Questions tagged [training]

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clarification on train, test and val and how to use/implement it

So far I think I understood the differences between the training, test and validation set. Basically it is like in this image: Training set: The data where the model is trained on Validation set: ...
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Issues with YOLO training

I want to detect plates using on this dataset, I follow this tutorial But an error generated in the beginnig of the training: Any idea how to fix this?
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How to run my ML training on azure? [closed]

Ok... so... I am doing an assignment with image classification and my computer is taking FAR too long. I have applied for access to the high power computer at my university but they're still 'getting ...
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1answer
12 views

How to construct validation set for time series for NN?

I would like to train my model with a validation set. As the data is a time series I have to use timeseriessplit: ...
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How to teach algorithm to mimic paths in a certain enviroment

I have a set of scenarios which represent the movement of a car in a certain environment containing some obstacles. So for each scenario I have the position of the car (x,y,t) and a description of the ...
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Training data for Tesseract

I have to train my tesseract to detect different variations of a letter for example, u,û,ü,ù should all mean u. Is this possible and if yes how should I train it and how should the dataset be made ...
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Issues with Implementation of CNN based on Paper

I am attempting to duplicate a CNN in a paper, and am having issues with bad accuracy and loss not decreasing past 40. As described in this paper, specifically on pages 6/7, the network architecture ...
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Training Deep Learning Models on NVIDIA Jetson Nano

I don't know is it a best place to ask this question. I know the NVIDIA Jetson Nano board is made for infer or deploy deep learning models. Now I am using NVIDIA Geforce 820M on my laptop that has ...
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How to avoid different accuracies when training with subsets?

when trying to train a CNN with randomly selected small subsets (each same size) of the training data set, I get different results in accuracy (the accuracy varies from 0.75 to 0.85). I determine the ...
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2answers
54 views

Why can we not split train test data with 0.01 as parameter or 99% training data

Most of the blogs mention about a good thumb rule to be 80-20 split for the train and test respectively. My special case is a time series dataset and it is for the stock prices, which IMO is very ...
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2answers
22 views

How to deal with a feature that has lot of categorical values?

I know this question has been asked before and I have tried a few things but those things are not working as expected for my usecase. I have a 500 length feature vector. One of these features is a ...
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Beating Roulette with Neural Networks, YoloV3, and PyTorch

Background: I am in my last semester of electrical engineering, and I am working on my senior design project. The senior design project is a two-semester design project in which students outline, or ...
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1answer
25 views

Accuracy noise patterns during model training

I'm training a logistic regression model on a small dataset. I have about 1300 samples that I split into a training and a testing set (70% and 30% respectively). The training seems ok, however when I ...
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Data leak when training on future data and testing on present [closed]

Given a time series dataset. Using simple train_test_split, then reversing the train to test and test to train i.e. using the future data to train and present data to test, where does one induce leak ...
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2answers
70 views

What can be the cause of a sudden explosion in the loss when training a CNN (Deeplab)

I am training the following deeplab CNN: https://github.com/tensorflow/models/tree/master/research/deeplab During training I see the following loss: The first 50k steps of the training the loss is ...
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1answer
45 views

Given a machine learning algorithm, what is the minimum size of the training set for it?

I understand that the more data we have, the more reliable is our model trained on that data. I also understand that the more parameters a machine learning model has, the more training data it ...
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9 views

Shrink the training set during the learning process

Is there any way to change the size of the training set during the learning process? For example, let's say we have four classes (with their distribution): [A (90%), B (5%), C(2%), D(3%)]. Can we ...
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1answer
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LSTM loss function and backpropagation

I'm trying to understand the connection between loss function and backpropagation. From what I understood until now, backpropagation is used to get and update matrices and bias used in forward ...
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1answer
94 views

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 ...
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1answer
25 views

How to prepare training data for deep learning models

I am working on a project which involves the application of deep learning models. I have collected training data. In collected images, I have more than one object in interest. I am not very clear how ...
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1answer
58 views

how is correct usage of the validation split in neural networks?

I have a dataset separated in train, test and validation splits. After each epoch, I evaluate the loss and accuracy in the validation split. When the loss in validation split is not better, I stop ...
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1answer
39 views

How to deal with a constant value as an output from neural network?

I am using feedforward neural network for regression and what I get as a result of prediction is a constant value visible on the graph below: Data I use are typical standardised tabular numbers. The ...
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1answer
24 views

On design of the training set: conceptual question

I am curious to know how training data should be constructed so that it scales to examples that are not a part of the training data. For example, the problem that I am facing right now is in the ...
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2answers
40 views

Resampling for imbalaced datasets: should testing set also be resampled?

Apologies for what is probably a basic question but I have not been able to find a definitive answer either in the literature or in the Internet. When dealing with an imbalanced dataset one possible ...
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1answer
17 views

Should inputs be shuffled for training a model with expected time dependency?

Consider a prediction model with numerical inputs and outputs. Suppose data is inserted tick by tick, i.e., when new data is available it is inserted asynchronously. Current output depends on current ...
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SRCNN - the colors disappear from the output

I'm training a custom CNN (built for academic purpose) to perform Super-Resolution. I based my work on this review. The input of the network is a RGB color image, so 3 channels of size image_width x ...
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2answers
53 views

Splitting training and test set with financial data

I am using trees algorithms (decision tree, random forest and XGBoost) to forecast the sign of the returns in the stock market (classification). I am using this article as a reference: http://...
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1answer
65 views

DC GAN with Batch Normalization not working

I'm trying to implement DC GAN as they have described in the paper. Specifically, they mention the below points Use strided convolutions instead of pooling or upsampling layers. Use only one fully ...
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2answers
64 views

Which service could I use to train my networks?

My laptop's Intel i7 3630QM 2.4GHZ, 8Gb RAM and GXForce 670M are clearly not sufficient... By reading some papers, I've written an SRGAN with Python Keras. At runtime there is no error but training ...
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2answers
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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. ...
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22 views

Architecture choice for variable input dimension sizes

So, I am doing this project where I have lets say a bunch of points. Each of those points can have a different number of RGB values. So lets say point 1 might have 30 RGB values, point 2 might have ...
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26 views

LSTM validation loss not improving

I'm a noob in the ML world and am currently building an LSTM to forecast the next page a user is going to visit on a website. My dataset is pretty much a mapping (with sliding window) from one page to ...
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20 views

Neural Network to generate data for another network?

I was wondering if its possible to develop and train a neural network that generates training data for another network (or possibly itself). I came to this thought wondering the difficulty in creating ...
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How strong is the generalization ability of YOLO?

I have a question about the generalization ability of YOLO or deep learning methods in general: I am working on vehicle detection and classification in highway traffic surveillance videos. As you ...
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1answer
13 views

Is it okay to use training data for validifying the trained model?

Currently, I have trained my model through 5-fold cross validation with very small amount of the sample (n=240). I used whole data set to train and got quite low performance in terms of accuracy, ...
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2answers
324 views

In the context of Deep Learning, what is training warmup steps

I found this term "training warmup steps" in some of the papers, what exactly does this term mean? Has it got anything to do with "learning rate"? If so, how does it affect?
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1answer
54 views

How to split a dataset into train and test sets for time series (multiple step-multiple output forecasting)?

I am trying to use a LSTM neural net to do multiple step / multiple output forecasting (I predict multiple values in one time knowing some values in the past). But, I have realized that I must be ...
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2answers
68 views

Is it correct to join training and validation set before inferring on test-set?

I would like to know if is a correct procedure to join training-set and validation-set together, in order to train the model on ...
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1answer
42 views

Neural network is getting partially trained

So I am writing my own neural network library using back-propagation as my training algorithm. Everything seems fine the error is getting decreased more and more at each iteration however when I am ...
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2answers
31 views

How to transform time series data to apply supervised learning algorithms to it?

Apologies in advance for what may be a very basic question. I have a dataset consisting of marketing calls to different clients, which include the timestamp for the call. My goal is to train a model ...
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How does permutation of training data improve convergence time when training a perceptron or neural network model? [duplicate]

I'm currently studying some basic concepts regarding Deep Learning and Neural Networks with this material. When discussing the training algorithm for a perceptron, the author states that looping ...
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1answer
37 views

Transformer for neural machine translation: is it possible to predict each word in the target sentence in a single forward pass?

I want to replicate the Transformer from the paper Attention Is All You Need in PyTorch. My question is about the decoder branch of the Transformer. If I understand correctly, given a sentence in the ...
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1answer
27 views

How interpret keras training loss without compare with validation loss?

I have several implementation of the same neural network, but each one with different starting parameter. This is one of my plot comparing the training loss of the base experiment with the training ...
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How to check performance of a model on a test set?

I have transformed my training set (predictor variables) using step_YeoJohnson for satisfying the assumptions of model. But now how do I run my model on test set which is not transformed and has ...
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25 views

Creating artificial blurred image to train neural network for super resolution

I would like to create blurred images from images obtained from large pdf with the intention of creating a training data set. I would like to create the blur as shown in the uploaded picture. Which ...
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17 views

Is there any formula that estimates the training time of a CNN?

Is there any formula which estimates the training time of a CNN? (with respect to the number of layers, the size of the dataset ...
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1answer
132 views

Smart data split (train/eval) for Object Detection

I am looking for a smart way of splitting object detection data (images with labelled objects inside them) while taking into account the distribution of the objects themselves and not just the images. ...
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3answers
60 views

How to know when to stop trainning a deep network?

I've been training several auto encoders containing two GRUs as encoder and decoder during last year. It occurred to me that ...
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Looking for approaches to large scale NER train test split without overlaps

I would like to train test split a list of texts with the associated entities so there are no entities overlapping splits. Ensuring no overlaps is challenging: I currently achieve it with 2 groupby ...
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1answer
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Why could an overfitted CNN model have a higher validation accuracy?

I am currently training a CNN model by using cifar10 images (50000 for training, another 10000 for validation). I plot training loss, validation loss and accuracy against training iteration: I am ...