def __init__(self, model_configs, model_dirs, weight_scheme="average"):
""" Initializes the ensemble experiment.
Args:
model_configs: A dictionary of all configurations.
model_dirs: A list of model directories (checkpoints).
weight_scheme: A string, the ensemble weights. See
`EnsembleModel.get_ensemble_weights()` for more details.
"""
super(EnsembleExperiment, self).__init__()
self._model_dirs = model_dirs
self._weight_scheme = weight_scheme
infer_options = parse_params(
params=model_configs["infer"],
default_params=self.default_inference_options())
infer_data = []
for item in model_configs["infer_data"]:
infer_data.append(
parse_params(params=item,
default_params=self.default_inferdata_params()))
self._model_configs = model_configs
self._model_configs["infer"] = infer_options
self._model_configs["infer_data"] = infer_data
print_params("Inference parameters: ", self._model_configs["infer"])
print_params("Inference datasets: ", self._model_configs["infer_data"])
def __init__(self,
model_configs,
model_dirs,
weight_scheme="average"):
""" Initializes the ensemble experiment.
Args:
model_configs: A dictionary of all configurations.
model_dirs: A list of model directories (checkpoints).
weight_scheme: A string, the ensemble weights. See
`EnsembleModel.get_ensemble_weights()` for more details.
"""
super(EnsembleExperiment, self).__init__()
self._model_dirs = model_dirs
self._weight_scheme = weight_scheme
infer_options = parse_params(
params=model_configs["infer"],
default_params=self.default_inference_options())
infer_data = []
for item in model_configs["infer_data"]:
infer_data.append(parse_params(
params=item,
default_params=self.default_inferdata_params()))
self._model_configs = model_configs
self._model_configs["infer"] = infer_options
self._model_configs["infer_data"] = infer_data
print_params("Inference parameters: ", self._model_configs["infer"])
print_params("Inference datasets: ", self._model_configs["infer_data"])
def __init__(self, model_configs):
""" Initializes the evaluation experiment.
Args:
model_configs: A dictionary of all configurations.
"""
super(EvalExperiment, self).__init__()
eval_options = parse_params(
params=model_configs["eval"],
default_params=self.default_evaluation_options())
eval_data = []
for item in model_configs["eval_data"]:
eval_data.append(parse_params(
params=item,
default_params=self.default_evaldata_params()))
self._model_configs = model_configs
self._model_configs["eval"] = eval_options
self._model_configs["eval_data"] = eval_data
print_params("Evaluation parameters: ", self._model_configs["eval"])
print_params("Evaluation datasets: ", self._model_configs["eval_data"])
def __init__(self, model_configs):
""" Initializes the inference experiment.
Args:
model_configs: A dictionary of all configurations.
"""
super(InferExperiment, self).__init__()
infer_options = parse_params(
params=model_configs["infer"],
default_params=self.default_inference_options())
infer_data = []
for item in model_configs["infer_data"]:
infer_data.append(parse_params(
params=item,
default_params=self.default_inferdata_params()))
self._model_configs = model_configs
self._model_configs["infer"] = infer_options
self._model_configs["infer_data"] = infer_data
print_params("Inference parameters: ", self._model_configs["infer"])
print_params("Inference datasets: ", self._model_configs["infer_data"])
def __init__(self, model_configs):
""" Initializes the evaluation experiment.
Args:
model_configs: A dictionary of all configurations.
"""
super(EvalExperiment, self).__init__()
eval_options = parse_params(
params=model_configs["eval"],
default_params=self.default_evaluation_options())
eval_data = []
for item in model_configs["eval_data"]:
eval_data.append(parse_params(
params=item,
default_params=self.default_evaldata_params()))
self._model_configs = model_configs
self._model_configs["eval"] = eval_options
self._model_configs["eval_data"] = eval_data
print_params("Evaluation parameters: ", self._model_configs["eval"])
print_params("Evaluation datasets: ", self._model_configs["eval_data"])
def __init__(self, model_configs):
""" Initializes the inference experiment.
Args:
model_configs: A dictionary of all configurations.
"""
super(InferExperiment, self).__init__()
infer_options = parse_params(
params=model_configs["infer"],
default_params=self.default_inference_options())
infer_data = []
for item in model_configs["infer_data"]:
infer_data.append(parse_params(
params=item,
default_params=self.default_inferdata_params()))
self._model_configs = model_configs
self._model_configs["infer"] = infer_options
self._model_configs["infer_data"] = infer_data
print_params("Inference parameters: ", self._model_configs["infer"])
print_params("Inference datasets: ", self._model_configs["infer_data"])
def __init__(self, model_configs):
""" Initializes the training experiment.
Args:
model_configs: A dictionary of all configurations.
"""
super(TrainingExperiment, self).__init__()
# training options
training_options = parse_params(
params=model_configs["train"],
default_params=self.default_training_options())
# for datasets
datasets_params = parse_params(
params=model_configs["data"],
default_params=self.default_datasets_params())
self._model_configs = model_configs
self._model_configs["train"] = training_options
self._model_configs["data"] = datasets_params
print_params("Datasets: ", self._model_configs["data"])
print_params("Training parameters: ", self._model_configs["train"])
ModelConfigs.dump(self._model_configs, self._model_configs["model_dir"])
def __init__(self, model_configs):
""" Initializes the training experiment.
Args:
model_configs: A dictionary of all configurations.
"""
super(TrainingExperiment, self).__init__()
# training options
training_options = parse_params(
params=model_configs["train"],
default_params=self.default_training_options())
# for datasets
datasets_params = parse_params(
params=model_configs["data"],
default_params=self.default_datasets_params())
self._model_configs = model_configs
self._model_configs["train"] = training_options
self._model_configs["data"] = datasets_params
print_params("Datasets: ", self._model_configs["data"])
print_params("Training parameters: ", self._model_configs["train"])
ModelConfigs.dump(self._model_configs, self._model_configs["model_dir"])
