Python tensorflow keras RandomForestModel get_config() 为空

关于get_config()，请注意docs的状态:

返回模型的配置.

Config是一个Python字典(可序列化)，其中包含

注意，get\u config()不保证返回

建议子类模型的开发人员重写此方法，

我想你能做的就是打电话给model.learner_params，了解你想要的细节:

import tensorflow_decision_forests as tfdf
import pprint

params_v2 = {
    "random_seed": 123456,
    "num_trees": 1000,
    "categorical_algorithm": "CART",
    "compute_oob_performances": True,
    "growing_strategy": "LOCAL",
    "honest": True,
    "max_depth": 8,
    "max_num_nodes": None
}

model = tfdf.keras.RandomForestModel().from_config(params_v2)
pprint.pprint(model.learner_params)

{'adapt_bootstrap_size_ratio_for_maximum_training_duration': False,
 'allow_na_conditions': False,
 'bootstrap_size_ratio': 1.0,
 'bootstrap_training_dataset': True,
 'categorical_algorithm': 'CART',
 'categorical_set_split_greedy_sampling': 0.1,
 'categorical_set_split_max_num_items': -1,
 'categorical_set_split_min_item_frequency': 1,
 'compute_oob_performances': True,
 'compute_oob_variable_importances': False,
 'growing_strategy': 'LOCAL',
 'honest': True,
 'honest_fixed_separation': False,
 'honest_ratio_leaf_examples': 0.5,
 'in_split_min_examples_check': True,
 'keep_non_leaf_label_distribution': True,
 'max_depth': 8,
 'max_num_nodes': None,
 'maximum_model_size_in_memory_in_bytes': -1.0,
 'maximum_training_duration_seconds': -1.0,
 'min_examples': 5,
 'missing_value_policy': 'GLOBAL_IMPUTATION',
 'num_candidate_attributes': 0,
 'num_candidate_attributes_ratio': -1.0,
 'num_oob_variable_importances_permutations': 1,
 'num_trees': 1000,
 'pure_serving_model': False,
 'random_seed': 123456,
 'sampling_with_replacement': True,
 'sorting_strategy': 'PRESORT',
 'sparse_oblique_normalization': None,
 'sparse_oblique_num_projections_exponent': None,
 'sparse_oblique_projection_density_factor': None,
 'sparse_oblique_weights': None,
 'split_axis': 'AXIS_ALIGNED',
 'uplift_min_examples_in_treatment': 5,
 'uplift_split_score': 'KULLBACK_LEIBLER',
 'winner_take_all': True}