Different Output from Libtorch c++than pytorch

username: Arki99
cooked: <p>I’m using the same traced model in both c++ and python but I’m getting different outputs.</p>
<p>Python Code</p>
<pre><code class="lang-auto">import cv2
import numpy as np 
import torch
import torchvision
from torchvision import transforms as trans


# device for pytorch
device = torch.device('cuda:0')

torch.set_default_tensor_type('torch.cuda.FloatTensor')

model = torch.jit.load("traced_facelearner_model_new.pt")
model.eval()

# read the example image used for tracing
image=cv2.imread("videos/example.jpg")

test_transform = trans.Compose([
            trans.ToTensor(),
            trans.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
        ])       

resized_image = cv2.resize(image, (112, 112))

tens = test_transform(resized_image).to(device).unsqueeze(0)
output = model(tens)
print(output)
</code></pre>
<p>C++ code</p>
<pre><code class="lang-auto">#include &lt;iostream&gt;
#include &lt;algorithm&gt; 
#include &lt;opencv2/opencv.hpp&gt;
#include &lt;torch/script.h&gt;


int main()
{
	try
	{
		torch::jit::script::Module model = torch::jit::load("traced_facelearner_model_new.pt");
		model.to(torch::kCUDA);
		model.eval();

		cv::Mat visibleFrame = cv::imread("example.jpg");

		cv::resize(visibleFrame, visibleFrame, cv::Size(112, 112));
		at::Tensor tensor_image = torch::from_blob(visibleFrame.data, { 1, visibleFrame.rows, visibleFrame.cols, 3 }, at::kByte);
		tensor_image = tensor_image.permute({ 0, 3, 1, 2 });
		tensor_image = tensor_image.to(at::kFloat);

		tensor_image[0][0] = tensor_image[0][0].sub(0.5).div(0.5);
		tensor_image[0][1] = tensor_image[0][1].sub(0.5).div(0.5);
		tensor_image[0][2] = tensor_image[0][2].sub(0.5).div(0.5);

		tensor_image = tensor_image.to(torch::kCUDA);
		std::vector&lt;torch::jit::IValue&gt; input;
		input.emplace_back(tensor_image);
		// Execute the model and turn its output into a tensor.
		auto output = model.forward(input).toTensor();
		output = output.to(torch::kCPU);
		std::cout &lt;&lt; "Embds: " &lt;&lt; output &lt;&lt; std::endl;

		std::cout &lt;&lt; "Done!\n";
	}
	catch (std::exception e)
	{
		std::cout &lt;&lt; "exception" &lt;&lt; e.what() &lt;&lt; std::endl;
	}
}
</code></pre>
<p>Python output:<br>
tensor([[-1.6270e+00, -7.8417e-02, -3.4403e-01, -1.5171e+00, -1.3259e+00,<br>
-1.1877e+00, -2.0234e-01, -1.0677e+00,  8.8365e-01,  7.2514e-01,<br>
2.3642e+00, -1.4473e+00, -1.6696e+00, -1.2191e+00,  6.7770e-01,<br>
-3.0230e-01, -1.5904e+00,  1.7433e+00, -7.8862e-01,  3.9448e-01,<br>
-1.7189e+00,  1.1014e+00, -2.2981e+00, -5.1542e-01, -1.1593e-01,<br>
6.5024e-01, -6.8557e-01, -7.0064e-01, -1.0784e+00, -7.7883e-01,<br>
1.3773e+00, -1.5619e+00, -2.0540e-01,  1.2147e+00,  7.3867e-01,<br>
1.1110e+00,  1.0524e-01, -1.1249e+00, -5.0620e-01, -5.2198e-01,<br>
1.3556e+00, -1.5315e+00,  1.0446e-01,  9.1795e-01,  2.7186e+00,<br>
-6.9594e-01,  7.4122e-01,  1.4757e+00,  1.2925e-01, -2.6900e-01,<br>
1.5588e+00, -1.0609e+00, -2.0121e-01, -6.8162e-01,  1.1572e-01,<br>
-1.7430e-01, -1.4399e+00,  1.4873e+00,  1.1772e+00,  8.0879e-01,<br>
-1.3121e-01, -2.0003e+00, -7.4500e-02, -4.1007e-01, -1.2315e+00,<br>
-1.1150e+00, -2.1979e+00, -1.2252e+00, -1.5357e+00,  2.3477e+00,<br>
-1.9694e+00,  1.8873e+00,  3.2776e-01, -7.6457e-01, -1.7912e+00,<br>
5.7192e-01, -2.5461e-01, -6.7235e-01, -3.1392e+00, -8.8816e-01,<br>
-6.2070e-01, -7.2750e-01,  2.4999e-01,  1.1434e+00,  1.0114e+00,<br>
3.4786e-01,  9.9722e-01, -4.8731e-01, -5.6572e-01,  1.2642e+00,<br>
-4.4803e-01, -1.4394e+00, -1.8629e-01,  5.3590e-01,  1.4678e+00,<br>
8.5147e-02, -2.0793e+00, -2.8566e-01,  2.9678e-01, -3.4123e-01,<br>
3.1120e-01,  7.2252e-01,  2.7816e+00,  1.0683e+00, -3.1785e+00,<br>
-6.7824e-01, -1.7665e-02,  5.2761e-01,  1.1141e-01, -1.6249e+00,<br>
-2.0966e+00,  1.2752e+00, -8.8363e-01, -1.9442e+00,  1.5579e+00,<br>
5.6738e-01, -3.4520e-01,  9.1841e-01,  7.5063e-02, -1.6585e+00,<br>
2.5177e-01, -1.3581e+00,  3.4045e-01,  1.2807e+00, -3.7098e-01,<br>
5.8744e-01,  9.2038e-01, -4.1502e-01, -1.4006e+00,  1.3954e+00,<br>
-1.1765e+00,  1.3100e+00,  2.1202e+00,  3.0595e+00,  1.7250e-01,<br>
-5.0746e-01, -1.1361e+00,  1.3437e+00, -8.2815e-02, -1.0477e+00,<br>
8.5581e-01,  2.4402e+00,  1.6616e+00, -1.9156e+00,  4.2771e-01,<br>
1.7761e+00,  1.5104e-01, -2.7037e-01, -6.1427e-02, -1.0483e+00,<br>
-2.2830e-01,  3.9742e-01, -6.7260e-01,  2.4361e+00, -7.6196e-01,<br>
1.0965e+00,  1.4753e+00,  8.5338e-01,  4.5726e-01, -1.8667e-01,<br>
-1.1761e+00, -8.8821e-02,  1.3202e-01,  1.5002e+00, -4.9365e-01,<br>
-1.0977e+00, -2.9104e-02, -3.5381e-01, -2.2095e-01,  9.3996e-01,<br>
-1.0770e+00,  9.3767e-01,  2.2430e+00, -7.1536e-01, -7.0468e-01,<br>
-2.1124e+00, -2.7435e+00,  1.7995e+00,  4.1688e-01,  4.2249e-01,<br>
1.1487e-01, -1.1160e-01,  2.0495e+00, -1.6678e+00, -2.2310e+00,<br>
3.1619e-01, -1.0459e-01, -5.3289e-01, -3.8420e-01, -1.3272e+00,<br>
-4.5785e-01, -1.3917e+00,  1.3051e-01, -1.6694e+00,  2.3753e+00,<br>
7.4885e-01,  2.2261e+00,  3.5489e-01,  2.2460e+00, -7.0667e-01,<br>
-3.1920e-01,  2.7467e-01, -1.4723e-01,  2.2449e-01,  3.0860e-01,<br>
-5.6551e-01,  1.3486e+00, -1.0313e+00, -1.8844e-01, -5.4212e-01,<br>
-8.9150e-01,  2.1663e-01, -2.3341e-02,  5.4041e-01, -2.8048e-01,<br>
-8.5421e-01, -1.3455e+00, -5.4566e-03,  3.3249e-01,  3.2633e-02,<br>
-7.2821e-01, -2.1179e+00, -4.3671e-01,  1.6922e-01, -1.5222e+00,<br>
-8.1076e-01, -4.5145e-01,  1.0031e+00,  3.8981e-01, -7.5108e-01,<br>
1.2772e+00,  1.0216e+00, -8.8832e-02,  7.2678e-01,  2.3863e-01,<br>
-7.2614e-01, -9.3102e-01,  1.0179e-01, -3.1820e-01,  1.7549e+00,<br>
2.4568e-02, -2.4448e-01,  6.6527e-01,  8.9161e-01,  2.4075e-01,<br>
7.7993e-01, -2.9786e-01,  3.7189e-01, -1.8534e+00,  1.2161e+00,<br>
-1.4340e-01, -8.4045e-01, -1.7490e-02, -6.3605e-02, -2.6961e-01,<br>
-6.0356e-02,  1.6479e-02,  8.4313e-02,  1.2867e+00, -1.8166e+00,<br>
-4.4236e-01,  1.9492e+00,  7.5414e-02, -1.1048e+00,  3.2055e-01,<br>
1.6554e+00,  1.6603e+00,  5.2739e-01, -8.8670e-02, -3.8753e-01,<br>
1.1036e+00, -8.2550e-02,  1.5303e+00,  7.2115e-01,  6.3496e-01,<br>
-5.9476e-01, -1.7111e+00, -7.4406e-02,  1.2575e+00,  1.0652e+00,<br>
3.3742e-01, -6.1574e-01, -7.7878e-01, -1.5626e+00,  2.0075e+00,<br>
7.8007e-01,  2.3359e+00, -5.8407e-01, -3.6670e-02, -1.8357e+00,<br>
-8.5492e-01, -7.9237e-02, -3.4835e+00,  1.8853e-01, -6.3243e-01,<br>
-1.4143e-01, -1.5573e+00,  1.3054e+00,  7.2289e-02, -3.3197e-01,<br>
-4.2815e-01, -9.9560e-01,  4.8308e-02, -1.0704e+00,  4.6133e-02,<br>
-2.7710e-01,  6.3607e-01, -1.2849e-01, -5.8321e-01, -6.4198e-01,<br>
6.8877e-01,  4.4855e-01, -9.9281e-01, -1.9603e-01, -1.3646e-01,<br>
-1.5132e+00, -1.8551e+00,  2.9994e+00,  1.9747e+00, -8.8294e-01,<br>
1.0297e+00,  5.4850e-01,  2.2204e+00, -1.9871e-02,  1.6224e+00,<br>
-1.3714e+00, -1.9999e-01, -1.8371e-01,  9.8869e-01,  1.7765e+00,<br>
2.1239e+00,  1.6547e-01, -3.8542e-01,  1.1274e+00, -3.9524e+00,<br>
-1.8184e-01, -9.8598e-01, -1.2485e-01, -7.8307e-01,  1.5246e+00,<br>
-2.3675e-01,  7.5133e-01, -1.8204e+00,  1.1964e+00,  6.9412e-01,<br>
-3.4246e+00, -6.2488e-01, -2.0008e-01, -1.4634e-01,  3.6126e-01,<br>
-6.2960e-01,  1.2811e+00, -2.0820e-01, -2.6770e-01,  1.0875e+00,<br>
-1.8656e+00, -1.7223e+00, -1.6199e+00, -1.6023e+00,  1.1000e-03,<br>
5.5017e-01,  1.9496e+00,  7.6847e-01, -1.2796e+00,  2.4125e+00,<br>
-1.0207e+00,  1.4682e+00,  6.9706e-04, -3.1195e-01,  8.4523e-01,<br>
1.1639e+00,  1.0964e+00,  8.0490e-01,  3.7047e-01,  4.5071e-01,<br>
1.0288e+00, -1.0690e+00, -1.0394e+00, -6.6745e-01, -2.9959e-01,<br>
1.2548e+00, -1.3682e+00, -1.3584e+00, -1.2101e+00, -9.2314e-01,<br>
-1.6717e+00,  1.9204e-01, -5.1889e-01,  6.6319e-01, -3.5625e-02,<br>
3.5143e+00,  7.8116e-01, -8.7697e-01, -3.8530e-01,  2.0860e+00,<br>
-1.5915e+00, -8.9022e-01, -5.0295e-01, -1.2801e+00,  1.8433e-01,<br>
-6.9138e-01,  7.6171e-01,  2.1874e-01, -9.5043e-01,  1.3584e+00,<br>
-1.0811e+00,  3.7449e-01,  1.4505e+00,  1.4932e+00, -1.0532e+00,<br>
-3.7828e-01,  1.7716e+00,  1.8390e-01, -1.4419e+00,  1.0288e+00,<br>
-1.6216e-01, -1.9189e+00, -1.0210e+00,  7.4068e-01,  7.0265e-01,<br>
1.6574e+00,  3.3080e-01, -2.9631e+00,  1.9505e-01, -2.5233e-01,<br>
-2.0795e+00, -1.4711e+00, -1.9923e+00,  3.1158e+00,  2.3007e+00,<br>
-1.4851e+00, -1.3739e+00, -3.8031e-01,  1.3879e+00,  6.2704e-01,<br>
4.0849e-01,  5.2626e-01, -5.3517e-01,  6.4794e-01,  1.3874e+00,<br>
1.1729e+00, -6.2420e-02,  1.6669e-01,  3.7647e-02, -1.8886e+00,<br>
7.9953e-01,  9.9094e-02,  3.3523e-01,  6.6596e-01, -2.0243e+00,<br>
6.9878e-01,  1.0356e+00,  4.0730e-01, -4.5905e-01,  2.0120e+00,<br>
-5.4535e-02, -1.4968e+00,  1.5344e-01, -2.9665e-01,  3.0098e-01,<br>
5.8679e-01,  2.0437e-01, -1.8587e+00,  6.7893e-02,  7.3112e-01,<br>
3.5927e-01,  1.2785e+00,  4.0530e-01,  8.8397e-01,  1.0595e+00,<br>
-6.2867e-01,  9.6102e-01, -1.6319e+00,  3.6489e-01, -4.1222e-01,<br>
1.8157e+00, -2.3874e+00, -2.0938e+00, -5.5133e-01,  1.8377e+00,<br>
-1.0041e+00,  7.4509e-02,  1.0751e+00,  1.6144e+00, -7.9048e-01,<br>
-8.2033e-01, -3.3595e+00,  1.1192e+00, -3.6376e-01, -5.9706e-02,<br>
-1.5762e+00, -7.6090e-01, -5.4732e-01, -2.5771e-01, -5.6112e-02,<br>
-8.0445e-01, -1.9105e+00,  4.5630e-01,  2.2545e+00, -1.7567e+00,<br>
-1.3612e+00,  1.2470e+00,  3.2429e-01,  1.2829e+00,  2.1712e+00,<br>
1.6078e+00,  1.1831e+00,  7.4726e-02,  3.6741e-01, -6.8770e-01,<br>
-7.1650e-01,  1.7661e-01]], device=‘cuda:0’,<br>
grad_fn=)</p>
<p>C++ output<br>
Embds: Columns 1 to 8 -84.6285  -14.7203   17.7419   47.0915   31.8170   57.6813    3.6089  -38.0543</p>
<p>Columns 9 to 16   3.3444  -95.5730   90.3788  -10.8355    2.8831  -14.3861    0.8706  -60.7844</p>
<p>Columns 17 to 24  30.0367  -43.1165   -5.6550   33.2033   -1.1758  105.3884   -9.8710   17.8346</p>
<p>Columns 25 to 32  17.0933   66.6854  119.4765   79.3748   30.2875  -77.4174    0.3317   -4.0767</p>
<p>Columns 33 to 40  -2.8686  -30.3538  -51.4344  -54.1199  -94.5696  -33.0847  -19.5770   54.3094</p>
<p>Columns 41 to 48   9.1542    1.8090   84.0233  -34.8189   79.6485  109.4215   10.2912  -47.0976</p>
<p>Columns 49 to 56  37.7219  -15.3790  -16.3427   22.2094 -110.2703  -47.8214  -40.3721   49.5144</p>
<p>Columns 57 to 64   7.0735  -69.1642  -87.2891    2.4904 -114.2314  -34.6742   77.0583   47.5493</p>
<p>Columns 65 to 72 -12.7955  -12.1884  -70.9220   61.2372  -23.0823  -14.9402   13.1899   77.5274</p>
<p>Columns 73 to 80  14.8980    3.9681  -12.4636   -2.8313  -26.5012   18.7349  -81.2809   27.7805</p>
<p>Columns 81 to 88   4.6502  -18.6308  -65.8188   -7.8959  -84.8021   18.9902   55.9421   -3.1461</p>
<p>Columns 89 to 96 -68.0309 -121.0718  -39.6810   79.0844   44.7410    5.4263  -55.5766  -46.9981</p>
<p>Columns 97 to 104 107.5576  -64.8779  -38.2952   27.7137   -3.9070   27.3118   -6.6422  -13.3164</p>
<p>Columns 105 to 112 104.2085    0.5082  -78.4771  -19.8312  -38.7756  -52.0113   55.9654  -14.9233</p>
<p>Columns 113 to 120  -9.7707   52.0167  -44.6636  -98.1208    4.3471   72.7285    1.8963  -15.4767</p>
<p>Columns 121 to 128 -15.4205  -42.2256  170.4943  -79.3618   -1.6385   11.5500   59.1987  -65.9982</p>
<p>Columns 129 to 136  -9.0985   33.3904   98.2815  -74.2509   11.8020  -89.1567   34.4861   43.4928</p>
<p>Columns 137 to 144 -56.4307   11.7731  -16.7437   31.0511  -46.6434  -20.9232   26.8300    3.2606</p>
<p>Columns 145 to 152  61.6599  -21.9810  -70.2742  -15.0909  -41.5298  -30.9954  -76.2638    0.6642</p>
<p>Columns 153 to 160   2.6916   47.7454   26.7200   21.0140  -44.8855   -6.4925  -65.3175  -45.4141</p>
<p>Columns 161 to 168 -17.8177  -31.5315  -32.9688   11.2705  -58.3355  -83.6264  -56.9800  -41.5826</p>
<p>Columns 169 to 176  14.9421  -66.3415  -19.4020   -8.9205   34.7736   -1.2142  -22.5419   40.3070</p>
<p>Columns 177 to 184  51.2629   37.0988  -84.1648  112.5778  -51.5290   56.4389  -17.4903   42.5482</p>
<p>Columns 185 to 192  57.6678  -29.1431   63.6813   17.9877  -59.6995   31.1782  -43.9503   42.7553</p>
<p>Columns 193 to 200  29.6934  -19.0927  -74.4936  -90.7978  -75.4938   41.4866    9.0591   52.9187</p>
<p>Columns 201 to 208 -89.2584  -50.5271  -46.8471  -67.3429   -1.2110   21.3874   86.3426  -33.9398</p>
<p>Columns 209 to 216  46.3358   17.8981 -100.1674  -50.8498  -55.5474  -42.1486    2.6009   79.9036</p>
<p>Columns 217 to 224  73.3729   41.6763  -82.8588   -2.8996   17.4613 -166.8535   68.3080   42.2190</p>
<p>Columns 225 to 232 -75.3225  -27.0393   40.7027  133.1041  -10.1574   85.9142  -17.5571  -11.0445</p>
<p>Columns 233 to 240 -46.6592   36.1900  -25.5837   23.5690  111.7863  116.6611   -3.4232  -14.3296</p>
<p>Columns 241 to 248 -10.1717  -26.3160  110.0413  -74.1527   66.8889   54.4394   -8.4007  -80.9817</p>
<p>Columns 249 to 256 -52.5828    0.9547  -78.9718   19.8881   68.5607    4.6896   82.5919   11.0848</p>
<p>Columns 257 to 264 -48.9090   49.7747  -90.9747  -22.6597   82.9919  -31.0079   33.3777  -80.8728</p>
<p>Columns 265 to 272  20.9312   24.9726   58.8175  -57.3928  -36.9511   41.7683  -22.7457   18.0902</p>
<p>Columns 273 to 280  33.3806   12.2698  -48.8019  -64.5811  -22.4971   13.0827   25.2252  -69.3366</p>
<p>Columns 281 to 288 -31.1383    9.3472  -41.4773  -45.0921  -29.0197   20.8469  -18.5003  101.1813</p>
<p>Columns 289 to 296  21.4998  -41.0139   13.0072   14.5900   47.8082    8.7939   -1.6898  -65.2906</p>
<p>Columns 297 to 304  98.5455  -36.5257  -13.4876   31.5104   67.0052   20.0974   80.6973  -59.4268</p>
<p>Columns 305 to 312  -9.8725  109.9801  -11.7113   76.0156   19.4814  -54.8399  -58.3198  -22.0197</p>
<p>Columns 313 to 320 -11.4874  -40.5763  -90.6195   61.3063    2.9030  -38.8599   49.8093   63.7094</p>
<p>Columns 321 to 328 -57.7285   41.2222   35.4600   21.2505   29.7755   40.5168  -36.1677  -35.7411</p>
<p>Columns 329 to 336  55.7660   46.6989   56.3559 -109.1042  -56.7988  -16.9920   32.8174   50.5294</p>
<p>Columns 337 to 344  13.8572   92.8637   59.6933   -0.8193  -69.0457   14.8087   20.9237   29.3850</p>
<p>Columns 345 to 352 -59.0192  -19.3695  -47.4750    1.2323  -18.9492  -63.6595   46.3948    1.5139</p>
<p>Columns 353 to 360  80.1003 -116.6856   18.4157   43.6484   14.6691  -26.1271  -60.0532   10.0214</p>
<p>Columns 361 to 368 -17.5375   11.3292   -6.1891   -2.1459  -24.8204    0.0574  147.1159   56.4644</p>
<p>Columns 369 to 376  20.6844   99.9769   -2.2026   45.3141   -5.9111   22.8332  -26.9914  -54.8931</p>
<p>Columns 377 to 384  13.0211  -22.7115  -55.9605 -102.6626  -41.1080   37.0626   64.1098  -87.8013</p>
<p>Columns 385 to 392  -4.5324  116.3614  -13.5869   29.3998  -29.8993  -19.1788   89.5348   33.3830</p>
<p>Columns 393 to 400  47.5617  -47.8952 -115.5733   18.6636   70.4700   38.7836   52.9221  -26.4590</p>
<p>Columns 401 to 408  57.7344  -46.9924 -107.3308 -104.5425   93.0818  -38.1794   28.5326   63.8123</p>
<p>Columns 409 to 416 -21.0296  -53.7937   46.5247   10.2387  -12.8996   85.9877   53.1290   48.6895</p>
<p>Columns 417 to 424 -66.8464   -2.3867   22.6467    7.4483   21.0441  -94.1917  -42.1939   15.9525</p>
<p>Columns 425 to 432  53.8263  113.8375   61.6334 -104.5839  -20.7676   78.8139  -22.6948 -127.5196</p>
<p>Columns 433 to 440  26.8981   20.7751   38.6938    0.1248  -14.7045  -67.0021  -51.5681   -8.1669</p>
<p>Columns 441 to 448  19.7874  -48.3975  -32.2947   81.1478   48.5060  -85.6838  -17.2948    4.0231</p>
<p>Columns 449 to 456  17.8500  173.0746   -8.2571   20.8623   -7.1263   78.6013   18.4043    6.9401</p>
<p>Columns 457 to 464 -55.3688   28.4737   21.1565  142.7567  -89.0954  -30.7984   62.5072   26.2824</p>
<p>Columns 465 to 472 -40.7608  -53.0610  -23.0218    2.4569   58.6491  -60.6084   15.7515  -54.9259</p>
<p>Columns 473 to 480 -44.9702   -8.3017  -71.4793  -84.7397 -114.3832  -15.3010   54.4510  -32.4508</p>
<p>Columns 481 to 488  75.7713   22.8518  -35.4634  -48.0759  -31.5085   -8.1592    6.5577  -23.7090</p>
<p>Columns 489 to 496  -0.2302  -68.3007   26.5670  -28.0143  -21.5935  -55.7180   -5.6677   56.4317</p>
<p>Columns 497 to 504  61.9337    9.6666  -12.2558  -60.3430  -30.2482   31.4843   71.7933   -8.8972</p>
<p>Columns 505 to 512  36.8830  -31.1061   51.6818    8.2866    1.7214   -2.9263  -37.4330   48.5854<br>
[ CPUFloatType{1,512} ]</p>
<p>Using<br>
Pytorch 1.6.0<br>
Libtorch 1.6.0<br>
Windows 10<br>
Cuda 10.1</p>

username: chetan_patil
cooked: <p>Hi,<br>
instead of using</p>
<pre><code class="lang-auto">tensor_image[0][0] = tensor_image[0][0].sub(0.5).div(0.5);
		tensor_image[0][1] = tensor_image[0][1].sub(0.5).div(0.5);
		tensor_image[0][2] = tensor_image[0][2].sub(0.5).div(0.5);
</code></pre>
<p>Just try <code>tensor_image = tensor_image.sub(0.5).div(0.5)</code></p>
<p>Also, it’s better to monitor the output tensor’s <code>sum</code> or <code>mean</code> rather than all the values in the output tensor.</p>

username: Arki99
cooked: <p>Hi,<br>
I tried that but I’m still facing the same issue.</p>