Contents
Why is my neural network not training?
Your Network contains Bad Gradients. You Initialized your Network Weights Incorrectly. You Used a Network that was too Deep. You Used the Wrong Number of Hidden Units.
Why are deep neural networks hard to train?
Unstable Gradient Problem. Nielsen claims that when training a deep feedforward neural network using Stochastic Gradient Descent (SGD) and backpropagation, the main difficulty in the training is the “unstable gradient problem”.
What is training error in neural network?
Frequently the test error of Artificial Neural Networks is presented with a monotonic decreasing behavior as a function of the iteration number, while the training error also continuously decreases. This behavior is, most of the times, illustrated by drawings instead of simulations or data from a real system.
Why is it hard to train a neural network?
The iterative training process of neural networks solves an optimization problem that finds for parameters (model weights) that result in a minimum error or loss when evaluating the examples in the training dataset.
Is there an algorithm to train a neural network?
In fact, there does not exist an algorithm to solve the problem of finding an optimal set of weights for a neural network in polynomial time. Mathematically, the optimization problem solved by training a neural network is referred to as NP-complete (e.g. they are very hard to solve).
How to improve the accuracy of neural networks?
In the process of training, we want to start with a bad performing neural network and wind up with network with high accuracy. In terms of loss function, we want our loss function to much lower in the end of training. Improving the network is possible, because we can change its function by adjusting weights.
How are neural networks trained to go downhill?
Repeat this process many times, and you will gradually go farther and farther downhill. You may sometimes get stuck in a small trough or valley, in which case you can follow your momentum for a bit longer to get out of it. Caveats aside, this strategy will eventually get you to the bottom of the mountain.