Why neural network gives 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. This can be confusing to beginners as the algorithm appears unstable, and in fact they are by design.
What is seeding in deep learning?
The “seed” is a starting point for the sequence and the guarantee is that if you start from the same seed you will get the same sequence of numbers. That said, you also want to test your experiments across different seed values.
Can a neural network be stuck with a small random weight?
In this case, the equations of the learning algorithm would fail to make any changes to the network weights, and the model will be stuck. It is important to note that the bias weight in each neuron is set to zero by default, not a small random value.
How does a neural network always have the same result?
To have always the same result, try to propose a fixed seed for the initial weights. Another option is that the neural network works with a random data division, taking randomly the order of input/target pairs to compute the error propagation phase.
Why do nodes have different weights in a neural network?
Specifically, nodes that are side-by-side in a hidden layer connected to the same inputs must have different weights for the learning algorithm to update the weights. This is often referred to as the need to break symmetry during training.
Why do we use nondeterministic algorithms in neural networks?
An alternate solution is to use nondeterministic algorithms. These are algorithms that use elements of randomness when making decisions during the execution of the algorithm. This means that a different order of steps will be followed when the same algorithm is rerun on the same data.