What is the problem with the tanh and sigmoid activation function?1 pointThey are discontinuous functionsYou can't take the derivativeThe derivative is near zero in many regionsThey are periodic functions
Question
What is the problem with the tanh and sigmoid activation function?1 pointThey are discontinuous functionsYou can't take the derivativeThe derivative is near zero in many regionsThey are periodic functions
Solution
The problem with the tanh and sigmoid activation functions is that the derivative is near zero in many regions. This leads to a problem called "vanishing gradients," where the weights and biases of a neural network are updated very little during training, making the learning process extremely slow. This is especially problematic in deep neural networks, where early layers can essentially stop learning.
Similar Questions
Which of the following statements about the sigmoid function is NOT true? The derivative of the sigmoid function can be negative. The sigmoid function is continuous and differentiable. The sigmoid function maps any input value to a value between 0 and 1. The sigmoid function can be used as an activation function in neural networks.
Which of the following is NOT a common activation function used in ANNs?a.Tanh (Hyperbolic Tangent)b.Sigmoidc.ReLU (Rectified Linear Unit)d.Euclidean
Which of the following activation functions is primarily used for neural networks designed to classify binary data?a)Hyperbolic tangent (tanh) functionb)Sigmoid functionc)Rectified Linear Unit (ReLU)d)Softmax function
Both of sigmoid function or perceptron decision function (step function) are differentiable.1 pointTrueFalse
The cut off value for the sigmoid function as discussed is :
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