2021년 2월 17일 (수) 08:24에 Pythagoras0님의 편집

2021-02-17T08:24:35Z

Pythagoras0: /* 메타데이터 */ 새 문단

2020-12-26T12:25:46Z

메타데이터: 새 문단

Pythagoras0: /* 노트 */ 새 문단

2020-12-21T08:35:19Z

노트: 새 문단

새 문서

== 노트 ==

===위키데이터===
* ID : [https://www.wikidata.org/wiki/Q4171168 Q4171168]
===말뭉치===
# When performing hyperparameter optimization, we are really searching for the best model we can find within our time constraints.<ref name="ref_12fb78eb">[https://blog.dataiku.com/narrowing-the-search-which-hyperparameters-really-matter Narrowing the Search: Which Hyperparameters Really Matter?]</ref>
# the violin plots show the distribution of importances for each hyperparameter, ordered by the median.<ref name="ref_12fb78eb" />
# We created the density distributions to explore the best values of this hyperparameter over the full set of datasets and recovered a similar shape to that in the original experiments.<ref name="ref_12fb78eb" />
# We found the number of estimators for random forest to be only the third most important hyperparameter.<ref name="ref_12fb78eb" />
# For more complex scenarios, it might be more effective to choose each hyperparameter value randomly (this is called a random search).<ref name="ref_610f9e74">[https://www.tensorflow.org/tensorboard/hyperparameter_tuning_with_hparams Hyperparameter Tuning with the HParams Dashboard]</ref>
# The Scatter Plot View shows plots comparing each hyperparameter/metric with each metric.<ref name="ref_610f9e74" />
# There are many other hyperparameter optimization libraries out there.<ref name="ref_4266f5c0">[https://ray.readthedocs.io/en/latest/tune.html Tune: Scalable Hyperparameter Tuning — Ray v1.0.1]</ref>
# As a user, you’re probably looking into hyperparameter optimization because you want to quickly increase your model performance.<ref name="ref_4266f5c0" />
# Multi-GPU & distributed training out of the box¶ Hyperparameter tuning is known to be highly time-consuming, so it is often necessary to parallelize this process.<ref name="ref_4266f5c0" />
# Most other tuning frameworks require you to implement your own multi-process framework or build your own distributed system to speed up hyperparameter tuning.<ref name="ref_4266f5c0" />
# So, traditionally, engineers and researchers have used techniques for hyperparameter optimization like grid search and random search.<ref name="ref_40819846">[https://www.mathworks.com/videos/applied-machine-learning-part-3-hyperparameter-optimization-1547849445386.html Applied Machine Learning, Part 3: Hyperparameter Optimization Video]</ref>
# Now, the main reason to do hyperparameter optimization is to improve the model.<ref name="ref_40819846" />
# And, although there are other things we could do to improve it, I like to think of hyperparameter optimizations as being a low-effort, high-compute type of approach.<ref name="ref_40819846" />
# One of the steps you have to perform is hyperparameter optimization on your selected model.<ref name="ref_d5654236">[https://www.freecodecamp.org/news/hyperparameter-optimization-techniques-machine-learning/ Hyperparameter Optimization Techniques to Improve Your Machine Learning Model's Performance]</ref>
# Now I will introduce you to a few alternative and advanced hyperparameter optimization techniques/methods.<ref name="ref_d5654236" />
# The library is very easy to use and provides a general toolkit for Bayesian optimization that can be used for hyperparameter tuning.<ref name="ref_d5654236" />
# This means that during the optimization process, we train the model with selected hyperparameter values and predict the target feature.<ref name="ref_d5654236" />
# More formally, a hyperparameter is a parameter from a prior distribution; it captures the prior belief, before data is observed (Riggelsen, 2008).<ref name="ref_9a99431e">[https://www.statisticshowto.com/hyperparameter/ Hyperparameter: Simple Definition]</ref>
# And since then the team has been getting a lot of questions about bayesian hyperparameter search – Is it faster than random search?<ref name="ref_7aadd54e">[https://www.wandb.com/articles/bayesian-hyperparameter-optimization-a-primer Bayesian Hyperparameter Optimization]</ref>
# In this post, I’ll try to answer some of your most pressing questions about Bayesian hyperparameter search.<ref name="ref_7aadd54e" />
# It’s tricky to find the right hyperparameter combinations for a machine learning model, given a specific task.<ref name="ref_7aadd54e" />
# In random search, other than defining a grid of hyperparameter values, we specify a distribution from which the acceptable values for the specified hyperparameters could be sampled.<ref name="ref_7aadd54e" />
# In this post, we'll go through a whole hyperparameter tuning pipeline step by step.<ref name="ref_162174ab">[https://www.sicara.ai/blog/hyperparameter-tuning-keras-tuner How to Perform Hyperparameter Tuning with Keras Tuner]</ref>
# Tuning them can be a real brain teaser but worth the challenge: a good hyperparameter combination can highly improve your model's performance.<ref name="ref_162174ab" />
# Shortly after, the Keras team released Keras Tuner, a library to easily perform hyperparameter tuning with Tensorflow 2.0.<ref name="ref_162174ab" />
# v1 is out on PyPI.https://t.co/riqnIr4auA Fully-featured, scalable, easy-to-use hyperparameter tuning for Keras & beyond.<ref name="ref_162174ab" />
# We believe optimization methods should not only return a set of optimized hyperparameters, but also give insight into the effects of model hyperparameter settings.<ref name="ref_22c5759f">[https://www.osti.gov/pages/servlets/purl/1556107 HyperSpace: Distributed Bayesian Hyperparameter Optimization (Journal Article)]</ref>
# HyperSpace leverages high performance computing (HPC) resources to better understand unknown, potentially non-convex hyperparameter search spaces.<ref name="ref_22c5759f" />
# When working on a machine learning project, you need to follow a series of steps until you reach your goal, one of the steps you have to execute is hyperparameter optimization on your selected model.<ref name="ref_8cf603ad">[https://www.analyticsvidhya.com/blog/2020/09/alternative-hyperparameter-optimization-technique-you-need-to-know-hyperopt/ An Alternative Hyperparameter Optimization Technique]</ref>
# Before I define hyperparameter optimization you need to understand what is a hyperparameter.<ref name="ref_8cf603ad" />
# Then hyperparameter optimization is a process of finding the right combination of hyperparameter values in order to achieve maximum performance on the data in a reasonable amount of time.<ref name="ref_8cf603ad" />
# This is a widely used traditional method that performing hyperparameter tuning in order to determine the optimal values for a given model.<ref name="ref_8cf603ad" />
# The key factor in all different optimization strategies is how to select the next set of hyperparameter values in step 2a, depending on the previous metric outputs in step 2d.<ref name="ref_2feda4f2">[https://www.knime.com/blog/machine-learning-algorithms-and-the-art-of-hyperparameter-selection Machine learning algorithms and the art of hyperparameter selection]</ref>
# One further simplification is to use a function with only one hyperparameter to allow for an easy visualization.<ref name="ref_2feda4f2" />
# This simplified setup allows us to visualize the experimental values of the one hyperparameter and the corresponding function values on a simple x-y plot.<ref name="ref_2feda4f2" />
# Whiter points correspond to hyperparameter values generated earlier in the process; redder points correspond to hyperparameter values generated later on in the process.<ref name="ref_2feda4f2" />
# (2019b), we used a single objective hyperparameter Bayesian optimization to optimize performance of spiking neuromorphic systems in terms of neural network's accuracy.<ref name="ref_141b2a4f">[https://www.frontiersin.org/articles/10.3389/fnins.2020.00667/full Bayesian Multi-objective Hyperparameter Optimization for Accurate, Fast, and Efficient Neural Network Accelerator Design]</ref>
# We showed how critical it is to use hyperparameter optimization techniques for designing any neuromorphic computing framework and how Bayesian approaches can help in this regard.<ref name="ref_141b2a4f" />
# , 2018; Tan et al., 2019; Wu et al., 2019), and Bayesian-based hyperparameter optimization (Reagen et al., 2017; Marculescu et al., 2018; Stamoulis et al., 2018).<ref name="ref_141b2a4f" />
# This selection does not impact the effectiveness or performance of our approach; rather, it only impacts the speed of searching the hyperparameter space and avoid trapping in local minima.<ref name="ref_141b2a4f" />
# We propose an efficient online hyperparameter optimization method which uses a joint dynamical system to evaluate the gradient with respect to the hyperparameters.<ref name="ref_9ae0e902">[https://openreview.net/forum?id=H1OQukZ0- Online Hyper-Parameter Optimization]</ref>
# The most widely used techniques in hyperparameter tuning are manual configuration, automated random search, and grid search 1.<ref name="ref_abffa124">[https://radiopaedia.org/articles/hyperparameter-machine-learning Hyperparameter (machine learning)]</ref>
# different hyperparameter values), you also need a way to evaluate each model's ability to generalize to unseen data.<ref name="ref_cf2109dd">[https://www.jeremyjordan.me/hyperparameter-tuning/ Hyperparameter tuning for machine learning models.]</ref>
# Recall that I previously mentioned that the hyperparameter tuning methods relate to how we sample possible model architecture candidates from the space of possible hyperparameter values.<ref name="ref_cf2109dd" />
# This is often referred to as "searching" the hyperparameter space for the optimum values.<ref name="ref_cf2109dd" />
# If we had access to such a plot, choosing the ideal hyperparameter combination would be trivial.<ref name="ref_cf2109dd" />
# I even consider the loss function as one more hyperparameter, that is, as part of the algorithm configuration.<ref name="ref_0417e61d">[https://quantdare.com/what-is-the-difference-between-parameters-and-hyperparameters/ What is the difference between parameters and hyperparameters?]</ref>
# Could it be considered one more hyperparameter or parameter?<ref name="ref_0417e61d" />
# A general hyperparameter optimization will consist of evaluating the performance of several models, those that different values combinations inside these ranges yield.<ref name="ref_0417e61d" />
# The number of folds needed for cross-validation is a good example of hyper-hyperparameter.<ref name="ref_0417e61d" />
# Hyperparameter tuning (or Optimization) is the process of optimizing the hyperparameter to maximize an objective (e.g. model accuracy on validation set).<ref name="ref_1206b280">[https://dzlab.github.io/ml/2020/08/16/mlflow-hyperopt/ Hyperparameter Tuning with MLflow and HyperOpt]</ref>
# train a simple TensorFlow model with one tunable hyperparameter: learning-rate and uses MLflow-Tensorflow integration for auto logging - link.<ref name="ref_1206b280" />
# The ideal hyperparameter values vary from one data set to another.<ref name="ref_4a16c564">[https://www.elastic.co/guide/en/machine-learning/current/hyperparameters.html Machine Learning in the Elastic Stack [7.10]]</ref>
# Hyperparameter optimization involves multiple rounds of analysis.<ref name="ref_4a16c564" />
# Each round involves a different combination of hyperparameter values, which are determined through a combination of random search and Bayesian optimization techniques.<ref name="ref_4a16c564" />
# If you explicitly set a hyperparameter, that value is not optimized and remains the same in each round.<ref name="ref_4a16c564" />
# In a random search, hyperparameter tuning chooses a random combination of values from within the ranges that you specify for hyperparameters for each training job it launches.<ref name="ref_d442ea8d">[https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-how-it-works.html How Hyperparameter Tuning Works]</ref>
# Given a set of input features (the hyperparameters), hyperparameter tuning optimizes a model for the metric that you choose.<ref name="ref_d442ea8d" />
# To solve a regression problem, hyperparameter tuning makes guesses about which hyperparameter combinations are likely to get the best results, and runs training jobs to test these values.<ref name="ref_d442ea8d" />
# When choosing the best hyperparameters for the next training job, hyperparameter tuning considers everything that it knows about this problem so far.<ref name="ref_d442ea8d" />
# Therefore, if an efficient hyperparameter optimization algorithm can be developed to optimize any given machine learning method, it will greatly improve the efficiency of machine learning.<ref name="ref_69185c59">[https://www.sciencedirect.com/science/article/pii/S1674862X19300047 Hyperparameter Optimization for Machine Learning Models Based on Bayesian Optimization]</ref>
# In this way, the hyperparameter tuning problem can be abstracted as an optimization problem and Bayesian optimization is used to solve the problem.<ref name="ref_69185c59" />
# Hyperparameter tuning is the process of determining the right combination of hyperparameters that allows the model to maximize model performance.<ref name="ref_8b73a695">[https://neptune.ai/blog/hyperparameter-tuning-in-python-a-complete-guide-2020 Hyperparameter Tuning in Python: a Complete Guide 2020]</ref>
# It fits the model on each and every combination of hyperparameter possible and records the model performance.<ref name="ref_8b73a695" />
# Instead of finding the values of p(y|x) where y is the function to be minimized (e.g., validation loss) and x is the value of hyperparameter the TPE models P(x|y) and P(y).<ref name="ref_8b73a695" />
# It uses information from the rest of the population to refine the hyperparameters and determine the value of hyperparameter to try.<ref name="ref_8b73a695" />
# In machine learning, a hyperparameter is a parameter whose value is used to control the learning process.<ref name="ref_696d6ddd">[https://en.wikipedia.org/wiki/Hyperparameter_(machine_learning) Hyperparameter (machine learning)]</ref>
# A hyperparameter is a parameter that is set before the learning process begins.<ref name="ref_f2206deb">[https://deepai.org/machine-learning-glossary-and-terms/hyperparameter Hyperparameter]</ref>
# We cannot know the best value for a model hyperparameter on a given problem.<ref name="ref_b35ed722">[https://machinelearningmastery.com/difference-between-a-parameter-and-a-hyperparameter/ What is the Difference Between a Parameter and a Hyperparameter?]</ref>
# Hyperparameter setting maximizes the performance of the model on a validation set.<ref name="ref_f5232007">[https://medium.com/@jorgesleonel/hyperparameters-in-machine-deep-learning-ca69ad10b981 Hyperparameters in Machine /Deep Learning]</ref>
# In machine learning, hyperparameter optimization or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm.<ref name="ref_3fee28aa">[https://en.wikipedia.org/wiki/Hyperparameter_optimization Hyperparameter optimization]</ref>
# A hyperparameter is a parameter whose value is used to control the learning process.<ref name="ref_3fee28aa" />
# Applied to hyperparameter optimization, Bayesian optimization builds a probabilistic model of the function mapping from hyperparameter values to the objective evaluated on a validation set.<ref name="ref_3fee28aa" />
# Population Based Training (PBT) learns both hyperparameter values and network weights.<ref name="ref_3fee28aa" />
# The most widely used method for hyperparameter optimization is the manual tuning of these hyperparameters, which demands professional knowledge and expert experience.<ref name="ref_b8df0b9a">[https://medium.com/pytorch/accelerate-your-hyperparameter-optimization-with-pytorchs-ecosystem-tools-bc17001b9a49 Accelerate your Hyperparameter Optimization with PyTorch’s Ecosystem Tools]</ref>
# Traditionally, hyperparameter optimization has been the job of humans because they can be very efficient in regimes where only a few trials are possible.<ref name="ref_b8df0b9a" />
# As a result, Hyperband evaluates more hyperparameter configurations and is shown to converge faster than Bayesian optimization on a variety of deep-learning problems, given a defined resources budget.<ref name="ref_b8df0b9a" />
# Optuna is an open source automatic hyperparameter optimization framework, particularly designed for machine learning.<ref name="ref_b8df0b9a" />
# Hyperparameter tuning is an art as we often call as “black function”.<ref name="ref_35b2e36c">[https://towardsdatascience.com/understanding-hyperparameters-and-its-optimisation-techniques-f0debba07568 Understanding Hyperparameters and its Optimisation techniques]</ref>
# Second, we discuss simple selection methods which only choose one of a finite set of given algorithms/hyperparameter configurations.<ref name="ref_9a5bfeab">[https://link.springer.com/chapter/10.1007/978-3-030-05318-5_1 Hyperparameter Optimization]</ref>
# The parameters and weights of the basis functions, and thus the full learning curve, can thereby be predicted for arbitrary hyperparameter configurations.<ref name="ref_9a5bfeab" />
# This page describes the concepts involved in hyperparameter tuning, which is the automated model enhancer provided by AI Platform Training.<ref name="ref_3063ba7d">[https://cloud.google.com/ai-platform/training/docs/hyperparameter-tuning-overview Overview of hyperparameter tuning]</ref>
# Hyperparameter tuning takes advantage of the processing infrastructure of Google Cloud to test different hyperparameter configurations when training your model.<ref name="ref_3063ba7d" />
# Hyperparameter tuning works by running multiple trials in a single training job.<ref name="ref_3063ba7d" />
# Hyperparameter tuning requires explicit communication between the AI Platform Training training service and your training application.<ref name="ref_3063ba7d" />
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← 이전 판		2020년 12월 26일 (토) 12:25 판
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Hyperparameter - 편집 역사

2021년 2월 17일 (수) 08:24에 Pythagoras0님의 편집

Pythagoras0: /* 메타데이터 */ 새 문단

Pythagoras0: /* 노트 */ 새 문단