Why do neural networks give different results?

Why do neural networks give different results?

Neural network algorithms are stochastic. This means they make use of randomness, such as initializing to random weights, and in turn the same network trained on the same data can produce different results. The random initialization allows the network to learn a good approximation for the function being learned.

Can a neural network be 100% accurate?

If your neural network got the line right, it is possible it can have a 100% accuracy. Remember that a neuron’s output (before it goes through an activation function) is a linear combination of its inputs so this is a pattern that a network consisting of a single neuron can learn.

What is it called when we use a model for getting predictions instead of training?

A final machine learning model is a model that you use to make predictions on new data.

How can I make my neural network more accurate?

Now we’ll check out the proven way to improve the performance(Speed and Accuracy both) of neural network models:

  1. Increase hidden Layers.
  2. Change Activation function.
  3. Change Activation function in Output layer.
  4. Increase number of neurons.
  5. Weight initialization.
  6. More data.
  7. Normalizing/Scaling data.

How do you know if a neural network is accurate?

Testing The Accuracy Of The Model

  1. The “subset” function is used to eliminate the dependent variable from the test data.
  2. The “compute” function then creates the prediction variable.
  3. A “results” variable then compares the predicted data with the actual data.

Why do I get 100% accuracy?

You are getting 100% accuracy because you are using a part of training data for testing. At the time of training, decision tree gained the knowledge about that data, and now if you give same data to predict it will give exactly same value.

Is Neural Network accurate?

We show experimentally that the accuracy of a trained neural network can be predicted surprisingly well by looking only at its weights, without evaluating it on input data. Furthermore, the predictors are able to rank networks trained on different, unobserved datasets and with different architectures.

Is the neural network always produces same / similar outputs?

EDIT: Two layers, an input layer of 2 inputs to 8 outputs, and an output layer of 8 inputs to 1 output, produces much the same results: 0.5+/-0.2 (or so) for each training case. I’m also playing around with pyBrain, seeing if any network structure there will work. Edit 2: I am using a learning rate of 0.1. Sorry for forgetting about that.

Why do I get the same result each time I Run my neural network?

If you want the results to be the same each time, for comparison and reproducibility, you can set the initial weights to the same values each time. This can be achieved by using the same value to seed the random number generator each time you run your program. Code Codes in MATLAB for Training Artificial Neural Network using…

What’s the mean value of a neural network?

I’m writing my own implementation of a neural network to test my knowledge, and while it seems to run okay, it converges such that the output is always the mean value (0.5 since I’m using logistic output activation) regardless of the input, and nothing I do seems to change anything.

How many neurons are there in a neural network?

Both of these networks have two inputs, one output, and I’ve tried a number of architectures, including 1 and 2 hidden layers with about 3-8 nodes in each. All of the neurons are using a logistic activation.