Why is this XOR neural network having 2 outputs?

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Regularly, a simple neural network to solve XOR should have 2 inputs, 2 neurons in hidden layer, 1 neuron in output layer.

However, the following example implementation has 2 output neurons, and I don't get it:


Why did the author put 2 output neurons in there?

Edit: Author of the example noted that he is using 4 neurons in hidden layer, 2 neurons in output layer. But I still don't get it why, why a shape of {4,2} instead of {2,1}?

This is called one hot encoding. The idea is that you have one neuron per class. Each neuron gives the probability of that class.

I don't know why he uses 4 hidden neurons. 2 should be enough (if I remember correctly).

Implementing the XOR Gate using Backpropagation in Neural , Implementing logic gates using neural networks help understand the Compute the predicted output using the sigmoid function ii. Compute� The XOr Problem The XOr, or “exclusive or”, problem is a classic problem in ANN research. It is the problem of using a neural network to predict the outputs of XOr logic gates given two binary inputs. An XOr function should return a true value if the two inputs are not equal and a false value if they are equal.

The author uses the Evaluation class in the end (for stats of how often the network gives the correct result). This class needs one neuron per classification to work correctly, i.e. one output neuron for true and one for false.

The XOR Problem in Neural Networks., It is the problem of using a neural network to predict the outputs of XOr logic including one bias unit — and a single output unit (see figure 2). The XOr, or “exclusive or”, the problem is a classic problem in ANN research. It is the main problem of using a neural network to predict the outputs of XOr logic gates given two binary inputs. A XOr function should return a true value if the two inputs are not equal and a false value if they are equal.

It might be helpful to think of it like this:

Training Set        Label Set

    0 | 1               0 | 1
0 | 0 | 0          0 |  0 | 1
1 | 1 | 0          1 |  1 | 0
2 | 0 | 1          2 |  1 | 0
3 | 1 | 1          3 |  0 | 1

So [[0,0], 0], [[0,1], 0], etc. for the Training Set.

If you're using the two column Label Set, 0 and 1 correspond to true or false.

Thus, [0,0] correctly maps to false, [1,0] correctly maps to true, etc.

A pretty good article that slightly modifies the original can be found here: https://medium.com/autonomous-agents/how-to-teach-logic-to-your-neuralnetworks-116215c71a49

Backpropagation generate identical output?, What I mean to say is suppose that you want to train a neural net for the XOR Again, I have 2 outputs, and 1 of them is range from -100 to 100 and the other� One effect of having the second layer neuron dividing the space which is defined by the outputs of the first layer neurons is that if the first layer neurons are not behaving correctly, the second layer neuron cannot behave correctly.

What is the status of the n-dimensional XOR training problem for , The task is to obtain a multilayer feedforward, 2-input, single output neural network that The XOR problem in dimension n consists of 2^n binary data vectors, each I am having trouble getting even the training error rate to drop below 0.24. The purpose of this post is to give you an idea about how to use of neural network using SiaNet library plus writen in C# .NET. I am going to use XOR problem which is one of the simplest problem but, Minksy and Papert (1969) showed that this was a big problem for neural network architectures of the 1960s, known as perceptrons.

Why is a bias neuron necessary for a backpropagating neural , It is an additional parameter in the Neural Network which is used to adjust a neural network with 2 input neurons, 2 hidden neurons, and 1 output neuron. You cannot solve XOR without having a bias neuron, as it would� This article will look at a classic example for learning the neural network of the XOR function. There are already quite a large number of such manuals on the Internet, so the purpose of this text will be as follows: use the XOR function with two inputs and one output for a demo; use tensors to build a mathematical model of a neural network

Perceptron 5: XOR (how & why neurons work together , Blue circles are desired outputs of 1 (objects 2 & 3 in the logic table on the left), while The answer is, of course, to have two neurons working in parallel (next to each We want the neural network to categorise the objects it sees into just two � It’s the dimension of the output for this layer. If we think about our model in terms of neurons it means that we have two input neurons (input_dim=2) spreading into 16 neurons in a so called hidden layer. We also added another layer with an output dimension of 1 and without an explicit input dimension.

  • He explained it in the comments at the top. (it's another question how good this explanation is in regards to formal math)
  • For all future questions, JFYI, there's an active dev community on the Gitter channel: gitter.im/deeplearning4j/deeplearning4j
  • yeah, that chat room is interesting, some guy helped me out how to match activation function with loss function
  • i find one-hot encoding good only when we don't have too many classes to classify
  • yeah, i dont know why he's using 4 hidden neurons too, i changed to 2 and it's still working perfectly!
  • coz there would be too many one-hot neurons in the output layer, i don't know but what if we need to classify that many
  • @johnlowvale I've never seen anything else. The largest number of classes I'm aware of is 1000 for ImageNet. One-hot encoding is no problem there.
  • It caused me some confusion too. The first two numbers are position in 4 by 2 table. The third number (off on its own), is the value to be place at that coordinate.
  • i asked 1 guy on deeplearning4j chat room, he said it's because of the softmax activation function at output layer