72 Since we are using the logistic function to transform a linear combination of the input into a non-linear output, how can logistic regression be considered a linear classifier? Linear regression is just like a …

Nov 30, 2018 · Relu's often tends to work better than sigmoid with hidden layers, because when input's to activation function is very high or very low (large negative number), the derivative of sigmoid …

Jun 12, 2016 · 77 While the choice of activation functions for the hidden layer is quite clear (mostly sigmoid or tanh), I wonder how to decide on the activation function for the output layer. Common …

For binary classification, it seems that sigmoid is the recommended activation function and I'm not quite understanding why, and how Keras deals with this. I understand the sigmoid function will produce …

Short version: I would like to calculate the expected value if you apply the sigmoid function 1 1+e−x 1 1 + e x to a normal distribution with expected value μ μ and standard deviation σ σ. If I'm correct this …

Oct 21, 2010 · I feel many might be interested in free parameters to alter the shape of the sigmoid function. Second for many applications you want to use a mirrored sigmoid function.

May 3, 2015 · It would help if you explain why you want something other than the $\tanh$ function. What is it about $\tanh$ that doesn't work for your purposes? Otherwise, people may waste a lot of time …

Dec 14, 2019 · Multi-label and single-Label determines which choice of activation function for the final layer and loss function you should use. For single-label, the standard choice is Softmax with …

Now this is the sum of convex functions of linear (hence, affine) functions in $ (\theta, \theta_0)$. Since the sum of convex functions is a convex function, this problem is a convex optimization. Note that if it …

The following code is written in Octave Programming language g =1./(1+e.^-(z) It computes a sigmoid function and can take scalar, vector or Matrix. For example if I put the above into a function