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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Understanding Weighted learning in Ensemble Classifiers

I'm currently studying Boosting techniques in Machine Learning and I happened to understand that in Algorithms like Adaboost, each of the training samples is given a weight depending on whether it was ...
AnonymousMe's user avatar
3 votes
1 answer
313 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 ...
Eric Cartman's user avatar
3 votes
0 answers
49 views

NN training with repetitive features

I posted the question also on ai.stackexchange but it didn't get any answers so I though I could try here. Here is a copy paste: Let's say you are training a NN in a RL setting where the state (i.e. ...
mkanakis's user avatar
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1 answer
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Training an ensemble of small neural networks efficiently in TensorFlow 2

I have a bunch of small neural networks (say, 5 to 50 feed-forward neural networks with only two hidden layers with 10-100 neurons each), which differ only in the weight initialization. I want to ...
MightyCurious's user avatar
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A Deep CNN model delivering better results with standardization, when compared with normalization

I developed a deep CNN model, based on the architecture discussed in this paper, to generate predictions for time series data. My training data is shown in the figure below: In order to train the ...
chupa_kabra's user avatar
3 votes
1 answer
493 views

Validation data shall be in broken down into batches or not?

I am using fit_generator to train the model. The training dataset is being read from a generator function which gives data in a constant batch size. Now I want to ...
yamini goel's user avatar
3 votes
0 answers
230 views

Training deep CNN with noisy dataset

I am training a Mask RCNN model with a train dataset that has been generated from some simple computer vision operations (color thresholding) and some morphological filtering. The train set captures ...
Gouda's user avatar
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1 answer
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How can I train a model to modify a vector by rewarding the model based on the modified vectors nearest neighbors?

I am experimenting with a document retrieval system in which I have documents represented as vectors. When queries come in, they are turned to vectors by the same method as used for the documents. The ...
RossDeVito's user avatar
3 votes
0 answers
288 views

How to add a new label to a multi-label dataset (like Open Images)

Given N classes in a multi-label dataset and a trained classifier C, how would we add a new class N+1 to the dataset, and fine-tune the trained classifier C such that it now predicts N+1 labels? (lets ...
cgcg's user avatar
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Multiple models vs. Single model for prediction

I am using the Darknet Convolutional Neural Networks to detect people (as in, humans) and furniture in a single image. If I train the model twice, one for people, one for furniture. I seem to get ...
Bob van Luijt's user avatar
2 votes
3 answers
89 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 ...
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Can the Apple M1's iGPU access the entire RAM as "video memory" when training with typical deep learning frameworks?

Can the Apple M1's iGPU access the entire RAM as "video memory" when training with typical deep learning frameworks (e.g., tensorflow_macos)? If not, what memory do they use as video memory?
Franck Dernoncourt's user avatar
2 votes
1 answer
432 views

In Orange Data Mining, how do I use results from clustering a training-set to test and score a test-set?

I am performing analysis on the well-known 'Adult' data-set, available on UCI using Orange Data Mining. In a PhD thesis, Pelleg (2004; pg 79) uses unsupervised clustering of the prescribed training ...
Paul Higgins's user avatar
2 votes
0 answers
21 views

Discussion about modern deep learning training strategies

Previously I have put a lot of effort into training networks appropriately. However, talking to colleagues, a lot of the things I did may be redundant due to novel optimizers and the theory of deep ...
Zador Pataki's user avatar
2 votes
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303 views

Your input ran out of data; interrupting training

I am trying to train ResNET50 for dogs and cats classification (Tensorflow2.3) using the following code: ...
root's user avatar
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Does it makes sense to train the model on whole data?

Suppose I am training an lstm model on a stock price data. So for first iteration say I have trained it on 80% of data and then tested it on rest of the 20% data and got the rmse value. Now after this ...
Stupid_Intern's user avatar
2 votes
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554 views

Multi-Core CPU training on Keras

I want to train models on a machine with multi-cores, I know training on GPU is better but I only have access now on CPU. Which parameters should I set using keras.models.Model.fit to utilize all ...
Thomas Artin's user avatar
2 votes
0 answers
87 views

When to stop the final model training?

Let's say I'm participating in a Kaggle image recognition competition. Firstly, I create a train/validation split and find the good hyperparameters for my model. Here the stopping criterion is when ...
SpaceCossack's user avatar
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445 views

Why does Adam optimizer work slower than Adagrad, Adadelta, and SGD for Neural Collaborative Filtering (NCF)?

I've been working on Neural Collaborative Filtering (NCF) recently to build a recommender system using Tensorflow Recommenders. Doing some hyperparameter tuning with different optimizers available in ...
bkaankuguoglu's user avatar
2 votes
0 answers
1k views

XGBoost incremental training for big datasets

I am trying to train an XGBoost model on a quite big dataset (tens of GB, almost a hundred). I have been trying to use some libraries such as Dask to deal with this problem, without any success due to ...
Mattia Surricchio's user avatar
2 votes
1 answer
258 views

Training a neural network with TWO possible correct outputs for one input

I have a system as a black box that has two correct outputs for a single input sample. now I want to train a neural network to generate at least one of the correct outputs for that input sample. what ...
Abolfazl Sajady's user avatar
2 votes
0 answers
272 views

How to calculate MAE and threshold in a multivariate time series

I'm trying to understand how to calculate the MAE in my time series and then the thresholds to understand which of my data in the test set are anomalies. I'm following this tutorial, which is based on ...
Fabio's user avatar
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19 views

Best Approach and Classifier for Binary Classification Problem

I am trying to build a binary classifier, and I am wondering what is the best approach for data segmentation, training/testing, performance evaluation, selecting classifier type, and overall approach ...
xyztg's user avatar
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2 votes
1 answer
579 views

What am I supposed to see on tensorboard images tab?

I'm training an object detection model with Tensorflow and monitor the training task with tensorboard. I was expecting in the Images tab of tensorboard that displayed images would show a bounding box (...
Patrick's user avatar
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1 answer
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what does the standard deviation plot around my learning curve indicate?

I plotted a learning curve below. There is a thick red band around the top portion of my training score. Why is it so high at the beginning? Below is a snippet of the code used: ...
Maths12's user avatar
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159 views

In YOLO training, what if two objects' centers fall in the same grid?

As I know, YOLO predicts one classification result (as well as some bounding boxes) for each grid. But when training yolo, what if two or more objects' centers fall in the same grid? How to choose the ...
Flicic Suo's user avatar
2 votes
0 answers
308 views

Will setting up time series data in this way cause data leakage?

I am trying to predict future stock market values using a gradient boosted tree model. As far as I know, gradient boosted trees use the data in one row, and only that row, to predict the target ...
Darcey BM's user avatar
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1 answer
2k views

Setting BATCH SIZE when performing multi-class classification with imbalanced dataset

I have a question regarding BATCH_SIZE on multi-class classification task with imbalanced data. I have 5 classes and a small dataset of around ...
Stefan Radonjic's user avatar
2 votes
1 answer
1k views

How to impute using simple imputer (custom function)

I am imputing my data using simple imputer from sklearn. i want to test many different ways of applying transformations to the data. i.e for logisitcic regression i would like to remove nans and ...
Maths12's user avatar
  • 506
2 votes
1 answer
23 views

What is the appropriate approach for training time series data against multiple, consecutive labels?

Let's say we have a time series $\{{\bf x}_i\}$ of features and are trying to learn to predict a time series $\{t_i\}$ using a neural network. Our goal is to be able to predict the time series value $...
Alexander Gruber's user avatar
2 votes
1 answer
208 views

Question about balancing training data for sentiment analysis (machine learning)

My question is about when to balance training data for sentiment analysis. Upon evaluating my training dataset, which has 3 labels (good, bad, neutral), I noticed there were twice as many neutral ...
Nore's user avatar
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0 answers
22 views

Negative correlation between OOB statistics and test set statistics during tuning of a RandomForest

I am tuning the parameters of a binary random forest classifier using a random search with a priority queue for training. After training with a fixed number of estimators (3000), the strategy is to ...
Net_Raider's user avatar
2 votes
3 answers
93 views

How to use External Data Sets in test set

I have a doubt regarding usage of external datasets like gdp rate, unemployment rate... etc., in test set for time series prediction. These datasets are historical and can be used along with train set,...
Anil B's user avatar
  • 21
2 votes
1 answer
28 views

Training pipelines where featurization/NLP is more expensive than backprop

I'm working on a document classification project and I'm using a neural net in tensorflow, where the features are 300-dimensional word embeddings, either from fastext or word2vec (yes I know that ...
generic_user's user avatar
2 votes
0 answers
45 views

Local RTX 2080 is 3x faster than V100 on GCP?

I have a gaming rig with an i9 CPU, 32GB RAM and RTX 2080, and I have a GCP VM with 4 vCPU, 52 GB RAM and V100. I try to train the same dataset using the same toolchain on both machines and these are ...
vaid's user avatar
  • 121
2 votes
2 answers
94 views

How to gather training data for simple voice commands?

I'm trying to build a machine learning model for recognizing simple voice commands like up, down, left, etc. On similar problems based on images, I'd just take the picture and assign a label to it. ...
Gautham Venkataraman's user avatar
2 votes
1 answer
100 views

Training a model where each response in the observation data has a different known varience

I have a dataset where each response variable is the number of successes of N Bernoulli trials with N and p (the probability of success) being different for each observation. The goal is to train a ...
dln's user avatar
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2 votes
0 answers
72 views

Performance diagnostics in mxnet gluon (e.g. plotting training vs validation loss over time)?

Tensorflow has tensorboard, is there any recommended way to plot classification error/loss over time in mxnet?
L Xandor's user avatar
  • 195
2 votes
1 answer
36 views

Correct approach to usage of class labels in cell imaging data

As part of a group project at university, we are given a series of videos of cell cultures over a 24 hour period. A number of these cells (the "knockout" cells) have had a particular gene removed, ...
OParry's user avatar
  • 21
2 votes
0 answers
839 views

How to train the generator in a recurrent GAN (Keras)

I am trying to train a Recurrent GAN that is meant to generate geospatial movement data (sequences of 3-tuples of latitude, longitude and time). You may simply consider it a sequences of vectors with ...
Jan Kaiser's user avatar
2 votes
0 answers
208 views

How to resolve the instability of average reward per episode in training of DQN (Deep Q-Network)?

what is shown when average reward per episode in training is unstable? If there is big difference between average reward per episode and final reward by test section, what we can say? For instance in ...
user10296606's user avatar
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2 votes
0 answers
28 views

Force Neural Net to attempt to predict every class

I am training a (deep) neural net to classify approximately 60 different classes. The range of occurrences of each class in the dataset is wide, 3 orders of magnitude from the most represented to the ...
TryingToTry's user avatar
2 votes
0 answers
487 views

Matlab: setting static iterations per epoch in a CNN

I'm building a convolutional neural network using Matlab's neural network toolbox. I have code designed to cross-train the network with different data sets, using the previous network's layers in ...
Liz Felton's user avatar
2 votes
0 answers
28 views

keras evaulate method results vary with equal testset

I trained segnet on a dataset of remote sensing imagery. When I run model.evaluate a set of metrics is returned. When I compile the model again with the same ...
ArnJac's user avatar
  • 131
2 votes
1 answer
2k views

Backpropagation with step or threshold activation function

I understand that gradient descent is local and it deals only with the inputs to the neuron, what it outputs and what it should output. In all I've seen, gradient descent needs the activation function ...
user196060's user avatar
2 votes
0 answers
36 views

Term for Methods of Representing Repeated Text in Classifier

A colleague told me that there are terms for two different methods of representing repeated text in the training set for a classifier, but he could not recall them. What are the terms for the options ...
Jacob Quisenberry's user avatar
2 votes
0 answers
88 views

How to select samples for a trainings set

My dataset contains half a million unlabeled entries with over 100 binary features. A third of these features are present in less than 1000 samples. I want to classify a few samples by hand (into ...
Jim's user avatar
  • 21
2 votes
2 answers
1k views

Ideal difference in the training accuracy and testing accuracy

In a data classification problem (with supervised learning), what should be the ideal difference in the training set accuracy and testing set accuracy? What should be the ideal range? Is a difference ...
girl101's user avatar
  • 1,161
1 vote
1 answer
38 views

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
  • 11
1 vote
1 answer
63 views

How to extract values from unstructured text

I'm implementing a tool which should extract values of interest from unstructured text entries. The data set is several hundred thousands of medical entries. Each entry is relatively short (around 100 ...
Jan's user avatar
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