Why is sigmoid a bad activation function?
The two major problems with sigmoid activation functions are: Sigmoid saturate and kill gradients: The output of sigmoid saturates (i.e. the curve becomes parallel to x-axis) for a large positive or large negative number. Thus, the gradient at these regions is almost zero.
Why is sigmoid not zero centered?
The sigmoid function is bound in the range of (0,1). Hence it always produces a non-negative value as output. Thus it is not a zero-centered activation function. The sigmoid function binds a large range of input to a small range of (0,1).
Is sigmoid zero centered?
Sigmoid outputs are not zero-centered. This is undesirable since neurons in later layers of processing in a Neural Network (more on this soon) would be receiving data that is not zero-centered.
Can we use sigmoid in hidden layer?
When using the Sigmoid function for hidden layers, it is a good practice to use a “Xavier Normal” or “Xavier Uniform” weight initialization (also referred to Glorot initialization, named for Xavier Glorot) and scale input data to the range 0-1 (e.g. the range of the activation function) prior to training.
Why is tanh activation better than sigmoid?
But, always mean of tanh function would be closer to zero when compared to sigmoid. It can also be said that data is centered around zero for tanh (centered around zero is nothing but mean of the input data is around zero. These are the main reasons why tanh is preferred and performs better than sigmoid (logistic).
Why are sigmoid functions used instead of anything else?
These requirements are all fulfilled by rescaling sigmoid functions. Both f(z) = 1 1 + e − z and f(z) = 0.5 + 0.5 z 1 + z fulfill them. However, sigmoid functions differ with respect to their behavior during gradient-based optimization of the log-likelihood.
How is the sigmoid function used in backprop?
The sigmoid derivative (greatest at zero) used in the backprop will help to push values away from zero. The sigmoid activation function shapes the output at each layer. E is the final error Y – Z. dZ is a change factor dependent on this error magnified by the slope of Z; if its steep we need to change more, if close to zero, not much.
How is a loss function used in gradient descent?
Now, the machine tries to perfect its prediction by tweaking these weights. It does so, by comparing the predicted value y with the actual value of the example in our training set and using a function of their differences. This function is called a loss function.
When to use a stochastic gradient descent model?
Stochastic Gradient Descent: When we train the model to optimize the loss function using only one particular example from our dataset, it is called Stochastic Gradient Descent.