Questions tagged [training]

Training is the part of machine learning whereby a model is "trained" on a define portion of a dataset to learn attributes and statistical features of the data. It's counterparts are called Testing and Validation. After training a model is tested and validated on another portion of the dataset.

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Which Frameworks/Libs Best Support Integer-Based Features, Scaling, Training, etc?

Papers such as Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference have interested me in exploring integer-based data science. In particular, I'm thinking of ...
ezekiel68's user avatar
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Training loss is much higher than validation loss

I am trying to train a neural network with 2 hidden layers to perform a multi class classification of 3 different classes. There is a huge imbalance to the classes, with the distribution being around ...
joseph wong's user avatar
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Understanding the desired behavior of the loss function of Variational Autoencoders

So I understood that when training VAE, we need to weight the KL part of the loss with a weight less than 1 so that the reconstruction loss can get a chance to learn (avoiding the posterior collapse). ...
user1407562's user avatar
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Why shouldn't we try to balance the test set?

Most advice I have found online is that we must not balance the test set. The test set should remain to be unseen. However, I failed to see how balancing the test set will cause us to leak knowledge ...
Fraïssé's user avatar
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Is it possible to detect early if a model is bad?

Let's say we have a model and have just started to fit it, the first epoch out of many. The first epoch shows awful results. Does it make sense to continue training hoping the results will be better ...
Putnik's user avatar
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Classification Threshold Optimization after GridSearchCV

In my machine learning problem I am using a CNN to classify images. Since my dataset is imbalanced I want to perform classification probability threshold tuning so I can find the optimal balance ...
Throwaway123's user avatar
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Optimal Data Split

I have a multiclass problem (3 classes) that looks to predict if someone will buy a product, neutral or not. I have initial features of in-app activity data such as likes, share, bookmark, share, ...
Marc Atanante's user avatar
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Naive Bayes classifier without training

I've a pooled group of individuals and a given number of features. So that my matrix looks like: individuals feature1 feature2 feature3 bob 1 0 1 ralph 0 1 1 mark 1 0 1 I want to discriminate ...
Lu_Ste's user avatar
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Imputation in train or test data

I'm having a rather simple question. Let's say i want to do a median imputation. I've read in some places that you should do: ...
Guilherme Raibolt's user avatar
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How to train multiple inputs for my model?

I'm a high school student and a newbie in Machine Learning. I just learned Machine Learning Crash Course by Google so my knowledge's still limited. I'm trying to build an Object Detection by myself ...
Nguyễn Phúc Khang's user avatar
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3 answers
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For cross validation should I use training set, or whole dataset?

I'm new to data science and I have a problem understanding what dataset to use when using cross validation for model evaluation. Let's say I have two models: LogisticRegression and ...
Michał Jurzak's user avatar
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Is using a stop gradient on a residual connection the same as not using the residual connection?

Stop gradient operation prevents the gradients to be calculated for the proceeding graph. However, skip connection outputs are added to the sub-network being skipped over.
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Challenges in Predicting Molecule Activity

I want to share a concern I have. I want to obtain a machine learning model that can predict whether a molecule exhibits biological activity. For this purpose, I have a set of molecules that do ...
Yasser Hayek's user avatar
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Choosing the number of episodes and iterations when training a RL model

I know the parameters chosen for training a RL model depend heavily on the model itself as well as the problem. Nevertheless, I am trying to train a bunch of these agents on different environments, ...
user152104's user avatar
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Which is the best binary classification model? Train and Test Accuracy are similar

I am building a binary classification model where classes are imbalanced but used SMOTE, I used 4 different models to compare performance and decide which to choose. They have same train and test ...
Sarah's user avatar
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How to train the AI to recognize soldiers' allegiance by armband?

If I hypothetically want to train an AI to recognize enemy soldiers by the color of their armband (for example green armband), should I feed the AI with only green armband soldiers or should I also ...
Henno's user avatar
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Retraining a TFlite Model for Fall Detection on Smartphone Accelerometer Data

I have developed a CNN model for Fall Detection using Keras and converted it to a TFlite(TensorFlow Lite) model for integration into an Android app. The app allows users to collect samples, which can ...
walt3rwhite's user avatar
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Cross entropy loss starts out very low

I'm working on making a transformer from scratch as described in the "Attention is All You Need" Paper. When training my model, my cross-entropy loss is always very low at the start. For ...
Justin Goodrich's user avatar
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Train test split in Graph Convolution Network for image classification

I am trying to construct a GCN for image classification where each pixel is a single node in the graph. However I want to train and test the model within the same graph, so I constructed a single ...
foobar's user avatar
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Help with running topology GAN (TopoGAN)

Link: https://github.com/TopoXLab/TopoGAN-ECCV2020 Sorry if this is the wrong place to ask, but I've been looking for help on how to work this out for a long time as I don't have much experience in ...
fds-mqdwqmqkdwl's user avatar
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CGAN - Odd Distribution gap, failure of convergence?

I am trying to train on some 1 dimensional data (675 samples, its very expensive to get more) and trying to match the distribution seen here: There are labels from 1-3 as to associated with the noise....
Shiro's user avatar
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Training GPT Model with Swagger Documents, Need Help with Model Fit

We are currently developing an application that performs actions based on user input using 'gpt-3.5-turbo-0613'. For example, if a user enters the prompt "create a user with the name Potter and ...
mostwelcome's user avatar
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How to train a layer multiple times in a pass for variable amount of input features?

I'm training an LSTM on multi-feature time-series location data. How can I design a network structure that can take one primary location's features, plus an arbitrary number $n$ of extra "...
rodriguezrrp's user avatar
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2 answers
44 views

KNN Accuracy training

After I developed my model using KNN I get the following accuracy: Train Accuracy :: 1 Test Accuracy :: 0.24 What is the accuracy of my model?
user150859's user avatar
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how to predict the arpu for a monthly cohort dynamically?

The main idea of this project, is to predict the ARPU (Average Revenue Per User) 11 month after subscription of a cohort with a monthly subscription, using minimum number of delays (a delay is a month ...
wassimdiai's user avatar
1 vote
1 answer
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Seeking guidance on understanding graphics card parameters for deep learning training

I am currently in the process of purchasing a new Nvidia graphics card for training deep learning models, and I have a few questions regarding the parameters involved and their relationship to the ...
ja1ba6's user avatar
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1 answer
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What is the meaning of K-folding Cross Validation's mean error?

I was just wondering the meaning of getting mean error of k-folding Cross Validation. The process is when I split a data set into k folding, and using k-1 as training set and the left 1 as the ...
cloudscomputes's user avatar
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Example of Pytorch Checkpointed Model Zoo

I am interested in the training dynamics of neural networks and would like to access a selection of trained (or more specifically partially trained) networks over the course of their training without ...
Rosco's user avatar
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23 views

What data should be used to train an ensemble of pre-trained models?

I split my dataset into train and test data. Then I trained logistic regression, SVM, and random forest models using pipelines with cross-validation and train data. I saved best-performing models with ...
Dmytro Horodetskyi's user avatar
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468 views

Easyocr Fine tune english_g2.pth text recognition model using my custom dataset

Am using easy OCR from the link below https://github.com/JaidedAI/EasyOCR I have custom dataset of 25000 images for training and 1000 images for validation in all_data folder generated. Max image ...
K manjunath's user avatar
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Longer DNN training times when using evolutionary algorithms

I am comparing my deep neural network (DNN) performance when using 2 types of optimizers: gradient-based Adam (properly tuned) and a population-based optimization algorithm (e.g., genetic algorithm (...
knowledge_seeker's user avatar
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Most popular frameworks for distributed training of pytorch

I've done mostly single GPU training using PyTorch. I've decided recently I wish to use a distributed approach for model training on a cluster with GPUs. But I'm unsure what framework to use. I gather ...
Stan Shunpike's user avatar
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Using both gradient clipping and learning rate warmup, should gradients be clipped during the warmup, or only once the warmup phase has completed?

I'm training a network using both learning rate warmup and an adaptive gradient clipping method outlined here. Is there a general consensus or anything in the literature relating to whether gradients ...
Molem7b5's user avatar
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77 views

Getting a dimension mismatch when training dataset on openAI CLIP

Here's the code I've written: ...
Manan Uppadhyay's user avatar
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25 views

Why is the efficiency of my neural network code reducing and what is causing the issue?

I am trying to implement a simple neural network from scratch to classify images from the MNIST dataset. However, I have noticed that the efficiency of the code decreases as I try to train the network ...
Tanmay Gejapati's user avatar
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Final Model Training Problem - Overfitting

I am working on a CNN project for multiclass classification. I implemented hyperparameter optimization to find the most suitable model, during which I got a best accuracy of 97.38%. I then took this ...
Zelreedy's user avatar
1 vote
2 answers
50 views

Data redundancy between train and test dataset - why is it bad (source needed)

I know that it is not OK to have too similar data in the train and test set (for example two pictures that differ by only one pixel). I'm trying to find a scientifically valid explanation why it is ...
user1633361's user avatar
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1 answer
70 views

Does the Transformer model has memory to store the state accross different data injection sequences(segments)?

I've trained a transformer model based on the pytorch tutorial: https://pytorch.org/tutorials/beginner/transformer_tutorial.html, But I found I've difficulties to understant this model's input and ...
Clock ZHONG's user avatar
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how can i store independent multiple time series datasets with same features in a matrix for training a model at once?

I have independent multiple time series datasets with same features. They are drilling proccesses in bone. Each dataset is a measurement. Is there a way to store all the datasets into a Matrix or ...
heyoka955's user avatar
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1 answer
20 views

Splitting data when combining multiple datasets

I have 13 small datasets from 12 different countries. All datasets have the same outcome and features, though have a different number of observations (ranging from ~50 to ~800). I would like to ...
jpsmith's user avatar
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The weighted average ensemble model does not train on the whole data

I am using custom data generator. I want to apply weighted average ensemble. The training set has 1042 samples, and validation indicates 298 samples. The batch size is 64. when I run this : ...
Zara Nz's user avatar
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1 answer
29 views

Overfitted model [duplicate]

A classic question with an unclear answer, is it better to have an overfitted model performing better on a Cross-Validation setting, or a non-overfitted model performing worse? In this context, higher ...
simon's user avatar
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How to label a transactions dataset made from scratch?

I have a question regarding creating a transactions dataset from scratch. I've created customer profiles and am generating transactions based on these profiles. The way I do this is based on the ...
pnav32's user avatar
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0 answers
121 views

ValueError: Found input variables with inconsistent numbers of samples: [283, 943]

I am trying yo split the data using train_test_split(), but I got this error: ...
Coco's user avatar
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0 answers
43 views

fine tuning open ai model with historical data

i'm trying to understand more about training models and unsure how to approach this problem. I have a bunch of historical financial data that I would like open ai to use as additional context when ...
Evan Bashir's user avatar
4 votes
1 answer
257 views

How can someone build a dataset for a "propensity to purchase" model?

Ok, this might seem a trivial question for some and it's not even a question, more like a discussion. I read the rules and I believe it's everything fine, so I'm gonna take my chances... Here's the ...
Andrew Joplh's user avatar
2 votes
3 answers
52 views

unbalanced data on train set and test set

I already have 2 datasets. One to use for training and one for testing. Both datasets are unbalanced (with similar percentages), with around 90% of label 1 . Will it be useful to balance the data if ...
mikeman's user avatar
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1 vote
1 answer
233 views

How do you retrain a model as new data comes in?

I'm just curious about real ML projects on production. I was wondering what is the way to go to retrain your models when you get new data? for example, let's suppose you've built a model with 2023 ...
Dani's user avatar
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5 votes
2 answers
1k views

Do model training pipeline should run on dev, staging and production environment?

I know it's a best practice to ship our code from dev to staging to production by including different level tests and validations that will help to confidently deploy on the production environment. ...
shaik moeed's user avatar
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1 answer
24 views

Train/val/test approach for hyperparameter tuning

When looking to train a model, does it make sense to have a 60-20-20 train val test split, first hyper parameter tuning over the training dataset, using the validation set, picking the best model. ...
Socorro's user avatar
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