def get_next_population(self, prev_response):
"""
Returns a new generation for a given experiment.
:param prev_response: the previous generation, *with* evaluation metrics for each Candidate
:return: a new generation, as a PopulationResponse object
"""
# Prepare a request for next generation
request_params = {
'version': self.version,
'experiment_id': self.experiment_id
}
request = ParseDict(request_params, PopulationRequest())
request.config = self.experiment_params_as_bytes
if prev_response:
request.evaluated_population_response.CopyFrom(prev_response)
# Ask for next generation
response = self._next_population_with_retry(request)
return response
def request_base_model(self):
# Update the request to query for 1 Keras model
params = copy.deepcopy(self.experiment_params)
params["evolution"] = {"population_size": 1}
params["LEAF"]["representation"] = "KerasNN"
# Prepare a request for next generation
request_params = {
'version': self.version,
'experiment_id': self.experiment_id
}
request = ParseDict(request_params, PopulationRequest())
request.config = self.extension_packaging.to_extension_bytes(params)
# Ask for the base model
response = self._next_population_with_retry(request)
# Convert the received bytes to a Keras model
model_bytes = response.population[0].interpretation
model_file = io.BytesIO(model_bytes)
keras_model = load_model(model_file)
# return the base model
return keras_model
