Questions tagged [softmax]
The softmax tag has no usage guidance.
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Numerical issue with softmax regression implementation on MNIST
I'm having numpy numerical issues with my implementation of softmax regression/multiclass logistic regression on the MNIST dataset.
The numpy exp and log numerical issue goes away when I divide the x ...
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Using auxiliary softmaxes to measure impact of each submodule on the final softmax classifier
I am attempting to assess the impact of various submodules (CNN 1D, CNN 2D, CNN 3D, FFNN) on the final classifier of the neural network that i am currently building. The neural network itself is ...
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What is the benefit of the exponential function inside softmax?
I know that softmax is:
$$ softmax(x) = \frac{e^{x_i}}{\sum_j^n e^{x_j}}$$
This is an $\mathbb{R}^n \implies \mathbb{R}^n$ function, and the elements of the output add up to 1. I understand that the ...
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Dealing with noise in softmax output
I have a device with an accelerometer and gyroscope (6-axis). The device sends live raw telemetry data to the model 40 samples for each input, 6 values per sample (accelerometer xyz, gyroscope xyz). ...
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Why does softmax give probability of 1 for outlier class?
I am trying to do open set classification by using a temperature scaled softmax and then interpreting the output probabilities as a confidence metric.
However, for complete outlier inputs, the ...
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PyTorch CrossEntropyLoss and Log_SoftMAx + NLLLoss give different results
As per PyTorch documentation CrossEntropyLoss() is a combination of LogSoftMax() and NLLLoss() function. However, calling CrossEntropyLoss() gives different results compared to calling LogSoftMax() ...
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How does softmax work for vectors?
In skipgram we predict the context words. That is the output layer before applying the softmax function is a number $V$ of words, where $V$ is the dictionary size. But each word is represented as a ...
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Fine tuning LR in my scenario
I am trying skip gram implementation from scratch (no pytorch or tensorflow, but just NumPy; actually CuPy because numpy does not run on GPU, but CuPy does) I am trying out what should be learning ...
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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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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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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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481
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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 = ...
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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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290
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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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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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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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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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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 <...
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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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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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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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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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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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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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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 ...
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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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"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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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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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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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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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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41
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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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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 ...
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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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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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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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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 ...
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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 ...
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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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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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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"? ...
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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 ...
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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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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 ...
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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.
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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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Keras Softmax is Hardmaxing for some reason
I am new to Keras and am bit confused at the moment:
...
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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 ...
