Does the loss of a model reflect its accuracy?
So these are my loss per 75 epochs:
Epoch: 75, loss: 47382825795584.000000
Epoch: 150, loss: 47382825795584.000000
Epoch: 225, loss: 47382825795584.000000
Epoch: 300, loss: 47382825795584.000000
Epoch: 375, loss: 47382825795584.000000
Epoch: 450, loss: 47382825795584.000000
Epoch: 525, loss: 47382825795584.000000
Epoch: 600, loss: 47382825795584.000000
Epoch: 675, loss: 47382825795584.000000
Epoch: 750, loss: 47382825795584.000000

And these are the values from predictions and targets respectively
Predictions: tensor([[ 8109436.0000,  7734814.0000,  8737677.0000, 11230861.0000,
          3795826.7500,  3125072.7500,  1699706.1250,  5337285.0000,
          3474238.5000]], grad_fn=&lt;TBackward&gt;)
----------------------------------------
Targets: tensor([[ 8111607.,  7580798.,  8749436., 11183578.,  3822811.,  3148031.,
          2343278.,  5360924.,  3536146.]])

And this is the accuracy of the first, and second elements inside predictions against the first, and second elements of targets
8109436.0000/8111607*100 #First element
Output: 99.9732358828528

print(7734814.0000/7580798*100) #Second element
Output: 102.03165946381898

So I'm really not sure what is going on. Because I have a large loss there is a 99% accuracy for the first element and 98% accuracy on the second element? I'm not the best at math, so I'm not sure about the last percentage.
Could someone explain if the loss reflects the accuracy?

Loss is only meaningful relatively (i.e. for comparison). Multiply your loss function by 10 and your loss is 10 times bigger on the same model. This doesn't tell you anything.
But using the same loss function, if model_1 gives a loss 10x smaller than model_2, then chances are model_1 will have better accuracy (although not 100% guarantied).
