HyperParameters

Original Link : https://keras.io/api/keras_tuner/hyperparameters/
Last Checked at : 2024-11-25

`HyperParameters` class

keras_tuner.HyperParameters()

Container for both a hyperparameter space, and current values.

A HyperParameters instance can be pass to HyperModel.build(hp) as an argument to build a model.

To prevent the users from depending on inactive hyperparameter values, only active hyperparameters should have values in HyperParameters.values.

Attributes

space: A list of HyperParameter objects.
values: A dict mapping hyperparameter names to current values.

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`Boolean` method

HyperParameters.Boolean(name, default=False, parent_name=None, parent_values=None)

Choice between True and False.

Arguments

name: A string. the name of parameter. Must be unique for each HyperParameter instance in the search space.
default: Boolean, the default value to return for the parameter. If unspecified, the default value will be False.
parent_name: Optional string, specifying the name of the parent HyperParameter to use as the condition to activate the current HyperParameter.
parent_values: Optional list of the values of the parent HyperParameter to use as the condition to activate the current HyperParameter.

Returns

The value of the hyperparameter, or None if the hyperparameter is not active.

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`Choice` method

HyperParameters.Choice(
    name, values, ordered=None, default=None, parent_name=None, parent_values=None
)

Choice of one value among a predefined set of possible values.

Arguments

name: A string. the name of parameter. Must be unique for each HyperParameter instance in the search space.
values: A list of possible values. Values must be int, float, str, or bool. All values must be of the same type.
ordered: Optional boolean, whether the values passed should be considered to have an ordering. Defaults to True for float/int values. Must be False for any other values.
default: Optional default value to return for the parameter. If unspecified, the default value will be:
- None if None is one of the choices in values
- The first entry in values otherwise.
parent_name: Optional string, specifying the name of the parent HyperParameter to use as the condition to activate the current HyperParameter.
parent_values: Optional list of the values of the parent HyperParameter to use as the condition to activate the current HyperParameter.

Returns

The value of the hyperparameter, or None if the hyperparameter is not active.

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`Fixed` method

HyperParameters.Fixed(name, value, parent_name=None, parent_values=None)

Fixed, untunable value.

Arguments

name: A string. the name of parameter. Must be unique for each HyperParameter instance in the search space.
value: The value to use (can be any JSON-serializable Python type).
parent_name: Optional string, specifying the name of the parent HyperParameter to use as the condition to activate the current HyperParameter.
parent_values: Optional list of the values of the parent HyperParameter to use as the condition to activate the current HyperParameter.

Returns

The value of the hyperparameter, or None if the hyperparameter is not active.

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`Float` method

HyperParameters.Float(
    name,
    min_value,
    max_value,
    step=None,
    sampling="linear",
    default=None,
    parent_name=None,
    parent_values=None,
)

Floating point value hyperparameter.

Example #1:

hp.Float(
    "image_rotation_factor",
    min_value=0,
    max_value=1)

All values in interval [0, 1] have equal probability of being sampled.

Example #2:

hp.Float(
    "image_rotation_factor",
    min_value=0,
    max_value=1,
    step=0.2)

step is the minimum distance between samples. The possible values are [0, 0.2, 0.4, 0.6, 0.8, 1.0].

Example #3:

hp.Float(
    "learning_rate",
    min_value=0.001,
    max_value=10,
    step=10,
    sampling="log")

When sampling="log", the step is multiplied between samples. The possible values are [0.001, 0.01, 0.1, 1, 10].

Arguments

name: A string. the name of parameter. Must be unique for each HyperParameter instance in the search space.
min_value: Float, the lower bound of the range.
max_value: Float, the upper bound of the range.
step: Optional float, the distance between two consecutive samples in the range. If left unspecified, it is possible to sample any value in the interval. If sampling="linear", it will be the minimum additve between two samples. If sampling="log", it will be the minimum multiplier between two samples.
sampling: String. One of “linear”, “log”, “reverse_log”. Defaults to “linear”. When sampling value, it always start from a value in range [0.0, 1.0). The sampling argument decides how the value is projected into the range of [min_value, max_value]. “linear”: min_value + value * (max_value - min_value) “log”: min_value * (max_value / min_value) ^ value “reverse_log”: (max_value - min_value * ((max_value / min_value) ^ (1 - value) - 1))
default: Float, the default value to return for the parameter. If unspecified, the default value will be min_value.
parent_name: Optional string, specifying the name of the parent HyperParameter to use as the condition to activate the current HyperParameter.
parent_values: Optional list of the values of the parent HyperParameter to use as the condition to activate the current HyperParameter.

Returns

The value of the hyperparameter, or None if the hyperparameter is not active.

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`Int` method

HyperParameters.Int(
    name,
    min_value,
    max_value,
    step=None,
    sampling="linear",
    default=None,
    parent_name=None,
    parent_values=None,
)

Integer hyperparameter.

Note that unlike Python’s range function, max_value is included in the possible values this parameter can take on.

Example #1:

hp.Int(
    "n_layers",
    min_value=6,
    max_value=12)

The possible values are [6, 7, 8, 9, 10, 11, 12].

Example #2:

hp.Int(
    "n_layers",
    min_value=6,
    max_value=13,
    step=3)

step is the minimum distance between samples. The possible values are [6, 9, 12].

Example #3:

hp.Int(
    "batch_size",
    min_value=2,
    max_value=32,
    step=2,
    sampling="log")

When sampling="log" the step is multiplied between samples. The possible values are [2, 4, 8, 16, 32].

Arguments

name: A string. the name of parameter. Must be unique for each HyperParameter instance in the search space.
min_value: Integer, the lower limit of range, inclusive.
max_value: Integer, the upper limit of range, inclusive.
step: Optional integer, the distance between two consecutive samples in the range. If left unspecified, it is possible to sample any integers in the interval. If sampling="linear", it will be the minimum additve between two samples. If sampling="log", it will be the minimum multiplier between two samples.
sampling: String. One of “linear”, “log”, “reverse_log”. Defaults to “linear”. When sampling value, it always start from a value in range [0.0, 1.0). The sampling argument decides how the value is projected into the range of [min_value, max_value]. “linear”: min_value + value * (max_value - min_value) “log”: min_value * (max_value / min_value) ^ value “reverse_log”: (max_value - min_value * ((max_value / min_value) ^ (1 - value) - 1))
default: Integer, default value to return for the parameter. If unspecified, the default value will be min_value.
parent_name: Optional string, specifying the name of the parent HyperParameter to use as the condition to activate the current HyperParameter.
parent_values: Optional list of the values of the parent HyperParameter to use as the condition to activate the current HyperParameter.

Returns

The value of the hyperparameter, or None if the hyperparameter is not active.

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`conditional_scope` method

HyperParameters.conditional_scope(parent_name, parent_values)

Opens a scope to create conditional HyperParameters.

All HyperParameters created under this scope will only be active when the parent HyperParameter specified by parent_name is equal to one of the values passed in parent_values.

When the condition is not met, creating a HyperParameter under this scope will register the HyperParameter, but will return None rather than a concrete value.

Note that any Python code under this scope will execute regardless of whether the condition is met.

This feature is for the Tuner to collect more information of the search space and the current trial. It is especially useful for model selection. If the parent HyperParameter is for model selection, the HyperParameters in a model should only be active when the model selected, which can be implemented using conditional_scope.

Examples

def MyHyperModel(HyperModel):
    def build(self, hp):
        model = Sequential()
        model.add(Input(shape=(32, 32, 3)))
        model_type = hp.Choice("model_type", ["mlp", "cnn"])
        with hp.conditional_scope("model_type", ["mlp"]):
            if model_type == "mlp":
                model.add(Flatten())
                model.add(Dense(32, activation='relu'))
        with hp.conditional_scope("model_type", ["cnn"]):
            if model_type == "cnn":
                model.add(Conv2D(64, 3, activation='relu'))
                model.add(GlobalAveragePooling2D())
        model.add(Dense(10, activation='softmax'))
        return model

Arguments

parent_name: A string, specifying the name of the parent HyperParameter to use as the condition to activate the current HyperParameter.
parent_values: A list of the values of the parent HyperParameter to use as the condition to activate the current HyperParameter.

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`get` method

HyperParameters.get(name)

Return the current value of this hyperparameter set.

HyperParameters