A sigmoid function is a mathematical function having an "S" shape (sigmoid curve). Often, sigmoid function refers to the special case of the logistic function defined by the formula S ( t ) = 1 / (1 + e^-t)
Sigmoid function (Wikipedia)
Questions tagged [sigmoid]
256 questions
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votes
1 answer
Pytorch Sigmoid Function what is e
I have a question on setting up the sigmoid function in pytroch.
So I define it as this
# Sigmoid function
def sigmoid(x):
return 1/(1 + torch.exp(-x))
But then looking at the sigmoid function from here…
MNM
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votes
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Unwanted [Nan] output in Python neural network
newbie here. Just switched over from JS to Python to build Neural nets but getting [Nan] outputs from it.
The weird thing is that my sigmoid func. doesn't seem to encounter any overflow but the derivative causes chaos.
import numpy as np
def…
neel g
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2 answers
Interpreting a sigmoid result as probability in neural networks
I've created a neural network with a sigmoid activation function in the last layer, so I get results between 0 and 1. I want to classify things in 2 classes, so I check "is the number > 0.5, then class 1 else class 0". All basic.
However, I would…
Tominator
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2 answers
Handling numpy.exp overflow when using function on 2d-array
I've got a 2d numpy array on which I want to use my function sigmoid(x) which is:
def sigmoid(x):
return 1 / (1 + np.exp(-x))
My problem is that I have inputs that are too big like 3000 and I get this warning:
RuntimeWarning: overflow…
Markus S
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Activation Function in Machine learning
What is meant by Activation function in Machine learning. I go through with most of the articles and videos, everyone states or compare that with neural network. I'am a newbie to machine learning and not that much familiar with deep learning and…
mohangraj
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votes
1 answer
Dataset values distribution for sigmoid and tanh
As many papers point out, for better learning curve of NN, it's better for dataset to be normalized in a way that values match Gaussian curve.
Does this apply only if we use sigmoid function as squashing function?
If not what deviation is best for…
gnivirht
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Sigmoid layer in Keras
I have a list of values, ranging from 15000 to 25000. I have to separate them into two categories, such that (approx) 20000 will end up in category 1 and the rest in category 2. I figured out that the sigmoid activation should work for this. I am…
chiru player
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Tensorflow sporadically negative values with tf.losses.sigmoid_cross_entropy
I'm using an Inception-V4 model to do a multi-label classification with tensorflow. The model has an output dimension of 51 and my labels are either one or zero for these 51 classes. I did not modify the Inception-V4 any further.
In order to learn…
andy
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votes
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what is the fastest way to calculate sigmoid?
I am trying to implement a recurrent neural network where sigmoid is chosen to be the activation function.
My first prototype is written in python and I find sigmoid is somehow the bottleneck of the program, accounts for ~30% of the total running…
bbvan
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F# Pick function based on input?
I have the following functions
let private sigmoid (z:float) =
1.0 / (1.0 + exp(-z))
let private sigmoidM (z : Matrix) : Matrix =
z.Map (fun x -> sigmoid(x))
let private sigmoidV (z:Vector) =
z.Map(fun x ->…
David Crook
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votes
1 answer
Binomial logit model with glmer doesn't yield a good fit to sigmoidal data
I am trying to fit a model to my data, which has a dependent variable that can be 0 or 1.
I tried to fit a binomial glmer to the data, but the fit is pretty bad as you can see below. This puzzles me because this is quite a sigmoid so I thought I…
FloLe
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votes
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1D classification using Tensorflow 2.0
I am total noob in Tensorflow and trying to implement sample code that implements binary classification using Tensorflow 2.0.
Here is my source code:
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
def sigmoid(x):
…
user3583807
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Change the precision of torch.sigmoid?
I want my sigmoid to never print a solid 1 or 0, but to actually print the exact value
i tried using
torch.set_printoptions(precision=20)
but it didn't work. here's a sample output of the sigmoid function :
before sigmoid :…
OneAndOnly
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Why XOR problem works better with bipolar representation?
I am undertaking a course in Neural Networks and the Professor introduced us to the XOR problem. I understand the XOR problem is not linearly separable and why we need to employ Neural Network for this problem.
However, he mentioned XOR works better…
Exploring
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Simulate sklearn logistic regression predict_proba with only coefficients and intercept
I will create dummy data and train sklearn Logistic regression on it. Then I would like to get the output of predict_proba but only with own coef_ and intercept_ calculation, but the results are different. The setting is following:
X = [[0,0,0],…
United121
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