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Questions tagged [sigmoid]

15 questions
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Difference in performance Sigmoid vs. Softmax

For the same Binary Image Classification task, if in the final layer I use 1 node with Sigmoid activation function and binary_crossentropy loss function, then the training process goes through pretty smoothly (92% accuracy after 3 epochs on…
Confucius Cat
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How to interpreter Binary Cross Entropy loss function?

I saw some examples of Autoencoders (on images) which use sigmoid as output layer and BinaryCrossentropy as loss function. The input to the Autoencoders is normalized [0..1] The sigmoid outputs values (value of each pixel of the image) [0..1] I…
Boom
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Why softmax training is more stable

I'm wondering about which activation function will be easier to train with (get better accuracy / smallest loss) - with SoftMax or sigmoid (for multiclass classification problem) According…
user3668129
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Which activation function for multi-class classification gives true probability (softmax vs sigmoid)

I'm wondering which activation function for multi class classification problem, give true probability. According to: https://ai.stackexchange.com/questions/37889/are-softmax-outputs-of-classifiers-true-probabilities it seems that the output…
user3668129
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Understanding intution behind sigmoid curve in the context of back propagation

I was trying to understand significance of S-shape of sigmoid / logistic function. The slope/derivative of sigmoid approaches zero for very large and very small input values. That is $σ'(z) ≈ 0$ for $z > 10$ or $z < -10$. So update to weights will…
Rnj
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If the input to the autoencoder is normalized, do we need to use sigmoid on the last layer?

According to: https://stackoverflow.com/questions/65307833/why-is-the-decoder-in-an-autoencoder-uses-a-sigmoid-on-the-last-layer The last layer activation function contains sigmoid in order to the output to be in range [0, 1]. If the input to the…
user3668129
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What loss function can I use?

My dataset consists of 5625 Arabic examples and 5625 synsets, and my model is CNN followed by a sigmoid classification layer. I constructed this 5625 synsets to 5625 classes, and my predicted output is a probability between 0 and 1 for each class.…
rahma touzi
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How do I calculate the New W1 and New W2?

You are training the following perceptron. The neuron in this perceptron has a sigmoid activation function. The sigmoid function is represented by the following equation: Using the update function for the weights: with a learning rate of η=1, and…
coder3
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The sum of multi-class prediction is not 1 using tensorflow and keras?

I am studying how to do text classification with multiple labels using tensorflow. Let's say my model is like: model = tf.keras.Sequential([ tf.keras.layers.Embedding(vocab_size, 50, weights=[embedding_matrix], trainable=False), …
Feng Chen
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Equivalent of sigmoid function with two thresholds

Is there a standard/frequently used/convenient equivalent to sigmoid function with two thresholds? Background When writing a likelihood of data for a binary classification problem one would often parametrize the probability to be in a class by a…
Roger V.
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Is this an unusual distribution for a sigmoid output from a neural network?

Shown here is the histogram of around 130K predictions of my deep neural network that is classifying some financial data. This is on the dev set but a similar distribution is also seen on the train set. The output is a sigmoid function with a binary…
BYZZav
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Binary classification works with softmax, but not sigmoid

I am doing a binary classification problem for seizure classification. I split the data into Training, Validation and Test with the following sizes and shapes dataset_X = (154182, 32, 9, 19), dataset_y = (154182, 1). The unique values for dataset_y…
Mohammed Nafie
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Improvement of the Keras model for a regression task

I am trying to build an expectation model for my (19502,3) data using Keras Sequential model. This dataset has been generated using a parametric sweep in a simulation software. With this model, I want to expect the length and the weight of an item…
Alex Moh
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Logistic Regression mapping formula

Sigmoid function predicts the probability value which is between 0 & 1. What is the formula in logistic regression that maps the predicted probabilities to either 1 or 0?
Apoorva
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Mapping values in Logistic Regression

When mapping probabilities obtained in logistic regression to 0s & 1s using the sigmoid function, we use a threshold value of 0.5. If the predicted probability lies above 0.5, then it gets mapped to 1, if the predicted probability lies below 0.5, it…
Apoorva
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