Batch tuner allows users to simply provide several configurations (i.e., choices of hyper-parameters) for their trial code. After finishing all the configurations, the experiment is done. Batch tuner only supports the type choice in [search space spec](../../../../../docs/SearchSpaceSpec.md).
Batch tuner allows users to simply provide several configurations (i.e., choices of hyper-parameters) for their trial code. After finishing all the configurations, the experiment is done. Batch tuner only supports the type choice in [search space spec](../../../../../docs/en_US/SearchSpaceSpec.md).
Suggested sceanrio: If the configurations you want to try have been decided, you can list them in searchspace file (using choice) and run them using batch tuner.
Grid Search performs an exhaustive searching through a manually specified subset of the hyperparameter space defined in the searchspace file. Note that the only acceptable types of search space are `choice`, `quniform`, `qloguniform`. **The number `q` in `quniform` and `qloguniform` has special meaning (different from the spec in [search space spec](../../../../../docs/SearchSpaceSpec.md)). It means the number of values that will be sampled evenly from the range `low` and `high`.**
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Grid Search performs an exhaustive searching through a manually specified subset of the hyperparameter space defined in the searchspace file. Note that the only acceptable types of search space are `choice`, `quniform`, `qloguniform`. **The number `q` in `quniform` and `qloguniform` has special meaning (different from the spec in [search space spec](../../../../../docs/en_US/SearchSpaceSpec.md)). It means the number of values that will be sampled evenly from the range `low` and `high`.**
[Autokeras](https://arxiv.org/abs/1806.10282) is a popular automl tools using Network Morphism. The basic idea of Autokeras is to use Bayesian Regression to estimate the metric of the Neural Network Architecture. Each time, it generates several child networks from father networks. Then it uses a naïve Bayesian regression estimate its metric value from history trained results of network and metric value pair. Next, it chooses the the child which has best estimated performance and adds it to the training queue. Inspired by its work and referring to its [code](https://github.com/jhfjhfj1/autokeras), we implement our Network Morphism method in our NNI platform.
If you want to know about network morphism trial usage, please check [Readme.md](../../../../../examples/trials/network-morphism/README.md) of the trial to get more detail.
If you want to know about network morphism trial usage, please check [Readme.md](../../../../../examples/trials/network_morphism/README.md) of the trial to get more detail.
[SMAC](https://www.cs.ubc.ca/~hutter/papers/10-TR-SMAC.pdf) is based on Sequential Model-Based Optimization (SMBO). It adapts the most prominent previously used model class (Gaussian stochastic process models) and introduces the model class of random forests to SMBO, in order to handle categorical parameters. The SMAC supported by nni is a wrapper on [the SMAC3 github repo](https://github.com/automl/SMAC3).
Note that SMAC on nni only supports a subset of the types in [search space spec](../../../../../docs/SearchSpaceSpec.md), including `choice`, `randint`, `uniform`, `loguniform`, `quniform(q=1)`.
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Note that SMAC on nni only supports a subset of the types in [search space spec](../../../../../docs/en_US/SearchSpaceSpec.md), including `choice`, `randint`, `uniform`, `loguniform`, `quniform(q=1)`.
