Hi, I got the above error of the nn.CrossEntropyLoss()
Traceback (most recent call last):
  File "./food101/food101_nas.py", line 467, in <module>
    loss1 = criterion(output1, labels)
  File "/usr/local/lib/python3.5/dist-packages/torch/nn/modules/module.py", line 477, in __call__
    result = self.forward(*input, **kwargs)
  File "/usr/local/lib/python3.5/dist-packages/torch/nn/modules/loss.py", line 862, in forward
    ignore_index=self.ignore_index, reduction=self.reduction)
  File "/usr/local/lib/python3.5/dist-packages/torch/nn/functional.py", line 1550, in cross_entropy
    return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction)
  File "/usr/local/lib/python3.5/dist-packages/torch/nn/functional.py", line 1407, in nll_loss
    return torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
RuntimeError: Expected object of type torch.cuda.LongTensor but found type torch.cuda.FloatTensor for argument #2 'target'

there is the part of the code
    criterion = nn.CrossEntropyLoss().cuda()
    for epoch in range(50):  # loop over the dataset multiple times
        running_loss = 0.0
        for i, data in enumerate(data_loader, 0):
            # get the inputs; data is a list of [inputs, labels]
            inputs, labels = data
            labels = labels.type(torch.cuda.FloatTensor)
            # zero the parameter gradients
            optimizer2.zero_grad()
            # forward + backward + optimize
            output1, output2 = net2(inputs)
            loss1 = criterion(output1, labels)

of loss1 = criterion(output1, labels) the programm stops
And i try to output the value of labels and output1, here are the results:
tensor([[ 0.3472,  0.2209, -0.5995,  0.5815, -0.5395, -0.0155,  2.4635,  2.0652,
         -0.2173,  2.0970, -0.9288,  1.7439,  2.1594, -0.3784,  0.3356,  0.4073,
          0.3096,  1.0668, -0.8825,  2.8969, -1.3266, -1.5186,  1.8897, -1.0376,
          0.4083, -3.1226,  1.1905,  3.9962,  2.8334,  2.7869, -0.5127, -4.0223,
         -1.6923,  2.1538, -0.2082, -2.7689, -0.9950, -1.1264,  0.7648, -1.8015,
         -1.1787, -0.6646,  1.1710,  2.5576,  1.2553, -1.6907, -1.9629, -2.4180,
         -1.6066, -0.0622, -3.0201,  1.1205, -0.0081,  1.2506,  0.7384, -0.4972,
         -0.2058,  0.5839,  0.6814,  3.6870, -1.0864, -1.5504,  0.2249,  0.2436,
          0.4892, -1.1034,  4.5987, -1.6100,  1.3722, -0.0430,  0.4520,  0.5140,
          1.2593, -1.5004, -1.1094, -0.2125,  2.0717, -2.4672,  1.7870, -0.2758,
         -0.5859, -0.5422, -3.7564, -2.0177,  1.5797, -3.3137,  0.0896, -1.4499,
         -2.0254,  2.7423, -4.7888,  0.0125, -1.6988,  1.9800, -0.4304,  0.0367,
          1.4657, -0.0832, -1.1632,  0.6637,  0.2876],
        [-1.0760, -2.3349, -0.2378, -0.0260, -1.9087, -0.5062,  2.7556,  2.8177,
          0.6555,  3.7096,  1.3478,  0.7260, -0.1354,  0.8245, -0.0649, -1.3409,
         -0.6787, -0.8940, -1.0808,  2.2057, -0.9994, -2.2736,  1.9989,  0.8456,
          2.5386, -1.7708, -1.1134,  2.9402,  2.4160,  1.9480, -2.1800, -1.9614,
         -1.7699, -2.3918,  1.1136, -0.7473,  2.8408,  0.5136,  0.1066, -0.1198,
         -2.7388,  1.0143, -0.2919, -1.4613, -1.4885, -0.7984,  2.3917, -0.3652,
         -0.9415,  0.4829,  3.9927,  0.8021,  2.0672,  2.1484, -1.5458,  2.6886,
          1.7743, -1.4343, -1.9249,  1.4623,  1.9435, -0.5579, -0.5783, -1.1952,
          0.1120, -1.6303,  4.4920, -1.4937,  1.7977,  1.6284, -3.0435,  0.0606,
          1.1746, -1.1102, -2.1779,  1.1458,  1.6065, -1.6678,  2.6499, -2.2413,
          1.3299, -1.9646, -3.0804,  1.4887,  2.5108, -1.3850,  0.2503,  0.6424,
          0.0941,  4.5158, -0.7314, -0.6402, -0.4716,  2.0102, -1.2146,  0.9128,
         -1.8152, -0.6944, -1.7823, -1.8926,  2.2456],
        [ 0.1301, -3.9154,  0.0021, -3.5201, -1.2929,  0.5564,  1.8020,  2.0753,
          1.8213, -0.0316,  0.6688, -0.8543, -0.5184, -1.0957, -1.2135, -0.1007,
          0.3097, -1.0970, -1.4665,  3.7728, -0.8016, -0.8768, -0.3117,  0.5693,
          2.2932, -1.0881, -0.9895,  2.3643,  4.3349,  3.4266, -0.0193,  0.8639,
         -1.9764, -0.2480,  0.7688,  0.1922,  0.1700,  2.3806,  1.2501, -2.3265,
         -0.7687,  0.3803, -0.1902, -1.1115, -0.3638, -0.7665, -3.4929, -0.9755,
          0.0180,  1.2906,  0.8780,  0.8353,  4.5028,  1.3965,  0.2595,  1.9385,
         -0.6293, -1.8133, -0.1595,  1.8847,  2.1214,  1.1154,  0.1183, -0.9148,
          0.1752,  0.5887,  1.3333,  0.5452, -0.5026,  0.2785, -0.4085,  2.6408,
         -1.1813,  0.3552, -4.1691, -1.4687,  2.3110, -1.3233,  2.4228, -0.4022,
          1.1015,  1.4589, -1.6527,  0.6953,  0.5717, -2.6545,  0.2260,  4.5496,
         -2.9178,  2.8515, -2.8933, -1.0091, -1.3126,  1.0695, -0.9941,  1.5877,
          0.3424,  0.4332, -0.4486, -3.1223,  2.7039],
        [-1.3958, -1.6683, -0.3244, -1.8912,  1.0913, -2.5487,  3.6205,  1.9874,
         -2.7681,  3.0277,  1.5184,  2.5858, -1.9175, -0.3118, -0.1551, -3.0004,
          1.2457,  2.3147,  0.4921, -0.6332, -2.1855,  2.8018,  7.3446, -2.2081,
          3.4884,  1.3904,  4.0372,  6.9340,  9.7749,  7.5051, -9.1866, -0.4472,
         -2.2257,  3.7294,  5.9419, -4.6607,  3.6848, -2.0865, -5.7268,  0.2872,
         -5.6435,  3.3136,  1.3436,  4.2567, -2.8445, -1.1460,  1.8317, -6.8653,
          1.1093, -1.0434, -4.2880, -2.9261,  4.1342,  4.2560, -1.7228,  8.0642,
         -3.9592,  3.0952, -1.5354, -0.6765,  0.6810, -1.4194, -1.0910, -4.5485,
          3.3561, -3.4030,  3.1959,  4.4669,  3.1454,  1.4789, -0.7964,  5.3485,
         -0.0700, -1.6954, -5.4768,  0.3164,  2.7968, -0.5237,  0.3240, -2.6250,
         -4.5888, -0.8958,  0.5304, -1.8871, -3.0368, -4.3204,  8.9819,  3.9074,
         -5.0982,  1.2394, -2.4371,  0.1130, -5.1622,  3.8740, -2.7696,  0.7971,
          3.4686, -0.9827, -1.6262, -2.0382,  0.9371]],
       device='cuda:0', grad_fn=<ThAddmmBackward>)
tensor([21., 45., 43., 87.], device='cuda:0')

I tried few of the solutions online, but nothing worked.