	Loss: nan, Accuracy: 34/64 (53.0000%)
Train Epoch: 1 [12800/49626 (26%)]	Loss: nan, Accuracy: 39/64 (60.0000%)
Train Epoch: 1 [13440/49626 (27%)]	Loss: nan, Accuracy: 34/64 (53.0000%)