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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An intuitive explanation of Adagrad, its purpose and its formula

It (Adagrad) adapts the learning rate to the parameters, performing smaller updates (i.e. low learning rates) for parameters associated with frequently occurring features, and larger updates (i.e. ...
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72 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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36 views

What are some ways to quickly evaluate the potential of a given NN architecture?

Main question Is there some way we can leverage general knowledge of how certain hyperparameters affect performance, to very rapidly get some sort of estimate for how good a given architecture could ...
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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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17 views

How to organize model training hyperparameters

I am working on multiple deep learning projects, most of them in the area of computer vision. For many of them I create multiple models, try different approaches, use various model architectures. And ...
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1answer
38 views

When training a CNN, what are the hyperparameters to tune first?

I am training a convolutional neural network for object detection. Apart from the learning rate, what are the other hyperparameters that I should tune? And in what order of importance? Besides, I read ...
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15 views

Is it normal to see oscillations in tested hyperparameters during bayesian optimisation?

I've been trying out bayesian hyperparameter optimisation (with TPE) on a simple CNN applied to the MNIST handwritten digit dataset. I noticed that over iterations of the optimisation loop, the tested ...
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18 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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19 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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33 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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49 views

Why do momentum techniques not work well for RNNs?

AFAIK, momentum is quite useful when training CNNs, and can speed-up the training substantially without any drop in validation accuracy. I've recently learned that it is not as helpful for RNNs ...
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15 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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31 views

Are there optimizers that schedule their learning rate, momentum etc. autonomously?

I'm aware there are some optimizer such as Adam that adjust the learning rate for each dimension during training. However, afaik, the maximum learning rate they can have is still determined by the ...
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21 views

Hyperparameter optimisation over entire range or shorter range of training episodes in Deep Reinforcement Learning

I am optimising hyperparameters for my deep reinforcement learning project (using PPO2, DQN and A2C) and was wondering: Should I find the optimum hyperparameters to get maximum reward from training ...
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1answer
48 views

How to use a deep learning network on new data-set?

I am trying to use a network for classification. This network works very well on the author's example data, but doesn't work on new data. Currently, I am using the popular EEG Motor Movement/Imagery ...
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105 views

Threshold selection for Siamese network hyper-parameter tuning

I'm interested in modeling a Siamese network for facial verification. I've already written a simple working model that inputs feature vectors generated from two CNNs with shared weights then outputs a ...
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20 views

Improving Recall of a Certain Class

Let's say that we have a test data set with $20,000$ observations for which we want to make a binary prediction for. When we apply our best trained model to this data set (e.g. logistic regression ...
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33 views

How do you scale your ML problems?

While I have limited resource usually to train my machine learning models, I often find that my hyperparameter optimization procedure is not necessary using all my GPU and CPU, and that is because the ...
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65 views

Why doesnt my lstm model for time series prediction improve after certain level of performance?

I created an lstm model which predicts multioutput sequeances. It takes variable length sequences as input. These sequences are padded with zero to obtain equal length. Note that the time series are ...
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50 views

How to use TensorFlow with hyperparameter tuning to optimize parameters for a robot simulator

I am trying to implement a DNN to optimize a set of 7 parameters that are used in a robot swarm simulator on the ARGoS platform. the program is a compiled C++ executable that reads the parameters from ...
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12 views

Larger neural network with almost similar training and validation error as smaller network

I am building a neural network that takes as input 202 units and outputs a 200 dimension continuous variable. While trying to find the best model, one of the parameters i tune is the the number of ...
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14 views

augmentation, cross-validation, test evaluation

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 ...
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14 views

Choosing best combinations from all possible combination expressions based few variables, unary operators, binary operators

I have a few financial variables of a stock universe like OHLC prices, volume, and other fundamentals with varying time-frequency. Using this set I'm creating an expression that gives the weights to ...