Questions tagged [training]

For questions about training networks, rules systems, or other AI system components.

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How to assess the goodness of a text generation algorithm

Take a RNN network fed with Shakespeare and generating Shakespeare-like text. Once a model seems mathematically fine, as can be assessed by observing its loss and accuracy over training epochs, how ...
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3answers
645 views

Is it okay to use publicly available Instagram videos to train an AI?

Since I haven't found any good training data for my university project, I want to use pictures and videos from public Instagram profiles. Am I allowed to do that?
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1answer
49 views

What problem does the neural network really solve?

In the image below taken from a Youtube video, the author explains that the neural network can be used to fit a relational graph for a set of data points shown by the green line. And that this is ...
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What's the benefit for using a Kalman filter for training a neural network compared to other optimization algorithms?

I found a paper about using an Unscented Kalman Filter(UKF) for traning a neural network. The UKF filter is modified so it works for parameter estimation. Assume that we have a neural network model $\...
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1answer
16 views

Validation accuracy less than training accuracy (with no sigh of overtraining)

I am working with a deep CNN with over 100k sample data. I divided it up into 75% training, 12.5% validation and 12.5% for testing. As I train my network, the training accuracy approaches near 100% ...
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1answer
27 views

Tensorflow object detection model total loss starts out good, but suddenly explodes up to high loss numbers

I'm training a Tensorflow object detection model with approx. 7500 images of two classes, which contains approx. 10,000 classes per class. I'm using Tensorflow 2.6.0, in case that is relavent. I am ...
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25 views

How big should the dataset for retraining ssd_mobilenet_v2 be?

I have retrained ssd_mobilenet_v2 using my own dataset with 2 classes (pen or pencil), using object detection API. For my project, I expect users to select specific pencils from all pencils and ...
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1answer
21 views

Using Human Confirmation in place of a loss Function for Training

Has there been any experimentation in designing an AI to prompt a human to judge the accuracy of it's outcomes? instead of using a loss function, a human can judge the accuracy of it's estimation ...
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Adding data to training results in loss random peaks

I have succesfully trained ssd_mobilenet_v2_keras for object detection, with a dataset of about 3700 images. Now I have more images to add. I tried adding only a few images (150-300) to see what ...
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1answer
97 views

Is there any way to train a neural network without using gradients?

The only algorithm I know for updation of weights of a neural network is based on gradients. The update equation can be roughly written as $$w \leftarrow w - \nabla_{w}L$$ where $\nabla_{w}L$ is the ...
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1answer
31 views

How to re-training an AI model to have smaller input image size

I need a PyTorch Model which can do road segmentation on OAK-D camera. The model provided requires Input Image Size: 896*512, which is too big for running on OAK-D camera. Thus I need to re-training ...
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What is meant by non-convergent limit cycles?

Limit cycle is a closed curve that is isolated i.e., no other closed curve near to it. You can read the following paragraph from here If there is (such) a closed curve, the nearby trajectories must ...
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Is stability an attribute of model or training algorithm used or combination of both?

From this answer, stability is attributed to learning algorithm A stable learning algorithm is one for which the prediction does not change much when the training data is modified slightly. At some ...
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Weight for Samples on SVM (Support Vector Machine)

There is a option sample_weight in fit(X[, y, sample_weight]) function (OneClassSVM, sklearn library). If I use the option ...
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1answer
44 views

What does it mean by strong or sufficient gradient for training in this context?

It has been mentioned in the research paper titled Generative Adversarial Nets that generator need to maximize the function $\log D(G(z))$ instead of minimizing $\log(1 −D(G(z)))$ since the former ...
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1answer
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Does average loss function in GAN training is just an approximation of value function and does not ensure convergence of generator and discriminator?

The value function on which convergence has been proved by the original paper of GAN is $$\min_G \max_DV(D, G) = \mathbb{E}_{x ∼ P_{data}}[\log D(x)] + \mathbb{E}_{z ∼ p_z}[log (1 - D(G(z)))]$$ and ...
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Why is the proof of convergence in the GAN paper not applicable practically?

This question is about generative adversarial networks and restricted to the research paper titled Generative Adversarial Nets. If I select a particular architecture of MLP as a generator and trained ...
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18 views

How to handle critical points during generator training?

Using an MLP as generator introduces multiple critical points in parameter space. You can read this excerpt from research paper titled Generative Adversarial Nets In practice, adversarial nets ...
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22 views

Training on the dataset in parts vs training on the whole dataset

What is the difference between these two situations? are they the same ? #1 : train a model 20 epochs on the whole dataset #2 : divide dataset into n-parts then train the model 20 epochs on each part ...
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1answer
31 views

How to calculate the gradient penalty proposed in "Improved Training of Wasserstein GANs"?

The research paper titled Improved Training of Wasserstein GANs proposed a gradient penalty in order to avoid undesired behavior due to weight clipping of the discriminator. We now propose an ...
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1answer
101 views

Mapping input vectors of variable length to output vectors of variable lengths with dummy variables

I have a general question about supervised ANNs that map inputs to outputs. It is possible to vary the length of the input and output vectors by inserting some dummy variables that will not be ...
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2answers
91 views

What is Lipschitz constraint and why it is enforced on discriminator?

The following is the abstract for the research paper titled Improved Training of Wasserstein GANs Generative Adversarial Networks (GANs) are powerful generative models, but suffer from training ...
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1answer
40 views

Can the optimal learning rate differ for different architectures?

In several courses and tutorials about neural networks, people often say that the learning rate (LR) should be the first hyper-parameter to be tuned before we tweak the others. For example, in this ...
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Feeding the output back to input in 3D CNN model

I am currently designing a Model which takes Input 3D Grid and Model Output at $t-1$. The model figure is described below I have two thoughts in training the model for above situation. Feed output $...
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19 views

Is there any existing mechanism that allows us to pass input from randomly selected layers of neural network per iteration?

Consider the following neural network with $\ell$ layers. $$i_0 \rightarrow h_1 \rightarrow h_2 \rightarrow h_3 \cdots \rightarrow h_{\ell-1} \rightarrow o_{\ell} ,$$ where $i, h, o$ stands for ...
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2answers
36 views

Train Validation Test Splitting After or Before Data Augmentation?

I have seen tutorials online saying that you should do data augmentation AFTER doing the train/val/test split. However, when I go online to read some research papers, I see numerous instances of ...
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1answer
41 views

What does it mean by "zeros the networks parameters gradients" in the context of training a neural network?

Consider the following PyTorch code ...
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4 views

How to estimate the number of training samples to train a HMM Model with Baum-Welch Algorithm

Is there a thumb rule that tells us how many data sample a HMM model needs to be trained by Baum-Welch Algorithm?
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29 views

Model not learning anything, what can be the problem?

I've trained a model for heart sound classification with transfer learning (MobileNet) on Physionet dataset, and it works fine. However, when I train it on my own dataset, it seems that it can not ...
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0answers
64 views

How to validate a trained HMM Model?

What are some approaches to validate a HMM Model ? I want to check if a trained model is good enough to put it into operation or if it does need more training. I am using the Baum Welch algorithm to ...
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24 views

Is the main difference between the logistic regression and the perceptron the activation function they use?

I went through a Stats StackExchange's post about the difference between logistic regression and perceptron, which is too long to get the key point. I'd like to consider the question in terms of the ...
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1answer
1k views

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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1answer
45 views

Can some of the weights be fixed during the training of a neural network?

Is it possible to exclude specific layers from the optimization? For example, let's say I have an input layer, 2 hidden layers, and the output layer. I know there is a perfect solution for my problem ...
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0answers
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Identifying rotating and resizing letters with background noise

I'm trying to complete a captcha, and here is what it looks like: Between captchas the calligraphy of the letters is the same, but the letters may be resized and rotated. And the background noise (...
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16 views

Train a model for different scenario and gather performance results in a single place

Recently, I extend a master's thesis. I am now in a training phase for the model associated with it. I have access to many node GPUs. I would like to train this model on different scenarios, e.g. ...
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1answer
68 views

Is it possible to overfit a model on infinite amounts of data?

This is a theoretical question. Is it possible to overfit a model on infinite amounts of data? Let me clarify there are no duplicates. Say, we have a generator function that produces data, with the ...
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23 views

Problems while transforming a 2D Variational Autoencoder into a 1D Version

I am trying to addapt the Keras variational autoencoder (VAE) here from a 2-D input/output (matrix of a picture) to a 1-D input/output (just a vector). I thought this would be a fearly easy task, but ...
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1answer
36 views

What is meant by "stable training" of a deep learning model?

I have read it said that the "stable training" of a deep learning model is important. What is meant by "stable training" of a deep learning model?
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How exactly is masking performed in the training part of the paper "Semi-Supervised Classification with Graph Convolutional Networks"?

I am struggling to understand the training part of the paper Semi-Supervised Classification with Graph Convolutional Networks (2017) by Thomas Kipf and Max Welling. The GitHub repo is here. I do not ...
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Necessity of likelihood in training energy-based models

Lately, I've been getting into energy-based models (EBMs) through some of Yann LeCun's recent talks, where he advocates the use of non-normalized models because it allows for more flexibility in the ...
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1answer
18 views

A neural network to learn the connection between two totally different type of images

I have a dataset of two different type of images. Say, I have images of a person and his all 10 fingerprints. I want to create a relation between them to predict one from another. How I can do that ...
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2answers
54 views

What is the process working on Tensorflow model.fit()? [closed]

I created a binary image classification model. The dataset contains about 500K images in each class, with ratio = Train : Validation : Test = 7 : 2 : 1. Total images = 1M I split my dataset into 5 ...
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1answer
30 views

Is it possible to design an AI with two inputs and a Boolean output?

I am having a difficult time explaining to my boss that what he is trying to achieve may not be possible or within reason. We have a database of 3 Million data points per computer across hundreds of ...
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0answers
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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 ...
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9 views

Choosing the size of the network for Neural Collaborative Filtering (NCF)?

I've been working on Neural Collaborative Filtering (NCF) recently to build a recommender system. After doing some hyperparameter tuning with various sizes for embedding and dense layers sizes, from ...
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7 views

If training a network to predict signal phase, should you use wrapped or unwrapped phase as the target data

I'm developing a network that will predict direction on the unit circle and I also want to predict the phase of each frequency bin at each time step. Would it better to train the network on wrapped or ...
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31 views

How to train feedforward network to recognize images?

Context I'm trying to create network for digits recognition. All digits are the same font and size of 40x40. I know that I can use feedforward network or CNN. I'd like to use the first one. Issue I ...
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21 views

Comparison between TD(0) and MC ( or GAE )?

I'm getting started with DRL and have trouble distinguishing TD(0), MC, and GAE; and which scenarios one's better than others. Here is what I understand so far: TD(0): increment learning, can learn ...
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1answer
46 views

How to properly use Flatten layer?

Context I'm trying to create net that will be able to recognize printed-like digits. Something like MNIST, but only for standard printing font. Images are of the size 40x40 and I'd like to put them ...
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1answer
24 views

Video Analysis: Providing a success score for a of a student carrying out a specific task

I have an AI/ML challenge in relation to video analysis and am unsure where to start. I am investigating an application that will grade students performance of carrying out a task, based on analysis ...

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