Questions tagged [hyper-parameters]

For questions related to the hyper-parameters of AI models and algorithms, which are parameters that are set before the learning process begins. For example, the number of hidden layers in a feed-forward neural network is usually a hyper-parameter.

Filter by
Sorted by
Tagged with
0 votes
2 answers
179 views

How to ensure that the ES-HyperNEAT algorithm generates an ANN in the substrate?

I'm trying to implement the ES-HyperNEAT algorithm using the original paper, as well as the pseudocode provided in the official user page. Occasionally, the algorithm would be unable to generate a ...
2 votes
1 answer
1k views

Could it make any sense to choose a larger dimension for the latent space of the VAE with respect to the original input?

Could it make any sense to choose a larger dimension for the latent space of the VAE with respect to the original input? For example, we may want to learn how to reconstruct a relatively low-...
1 vote
0 answers
17 views

Looking for a way to train a model to learn optimal parameters/hyperparameters of clustering

I have 5000 docs, each is a review. For each review, i'm plotting the sentences in a semantic dimension. Now, I'm applying clustering to these points for each review. The success of my model depends ...
-1 votes
1 answer
263 views

GPT beam search length (number of tokens)

Background: I'm currently trying to use GPT to give me numerical scores, and looking for tips on prompt design, see my previous StackExchange post. To craft good prompts it seems important to have a ...
0 votes
1 answer
37 views

Patterns binary classification - model doesn't overfit

I am working on a very basic binary classification problem. For each set of four float numbers $(x,y,z,w)$, I want to check if they fall or not into one category. I have written a model with 3 dense ...
2 votes
1 answer
285 views

What's the architecture and size of neural-network-based reward models as used in reinforcement learning by human feedback

My rough understanding of RLHF as used for ChatGPT in a nutshell is this: A reward model is trained using comparisons of different responses to the same prompt. Human trainers rank these responses ...
0 votes
0 answers
12 views

Incorporating HiPlot and Keras

I just started to learn about Keras and train some models, and I came across HiPlot which is used for tuning hyperparameters. I was wondering if HiPlot can also be used to see what parameters would ...
14 votes
2 answers
15k views

How large should the replay buffer be?

I'm learning DDPG algorithm by following the following link: Open AI Spinning Up document on DDPG, where it is written In order for the algorithm to have stable behavior, the replay buffer should ...
0 votes
1 answer
92 views

How to approach a toy classification problem using a neural network?

The toy problem: 50 unique numbers are randomly selected from number 0 to 99. If number 1 appears in the selection but number 2 doesn't, the selection is labelled as "1". If number 2 ...
0 votes
1 answer
79 views

Do different architectures really make a difference or is it just a matter of the training process?

I was wondering which influence different architectures for deep learning truly have on the performance. Of course, substantial changes in the paradigms we use when building neural networks (such as ...
1 vote
1 answer
106 views

Are there any guidelines on picking hyperparameters for Deep Reinforcement Learning?

I am trying to learn machine learning from Andrew NG's Machine learning specialization on Coursera. In the chapter about reinforcement learning Andrew NG said that if you do not select correct ...
0 votes
0 answers
37 views

Which of these 3 mutation rates is the best in terms of performance?

I am need some comments since I am conducting experiments with 3 different mutation rates and hesitate to choose the best one. I ...
4 votes
1 answer
2k views

What's the relationship between number of heads and embedding dimension in Transformers?

I am reading the book: Natural Language Processing with Transformers. It has the following paragraph Although head_dim does not have to be smaller than the number of embedding dimensions of the ...
0 votes
2 answers
592 views

What should I do if my validation score is good, but my test score is bad?

I've trained my artificial neural network, and, as per standard practice, I've picked out the one neural network throughout training that did the best on my validation dataset. That is, the neural ...
0 votes
2 answers
139 views

How to reduce the number of clusters produced by the Markov Clustering Algorithm?

I have used the Markov Clustering Algorithm (MCL) to cluster tweets, based on their similarity. However, I got a too high number of clusters, and most of the clusters have only one tweet. Any ...
1 vote
0 answers
31 views

How to make my neural networks designs more robust

Whenever, I design a neural network to solve a novel problem (requires a novel loss function i.e. not image classification) it always ends up being hypersensitive to batch size and learning rate. ...
1 vote
2 answers
2k views

Is there any relationship between the batch size and the number of epochs?

I am currently running a program with a batch size of 17 instead of batch size 32. The benchmark results are obtained at a batch size of 32 with the number of epochs 700. Now I am running with batch ...
9 votes
3 answers
18k views

How to determine the embedding size?

When we are training a neural network, we are going to determine the embedding size to convert the categorical (in NLP, for instance) or continuous (in computer vision or voice) information to hidden ...
0 votes
1 answer
185 views

Which paper describes the effect of learning_starts in Reinforcement Learning?

I have seen many popular RL libraries have a learning_start parameter. This allows the agent to collect enough experiences before training on the replay_buffer. However, I am unable to find the paper ...
1 vote
1 answer
181 views

How many singular vectors do we need to calculate for SVD?

In the geometrical interpretation of SVD, the data points that we have need to be imagined as points in high dimensional space (say $d$-dimensional space). But we need to find a hyperplane in $k-$...
3 votes
2 answers
2k views

How many training steps does it usually take to train an RL model?

This is my model average rewards as follow image. How to tell if it is undertrained or not convergent? How many training steps does it usually take to train an RL model? And I'm using PPO to train.
1 vote
1 answer
296 views

Is the described Q-table considered large?

I never saw any rule of thumb as to what size is said as large for a q-table but I have a Q-table with like 2500 entries. Is it considered large for a tabular approach? Anyone from experience can ...
3 votes
1 answer
638 views

Why is a large replay buffer inefficient?

Open AI spin up says ... the replay buffer should be large enough to contain a wide range of experiences, but it may not always be good to keep everything. If you only use the very-most recent data, ...
2 votes
1 answer
494 views

For continuing tasks, is the choice of episode length completely arbitrary?

Let's say I'm training a reinforcement learning agent to act in some environment that perpetually continues to give the agent opportunities to earn rewards, and there is no cap on the score and there ...
2 votes
0 answers
204 views

What is the correct dimension of mu/logvar and z in the VAE?

I'm having a problem to understand the needed dimensions of a VAE, especially for mu, logvar and ...
66 votes
4 answers
119k views

How to select number of hidden layers and number of memory cells in an LSTM?

I am trying to find some existing research on how to select the number of hidden layers and the size of these of an LSTM-based RNN. Is there an article where this problem is being investigated, i.e., ...
0 votes
1 answer
243 views

Why should data batches in a neural network have an equal size?

Why should data batches in a neural network have an equal size? I have seen some recent research works on making the batch size dynamic, but still, I can't find an answer to my question.
5 votes
1 answer
361 views

How do I design a neural network that breaks a 5-letter word into its corresponding syllables?

I am going to design a neural network which will be able to break a 5-letter word into its corresponding syllables (hybrid syllables, I mean it will not strictly adhere to grammatical syllable rules ...
0 votes
3 answers
278 views

What can I infer if my model is converging extremely fast?

I am running a model with fixed hyperparameters. To my surprise/shock, the model converged extremely fast with the least loss possible. I want to know the causes of this phenomenon. I have the ...
0 votes
2 answers
115 views

Why data required for hyperparameter tuning is considered as an additional data?

Any parametric model may have parameters as well as hyperparameters. Learning algorithm deals with parameters and hyperparameters should be dealt outside learning algorithm. Consider the following ...
3 votes
1 answer
1k views

What is the intuition behind the number of filters/channels for each convolutional layer?

After having chosen the number of layers for a convolutional neural network, we must also choose the number of filters/channels for each convolutional layer. The intuition behind the filter's spatial ...
6 votes
1 answer
2k views

Should I be decaying the learning rate and the exploration rate in the same manner?

Should I be decaying the learning rate and the exploration rate in the same manner? What's too slow and too fast of an exploration and learning rate decay? Or is it specific from model to model?
3 votes
2 answers
123 views

Does this hyperparameter optimisation approach yield the optimal hyperparameters?

Say I have a ML model which is not very costly to train. It has around say 5 hyperparameters. One way to select best hyperparameters would be to keep all the other hyperparamaters fixed and train ...
5 votes
1 answer
2k views

How many weights does the max-pooling layer have?

How many weights does the max-pooling layer have? For example, if there are 10 inputs, a pooling filter of size 2, stride 2, how many weights, including bias, does a max-pooling layer have?
4 votes
1 answer
647 views

Why is the number of output channels 16 in the hidden layer of this CNN?

In this tutorial from Jeremy Howard: What is torch.nn really? he has an example towards the end where he creates a CNN for MNIST. In nn.Conv2d, he makes the ...
0 votes
2 answers
263 views

Is it true that batch size of form $2^k$ gives better results?

I am confused among the following in selecting the batch size for my model. #1: powers of 2 I generally see that batch sizes are in powers of two: 32, 64, 128, 256. #2: maximum GPU Suppose my GPU ...
9 votes
1 answer
1k views

What causes a model to require a low learning rate?

I've pondered this for a while without developing an intuition for the math behind the cause of this. So what causes a model to need a low learning rate?
5 votes
2 answers
257 views

In a neural network, by how much does the number of neurons typically vary from layer to layer?

In a neural network, by how much does the number of neurons typically vary from layer to layer? Note that I am NOT asking how to find the optimal number of neurons per layer. As a hardware design ...
3 votes
3 answers
2k views

Why must the momentum factor be in the range 0-1?

Why is it a bad idea to have a momentum factor greater than 1? What are the mathematical motivations/reasons?
3 votes
0 answers
40 views

Which hyper-parameters are considered in neural architecture search?

I want to understand automatic Neural Architecture Search (NAS). I read already multiple papers, but I cannot figure out what the actual search space of NAS is / how are classical hyper-parameters ...
2 votes
0 answers
63 views

Given a 2-layer GCN, can we choose the dimensions of the 2nd weight matrix, such that this architecture has the same capacity as a 1-layer GCN?

This might be more of a question about nested function classes: For $k$ class node classification in a graph with $n$ nodes, and $d$ feature vector. I want to compare Architecture I: the GCN model of ...
2 votes
1 answer
101 views

What components of reinforcement learning influence the result the most?

I'm working on my thesis concerning a reinforcement learning problem and am trying to prioritise my time on different components of it: Formalising the agent environment (like the design of state-, ...
2 votes
1 answer
653 views

How to design a neural network to predict the quadrant where a given point lies?

I've written a single perceptron that can predict whether a point is above or below a straight-line graph, given the correct training data and using a sign activation function. Now, I'm trying to ...
4 votes
1 answer
87 views

How do you efficiently choose the hyper-parameters of a neural network?

How do you efficiently choose the hyper-parameters of a neural network (e.g. the learning rate, number of layer, weights, etc.)?
3 votes
2 answers
428 views

When can I call an entity a hyperparameter?

As per my knowledge, any entity that is learnable by a training algorithm can be called a parameter. Weights of a neural network are called parameters because of this reason only. But I have doubts ...
4 votes
1 answer
127 views

When using Neural Architecture Search, how are the hyper-parameters chosen?

I have read a lot about NAS, but I still do not understand one concept: When setting up a neural network, hyperparameters (such as the learning rate, dropout rate, batch size, filter size, etc.) need ...
3 votes
1 answer
113 views

What is the most statistically acceptable method for tuning neural network hyperparameters on very small datasets?

Neural networks are usually evaluated by dividing a dataset into three splits: training, validation, and test The idea is that critical hyperparameters of the network such as the number of epochs ...
3 votes
1 answer
480 views

Why is the input layer of a neural network usually not counted?

I came across the following statement from the caption of figure 7.8 from the textbook Neural Networks and Neural Language Models the input layer is usually not counted when enumerating layers Why ...
2 votes
0 answers
242 views

How will the filter size affect the transpose convolution operation?

After a series of convolutions, I am up-sampling a compressed representation, I was curious what is the methodology I should follow to choose an optimum kernel size for up-sampling. How will the ...
6 votes
2 answers
4k views

What are the best hyper-parameters to tune in reinforcement learning?

Obviously, this is somewhat subjective, but what hyper-parameters typically have the most significant impact on an RL agent's ability to learn? For example, the replay buffer size, learning rate, ...