Machine Learning MCQ - Which of the following is true about dropout in a neural network

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Machine Learning MCQ - Application of dropout in a neural network to reduce overfitting

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1. Which of the following is true about dropout?

a) Dropout leads to sparsity in the trained weights

b) At test time, dropout is applied with inverted keep probability

c) The larger the keep probability of a layer, the stronger the regularization of the weights in that layer

d) Dropout is applied to different layers of a neural network, but not the output layer

 

Answer: (d) Dropout is applied to different layers of a neural network, but not the output layer Dropout is a machine learning technique that randomly disables a portion of neurons in a neural network during training to prevent overfitting. It works by randomly "dropping out" (setting to zero) a fraction of the neurons (units) in a layer during each forward pass in training. This forces the network to become more robust by preventing it from relying too heavily on any one neuron, thus encouraging the network to learn more diverse features. Dropout can be applied on input layer (to remove deemed to be irrelevant data), and hidden layers (because much of the intermediate processing would end up noise) of a neural network but not on the output layer.   What is dropout?  The term “dropout” refers to dropping out the nodes (input and hidden layer) in a neural network. All the forward and backwards connections with a dropped node are temporarily removed, thus creating new network architecture out of the parent network. The nodes are dropped by a dropout probability of p.   Why dropout is not used in output layer? Dropout is typically not used in the output layer of a neural network because the output layer is responsible for making final predictions, and this layer should produce deterministic and stable results. Random dropout could interfere with the reliability of those predictions.  Alternate to dropout at the output layer? If needed one could use any other regularization techniques that do not affect the stability of the prediction at the output layer. Why not option (b)? Keep probability is the probability of retaining neurons during dropout. Also, dropout is applied during training but not during testing phase.  Why not option (c)? Having a larger keep probability (say 95% of neurons are kept during dropout) may lead to overfit problem. In such cases, dropout may not be effective.

 

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What is dropout in a neural network and why is it used?

Where can we use dropout in a neural network?

Why we cannot use dropout technique in the output layer of a neural net?

If at all you need to use some technique to overcome overfitting in the output layer, what we can do?

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