Questions tagged [training]

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

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33 views

What is a good convergence criterion for Q-learning in a stochastic environment?

I have a stochastic environment and I'm implementing a Q-table for the learning that happens on the environment. The code is shown below. In short, there are ten states (0, 1, 2,...,9), and three ...
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1answer
53 views

When using PCA for dimensionality reduction of the feature vectors to speed up learning, how do I know that I'm not letting the model overfit?

I'm following Andrew Ng's course for Machine Learning and I just don't quite understand the following. Using PCA to speed up learning Using PCA to reduce the number of features, thus lowering the ...
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1answer
142 views

Is it practical to train AlphaZero or MuZero (for indie games) on a personal computer?

Is it practical/affordable to train an AlphaZero/MuZero engine using a residential gaming PC, or would it take thousands of years of training for the AI to learn enough to challenge humans? I'm having ...
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33 views

Data Augmentation of store images using handwritten labels

I am new to AI and NN. I've started learning using Geron's book on Tensorflow. My first project ("Smart Shelf") is to determine which items in a store have been purchased and need refilled. ...
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1answer
26 views

Number Series Continuation? [closed]

I am new to AI. I have a series of numbers ranging from x to y and I have a lot of data to train with What I am trying to do is, let's say from 0 to 1, I train it with data calculated over time and ...
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10 views

Dividing datasets to reveal opposing signals

I have a set of data to train with comprising of 100 elements, 50 of which support theory A, and 50 support theory B. It turns out that both theories are valid, but the signal that A is valid is the ...
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0answers
28 views

Single-value loss/training in a CNN with a tensor output

I am playing around with an idea of using using Q-learning with a DQN (Deep Q-Network), to determine the optimal position of a number of 'units' on a grid of allowed locations, according to some ...
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2answers
52 views

Do AlphaZero/MuZero learn faster in terms of number of games played than humans?

I don't know much about AI and am just curious. From what I read, AlphaZero/MuZero outperform any human chess player after a few hours of training. I have no idea how many chess games a very talented ...
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1answer
73 views

How to train a policy model incrementally to solve a problem similar to the vehicle routing problem?

I have a problem similar to the vehicle routing problem (VRP) that I want to solve with reinforcement learning. In this problem, the agent starts from the point $(x_0, y_0)$, then it needs to travel ...
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0answers
12 views

How are the parameters $\alpha_i$ of hard attention trained?

I have a question about Show, Attend and Tell: Neural Image CaptionGeneration with Visual Attention paper by Xu. The basic mechanism of stochastic hard attention is that each pixel of the input image ...
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0answers
41 views

How to train/update neural networks faster without a decrease in performance?

I noticed that there are many studies in recent years on how to train/update neural networks faster/quicker with equal or better performance. I find the following methods(except the chips arms race): ...
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1answer
64 views

How to predict the best from a set of messages - best practice

Suppose I have a set of messages A,B,C,D and I want to produce the best message for a website user at a given time. For training I plan to show random users a random single message [A/B/C/D] and fill ...
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0answers
40 views

Offline/Batch Reinforcement Learning: when to stop training and what agent to select

Context: My team and I are working on a RL problem for a specific application. We have data collected from user interactions (states, actions, rewards, etc.). It is too costly for us to emulate agents....
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18 views

Neural Network trains towards 1 despite target

So I'm trying to make my first neural network and have just finished my back propagation functions. I got the algebra from brilliant and thought I'd understood it, but my bug proves otherwise. The bug ...
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2answers
24 views

Semantic segmentation CNN outputs all zeroes

I'm using MATLAB 2019, Linux, and UNet (a CNN specifically designed for semantic segmentation). I'm training the network to classify all pixels in an image as either cell or background to get ...
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0answers
39 views

Is training on single game each time appropriate for an agent to learn to play checkers

I was facing a problem I mentioned in a previous question but after a while, I realize that maybe the problem in the dataset not in the learning rate. I build the dataset from white positions only i.e ...
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0answers
25 views

The last target name is missed in the test set

I am training a neural network with a dataset that has 51 classes and 6766 data in it. I used 80% for the training set, 10% for validation, and 10% for the test. After training I got confusion matrix ...
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0answers
37 views

DDQN Agent in Othello (Reversi) game struggle to learn

This is my first question on this forum and I would like to welcome everyone. I am trying to implement DDQN Agent playing Othello (Reversi) game. I have tried multiple things but the agent which seems ...
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0answers
60 views

Is the performance of a neural network, which was trained with encrypted data and weights, affected if the weights are decrypted?

Suppose that a neural network is trained with encrypted (for example, with homomorphic encryption and, more precisely, with the Paillier partial scheme) data. Moreover, suppose that it is also trained ...
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1answer
95 views

During neural network training, can gradients leak sensitive information in case training data fed is encrypted (homomorphic)?

Some algorithms in the literature allow recovering the input data used to train a neural network. This is done using the gradients (updates) of weights, such as in Deep Leakage from Gradients (2019) ...
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0answers
45 views

Why is there a Uniform and Normal version of He / Xavier initialization in DL libraries?

Two of the most popular initialization schemes for neural network weights today are Xavier and He. Both methods propose random weight initialization with a variance dependent on the number of input ...
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34 views

Is there any rule of thumb to determine the amount of data needed to train a CNN

I am training an AlexNet Convolutional Neural Network to classify images in a dataset. I want to know if there is any general rule for using data augmentation in training a neural network. How can I ...
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1answer
55 views

Can someone explain me what does this loss curve says?

I was training a CNN model on TensorFlow. After a while I came back and saw this loss curve: The green curve is training loss and the gray one is validation loss. I know that before epoch 394 the ...
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1answer
99 views

Can the (sparse) categorical cross-entropy be greater than one?

I am using AlexNet CNN to classify my dataset which contains 10 classes and 1000 data for each class, with 60-30-10, splits for train, validation, and test. I used different batch sizes, learning ...
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1answer
28 views

Why do we need to provide false labels to the discriminator on purpose to train GANs?

This is the tutorial that I used to learn about GANs. In this tutorial, it taught us to intentionally provide false labels to "fool" the discriminator, but does it make the discriminator ...
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1answer
25 views

Are there any downsides of using a fixed seed for a neural network's weight initialization?

For example, if we set the random seed to be 0, will we run into any problems? For example, maybe for seed 0, we can only reach a certain training error, but other seeds will converge to a much lower ...
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0answers
62 views

Why should the weight updates be proportional to input?

I'm reading the book Grokking Deep Learning. Regarding weight updates during training, it has the following code and explanation: ...
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0answers
15 views

Effect of Batch Size on a Translation Model's Validation Scores

This is a general question. I am training a neural machine translation model with different batch sizes: 16, 32, and 64 for the same amount of epochs (10). I am getting very low validation scores for ...
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28 views

When training deep learning models for object detection in images, do you need a large number of images, or a large number of training samples?

I am training a deep learning model for object detection. The consensus is that the more images that you have, the better the results will be. All the tutorials that I have seen say that more images ...
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0answers
18 views

How does scheduled sampling for transformers work?

I was reading this paper which applies a modified version of the transformers for traffic forecasting. I am somewhat familiar with the transformer architecture and how it functions, but, in the paper, ...
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1answer
27 views

Will changing the dimension reduction size of a neural network (i.e. SSD ResNet-50) change the overall outcome and accuracy of the model?

I am training a convolutional neural network to detect objects (weeds amongst crops, in my case) using TensorFlow. The original dimensions of the raw training photos are 4000 x 3000 pixels, which must ...
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1answer
56 views

Should we also shuffle the test dataset when training with SGD?

When training machine learning models (e.g. neural networks) with stochastic gradient descent, it is common practice to (uniformly) shuffle the training data into batches/sets of different samples ...
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0answers
24 views

Could the data augmentation lead to the model learning features which corresponds to data augmented data and not to the real data?

I am trying to train a Unet network with Synthetic data to do binary segmentation due to the fact that is is not easy to collect real data. And there is something in the training process that I do not ...
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1answer
96 views

Are there any rules for choosing batch size? [duplicate]

I am training a CNN with a batch size of 128, but I have some fluctuations in the validation loss, which are greater than one. I want to increase my batch size to 150 or 200, but, in the code examples ...
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0answers
30 views

How do I derive the gradient of the log-likelihood of an RBM?

In a Restricted Boltzmann Machine (RBM), the likelihood function is: $$p(\mathbf{v};\mathbf{\theta}) = \frac{1}{Z} \sum_{\mathbf{h}} e^{-E(\mathbf{v},\mathbf{h};\mathbf{\theta})}$$ Where $E$ is the ...
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0answers
30 views

Training Object Detection model on just 10 images

I am trying to train an object detection model using Mask-RCNN with Resnet50 as backbone. I am using the pre-trained models from PyTorch's Torchvision library. I have only 10 images that I can use to ...
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0answers
38 views

How to have closer validation loss and training loss in training a CNN

I am using an AlexNet architecture as my Convolutional Neural Network. A learning rate of 0.00007 and 128 batch_size. I have 20000 data and 10% test, 40% validation, and 50% for training. I used 100 ...
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0answers
20 views

How long will it take to train SSD V1 Mobilenet image recognition algorithm?

I am training a deep learning algorithm using an NVIDIA GEFORCE RTX GPU, with 16 GB RAM. I went through my image database and generated 50,000 training samples with the Labelimg software; the images ...
1
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1answer
72 views

Why does the training time of SVMs dramatically decrease after applying dimensionality reduction to the features?

Training an SVM with an RBF kernel model with c = 5.5 and gamma = 1.06, for a 5-class classification problem on the NSL-KDD ...
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2answers
76 views

What is the right way to train a generator in a GAN?

I am not fully understanding how to train a GAN's generator. I have a few questions below, but let me first describe what I am doing. I am using the MNIST dataset. I generate a batch of random images ...
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1answer
38 views

Training while predicting on dataset

I have been trying to figure out whether if I train a model and then while predicting is it possible to train images too just like humans Somehow converting valid images to the dataset by asking us ...
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0answers
19 views

Role of autoencoder in Hierarchical Extreme Learning Machine

I want to build HELM neural network that consists of autoencoder (AE) and one class classification (OC). HELM with AE and OC have following shape: That is, hidden layer output of AE is input of OC. ...
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1answer
75 views

How does back-propagation through time work for optimizing the weights of a bidirectional RNN?

I am aware that back-propagation through time is used for training the recurrent neural network. But I am not able to understand how this happens for the bi-directional versions of the recurrent ...
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1answer
31 views

How to train the NN of simple agents given a reward system?

I'm not an expert in AI or NN, I gathered most of the information I have from the internet, and I'm looking for advice and guidance. I'm trying to design a NN that is going to be used by all the ...
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1answer
24 views

Is training a CNN object detector on an image containing multiple targets that are not all annotated will teach it to miss targets?

I want to train a convolutional neural network for object detection (say YOLO) to detect faces. Consider this image: In this training image, I have many people, but only 2 of them are annotated. Is ...
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2answers
45 views

Is stable learning preferable to jumps in accuracy/loss

A stable/smooth learning validation curve often seems to keep improving over more epochs than an unstable learning curve. My intuition is that dropping the learning rate and increasing the patience of ...
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0answers
37 views

Training a CNN for semantic segmentation of large 4600x4600px images

I am trying to implement a CNN (U-Net) for semantic segmentation of similar large grayscale ~4600x4600px medical images. The area I want to segment is the empty space (gap) between a round object in ...
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0answers
27 views

Why scaling down the parameter many times during training will help the learning speed be the same for all weights in Progressive GAN?

The title is one of the special things in Progressive GAN, a paper of the NVIDIA team. By using this method, they introduced that Our approach ensures that the dynamic range, and thus the learning ...
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0answers
50 views

What is the time complexity for training a single-hidden layer auto-encoder?

What is the time complexity for training a single-hidden layer auto-encoder, for 1 epoch? You can assume that there are $n$ training examples, $m$ features, and $k$ neurons in the hidden layer, and ...
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1answer
41 views

Does it make sense to train images (for object detection algorithms) with cameras that will not be used to collect future data?

I am training an algorithm to identify weeds within crops using the YOLOv5 algorithm. This algorithm will be used in the future to identify weeds in images collected by unmanned aircraft (drones) ...

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