Questions tagged [hyperparameter-optimization]

For questions related to the concept of hyper-parameter optimization, that is, the task of finding the best hyper-parameters for a particular learning algorithm (e.g. gradient descent) or model (e.g. a multi-layer neural network) using an optimization method (e.g. Bayesian optimization or genetic algorithms).

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30 views

Different learning rates for different layers

I wonder, whether there are heuristic rules for optimal selection of learning rates for different layers. I expect, that there is no general recipe, but probably there are some some choices that maybe ...
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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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41 views

Why doesn't anyone use reinforcement learning to find the best possible alternative to backpropagation?

To be clear, I'm very uninformed on the topic of alternative learning algorithms to backprop, all my knowledge comes from articles like these: lets-not-stop-at-backprop backprop-alternatives we-need-a-...
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73 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 ...
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16 views

How to manually optimize Neural Networks the most systematical way?

Do you have any ideas or guidance on how to do manual neural network optimization in the most systematic way? Especially when models train longer and the effects of hyperparameter fitting are very ...
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12 views

Is it valid to implement hyper-paramter tuning and THEN cross validation

I have a multi-label classification task I am implementing. I have done a hyper-parameter tuning to determine the best configuration for my neural network. Is it valid to do this (determine the best ...
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1answer
36 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 $k-$...
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14 views

Are there guiding principles as to which activation functions suit a given RL algorithm?

Are there rules of thumb as to which activation functions work well (or which one would not) on the policy and value network of a class of RL algorithms? For hidden layers and for the output layer. ...
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2answers
98 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, ...
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13 views

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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16 views

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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33 views

Is there any way to determine/estimate the number of rounds for the whole Federated Learning process?

In Federated Learning (FL) the process ends until the model converges or reached certain accuracy. My question: Is there any way to determine/estimate the number of rounds for the whole process?
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1answer
50 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 ...
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2answers
26 views

Do larger numbers of hidden layers have a bigger effect on a classification model's accuracy?

I trained different classification models using Keras with different numbers of hidden layers and the same number of neurons in each layer. What I found was the accuracy of the models decreased as the ...
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19 views

How to Select Model Parameters for Transformer (Heads, number of layers, etc)

Is there a general guideline on how the Transformer model parameters should be selected, or the range of these parameters that should be included in a hyperparameter sweep? Number of heads Number of ...
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1answer
56 views

What to do with a GAN that trained well but got worse over time?

I am training a WGAN-GP network based on the following paper, though I am using a different dataset. Now, for the first ~ 60-70 epochs, my network trained really well, which I could see in the loss ...
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1answer
50 views

Is it possible to train one part of the network with a particular learning rate and the other part with a different one?

I have a combined network consisting of two parts: one is for images and the other is for numerical data. Each sample is matched with a numerical case by an ID. For this combined network, a ...
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1answer
83 views

Bayesian hyperparameter optimization, is it worth it?

In the Deep Learning book by Goodfellow et al., section 11.4.5 (p. 438), the following claims can be found: Currently, we cannot unambiguously recommend Bayesian hyperparameter optimization as an ...
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1answer
103 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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1answer
59 views

What is the impact of changing the crossover and mutation rates?

What is the impact of using a: low crossover rate high crossover rate low mutation rate high mutation rate
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82 views

Why does PPO lead to a worse performance than TRPO in the same task?

I am training an agent with an Actor-Critic network and update it with TRPO so far. Now, I tried out PPO and the results are drastically different and bad. I only changed from TRPO to PPO, the rest of ...
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1answer
55 views

How should I change the hyper-parameters of the C51 algorithm, in order to obtain higher reward?

I have a scenario where, in an ideal situation, the greedy approach is the best, but when non-idealities are introduced which can be learned, DQN starts doing better. So, after checking what DQN ...
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1answer
48 views

What is the difference between sensitivity analysis and parameter tuning?

I tried different values of genetic algorithm operators: many crossover rates from 20% to 80% many crossover rates from 1% to 20% varying the population size The study of different parameter values ...
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0answers
195 views

How to determine the number of fully connected layers for a convolutional neural network?

How many fully connected layers should be added to a convolutional neural network? Does it depend on input size to the fully connected layer? If so, how do we decide? What if the input size of the ...
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2answers
65 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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37 views

Unable to meet desired mean squared error

I wish to get MSE < 0.5 on test data (https://easyupload.io/zr7xf3) which is 20% of given data chosen randomly. But I am reaching 0.73 using both plain Ridge Regression as well as a neural network ...
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47 views

How to optimize my GAN generator and discriminator models' structures?

I'm using Tensorflow to feed a DCGAN 3000 320x320 colored images of cars. The goal is to generate new cars. I've been training on Google Colab for the past 10 hours or so. I guess I can expect results ...
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120 views

Rules of thumb for hidden layer sizes [duplicate]

I am quite new to neural networks, and would like to save myself some of the learning curve by having some rules of thumb about hidden layer sizes. I would also like to have a rule of thumb for the ...
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2answers
325 views

What is the meaning of "exploration" in reinforcement and supervised learning?

While exploration is an integral part of reinforcement learning (RL), it does not pertain to supervised learning (SL) since the latter is already provided with the data set from the start. That said, ...
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1answer
420 views

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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1answer
144 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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1answer
633 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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0answers
23 views

Best/quickest approach for tuning the hyperparameters of a restricted boltzmann machine

I have an RBM model which takes extremely long to train and evaluate because of the large number of free parameters and the large amount of input data. What would be the most efficient way of tuning ...
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1answer
70 views

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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1answer
51 views

96.91% accuracy on MNIST after 2 hours of training using custom made neural net library. Ways to improve?

I wanted to understand back-propagation so I made a basic neural network library. I used momentum, with learning rate = $0.1$, beta = $0.99$, epochs = $200$, batch size = $10$, loss function is cross ...
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1answer
316 views

Why is my Soft Actor-Critic's policy and value function losses not converging?

I'm trying to implement a soft actor-critic algorithm for financial data (stock prices), but I have trouble with losses: no matter what combination of hyper-parameters I enter, they are not converging,...
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0answers
25 views

Deriving hyperparameter updates in Online Interactive Collaborative Filtering

I've been going through "Online Interactive Collaborative Filtering Using Multi-Armed Bandit with Dependent Arms" by Wang et al. and am unable to understand how the update equations for the ...
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0answers
37 views

How many training runs are needed to obtain a credible value for performance?

I'm trying to optimize a neural network. For that, I'm changing parameters like the batch size, learning rate, weight initialization, etc. A neural network is not a deterministic algorithm, so, in ...
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0answers
60 views

How do we choose the filters for the convolutional layer of a convolution neural network?

Since the hidden layers of a CNN work as a trainable feature extractor, more detailed content based on a larger number of pixels shall require bigger filter sizes. But for cases where localized ...
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0answers
35 views

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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2answers
50 views

Is it theoretically possible (or impossible) that principal component analysis worsens the performance of the model?

In case I had a prediction model and decided to add a PCA step prior to the model, is it theoretically possible/impossible that the number of output dimensions that is better for all tests may perform ...
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1answer
50 views

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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2answers
413 views

How is a validation set used to tune the hyperparameters in a non-biased way, if the new models depends on the values of these?

I've built a neural network from the scratch, choosing arbitrary numbers for the hyperparameters: learning rate, number of hidden layers and neurons for these, number of epochs and size of mini ...
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1answer
54 views

After having selected the best model with cross-validation, for how long should I train it?

When using k-fold cross-validation in a deep learning problem, after you have computed your hyper-parameters, how do you decide how long to train your final model? My understanding is that, after the ...
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0answers
75 views

Why can't I Hyper tune my KNNBasic Algorithm?

I've been trying to hyper tuning my KNNBasic algorithm by the help of grid search for recommendation system for movie review data. The problem is that both of my KNNBasicTuned and KNNBasicUntuned ...
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2answers
156 views

Why do we need both the validation set and test set?

I know that this has been asked a hundred times before, however, I was not able to find a question (and an answer) which actually answered what I wanted to know, respectively, which explained it in a ...
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0answers
83 views

Are there principled ways of tuning a neural network in case of overfitting and underfitting?

Whenever I tune my neural network, I usually take the common approach of defining some layers with some neurons. If it overfits, I reduce the layers, neurons, add dropout, utilize regularisation. ...
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3answers
5k views

What kind of optimizer is suggested to use for binary classification of similar images?

I have spent some time searching Google and wasn't able to find out what kind of optimization algorithm is best for binary classification when images are similar to one another. I'd like to read ...
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22 views

When stacking LSTM's, should the hidden units increase?

I'm using Weights and Biases to do some hyperparameter sweeping for a supervised sequence-to-sequence problem I'm working on. One thing I noticed is that the sweeps with a gradually increasing number ...
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1answer
82 views

How to fairly conduct a model performance with 5-fold cross validation after augmentation?

I have, say, a (balanced) data-set with 2k images for binary classification. What I have done is that randomly divided the data-set into 5 folds; copy-pasted all 5-fold data-set to have 5 exact ...