What does the dropout regularization technique do during training?Question 3AnswerA.Increases the learning rate dynamically during trainingB.Adds a penalty term to the loss function based on weight magnitudesC.Adds random noise to the input dataD.Sets a fraction of randomly chosen activations to zero
Question
What does the dropout regularization technique do during training?Question 3AnswerA.Increases the learning rate dynamically during trainingB.Adds a penalty term to the loss function based on weight magnitudesC.Adds random noise to the input dataD.Sets a fraction of randomly chosen activations to zero
Solution
The dropout regularization technique during training sets a fraction of randomly chosen activations to zero. This is done to prevent overfitting of the model to the training data. Here's a step-by-step explanation:
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During training, dropout is implemented by only keeping a neuron active with some probability p (a hyperparameter), or setting it to zero otherwise.
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By doing this, it forces the network to learn redundant representations, and ensures that the neurons inside the network are not co-adapting too much to the training data, hence improving the generalization ability of the model.
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When testing, no neurons are dropped out. Instead, their outputs are scaled down by a factor of p, to balance for the fact that more neurons are active than at training time.
So, the correct answer to your question is D. Sets a fraction of randomly chosen activations to zero.
Similar Questions
Dropout is a technique used to prevent overfitting by randomly turning off a fraction of neurons during training.Group of answer choicesTrueFalse
How does early stopping act as a form of regularization?Question 8AnswerA.It stops the training process early to prevent overfitting.B.It adds a dropout layer to the neural network.C.It penalizes large weights in the model.D.It introduces random noise to the input data.
What is the primary purpose of regularization techniques in deep learning?Question 2AnswerA.To increase model complexityB.To introduce noise in the dataC.To reduce model biasD.To increase model variance
Which of the following techniques performs similar operations as a dropout in a neural network?Question 5Select one:A.StackingB.NoneC.BoostingD.Bagging
Question 3Which of the following statements about regularization is TRUE? 1 pointRegularization always reduces the number of selected features. Regularization increases the likelihood of overfitting relative to training data. Regularization decreases the likelihood of overfitting relative to training data.Regularization performs feature selection without a negative impact in the likelihood of overfitting relative to the training data.
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