You are training an RNN, and find that your weights and activations are all taking on the value of NaN (“Not a Number”). Which of these is the most likely cause of this problem?Question 8Answera. Exploding gradient problem.b.Vanishing gradient problem.c. ReLU activation function g(.) is used to compute g(z), where z is too large.d. Sigmoid activation function g(.) is used to compute g(z), where z is too large.
Question
You are training an RNN, and find that your weights and activations are all taking on the value of NaN (“Not a Number”). Which of these is the most likely cause of this problem?Question 8Answera. Exploding gradient problem.b.Vanishing gradient problem.c. ReLU activation function g(.) is used to compute g(z), where z is too large.d. Sigmoid activation function g(.) is used to compute g(z), where z is too large.
Solution
The most likely cause of the problem where weights and activations are all taking on the value of NaN (“Not a Number”) in an RNN is the Exploding gradient problem (option a).
Here's why:
The Exploding gradient problem occurs when the gradient becomes too large and causes an overflow (i.e., values that are too large to be represented in the computer's memory), which is often represented as NaN. This typically happens when the values of the weights in the neural network are too
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