Feb 26, 2018 · The tanh function on the other hand, has a derivativ of up to 1.0, making the updates of W and b much larger. This makes the tanh function almost always better as an …

To see this, calculate the derivative of the tanh function and notice that its range (output values) is [0,1]. The range of the tanh function is [-1,1] and that of the sigmoid function is [0,1] Avoiding …

Tanh. The tanh non-linearity is shown on the image above on the right. It squashes a real-valued number to the range [-1, 1]. Like the sigmoid neuron, its activations saturate, but unlike the …

I'm aware the LSTM cell uses both sigmoid and tanh activation functions internally, however when creating a stacked LSTM architecture does it make sense to pass their outputs through an …

Nov 29, 2020 · While studying GAN, I found out that ReLU activation is used at the intermediate layers, and tanh or sigmoid is used at the last layer of the generator. I'm curious about why …

Apr 4, 2017 · I usually see sigmoid or tanh being used as activation in neural networks. Why not linear slope given that linearity is simpler mathematically?

In the backpropagation algorithm when the output activation function is tanh and the number of classes is 2 (binary problem), the value obtained at the output layer is in the range between -1 …

The most common activation functions are LOG and TanH. These functions have a compact range, meaning that they compress the neural response into a bounded subset of the real …

Apr 4, 2017 · The activation functions I have seen in practice are either sigmoid or tanh. Why isn't step function used? What is bad about using a step function in an activation function for neural …

Mar 18, 2015 · I'm trying to understand the pros and cons of using tanh (map -1 to 1) vs. sigmoid (map 0 to 1) for my neuron activation function. From my reading it sounded like a minor thing …