Table of Contents
- 1 Why do we need more than one hidden layer?
- 2 How many hidden units should I use?
- 3 What is the danger to having too many hidden units in your network?
- 4 Why are more layers better?
- 5 What does increasing the number of hidden layers do?
- 6 What is the number of units in the input hidden and output?
- 7 How do you find the net input to the output unit?
- 8 Do we need to count the number of parameters in deep learning?
In theory, multiple hidden layers result in a composition of representations with increased abstraction higher up the hierarchy. The idea is compositionality, you want each lower level layer to feed a layer above such that the upper layer builds features based on the composition of features from the lower layers.
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.
Is 2 hidden layers enough?
Since any Boolean function can be written in DNF -form, two hidden layers are sufficient for a multilayer network to realize any polyhedral di- chotomy. Two hidden layers are sometimes also necessary, e.g. for realizing the “four-quadrant” dichotomy which generalizes the XOR function [4].
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. (overfitting – A network that is not sufficiently complex can fail to detect fully the signal in a complicated data set, leading to underfitting.
Why are more layers better?
The more data samples you have, the more you can add up layers and nodes to the configuration, with the result of having better performances, i.e. a Neural Network which better approximate the (ideal and purely hypothetical) mathematical function introduced above.
Does adding more hidden units increase accuracy?
Simplistically speaking, accuracy will increase with more hidden layers, but performance will decrease. But, accuracy not only depend on the number of layer; accuracy will also depend on the quality of your model and the quality and quantity of the training data.
An inordinately large number of neurons in the hidden layers can increase the time it takes to train the network. The amount of training time can increase to the point that it is impossible to adequately train the neural network.
Number of units in the input, hidden and output layers are respectively 3, 4 and 2. From the diagram, we have i = 3, h = 4 and o = 2. Note that the red colored neuron is the bias for that layer.
What is the error value of a single output neuron?
The error value indicates how much the network’s output (actual) is off the mark from the expected output (target). We use the Mean Squared Error function to calculate the error. The error value of a single output neuron is a function of its actual value and the target value.
How do you find the net input to the output unit?
The net input to the output unit is computed: net = ∑ iwiii. If net is greater than the threshold θ, the unit is turned on, otherwise it is turned off. Then the response is compared with the actual category of the input vector. If the vector was correctly categorized, then no change is made to the weights.
Do we need to count the number of parameters in deep learning?
W hy do we need to count the number of parameters in a deep learning model again? We don’t. But in cases where we need to reduce the file size of the model or even reduce the time taken for model inference, knowing the number of parameters before and after model quantization would come in handy.