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
219
votes
16 answers
How to calculate a logistic sigmoid function in Python?
This is a logistic sigmoid function:
I know x. How can I calculate F(x) in Python now?
Let's say x = 0.458.
F(x) = ?
Richard Knop
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57
votes
2 answers
What is the difference between a sigmoid followed by the cross entropy and sigmoid_cross_entropy_with_logits in TensorFlow?
When trying to get cross-entropy with sigmoid activation function, there is a difference between
loss1 = -tf.reduce_sum(p*tf.log(q), 1)
loss2 = tf.reduce_sum(tf.nn.sigmoid_cross_entropy_with_logits(labels=p, logits=logit_q),1)
But they are the…
D.S.H.J
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38
votes
2 answers
Fit sigmoid function ("S" shape curve) to data using Python
I'm trying to fit a sigmoid function to some data I have but I keep getting:ValueError: Unable to determine number of fit parameters.
My data looks like this:
My code is:
from scipy.optimize import curve_fit
def sigmoid(x):
return…
user88484
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27
votes
5 answers
ReLU derivative in backpropagation
I am about making backpropagation on a neural network that uses ReLU.
In a previous project of mine, I did it on a network that was using Sigmoid activation function, but now I'm a little bit confused, since ReLU doesn't have a derivative.
Here's an…
Gergely Papp
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25
votes
6 answers
Keras retrieve value of node before activation function
Imagine a fully-connected neural network with its last two layers of the following structure:
[Dense]
units = 612
activation = softplus
[Dense]
units = 1
activation = sigmoid
The output value of the net is 1, but I'd like to know…
johk95
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21
votes
2 answers
Keras Binary Classification - Sigmoid activation function
I've implemented a basic MLP in Keras with tensorflow and I'm trying to solve a binary classification problem. For binary classification, it seems that sigmoid is the recommended activation function and I'm not quite understanding why, and how Keras…
Daniel Whettam
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18
votes
1 answer
Binary classification with Softmax
I am training a binary classifier using Sigmoid activation function with Binary crossentropy which gives good accuracy around 98%.
The same when I train using softmax with categorical_crossentropy gives very low accuracy (< 40%).
I am passing the…
AKSHAYAA VAIDYANATHAN
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17
votes
6 answers
optimal way of defining a numerically stable sigmoid function for a list in python
For a scalar variable x, we know how to write down a numerically stable sigmoid function in python:
def sigmoid(x):
if x >= 0:
return 1. / ( 1. + np.exp(-x) )
else:
return exp(x) / ( 1. + np.exp(x) )
For a list of scalars,…
RandomWalker
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16
votes
2 answers
Sigmoid output - can it be interpreted as probability?
Sigmoid function outputs a number between 0 and 1. Is this a probability or is it merely a 'yes or no' depending on whether it's above or below 0.5?
Minimal example:
Cats vs dogs binary classification. 0 is cat, 1 is dog.
Can I perform the…
Voy
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14
votes
2 answers
sigmoid in python that can take scalar, vector or matrix
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 sigmoid(z), where z=0, the result will…
sunny
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14
votes
3 answers
logistic / sigmoid function implementation numerical precision
in scipy.special.expit, logistic function is implemented like the following:
if x < 0
a = exp(x)
a / (1 + a)
else
1 / (1 + exp(-x))
However, I have seen implementations in other languages/frameworks that simply do
1 / (1 +…
colinfang
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6
votes
2 answers
Training MSE loss larger than theoretical maximum?
I am training a keras model whose last layer is a single sigmoid unit:
output = Dense(units=1, activation='sigmoid')
I am training this model with some training data in which the expected output is always a number between 0.0 and 1.0.
I am…
oooliverrr
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6
votes
2 answers
tf.keras.metrics.MeanIoU with sigmoid layer
I have a network for semantic segmentation and the last layer of my model applies a sigmoid activation, so all predictions are scaled between 0-1. There is this validation metric tf.keras.metrics.MeanIoU(num_classes), which compares classified…
WillemBoone
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6
votes
2 answers
Sigmoid with Large Number in JavaScript
From what I understand you use a sigmoid function to reduce a number to the range of 0-1.
Using the function found in this library
function sigmoid(z) {
return 1 / (1 + Math.exp(-z));
}
This works for a numbers 1-36. Any number higher than this…
Michael Warner
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6
votes
2 answers
Sigmoid function returns 1 for large positive inputs
I wrote the following function in Python to calculate sigmoid function of a scalar, vector or matrix.
def sigmoid(z):
sig = 1.0/(1.0 + np.exp(-z))
return sig
For relatively large positive values of z, e^-z returns a very small value close…
Supratim Haldar
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