def test_hyperparameters_values_proto():
values = keras_tuner_pb2.HyperParameters.Values(
values={
"a": keras_tuner_pb2.Value(int_value=1),
"b": keras_tuner_pb2.Value(float_value=2.0),
"c": keras_tuner_pb2.Value(string_value="3"),
})
# When only values are provided, each param is created as `Fixed`.
hps = hp_module.HyperParameters.from_proto(values)
assert hps.values == {"a": 1, "b": 2.0, "c": "3"}
def to_proto(self):
if isinstance(self.values[0], six.string_types):
values = [
keras_tuner_pb2.Value(string_value=v) for v in self.values
]
elif isinstance(self.values[0], six.integer_types):
values = [keras_tuner_pb2.Value(int_value=v) for v in self.values]
else:
values = [
keras_tuner_pb2.Value(float_value=v) for v in self.values
]
return keras_tuner_pb2.Condition(
parent=keras_tuner_pb2.Condition.Parent(name=self.name,
values=values))
def to_proto(self):
if isinstance(self.value, six.integer_types):
value = keras_tuner_pb2.Value(int_value=self.value)
elif isinstance(self.value, float):
value = keras_tuner_pb2.Value(float_value=self.value)
elif isinstance(self.value, six.string_types):
value = keras_tuner_pb2.Value(string_value=self.value)
else:
value = keras_tuner_pb2.Value(boolean_value=self.value)
return keras_tuner_pb2.Fixed(
name=self.name,
value=value,
conditions=[c.to_proto() for c in self.conditions],
)
def to_proto(self):
fixed_space = []
float_space = []
int_space = []
choice_space = []
boolean_space = []
for hp in self.space:
if isinstance(hp, Fixed):
fixed_space.append(hp.to_proto())
elif isinstance(hp, Float):
float_space.append(hp.to_proto())
elif isinstance(hp, Int):
int_space.append(hp.to_proto())
elif isinstance(hp, Choice):
choice_space.append(hp.to_proto())
elif isinstance(hp, Boolean):
boolean_space.append(hp.to_proto())
else:
raise ValueError("Unrecognized HP type: {}".format(hp))
values = {}
for name, value in self.values.items():
if isinstance(value, float):
val = keras_tuner_pb2.Value(float_value=value)
elif isinstance(value, six.integer_types):
val = keras_tuner_pb2.Value(int_value=value)
elif isinstance(value, six.string_types):
val = keras_tuner_pb2.Value(string_value=value)
elif isinstance(value, bool):
val = keras_tuner_pb2.Value(boolean_value=value)
else:
raise ValueError("Unrecognized value type: {}".format(value))
values[name] = val
return keras_tuner_pb2.HyperParameters(
space=keras_tuner_pb2.HyperParameters.Space(
fixed_space=fixed_space,
float_space=float_space,
int_space=int_space,
choice_space=choice_space,
boolean_space=boolean_space,
),
values=keras_tuner_pb2.HyperParameters.Values(values=values),
)
def to_proto(self):
if isinstance(self.values[0], six.string_types):
values = [keras_tuner_pb2.Value(string_value=v) for v in self.values]
default = keras_tuner_pb2.Value(string_value=self.default)
elif isinstance(self.values[0], six.integer_types):
values = [keras_tuner_pb2.Value(int_value=v) for v in self.values]
default = keras_tuner_pb2.Value(int_value=self.default)
else:
values = [keras_tuner_pb2.Value(float_value=v) for v in self.values]
default = keras_tuner_pb2.Value(float_value=self.default)
return keras_tuner_pb2.Choice(
name=self.name,
ordered=self.ordered,
values=values,
default=default,
conditions=[c.to_proto() for c in self.conditions],
)