What is the danger to having too many hidden units in your neural network?

If you have too few hidden units, you will get high training error and high generalization error due to underfitting and high statistical bias. If you have too many hidden units, you may get low training error but still have high generalization error due to overfitting and high variance.

What is hidden units in neural network?

In neural networks, a hidden layer is located between the input and output of the algorithm, in which the function applies weights to the inputs and directs them through an activation function as the output. In short, the hidden layers perform nonlinear transformations of the inputs entered into the network.

What does increasing the number of hidden layers do?

1) Increasing the number of hidden layers might improve the accuracy or might not, it really depends on the complexity of the problem that you are trying to solve. Where in the left picture they try to fit a linear function to the data.

What is the most preferred hidden layer activation in deep neural networks *?

Rectified Linear Unit is the most used activation function in hidden layers of a deep learning model. The formula is pretty simple, if the input is a positive value, then that value is returned otherwise 0.

How many neurons should be hidden in a neural network?

The number of hidden neurons should be between the size of the input layer and the size of the output layer. The number of hidden neurons should be 2/3 the size of the input layer, plus the size of the output layer. The number of hidden neurons should be less than twice the size of the input layer.

How do you calculate the number of neurons in a layer?

The number of hidden neurons should be between the size of the input layer and the size of the output layer. The number of hidden neurons should be 2/3 the size of the input layer, plus the size of the output layer.

Is it possible to use a neural network with two layers?

Problems that require two hidden layers are rarely encountered. However, neural networks with two hidden layers can represent functions with any kind of shape. There is currently no theoretical reason to use neural networks with any more than two hidden layers.

Do more hidden units mean better results?

Also Hinton has some experiments which show that more hidden units means a better representation of the input and hence better results. This is especially present when using rectified linear units. – elaRosca Jan 12 ’14 at 10:06