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Does ReLU have vanishing gradient problem?
ReLU has gradient 1 when input > 0, and zero otherwise. Thus, multiplying a bunch of ReLU derivatives together in the backprop equations has the nice property of being either 1 or 0. There is no “vanishing” or “diminishing” of the gradient.
What is one reason the ReLU activation function is better at preventing the vanishing gradient problem than the sigmoid activation function?
An advantage to ReLU other than avoiding vanishing gradients problem is that it has much lower run time. max(0,a) runs much faster than any sigmoid function (logistic function for example = 1/(1+e^(-a)) which uses an exponent which is computational slow when done often).
When does the gradient of a ReLU function vanish?
Gradients of different activation functions ReLU has gradient 1 when input > 0, and zero otherwise. Thus, multiplying a bunch of ReLU derivatives together in the backprop equations has the nice property of being either 1 or 0. There is no “vanishing” or “diminishing” of the gradient.
Is there an improvement to the vanishing gradient problem?
This post will examine the vanishing gradient problem, and demonstrate an improvement to the problem through the use of the rectified linear unit activation function, or ReLUs. The examination will take place using TensorFlow and visualizing with the TensorBoard utility.
What does the first derivative of Relu look like?
This function and it’s first derivative look like: As can be observed, the ReLU activation simply returns its argument x whenever it is greater than zero, and returns 0 otherwise. The first derivative of ReLU is also very simple – it is equal to 1 when x is greater than zero, but otherwise it is 0.
Vanishing gradients usually happen while using the Sigmoid or Tanh activation functions in the hidden layer units. Looking at the function plot below, we can see that when inputs become very small or very large, the sigmoid function saturates at 0 and 1 and the tanh function saturates at -1 and 1.