Questions tagged [softmax]
The softmax tag has no usage guidance.
29
questions
0
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2answers
31 views
What if multiple final prediction values for multi-class Neural Network are equal
If, for example, your final prediction for a multi-class problem, say for ["mouse","cat","dog","lion"], is [0.1,0.3,0.3,0.3], should the neural network predict that this data is "cat","dog" or "lion"? ...
0
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0answers
10 views
Not regularizing bias term in gradient descent for softmax
I'm writing a gradient descent function for a multi-class classifier using softmax.
I'm a bit confused about how regularization should work in the gradient function. I've specified my matrix, X, such ...
1
vote
0answers
62 views
Generalized softmax derivative for implementation with any loss function
I am currently taking some deep learning and neural network (NN) courses, and in addition to performing the course work, am implementing my own "toolkit" of NN techniques to better my understanding of ...
0
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1answer
22 views
When can you reorder log operations?
For example, you can reorder a softmax + nl (negative likelihood)
to log_softmax + nll (negative log-likelihood)
Essentially changing log(softmax(x)) to softmax(log(x))
However, what are the ...
2
votes
1answer
52 views
Problem with chain rule in softmax layer when differentiated separately
I have some problems with backpropagation in softmax output layer. I know how it should work but if I try to apply the chain rule in the classical way, I get different results compared to when Softmax ...
1
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0answers
15 views
Can someone please explain Lovasz softmax loss? as its a bit difficult to understand why it works well from the original paper [duplicate]
Lovasz Softmax is used a lot these days for segmentation problem and the original paper is really bad at explaining why it works.
1
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0answers
28 views
Balance two crossentropy losses with different number of neurons
I have a model with a few outputs, each output with shape:
Shape: (batch_size, labels_1) -> softmax -> ...
0
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0answers
19 views
Keras Softmax is Hardmaxing for some reason
I am new to Keras and am bit confused at the moment:
...
-1
votes
1answer
376 views
Softmax gives output vector whose sum is greater than 1 in Pytorch
I am a newbie to PyTorch. I was trying out the following network architecture to train a multi-class classifier. I used Softmax at the output layer and cross entropy as the loss function. However, the ...
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0answers
199 views
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0answers
38 views
Multi-label classification with missing labels
I have a neural network that generates a vector that represents the class probabilities. Since it is a multilabel classification problem, I'm supposed to train the network using sigmoid + binary cross-...
0
votes
1answer
76 views
How to calculate Temperature variable in softmax(boltzmann) exploration
Hi I am developing a reinforcement learning agent for a continous state/discrete action space. I am trying to use boltmzann/softmax exploration as action selection strategy. My action space is of size ...
1
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0answers
24 views
temperature variable in boltzmmann-exploration in reinforcement learning
I have been using epsilon greedy action selection strategy and recently have come across boltzmann(softmax) action selection strategy. One thing I am not clear about boltzmann exploration is the ...
1
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0answers
114 views
boltzmann-exploration(softmax exploration) in reinforcement learning
I have started learning reinforcement learning and as a part of it I am exploring the action selection strategies available. I am comparing epsilon-greedy vs boltzmann exploration(softmax exploration)....
7
votes
2answers
3k views
Gumbel-Softmax trick vs Softmax with temperature
From what I understand, the Gumbel-Softmax trick is a technique that enables us to sample discrete random variables, in a way that is differentiable (and therefore suited for end-to-end deep learning)....
3
votes
1answer
78 views
Softmax activation predictions not summing to 1
I am a beginner with rnns, consider this sample code
...
1
vote
1answer
301 views
Backpropagation with log likelihood cost function and softmax activation
In the online book on neural networks by Michael Nielsen, in chapter 3, he introduces a new cost function called as log-likelihood function defined as below
$$ C = -ln(a_y^L) $$
Suppose we have 10 ...
0
votes
0answers
49 views
How exactly should i use softmax activation in last layer of a Neural Network
I am developing a digit classifer with MNIST Dataset. I have read that for classification problems softmax activation function is used, as it maps last layer neuron's outputs into probabilities.
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2
votes
0answers
72 views
Can I turn any binary classification algorithms into multiclass algorithms using softmax and cross-entropy loss?
Softmax + cross-entropy loss for multiclass classification is used in ML algorithms such as softmax regression and (last layer of) neural networks. I wonder if this method could turn any binary ...
1
vote
2answers
681 views
Do I need to standardize my one hot encoded labels?
I'm trying to do a simple softmax regression where I have features (2 columns) and a one hot encoded vector of labels (two categories: left = 1 and Right = 0). Do I need to standardize just the ...
0
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2answers
45 views
Is the Cross Entropy Loss important at all, because at Backpropagation only the Softmax probability and the one hot vector are relevant?
Is the Cross Entropy Loss (CEL) important at all, because at Backpropagation (BP) only the Softmax (SM) probability and the one hot vector are relevant?
When applying BP, the derivative of CEL is the ...
2
votes
1answer
2k views
Pytorch doing a cross entropy loss when the predictions already have probabilities
So, normally categorical cross-entropy could be applied using a cross-entropy loss function in PyTorch or by combing a logsoftmax with the negative log likelyhood function such as follows:
...
0
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0answers
81 views
Thresholding in intermediate layer using Gumbel Softmax
In a neural network, for an intermediate layer, I need to threshold the output. The output of each neuron in the layer is a real value, but I need to binarize it (to 0 or 1). But with hard ...
2
votes
1answer
162 views
Why use different variations of Softmax in training and validation for neural networks with Pytorch?
Specifically, I'm working on a modeling project, and I see someone else's code that looks like
...
1
vote
2answers
248 views
Why do we use a softmax activation function in Convolutional Autoencoders?
I have been working on an image segmentation project where I have created a convolutional autoencoder. I saw this image and implemented it using Keras.
At the output layer, the author has used the ...
0
votes
0answers
169 views
Why is the last layer of a Tensorflow hub model not fed directly to softmax?
I was checking out this example using BERT model for movie review sentiment analysis, and a few others on how to use Tensorflow hub models. A common pattern that I notice is the output from the last ...
1
vote
1answer
54 views
Softmax function result for already normalized probabilities
Isn't the aim of softmax function normalizing the probabilities such that they all sum to 1? So when we apply this method to the already normalized numbers, it would change them. what do these new ...
1
vote
1answer
32 views
Does the score output of a classification model has a global meaning?
The scores-output layer contains the class scores that the model generated for the current sample and it is passed thru the softmax layer to get the final output in the form of a probabilities vector.
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3
votes
1answer
380 views
Multiclass Classification with Decision Trees: Why do we calculate a score and apply softmax?
I'm trying to figure out why when using decision trees for multi class classification it is common to calculate a score and apply softmax, instead of just taking the averages of the terminal nodes ...
