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]
212 questions
176
votes
15 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
- 73,317
- 142
- 374
- 539
53
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
- 541
- 1
- 7
- 6
23
votes
5 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
- 833
- 9
- 26
22
votes
1 answer
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
- 573
- 1
- 3
- 20
22
votes
3 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
- 589
- 1
- 4
- 9
17
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
- 305
- 1
- 2
- 9
16
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
- 2,229
- 5
- 26
- 45
12
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
- 443
- 5
- 9
12
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
- 573
- 1
- 8
- 23
11
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
- 17,887
- 11
- 69
- 146
6
votes
4 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
- 98
- 7
5
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
- 2,938
- 1
- 30
- 43
5
votes
1 answer
finding a point on a sigmoidal curve in r
Here is a data set:
df <- data.frame('y' = c(81,67,54,49,41,25), 'x' =c(-50,-30,-10,10,30,50))
So far, I know how to fit a sigmoidal curve and display it on screen:
plot(df$y ~ df$x)
fit <- nls(y ~ SSlogis(x, Asym, xmid, scal), data =…
B C
- 310
- 3
- 15
5
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
- 1,976
- 2
- 12
- 24
5
votes
5 answers
Compute e^x for float values in System Verilog?
I am building a neural network running on an FPGA, and the last piece of the puzzle is running a sigmoid function in hardware. This is either:
1/(1 + e^-x)
or
(atan(x) + 1) / 2
Unfortunately, x here is a float value (a real value in…
rohan32
- 432
- 1
- 6
- 16