Question: In a dropout layer, instead of zeroing out activations at test time, we multiply the weights by 1 p , where p is the probability

In a dropout layer, instead of zeroing out activations at test time, we multiply the weights by 1 p, where p is the probability that an activation is set to zero during training. Explain why the multiplication by 1 p is necessary for the neural network to make meaningful predictions.

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