A HParams object holds hyperparameters used to build and train a model,
such as the number of hidden units in a neural net layer or the learning rate
to use when training.
You first create a HParams object by specifying the names and values of the
hyperparameters.
To make them easily accessible the parameter names are added as direct
attributes of the class. A typical usage is as follows:
# Create a HParams object specifying names and values of the model
# hyperparameters:
hparams = HParams(learning_rate=0.1, num_hidden_units=100)
# The hyperparameter are available as attributes of the HParams object:
hparams.learning_rate ==> 0.1
hparams.num_hidden_units ==> 100
Hyperparameters have type, which is inferred from the type of their value
passed at construction type. The currently supported types are: integer,
float, boolean, string, and list of integer, float, boolean, or string.
You can override hyperparameter values by calling the
parse() method, passing a string of comma separated
name=value pairs. This is intended to make it possible to override
any hyperparameter values from a single command-line flag to which
the user passes 'hyper-param=value' pairs. It avoids having to define
one flag for each hyperparameter.
The syntax expected for each value depends on the type of the parameter.
See parse() for a description of the syntax.
Example:
# Define a command line flag to pass name=value pairs.
# For example using argparse:
import argparse
parser = argparse.ArgumentParser(description='Train my model.')
parser.add_argument('--hparams', type=str,
help='Comma separated list of "name=value" pairs.')
args = parser.parse_args()
...
def my_program():
# Create a HParams object specifying the names and values of the
# model hyperparameters:
hparams = tf.contrib.training.HParams(
learning_rate=0.1,
num_hidden_units=100,
activations=['relu', 'tanh'])
# Override hyperparameters values by parsing the command line
hparams.parse(args.hparams)
# If the user passed `--hparams=learning_rate=0.3` on the command line
# then 'hparams' has the following attributes:
hparams.learning_rate ==> 0.3
hparams.num_hidden_units ==> 100
hparams.activations ==> ['relu', 'tanh']
# If the hyperparameters are in json format use parse_json:
hparams.parse_json('{"learning_rate": 0.3, "activations": "relu"}')
Args
hparam_def
Serialized hyperparameters, encoded as a hparam_pb2.HParamDef
protocol buffer. If provided, this object is initialized by
deserializing hparam_def. Otherwise **kwargs is used.
model_structure
An instance of ModelStructure, defining the feature
crosses to be used in the Trial.
**kwargs
Key-value pairs where the key is the hyperparameter name and
the value is the value for the parameter.
Raises
ValueError
If both hparam_def and initialization values are provided,
or if one of the arguments is invalid.
If a non-negative integer, JSON array elements and object members
will be pretty-printed with that indent level. An indent level of 0, or
negative, will only insert newlines. None (the default) selects the
most compact representation.
separators
Optional (item_separator, key_separator) tuple. Default is
(', ', ': ').
sort_keys
If True, the output dictionaries will be sorted by key.
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