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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How to get numpy.exp to work with an array as well as a scalar?
I am working on an assignment that asks me to code the sigmoid function in Python. I'm very new to this so I sort of understand how the Sigmoid function works.
The assignment asks that I code it so that the function will work whether z is an array…
alpablo20
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Tensorflow 2.2 [predictions must be >= 0] [Condition x >= y did not hold element-wise:] [x (dense_1/Sigmoid:0) = ]
I am training a Binary detection architecture using TensorFlow 2.2 and Keras. Previously, I had this working if I loaded the data in the same script as the training of the model. However, when I use a larger dataset (x6 more samples, same ratio of …
user14397621
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How to get a sigmoid function between 0 and 1 for probability of correct answer?
I am trying to simulate some data where a response can either be right (1) or wrong (0). I am thus trying to find a distribution in which there are four conditions (in this case degrees of a circle).
Thus, the x axis is pi/2, pi, pi1.5, 2pi. I have…
sallicap
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How to use sigmoid function in python when developing a NeuralNetwork
I am trying to create a neural network, but when i try implementing the sigmoid function (importing, or creating it manually like in this case) it just says that the variable sigmoid does not exist
Here is my code
Also, i am using visual studio code…
Alexander Siles
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Syntax of a SIgmoid Function - Javascript
I've been trying to make this function work to no avail. Could anyone potentially shed some light?
It is a Sigmoid with some applied boundaries/transormations.
Here's the link to the function in desmos. That is working perfectly. It looks like…
Specific
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Wrong dimensions XOR neural network python
I'm trying to build an XOR neural network in python with one hidden layer but I'm hitting a problem with dimensions and I can't figure out why I'm getting the wrong dimensions in the first place because the math looks correct to me.
The dimensions…
n_1
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Difference in having Sigmoid activation function instead of linear activation and using sigmoid in loss
I am fairly new to the loss-functions and I have a 800 binary classification problem (meaning 800 neurons at the output that are not effected by eachother - probablity of each is 0 or 1). Now looking at the Documentations from:…
Yahya Nik
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Tensorflow sigmoid keeps on satuarting
so i have this environment and reward design that i specially designed to be close to -1, 0 and 1, so (as i got told) the sigmoid wouldn't saturate, also i kept the reward design fairly simple with ca -1 and 1 reward for end goal.
so i am using DDPG…
user10795232
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Using two different sigmoid-fucntions in Neuralnet in R
I am trying to repeat a study (https://www.sciencedirect.com/science/article/pii/S0957417410011711)
In the study they use two different functions, one for the hidden layer and one for the output. On page 5314 they write "A tangent sigmoid transfer…
Patrik Per Lilja
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Usage of sigmoid activation function in Keras
I have a big dataset composed of 18260 input field with 4 outputs. I am using Keras and Tensorflow to build a neural network that can detect the possible output.
However I tried many solutions but the accuracy is not getting above 55% unless I use…
Ahmad Hijazi
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Why is the code for a neural network with a sigmoid so different than the code with softmax_cross_entropy_with_logits?
When using neural networks for classification, it is said that:
You generally want to use softmax cross-entropy output, as this gives you the probability of each of the possible options.
In the common case where there are only two options, you want…
rwallace
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2-Class Support Vector Machine using Custom Kernel
Is there a sample python code for 2-class SVM classification using the custom kernel or sigmoid kernel?
The code below uses 3-class classification. How to modify this to a 2-class SVM?…
Christel Junco
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Does sigmoid_cross_entropy produce the mean loss over the whole batch?
I have a multi-label classification task and there are 6 labels. Any sample may have none or some labels to be 1. I have use the loss in tensorflow:
self.loss = tf.losses.sigmoid_cross_entropy(self.labels, self.logits)
Every time a batch (1000) of…
yanachen
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Sigmoid operation implementation in Tensorflow
For learning purposes I have a task to make a linear and sigmoid operations in tensorflow.
I managed to do the linear op:
def linear_op_forward(X, W):
''' linear operation'''
return np.dot(X, W.T)
def linear_op_backward(op, grads):
''' Linear…
Alex Nikitin
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The right way to calculate the derivative of sigmoid function in Python
I was walking through this article. In the code, the author mentions that the following function finds the derivative:
# convert output of sigmoid function to its derivative
def sigmoid_output_to_derivative(output):
return output*(1-output)
I…
Simplicity
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