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"])