For a classification task, instead of random weight initializations in a neuralnetwork, we set all the weights to zero. Which of the following statements is true?Question 15Answera.There will not be any problem and the neural network will train properlyb.None of thesec.The neural network will train but all the neurons will end up recognizing the samethingd.The neural network will not train as there is no net gradient change
Question
For a classification task, instead of random weight initializations in a neuralnetwork, we set all the weights to zero. Which of the following statements is true?Question 15Answera.There will not be any problem and the neural network will train properlyb.None of thesec.The neural network will train but all the neurons will end up recognizing the samethingd.The neural network will not train as there is no net gradient change
Solution
The correct answer is c. The neural network will train but all the neurons will end up recognizing the same thing.
Here's why:
When all the weights are initialized to zero, every neuron in the network computes the same output. This is because the output is a function of the input and the weights, and since all weights are the same, all neurons produce the same output.
During backpropagation, all the neurons will have the same gradient and hence will update the same way. This makes the neurons symmetric and they will continue to be so on subsequent passes, meaning they will learn the same features during training.
This is a problem because one of the main advantages of a neural network is its ability to learn complex patterns by combining different features learned by different neurons. If all neurons are learning the same thing, then
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