def get_jacs(
self,
input_data: InputDataGLM
):
if self.noise_model is None:
raise ValueError("noise_model is None")
else:
if self.noise_model == "nb":
from batchglm.api.models.tf1.glm_nb import Estimator
elif self.noise_model == "norm":
from batchglm.api.models import Estimator
elif self.noise_model == "beta":
from batchglm.api.models.tf1.glm_beta import Estimator
else:
raise ValueError("noise_model not recognized")
provide_optimizers = {"gd": True, "adam": True, "adagrad": True, "rmsprop": True,
"nr": False, "nr_tr": False,
"irls": False, "irls_gd": False, "irls_tr": False, "irls_gd_tr": False}
estimator = Estimator(
input_data=input_data,
quick_scale=False,
provide_optimizers=provide_optimizers,
provide_fim=False,
provide_hessian=False,
init_a="standard",
init_b="standard"
)
estimator.initialize()
# Do not train, evaluate at initialization!
estimator.train_sequence(training_strategy=[
{
"convergence_criteria": "step",
"stopping_criteria": 0,
"use_batching": False,
"optim_algo": "gd",
"train_mu": False,
"train_r": False
},
])
estimator_store = estimator.finalize()
return estimator_store.gradients.values
def get_hessians(self, input_data: InputDataGLM):
if self.noise_model is None:
raise ValueError("noise_model is None")
else:
if self.noise_model == "nb":
from batchglm.api.models.tf1.glm_nb import Estimator
elif self.noise_model == "norm":
from batchglm.api.models import Estimator
elif self.noise_model == "beta":
from batchglm.api.models.tf1.glm_beta import Estimator
else:
raise ValueError("noise_model not recognized")
provide_optimizers = {
"gd": True,
"adam": True,
"adagrad": True,
"rmsprop": True,
"nr": False,
"nr_tr": False,
"irls": False,
"irls_gd": False,
"irls_tr": False,
"irls_gd_tr": False
}
estimator = Estimator(input_data=input_data,
quick_scale=False,
provide_optimizers=provide_optimizers,
provide_fim=False,
provide_hessian=False,
init_a="standard",
init_b="standard")
estimator.initialize()
estimator_store = estimator.finalize()
return -estimator_store.fisher_inv