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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Can I modify my training datasets (X_train and Y_train) while fitting the model?
I am new to ML and I am trying to train a forecasting model. The target variable (Y_train) has multiple columns, all of Boolean type. The features table (X_train), according to my approach, in the ...
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How can I tell if my CNN tuning made a difference?
I'm working on a detection CNN, estimates pose for some classes of objects. I am able to compute a bunch of different metrics on performance, things like position error, rotation error, tracking ...
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Training with few samples, dropping training loss but constant validation loss
I am training a resnet50-based model using transfer learning. My dataset has 10 classes and about 10 occurrences per class, so it is very small. The training loss is decreasing steadily to 0.07 for ...
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Holding batch size constant, will a bigger dataset consume more GPU memory?
If you hold (mini) batch size constant (as well as everything else) but increase the number of examples (and therefore the number of training iterations), should you expect a (significant) increase in ...
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Confusion over training accuracy vs. training loss
I had a small discussion with my friends on overfitting and we became confused over the two terms: "training accuracy" and "training loss (or cost)". This is the first time I've ...
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How was the word2vec model trained?
Let's take the CBOW (continuous bag of words) model as the example.
Suppose that, there are $c$ context words, each of which is a one-hot encoding vector. So the total number of elements of input ...
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Very basic but how to understand data statistically for machine learning?
I’m trying to solidify my statistics so I really know how to use them in my analysis/models. However my concept of statistical testing gets completely messed up by context. I’m unsure defining exactly ...
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PyTorch ResNet implementation's Training Loss increasing with every Epochs
I'm implementing a ResNet network from scratch using PyTorch. This network is unique to my requirements, since I need to perform Image Classification for Satellite Imagery with 14 different channels ...
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How do we modify the early stopping procedure to account for better losses after initial rise in losses?
I have a question regarding the usage of early stopping in the training of my forecasting model. Curious about how the training would go without early stopping, I observed that the test loss seems to ...
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Workflow when making a machine learning model
I'm new to data science, and kinda confused with the workflow and steps to make a model. Before learning the math and concepts behind the algorithms like SVM, linear regressions, etc, I would just ...
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How to calculate the training accuracy of a decision tree?
The hint given was to construct a confusion matrix.
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what is the difference between window size and context length of language model?
is window size and context length of language model one and the same thing?
******** following text is added as question with ONLY above text was not allowed *****
I am trying to understand how GPT ...
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Is it a problem to use the test dataset for the hyperparameter tuning, when I want to compare 2 classification algorithms on the 10 different dataset?
I know that we should use the validation set to perform hyperparameter tuning and that test dataset is not anymore really the test if it is used for hyperparameter tuning. But is this a problem if i ...
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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 ...
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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 ...
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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).
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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 ...
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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 ...
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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 ...
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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, ...
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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
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ralph
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mark
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I want to discriminate ...
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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:
...
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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 ...
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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 ...
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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 ...
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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, ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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....
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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 ...
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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 "...
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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?
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 (...
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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 ...
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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 ...
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Getting a dimension mismatch when training dataset on openAI CLIP
Here's the code I've written:
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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 ...
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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 ...
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