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.

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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 ...
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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.
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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, ...
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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 ...
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What is a good first guess to set hyper parameters for Adam optimiser for Faster RCNN?

Is there a way to make an educated guess on how to set the hyper parameters for Adam optimiser for Faster RCNN depending on the size of the dataset and the loss function? I use the default values for ...
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Should hyper-parameters used in the mutation operator be fixed or variable?

Let's say I have a function $f(p_{i,t})$ and want to optimize $p_{i,t}$ so that I minimize a certain loss. Here, $p_{i,t}$ is the individual $i$ at iteration $t$. Now, $p_{i,t}$ can live anywhere ...
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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.
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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-...
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How to identify clusters of best hyperparameters from noisy hyperparameter optimization?

I'd like to optimize the hyperparameters for a random forest model that is already somewhat time-consuming to train. With adding cross-validation and multiple hyperparameter combinations, I might be ...
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3 answers
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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 ...
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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 ...
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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-, ...
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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 ...
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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 ...
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How Long Can BPTT Truncated?

I wanted to ask what is, in general, the maximum value (the order of magnitude) of the number of time steps I can back-propagate in the past using TBTT (Truncated Backpropagation Through Time) in an ...
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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 ...
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Loss & accuracy curves from learning rate range test interpretation

I am working on a project doing experiments with the Learning Rate Range Test (See "A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and ...
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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 ...
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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 ...
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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 ...
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1 answer
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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 $k-$...
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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 ...
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Optimal episode length in reinforcement learning

I have a custom environment for stock trading where an episode can be as long as 2000-3000 steps. I've run several experiments with td3 and sac algorithms, average reward per episode flattens after ...
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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, ...
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2 votes
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Is there an optimal number of species for NEAT?

Is there an optimal number of species for NEAT? Since too low and too high is bad, I am thinking about adjusting the threshold of the distance function at runtime in order to have the number of ...
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Hyperparameters for Reproducing the Results of IRGAN on MovieLens 1M

I am trying to reproduce results reported for IRGAN (information retrieval GAN) on the MovieLens 1M dataset. The results I want to reproduce and their sources are listed in the table below. Model ...
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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 ...
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2 votes
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Is it a good idea to use different width and height of the kernel in a CNN?

I always see that the width and height of the kernel are the same. But is it a good idea to use different numbers? Recently I tried to use GoogLeNet (which expects images to be 224x224) on my images (...
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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 ...
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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 ...
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How does noise input size affect fake image generation with GANs?

In Generative Adversarial Networks, the Generator takes noise vector as input and feeds it forward to create an image. The noise vector consists of random numbers sampled from the normal distribution. ...
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For episodic tasks with an absorbing state, why can't we both have $\gamma=1$ and $T= \infty$ in the definition of the return?

For episodic tasks with an absorbing state, why can't $\gamma=1$ and $T= \infty$? In Sutton and Barto's book, they say that, for episodic tasks with absorbing states that becomes an infinite sequence, ...
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2 votes
1 answer
799 views

What should the value of epsilon be in the Q-learning?

I am trying to understand Reinforcement Learning and already explored different Youtube videos, blog posts, and Wikipedia articles. What I don't understand is the impact of $\epsilon$. What value ...
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1 vote
2 answers
178 views

Why is an embedding of dimension 400 enough to represent 70000 words?

I am learning PyTorch on Udacity. In lesson 8, section 11: Training the Model, the instructor writes: Then I have my embedding and hidden dimension. The embedding dimension is just a smaller ...
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Should the range and initial values of weights and biases be adjusted to fit input and output data?

As a routine (in typical everyday tasks) of a data scientist, should they usually decide about weights and biases range and initial values as a function of which data they are planning to insert as ...
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2 votes
1 answer
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Which hyperparameters in neural network are accesible to users adjustment

I am new to Neural Networks and my questions are still very basic. I know that most of neural networks allow and even ask user to chose hyper-parameters like: amount of hidden layers amount of ...
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Why one unit in the layers of neural network is not enough?

In a deep connected network, when every unit gets all the input features(X) so it has one parameter for every feature and every unit tweaks its parameters for loss optimization. What if we use only ...
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2 votes
1 answer
321 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 ...
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What is the best activation function for the embedding layer in a deep auto-encoder?

I am designing a deep autoencoder for graph embedding (exactly node embedding) following this paper SDNE. In the original paper, they used the sigmoid activation for all hidden layers in the ...
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1 vote
1 answer
419 views

How to determine the number of hidden layers and units of a deep auto-encoder?

I am using a deep autoencoder for my problem. However, the way I choose the number of hidden layers and hidden units in a hidden layer is still based on my feeling. The size of the model that ...
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3 votes
1 answer
1k views

How should I choose the target's update frequency in DQN?

I have been dealing with a problem that I'm trying to solve with DQN. A general question that I have is regarding the target's update frequency. How should it change? Depending on what factor do we ...
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3 votes
1 answer
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How are training hyperparameters determined for large models?

When training a relatively small DL model, which takes several hours to train, I typically start with some starting points from literature and then use a trial-and-error or grid-search approach to ...
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1 answer
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Why does every neuron in hidden layers of a multi-layer perceptron typically have the same activation function? [duplicate]

Why does every neuron in a hidden layer of a multi-layer perceptron (MLP) typically have the same activation function as every other neuron in the same or other hidden layers (so I exclude the output ...
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2 votes
1 answer
50 views

How exactly does nested cross-validation work?

I have trouble understanding how nested cross-validation works - I understand the need for two loops (one for selecting the model, and another for training the selected model), but why are they nested?...
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4 votes
1 answer
568 views

What made your DDPG implementation on your environment work?

I am working on scheduling problem that has inherent randomness. The dimensions of action and state spaces are 1 and 5 respectively. I am using DDPG, but it seems extremely unstable, and so far it ...
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Should we start with a small batch-size and increase during training to improve sample efficiency?

Just made an interesting observation playing around with the stable-baseline's implementation of PPO and the BipedalWalker environment from OpenAI's Gym. But I believe this should be a general ...
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1 vote
1 answer
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How to know if the hyperparameters of a neural network relate to each other?

According this thread some hyperparameters are independent from each other while some are directly related. One of the answers give an example where two hyperparameters affect each other. For ...
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Why is the number of neurons used in various neural networks power of 2?

I have noticed that almost all tutorials take the number of neurons as a power of 2. Is there any proper mathematical and well-proven reason for that? If you sometimes change it to some other odd ...
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1 answer
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What is relation between gradient descent and regularization in deep learning?

Gradient descent is used to reduce the loss and regularization is used to fight over-fitting. Is there any relation between gradient descent and regularization, or both are independent of each other?...
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How can I do hyperparameter optimization for a CNN-LSTM neural network?

I have built a CNN-LSTM neural network with 2 inputs and 2 outputs in Keras. I trained the network with model.fit_generator() (and not ...
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