Hello Brian!



 ApeelingPotato:

The issue I have is when I feed an input tensor into the neural network, it feeds out a tensor with the same value single float value for each input. I want the output to be specific with each input.


Do I understand correctly?
You feed input1 – a single input tensor consisting of 1040 floats — into
your network, and you get outputA – a single float.  You then feed in a
different input tensor, input2, but get the same output, outputA.
This seems very odd.
Could you post a simple, complete, runnable script, together with its
output, that demonstrates this issue?
I have in mind something like:
import torch
import ...

print (torch.__version__)

input1 = <some explicit numerical input tensor>
input2 = <another explicit numerical input tensor>
input3 = <yet another>

class Net(nn.Module):
    <your specific network that displays this issue>

net = Net()

output1 = net (input1)
print ('input1 = ...')
print (input1)
print ('output1 = ...')
print (output1)

output2 = net (input2)
print ('input2 = ...')
print (input2)
print ('output2 = ...')
print (output2)

output3 = net (input3)
print ('input3 = ...')
print (input3)
print ('output3 = ...')
print (output3)

print ('output2 - output1 =', output2 - output1)
print ('output3 - output1 =', output3 - output1)

The idea is that others should be able to run the script you post, and
by doing so, reproduce the output you post.
Best.
K. Frank