What is the meaning of the error rate in Neural networks? I'm a beginner with neural networks. I get that the error should be low. But what does that number really mean?
I created a simple neural network which has a error of 1.5. Is that too high? What are the consequences? 
 A: The error basically signifies how well your network is performing on a certain (training/testing/validation) set. Having a low error is good, will having a higher error is certainly bad. The error is calculated through a loss function, of which there are several. 
One of these for example is the Mean Squared Error, which will calculate the distance between the wanted input and the real input, and squaring this value.
So if your network is outputting [0.5] for example, but you want it to output [0]. Your error will be (0.5 - 0)^2 = 0.25. But this error is just used to signify how well the network is performing.
During backpropagation, the network uses something called error responsibility through which it calculates how much it should change connection weights and biases. 
About your 1.5 error: this is fairly high! Normally, your error should be between 0 and 1. A 'good' error is anywhere between 0 and 0.05. 
A: The error is a measure of the difference between what the ANN predicts and the real Label of data.
for example for a simple "And" inputs and label(output) is like:
input1-input2-output
  0          0          0
  0          1          0
  1          0          0
  1          1          1
if for example, the network predicts 1 for inputs 0 and 0 it's wrong and it will be added to the Error.
so basically Error is a measure showing how much the network is Wrong!
