Sobol is a type of random sampling supported by sweep job types. from azure.ai.ml.sweep import Normal, Uniform, RandomParameterSampling After creating your command job, you can use the sweep parameter to define the sampling algorithm. In random sampling, hyperparameter values are randomly selected from the defined search space. Some users do an initial search with random sampling and then refine the search space to improve results. It supports early termination of low-performance jobs. Random sampling supports discrete and continuous hyperparameters. Azure Machine Learning supports the following methods: Specify the parameter sampling method to use over the hyperparameter space. keep_probability has a uniform distribution with a minimum value of 0.05 and a maximum value of 0.1.įor the CLI, you can use the sweep job YAML schema, to define the search space in your YAML: search_space: learning_rate has a normal distribution with mean value 10 and a standard deviation of 3. This code defines a search space with two parameters - learning_rate and keep_probability. Keep_probability=Uniform(min_value=0.05, max_value=0.1), LogNormal(mu, sigma) - Returns a value drawn according to exp(Normal(mu, sigma)) so that the logarithm of the return value is normally distributedĪn example of a parameter space definition: from azure.ai.ml.sweep import Normal, Uniform.Normal(mu, sigma) - Returns a real value that's normally distributed with mean mu and standard deviation sigma.LogUniform(min_value, max_value) - Returns a value drawn according to exp(Uniform(min_value, max_value)) so that the logarithm of the return value is uniformly distributed.Uniform(min_value, max_value) - Returns a value uniformly distributed between min_value and max_value.The Continuous hyperparameters are specified as a distribution over a continuous range of values: QLogNormal(mu, sigma, q) - Returns a value like round(exp(Normal(mu, sigma)) / q) * q.QNormal(mu, sigma, q) - Returns a value like round(Normal(mu, sigma) / q) * q.QLogUniform(min_value, max_value, q) - Returns a value like round(exp(Uniform(min_value, max_value)) / q) * q.QUniform(min_value, max_value, q) - Returns a value like round(Uniform(min_value, max_value) / q) * q.The following advanced discrete hyperparameters can also be specified using a distribution: In this case, batch_size one of the values and number_of_hidden_layers takes one of the values. Number_of_hidden_layers=Choice(values=range(1,5)), Choice can be:īatch_size=Choice(values=), Discrete hyperparametersĭiscrete hyperparameters are specified as a Choice among discrete values. Hyperparameters can be discrete or continuous, and has a distribution of values described by a Tune hyperparameters by exploring the range of values defined for each hyperparameter. The process is typically computationally expensive and manual.Īzure Machine Learning lets you automate hyperparameter tuning and run experiments in parallel to efficiently optimize hyperparameters. Hyperparameter tuning, also called hyperparameter optimization, is the process of finding the configuration of hyperparameters that results in the best performance. Model performance depends heavily on hyperparameters. For example, with neural networks, you decide the number of hidden layers and the number of nodes in each layer. Hyperparameters are adjustable parameters that let you control the model training process.
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