Questions tagged [accuracy]

For questions related to the accuracy metric/measure, which is the number of correct predictions divided by the total number of predictions.

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Is the neural network 100% accurate on training data if epoch loss is minimized to 0?

This seems like a silly, trivial question, but I just want to confirm it in case I'm missing something. I'm trying to train a ReLU neural network, which is supposed to be a function that satisfies ...
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Is the accuracy the best metrics to evaluate the performance of Deep Learning model? [closed]

Consider a model A that achieved an test accuracy of 99% on dataset-A with the size of 200 images and a model B that achieved ...
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Where can I find authentic references on "categorical cross entropy" and "categorical accuracy metric"?

My Python source code uses TensorFlow and Keras to implement a neural network. The Keras source code uses something called "categorical cross-entropy" and "categorical accuracy metric&...
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Confusion Matrix Measures vs Accuracy level in Neural Network Model

I'm working on a classification machine learning problem with two classes: high and low, which are derived from another numerical column x. Previously, if x>100, the sample is considered ...
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Is the average accuracy of each class (computed from the confusion matrix) equal to the accuracy calculated from cross-validation?

When I calculate the accuracy using cross-validation, it gives me a different accuracy than when I calculate using the confusion matrix. Why does it give a different accuracy? Is the accuracy ...
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Test accuracy decreases during my train process

I want to train a neural network model with the arcface loss function and try to combine it with domain adaption. But when the training process continues, I find the test accuracy first increases and ...
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Is a test accuracy of 0.74 good enough, given a dataset of about 700 samples, and, if not, how can I improve it?

I am new to neural networks. I am trying to solve a binary classification problem. Specifically, I want to determine whether a patient has or not a certain disease based on the dataset. The dataset ...
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93 views

Bert vs Sentence-Bert

I read a paper about Rumor detection and they used BERT as an unsupervised language representation, fine-tuning it using a small dataset, and combining it with a supervised learning model to provide ...
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1 answer
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Should I continue training if the neural network attains 100% training accuracy?

I have a neural network where there are two hidden layers. Each hidden layer has 128 neurons. The input layer has 20 inputs, and the output layer has 3 outputs. I have 1 million records of data. 80% ...
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102 views

What is the difference between the definition of "accuracy" in machine learning and federated learning?

What is the difference between the definition of "accuracy" in machine learning and federated learning? In particular, how is the accuracy calculated in the following paper: Cai, Lingshuang,...
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Is it ok to have an accuracy of 65% and a sensitivity of 90% with Naive Bayes for sentiment analysis?

I am creating a sentiment analysis model using Naive Bayes. When I test the model, I get an average accuracy of 65%; however, the sensitivity of the model is much higher, 90%. So, I am wondering if ...
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1 answer
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Is it a good practice to pad signal before feature extraction?

Is padding, before feature extraction with VGGish, a good practice? Our padding technique is to find the longest signal (which is loaded .wav signal), and then, in ...
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Do larger numbers of hidden layers have a bigger effect on a classification model's accuracy?

I trained different classification models using Keras with different numbers of hidden layers and the same number of neurons in each layer. What I found was the accuracy of the models decreased as the ...
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124 views

Accuracy Not Going Above 30%

I am trying to make a big classification model using the coco2017 dataset. Here is my code: ...
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1 answer
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Is it possible that the model is overfitting when the training and validation accuracy increase?

I am aware of similar questions that have been asked, and I have gone through many. I want to bring my case to SE to understand better what my results are. I am working with a large dataset (around ...
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What are possible ways to combat overfitting or improve the test accuracy in my case?

I have asked a question here, and one of the comments suggested that this is a case of severe overfitting. I made a neural network, which uses residual boosting (which is done via a KNN), and I am ...
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Could I just choose the other (non-predicted) class when the accuracy is low?

I have a binary classification problem. My neural network is getting between 10% and 45% accuracy on the validation set and 80% on the training set. Now, if I have a 10% accuracy and I just take the ...
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Why is my siamese network learning very well in e.g. 1 out of every 5 runs?

Why is my siamese network learning very well in e.g. 1 out of every 5 runs? The rest of the time it's not learning and maintains an accuracy of 0.5. Any explanations? Is the contrastive loss taken in ...
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Is there a way, while training (with contrastive learning) the embedding network, to find the test accuracy?

I aim to do action recognition in videos on a private dataset. To compare with the existing state-of-the-art implementations, other guys published their code on Github, like the one here (for the ...
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Why would my neural network have either an accuracy of 90% or 10% on the validation data, given a random initialization?

I'm making a custom neural network framework (in C++, if that is of any help). When I train the model on MNIST, depending on how happy the network is feeling, it'll give me either 90%+ accuracy, or ...
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What is the effect of too harsh regularization?

While training a CNN model, I used an l1_l2 regularization (i.e. I applied both $L_1$ and $L_2$ regularization) on the final layers. While training, I saw the ...
2 votes
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73 views

Why do I get higher average dice accuracy for less data

I am working on image segmentation of MRI thigh images with deep learning (Unet). I noticed that I get a higher average dice accuracy over my predicted masks if I have less samples in the test data ...
1 vote
1 answer
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Why does the accuracy drop while the loss decrease, as the number of epochs increases?

I've been trying to find the optimal number of epochs that I should train my neural network (that I just implemented) for. The visualizations below show the neural network being run with a variable ...
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Why does loss and accuracy for a multi label classification ann does not change overtime?

I have run into a strange behavior of my multi label classification ANN ...
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Keras model accuracy not improving beyond threshold

I am currently working on a public project for the National Weather Model. We are experimenting with using a recurrent neural network to replace the output of a quadratic formula that is in use. The ...
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1 answer
496 views

Which kind of data does sigmoid kernel performance well?

While I was playing with some hyperparameters, I came to a wired situation. My dataset is IRIS dataset to be specific. SVM algorithm has some hyperparameters that we can tune, such as Kernels, and C ...
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32 views

Low accuracy during training for text summarization

I am trying to implement an extractive text summarization model. I am using keras and tensorflow. I have used bert sentence embeddings and the embeddings are fed into an LSTM layer and then to a Dense ...
1 vote
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Why is the accuracy of my model very low on a separate dataset from the training and test datasets?

I am working on stock price prediction project, I am using the support vector regression (SVR) model for it. As I am splitting my data into train and test, I am getting high accuracy while predicting ...
3 votes
1 answer
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Accuracy dropped when I ran the program the second time

I was following a tutorial about Feed-Forward Networks and wrote this code for a simple FFN : ...
2 votes
1 answer
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Why am I getting a difference between training accuracy and accuracy calculated with Keras' predict_classes on a subset of the training data?

I'm trying to solve a binary classification problem with AlexNet. I split the original dataset into training and validation datasets using a 70/30 ratio. I have trained my neural network with a ...
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1 answer
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Why is there more than one way of calculating the accuracy?

Some sources consider the true negatives (TN) when computing the accuracy, while some don't. Source 1: https://medium.com/greyatom/performance-metrics-for-classification-problems-in-machine-learning-...
1 vote
1 answer
63 views

Choosing Data Augmentation smartly for different application

I'm trying to understand the role of data augmentation and how it can affect the performance/accuracy of a deep model. My target application is fire detection (on video frames), with almost 15K ...
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2 answers
349 views

Should I choose the model with highest validation accuracy or the model with highest mean of training and validation accuracy?

I'm training a deep network in Keras on some images for a binary classification (I have around 12K images). Once in a while, I collect some false positives and add ...
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1 answer
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Given the precision and recall of this model, what can I say about it?

The following table shows the precision and recall values I obtained for three object detection models. The goal is to find the best object detection model for that particular data set. I evaluate ...
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Effect of batch size and number of GPUs on model accuracy

I have a data set that was split using a fixed random seed and I am going to use 80% of the data for training and the rest for validation. Here are my GPU and batch size configurations use ...
1 vote
1 answer
88 views

Can we calculate mean recall and precision

I'm evaluating the accuracy in detecting objects for my image data set using three deep learning algorithms. I have selected a sample of 30 images. To measure the accuracy, I manually count the number ...
1 vote
0 answers
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Sample size for the evaluation of Deep Learning Models

I'm evaluating the performance and accuracy in detecting objects for my data set using three deep learning algorithms. In total there are 24,085 images. I measure the performance in terms of time ...
1 vote
1 answer
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How to explain peak in training history of a convolutional neural network?

I am training a simple convolutional neural network to recognize two types of 1024-point frequency spectra (FFT). This is the model I'm using: ...
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2 answers
2k views

Why don't people always use TensorFlow Lite, if it doesn't decrease the accuracy of the models?

I have been exploring edge computation for AI, and I came across multiple libraries or frameworks, which can help to convert the model into a lite format, which is suitable for edge devices. ...
3 votes
1 answer
359 views

What is the relationship between the training accuracy and validation accuracy?

During model training, I noticed various behaviour in between training and validation accuracy. I understand that 'The training set is used to train the model, while the validation set is only used to ...
2 votes
1 answer
44 views

What does top N accuracy mean?

Places205-VGG, a CNN trained model for 205 scene categories of Places Database with 2.5 million images Places205 dataset has ...
3 votes
2 answers
624 views

How to express accuracy of a regression ANN that uses MSE loss function?

I have a regression MLP network with all input values between 0 and 1, and am using MSE for the loss function. The minimum MSE over the validation sample set comes to 0.019. So how to express the '...
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Should I choose a model with the smallest loss or highest accuracy?

I have two Machine Learning models (I use LSTM) that have a different result on the validation set (~100 samples data): Model A: Accuracy: ~91%, Loss: ~0.01 Model B: Accuracy: ~83%, Loss: ~0.003 The ...
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7 votes
4 answers
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Why is my test error lower than the training error?

I am trying to train a CNN regression model using the ADAM optimizer, dropout and weight decay. My test accuracy is better than training accuracy. But, as far as I know, usually, the training accuracy ...