machine learning - tanh activation function vs sigmoid activation ...
Having stronger gradients: since data is centered around 0, the derivatives are higher. 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 …
machine learning - Why is tanh almost always better than sigmoid …
2018年2月26日 · @elkout says "The real reason that tanh is preferred compared to sigmoid (...) is that the derivatives of the tanh are larger than the derivatives of the sigmoid." I think this is a non-issue. I never seen this being a problem in the literature. If it bothers you that one derivative is smaller than another, you can just scale it.
tanh vs. sigmoid in neural net - Cross Validated
2015年3月18日 · 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 with marginal differences. In practice for my problems I find that the sigmoid is easier to train and strangely, the sigmoid appears to find general solution better.
machine learning - Why is sigmoid or tanh better than linear slope …
2017年4月4日 · The TANH and Sigmoid function introduce this needed non-linearity. Neural networks have to implement complex mapping functions hence they need activation functions that are non-linear in order to bring in the much needed non-linearity property that enables them to approximate any function.
Do we still need to use tanh and sigmoid activation functions in …
2018年4月12日 · Any function approximated with ReLU activation functions is going to be piecewise linear. So, it takes a lot of piecewise linear functions to fit to a smooth function like sin. Meanwhile, tanh is very smooth, and it doesn't take as many tanh primitives to build something that closely resembles a sine wave.
Why use tanh function at the last layer of generator in GAN?
2020年11月30日 · This is due to the fact that when generating the images, they are typically normalized to be either in the range [0,1] or [-1,1]. So if you want your output images to be in [0,1] you can use a sigmoid and if you want them to be in [-1,1] you can use tanh.
Glorot/ Xavier Init: for sigmoid and tanh? - Cross Validated
2020年1月25日 · My question is about Xavier Glorot Init.The assumptions that they make are that they approximate the activation function linearly, that this function has f'(0) = 1 and that we set the bias to 0, as well as that the input features are normalized (because all inputs should have the same variance).
Relu vs Sigmoid vs Softmax as hidden layer neurons
2016年6月14日 · Sigmoids saturate and kill gradients. Sigmoid outputs are not zero-centered. tanh. Like the sigmoid neuron, its activations saturate, but unlike the sigmoid neuron its output is zero-centered. Therefore, in practice the tanh non-linearity is always preferred to the sigmoid nonlinearity. ReLU
Activation function between LSTM layers - Cross Validated
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 activation function (e.g. ReLU)? So do we prefer this:
Neural Network: Matlab uses different activation functions for ...
2020年8月19日 · The function $\tanh$ returns values between -1 and 1, so it is not a probability. If you wished, you could use $\sigma(x)$ as an activation function. But $\tanh$ is preferred because having a stronger gradient and giving positive and negative outputs makes it easier to optimize. See: tanh activation function vs sigmoid activation function