Questions tagged [training]

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

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12
votes
3answers
31k views

How do I choose the optimal batch size?

Batch size is a term used in machine learning and refers to the number of training examples utilised in one iteration. The batch size can be one of three options: batch mode: where the batch size is ...
4
votes
4answers
1k views

What is the difference between training and testing in reinforcement learning?

In reinforcement learning (RL), what is the difference between training and testing an algorithm/agent? If I understood correctly, testing is also referred to as evaluation. As I see it, both imply ...
7
votes
5answers
2k views

How can action recognition be achieved?

For example, I would like to train my neural network to recognize the type of actions (e.g. in commercial movies or some real-life videos), so I can "ask" my network in which video or movie (and at ...
13
votes
3answers
2k views

Why are the initial weights of neural networks randomly initialised?

This might sound silly to someone who has plenty of experience with neural networks but it bothers me... Random initial weights might give you better results that would be somewhat closer to what a ...
13
votes
2answers
2k views

How are generative adversarial networks trained?

I am reading about generative adversarial networks (GANs) and I have some doubts regarding it. So far, I understand that in a GAN there are two different types of neural networks: one is generative ($...
12
votes
2answers
6k views

Which layer in a CNN consumes more training time: convolution layers or fully connected layers?

In a convolutional neural network, which layer consumes more training time: convolution layers or fully connected layers? We can take AlexNet architecture to understand this. I want to see the time ...
4
votes
1answer
606 views

How would you encode your input vector/matrix from a sequence of moves in game like tasks to train an AI? e.g. Chess AI?

I've seen data sets for classification / regressions tasks in domains such as credit default detection, object identification in an image, stock price prediction etc. All of these data sets could ...
4
votes
0answers
83 views

Forcing a neural network to be close to a previous model - Regularization through given model

I'm wondering, has anyone seen any paper where one trains a network but biases it to produce similar outputs to a given model (such as one given from expert opinion or it being a previously trained ...
1
vote
1answer
110 views

How many layers exists in my neural network?

I have a neural network model defined as below. How many layers exist there? Not sure which ones to count when we are asked about the number. ...
1
vote
1answer
47 views

Choosing Data Augmentation smartly for different application

I'm trying to understand the role of data augmentation and how it can affect the performance/accuracy of a deep model. My target application is fire detection (on video frames), with almost 15K ...
5
votes
5answers
2k views

How does an activation function's derivative measure error rate in a neural network?

A blog post called "Text Classification using Neural Networks" states that the derivative of the output of a sigmoid function is used to measure error rates. What is the rationale for this? I ...
3
votes
2answers
2k views

Is pooling a kind of dropout?

If I got well the idea of dropout, it allows improving the sparsity of the information that comes from one layer to another by setting some weights to zero. On the other hand, pooling, let's say max-...
2
votes
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 ...
2
votes
1answer
35 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?
2
votes
1answer
48 views

What is the AI discipline where an algorithm learns from an initial training set, but then refines its learning as it uses that training?

Imagine a system that is trained to manipulate dampers to manage air flow. The training data includes damper state and flow characteristics through a complex system of ducts. The system is then given ...
1
vote
2answers
92 views

Drone training, how to train without training data?

I setupped a small drone simulator using PhysX, the time step is at 200 hz, while motors update like regular ESCs (at 50 Hz). I computed the inertia matrix, tweaked a bit mass of components to be real,...
0
votes
2answers
112 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 ...
0
votes
1answer
41 views

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 ...
-4
votes
2answers
685 views

What are the approaches to predict sequence of π numbers? [closed]

Given list of fixed numbers from a mathematical constant such as Pi, is it is possible to train AI to attempt to predict the next numbers? Which AI or neural network would be more suitable for this ...