Questions tagged [softmax]

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I have a question about Transformer's Q, K, V

I think the cosine similarity of negative values has its own meaning. If you softmax the cosine similarity of Q and K, wouldn't it prevent Transformer from using information with the opposite meaning?
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1 answer
28 views

Why does softmax perform well on MNIST but poorly on EMNIST letters?

I am learning about softmax regression using Dive into Deep Learning. I have a very basic question on why softmax performs well on one dataset and poorly on another. I tried modifying the results from ...
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1 answer
18 views

error useing soft max gives outputs greater than 1

I am using Hugging Face AutoModelForSequenceClassification, model is roberta, using it for text classification. There are 3 classes. The output is: ...
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0 answers
161 views

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 = ...
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1 answer
12 views

Why is the optimal output out of domain in A2C?

If each state has an optimal action, then the optimal actions distribution vector is a one-hot vector kind of like [0,0,1,0,0,0]. But with algorithms like A2C, we ...
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4 views

How to get logit matrix from a customized CNN?

This is my model ...
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1 answer
138 views

How to understand a large result of torch.nn.NLLLoss() with correct predicts?

I'm learning the usage of torch.nn.NLLLoss() and torch.nn.LogSoftmax(), and I'm confused about the results of them. For example: ...
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5 votes
1 answer
116 views

What is the advantage of using Euler's number (e^x) instead of another base in the softmax equation?

I understand the softmax equation is $\boldsymbol{P}(y=j \mid x)=\frac{e^{x_{j}}}{\sum_{k=1}^{K} e^{x_{k}}}$ My question is: why use $e^x$ instead of say, $3^x$. I understand $e^x$ is it's own ...
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1 answer
25 views

Normalizing softmax by dividing by its maximum?

Reading this paper, I'm struggling to understand the step with the question mark (page 3). The formula for $\textbf r$ uses $\textbf q_i$ (no tilde), but the numeric values in the following paragraph ...
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What configuration of output neurons to use for detecting bias

I am trying to make a deep learning model that detects political bias in media articles for my local community. There are two political parties here and I have a dataset of biased articles from both. ...
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How to calculate the expression for the gradient of softmax + cross entropy with respect to weights?

I'm learning cs231n on my own. The Softmax classifier has the following loss function: to make this clear: $L_i$ is the loss for a particular training input $f_j$ is the $j$th element of the vector, ...
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Sample variance matrix normal distribution in R

I'm trying to perform a multinomial logistic regression in R employing the Metropolis-Hastings algorithm, considering a Matrix Normal Distribution as proposal. I'm using the function rmatrixnorm() ...
1 vote
0 answers
29 views

Deriving a binary logistic classifier from a multi class logistic classifier

Given a multi class logisitic classifier $f(x)=argmax(softmax(Ax + \beta))$, and a specific class of interest $y$, is it possible to construct a binary logistic classifier $g(x)=(\sigma(\alpha^T x + b)...
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CNN Eliminate Wrong Results

I extracted images of human faces from the videos, but the model also recorded images without faces. I wrote CNN for emotion classification. In the obvious pictures, the probability is closer to a ...
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0 answers
37 views

using logsumexp in softmax

I saw this equation in somebody's code which is an alternative approach to implementing the softmax in order to avoid underflow by division by large numbers. softmax = e^(matrix - logaddexp(matrix)) = ...
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1 answer
1k views

neural network binary classification softmax logsofmax and loss function

I am building a binary classification where the class I want to predict is present only <2% of times. I am using pytorch The last layer could be logosftmax or <...
1 vote
1 answer
285 views

How to prove Softmax Numerical Stability?

I was playing around with the softmax function and tried around with the numerical stability of softmax. If we increase the exponent in the numerator and denominator with the same value, the output of ...
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1 answer
207 views

Can a single label be a vector/matrix in a neural network and not a scalar?

My training data consists of individual sentences and each sentence has a few labels (say 10) and each of these labels has a discrete score from 1-10 -- so in essence, a single training example has a ...
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1 answer
22 views

Train a model when input can contain a smaller options output with the correct output

I have service order lines to charge customers, each line needs to be set to an actual product. If the customer had only one product, so all lines are set to that product. But, if the there are many ...
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128 views

Is there a Softmax-like transformation with scale-invariance and linarity?

At the moment I'm using XGBoost to generate a prediction of probabilities with a custom objective-function to build something like an expert system. To do so I need to transform the raw XGBoost ...
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1 vote
0 answers
27 views

What meaning does exp function have? [closed]

Is there any problem that solution(or algorithm) would be exp function? Let's say f(x)=2^x. It's an answer of a problem when you would like to know how many pieces would be made when you fold a paper ...
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1 vote
1 answer
161 views

Should I apply Softmax before calculating metrics Precision or similar?

I am using PyTorch Lightning (there is no tag for this and I don't have enough reputation to create one) and am facing a multi classification problem. My loss function is ...
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2 votes
1 answer
152 views

Keras: Custom output layer for multiple multi-class classifications

Hello, I’m quite new to machine learning and I want to build my first custom layer in Keras, using Python. I want to use a dataset of 103 dimensions to do classification task. The last fully connected ...
1 vote
0 answers
27 views

Is the Cross entropy cost function the same as the Cross entropy loss?

Is the Cross entropy cost function defined as $J(\Theta) = -\frac{1}{m}\sum_{i=1}^{m}\sum_{k=1}^{K}y_{k}^{(i)}log(\hat{p}_{k}^{(i)})$ the same as the one implemented in ...
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1 answer
81 views

"Up or down but not sideways" bimodal time series prediction - what is the best way to model it?

Say I have a time series (e.g. bitcoin price). I want to predict tomorrow's price, specifically tomorrow's % change in price from today. Let's say this is gaussian distributed, with the mean at 0%. If ...
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0 answers
95 views

Softmax Derivative

I've been trying to build a neural network from scratch in python over the last few weeks. I've gotten it working with sigmoid, but trying to implement softmax has been killing me, entirely thanks to ...
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1 answer
55 views

Multiple targets in a classification problem

I have a vector of length $n \gt 4$ which has exactly 4 targets, so for example [0, 0, 0, 1, 0, 1, 0, 1, 1]. I would like to know how I can modify the softmax ...
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3 votes
1 answer
245 views

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 ...
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2 answers
45 views

Attention transformation - matrices

Could somebody explain which matrix dimension should be found here - K? and if it is for example 3X3, should I use just 9?
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2 answers
37 views

In classification task, is it possible that a truly classified data has a higher loss compared to a miscallisfied data?

Given a classifier using softmax, is it possible that say, for data point a which our model has correctly classified, has higher loss compared to data point ...
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1 vote
0 answers
32 views

Convnet with peculiar loss function not learning!

Im using this loss function: ...
1 vote
1 answer
98 views

Derivative of a custom loss function with the logistic function

I have costum loss function with $\mu ,p, o, u, v$ as variables and $\sigma$ is the logistic function. I need to derive this loss function. Due to multiple variables in the loss function, I need to ...
1 vote
0 answers
116 views

Using 2 nodes in the output sigmoid activation function for 2 mutually exclusive classes is somehow giving good results than softmax

I know for two mutually exclusive classes softmax is the best activation function in the output layer. However, somehow (2, softmax) and even (1,sigmoid) are giving average results and (2, sigmoid) as ...
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1 vote
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20 views

Ensemble of different reservoirs (echo state networks)

Suppose I want to do reservoir computing to classify the input to the proper category (e.g. recognizing a handwritten letter). Ideally, after training a single reservoir and testing it, there would be ...
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3 votes
3 answers
2k 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 ...
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1 answer
307 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 ...
2 votes
0 answers
1k 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 ...
1 vote
0 answers
239 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 ...
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1 vote
2 answers
190 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 ...
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1 vote
3 answers
48 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"? ...
1 vote
0 answers
228 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 votes
1 answer
28 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 ...
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2 votes
1 answer
72 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 vote
0 answers
25 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 vote
0 answers
100 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 -> ...
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0 answers
35 views

Keras Softmax is Hardmaxing for some reason

I am new to Keras and am bit confused at the moment: ...
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-1 votes
1 answer
2k 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 ...
1 vote
0 answers
136 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-...
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1 vote
2 answers
632 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 ...
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1 vote
0 answers
55 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 ...
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