Newest 'softmax' Questions

3

votes

2answers

37 views

Dot product for similarity in word to vector computation in NLP

In NLP while computing word to vector we try to maximize log(P(o|c)). Where P(o|c) is probability that o is outside word, given that c is center word. Uo is word vector for outside word Vc is word ...

asked Jan 14 at 15:55

Vivek Dani

1311 bronze badge

0

votes

0answers

16 views

Dependent Classifiction

Most of the classification problems I have seen assume that the classes are kind of independent. For example, a digit classification uses a softmax output that penalizes the output prediction error ...

classification softmax

asked Nov 18 '20 at 15:39

dsp_guy2020

1

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votes

0answers

12 views

In multinomial logistic regression, how to explain the softmax outputs properly?

I tried to solve multiclass problem ("cat", "dog", "horse") problem and figured out that the more words in test text, the more difference between classes. I grouped the ...

classification logistic-regression softmax

asked Nov 18 '20 at 10:21

jas_0n

233 bronze badges

0

votes

1answer

46 views

Softmax regression cost function code [closed]

I really do not understand what does this code do M = sparse.coo_matrix(([1]*n, (Y, range(n))), shape=(k,n)).toarray() The code is related to calculating the ...

machine-learning multilabel-classification scipy softmax sparse

asked Oct 19 '20 at 11:15

Mostafa Atallah

111 bronze badge

2

votes

0answers

39 views

Precision-Recall Curve Intuition for Multi-Class Classification Utilizing SoftMax Activation [closed]

I am running a CNN image multi-class classification model with Keras/Tensorflow and have established about a 90% overall accuracy with my best model trial. I have 10 unique classes I am trying to ...

python classification multiclass-classification softmax

asked Oct 16 '20 at 18:37

Coldchain9

1592 bronze badges

1

vote

0answers

49 views

Valid approach? LogSoftmax during training, Softmax during inference

I am training a classifier assigning one of four possible classes to each frame in a preprocessed audio stream using pytorch. I am using cross-entropy loss as the loss function for training. It is ...

pytorch softmax

asked Aug 20 '20 at 15:05

Scipio

133 bronze badges

1

vote

2answers

41 views

Lower loss always better for Probabilistic loss functions?

I am working on an neural net int Tensorflow that predicts percentages for win, draw, loss for given data of a game. The labels I provide are always {1, 0, 0}, {0, 1, 0} or {0, 0, 1}. After some ...

neural-network training loss-function probability softmax

asked Jul 2 '20 at 13:37

Evator

132 bronze badges

1

vote

3answers

42 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"? ...

machine-learning neural-network softmax

asked May 19 '20 at 13:28

Conor Kirby

111 bronze badge

0

votes

0answers

20 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 ...

machine-learning regularization softmax learning

asked May 16 '20 at 15:39

Test Student

1

vote

0answers

116 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 ...

machine-learning neural-network backpropagation activation-function softmax

asked Apr 15 '20 at 9:49

Krothagon

112 bronze badges

0

votes

1answer

23 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 ...

logistic-regression math softmax

asked Mar 12 '20 at 22:37

katiex7

1995 bronze badges

2

votes

1answer

57 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 ...

neural-network backpropagation softmax

asked Dec 13 '19 at 7:39

Display name

1534 bronze badges

1

vote

0answers

16 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.

loss-function softmax

asked Dec 5 '19 at 11:53

Prativa Das

111 bronze badge

1

vote

0answers

38 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 -> ...

classification loss-function softmax imbalanced-learn

asked Dec 4 '19 at 13:15

Daniel Möller

1089 bronze badges

0

votes

0answers

20 views

Keras Softmax is Hardmaxing for some reason

I am new to Keras and am bit confused at the moment: ...

keras softmax

asked Dec 3 '19 at 2:27

Tank

2252 silver badges7 bronze badges

-1

votes

1answer

833 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 ...

neural-network multiclass-classification pytorch softmax

asked Nov 14 '19 at 12:40

Ms_curious_potato

11 bronze badge

2

votes

0answers

46 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-...

multiclass-classification multilabel-classification softmax

asked Oct 26 '19 at 19:31

robowolf

211 bronze badge

0

votes

2answers

244 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 ...

deep-learning reinforcement-learning dqn ai softmax

asked Sep 27 '19 at 14:51

cvg

4553 silver badges16 bronze badges

1

vote

0answers

29 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 ...

deep-learning reinforcement-learning ai softmax deepmind

asked Sep 26 '19 at 8:01

cvg

4553 silver badges16 bronze badges

1

vote

0answers

156 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)....

reinforcement-learning softmax

asked Sep 25 '19 at 15:29

cvg

4553 silver badges16 bronze badges

14

votes

3answers

6k 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)....

neural-network deep-learning attention-mechanism softmax

asked Aug 29 '19 at 10:30

4-bit

1411 silver badge3 bronze badges

3

votes

1answer

198 views

Softmax activation predictions not summing to 1

I am a beginner with rnns, consider this sample code ...

keras tensorflow rnn softmax

asked Aug 24 '19 at 7:40

user329387

311 bronze badge

1

vote

1answer

479 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 ...

neural-network backpropagation softmax

asked Aug 18 '19 at 13:18

Nagabhushan S N

4845 silver badges17 bronze badges

3

votes

0answers

132 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 ...

machine-learning multiclass-classification softmax

asked Jul 30 '19 at 6:22

CouldntLoginToMyPreviousAcc

312 bronze badges

2

votes

3answers

2k 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 ...

machine-learning softmax

asked Jul 26 '19 at 13:39

José Lucas Araújo dos Santos

212 bronze badges

0

votes

2answers

65 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 ...

deep-learning loss-function backpropagation softmax

asked Jul 20 '19 at 6:07

clemens_m_b

1

3

votes

1answer

3k 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: ...

neural-network loss-function probability pytorch softmax

asked Jul 18 '19 at 21:56

user3023715

1831 silver badge5 bronze badges

2

votes

1answer

286 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 ...

neural-network deep-learning pytorch softmax

asked Jul 10 '19 at 21:11

Anon

1234 bronze badges

2

votes

2answers

404 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 ...

convolution image-recognition autoencoder activation-function softmax

asked Jun 15 '19 at 5:37

Shubham Panchal

1,7224 silver badges18 bronze badges

1

vote

1answer

62 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 ...

cnn normalization softmax

asked May 22 '19 at 22:27

Fatemeh Asgarinejad

1,1443 silver badges15 bronze badges

1

vote

1answer

34 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. ...

classification object-detection softmax

asked May 20 '19 at 12:33

Mark.F

1,7682 gold badges11 silver badges24 bronze badges

3

votes

1answer

430 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 ...

decision-trees xgboost softmax

asked Sep 27 '17 at 1:05

Caleb

1312 bronze badges

56

votes

5answers

105k views

Cross-entropy loss explanation

Suppose I build a neural network for classification. The last layer is a dense layer with Softmax activation. I have five different classes to classify. Suppose for a single training example, the <...

machine-learning neural-network deep-learning softmax

asked Jul 10 '17 at 10:26

enterML

2,6317 gold badges24 silver badges37 bronze badges

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Questions tagged [softmax]

Dot product for similarity in word to vector computation in NLP

Dependent Classifiction

In multinomial logistic regression, how to explain the softmax outputs properly?

Softmax regression cost function code [closed]

Precision-Recall Curve Intuition for Multi-Class Classification Utilizing SoftMax Activation [closed]

Valid approach? LogSoftmax during training, Softmax during inference

Lower loss always better for Probabilistic loss functions?

What if multiple final prediction values for multi-class Neural Network are equal

Not regularizing bias term in gradient descent for softmax

Generalized softmax derivative for implementation with any loss function

When can you reorder log operations?

Problem with chain rule in softmax layer when differentiated separately

Can someone please explain Lovasz softmax loss? as its a bit difficult to understand why it works well from the original paper [duplicate]

Balance two crossentropy losses with different number of neurons

Keras Softmax is Hardmaxing for some reason

Softmax gives output vector whose sum is greater than 1 in Pytorch

Multi-label classification with missing labels

How to calculate Temperature variable in softmax(boltzmann) exploration

temperature variable in boltzmmann-exploration in reinforcement learning

boltzmann-exploration(softmax exploration) in reinforcement learning

Gumbel-Softmax trick vs Softmax with temperature

Softmax activation predictions not summing to 1

Backpropagation with log likelihood cost function and softmax activation

Can I turn any binary classification algorithms into multiclass algorithms using softmax and cross-entropy loss?

Do I need to standardize my one hot encoded labels?

Is the Cross Entropy Loss important at all, because at Backpropagation only the Softmax probability and the one hot vector are relevant?

Pytorch doing a cross entropy loss when the predictions already have probabilities

Why use different variations of Softmax in training and validation for neural networks with Pytorch?

Why do we use a softmax activation function in Convolutional Autoencoders?

Softmax function result for already normalized probabilities

Does the score output of a classification model has a global meaning?

Multiclass Classification with Decision Trees: Why do we calculate a score and apply softmax?

Cross-entropy loss explanation

Hot Network Questions

Questions tagged [softmax]

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