def main(config="../../config.yaml", namespace=""): # obtain config if isinstance(config, str): config = load_job_config(config) backend = config.backend work_mode = config.work_mode lr_param = { "name": "hetero_lr_0", "penalty": "L2", "optimizer": "rmsprop", "tol": 0.0001, "alpha": 0.01, "max_iter": 10, "early_stop": "diff", "batch_size": -1, "learning_rate": 0.15, "init_param": { "init_method": "zeros" }, "cv_param": { "n_splits": 3, "shuffle": False, "random_seed": 103, "need_cv": True } } pipeline = common_tools.make_normal_dsl(config, namespace, lr_param, is_cv=True) # fit model pipeline.fit(backend=backend, work_mode=work_mode) # query component summary common_tools.prettify(pipeline.get_component("hetero_lr_0").get_summary())
def main(config="../../config.yaml", namespace=""): # obtain config if isinstance(config, str): config = load_job_config(config) lr_param = { "name": "hetero_lr_0", "penalty": "L2", "optimizer": "nesterov_momentum_sgd", "tol": 1e-05, "alpha": 0.0001, "max_iter": 1, "early_stop": "diff", "multi_class": "ovr", "batch_size": -1, "learning_rate": 0.15, "init_param": { "init_method": "zeros" } } pipeline = common_tools.make_normal_dsl(config, namespace, lr_param, is_ovr=True) # fit model pipeline.fit() # query component summary common_tools.prettify(pipeline.get_component("hetero_lr_0").get_summary())
def main(config="../../config.yaml", namespace=""): # obtain config if isinstance(config, str): config = load_job_config(config) lr_param = { "name": "hetero_lr_0", "penalty": "L2", "optimizer": "rmsprop", "tol": 0.0001, "alpha": 0.01, "max_iter": 30, "early_stop": "diff", "batch_size": 320, "batch_strategy": "random", "learning_rate": 0.15, "init_param": { "init_method": "zeros" }, "sqn_param": { "update_interval_L": 3, "memory_M": 5, "sample_size": 5000, "random_seed": None }, "cv_param": { "n_splits": 5, "shuffle": False, "random_seed": 103, "need_cv": False }, "callback_param": { "callbacks": ["ModelCheckpoint"], "save_freq": "epoch" } } pipeline = common_tools.make_normal_dsl(config, namespace, lr_param) # dsl_json = predict_pipeline.get_predict_dsl() # conf_json = predict_pipeline.get_predict_conf() # import json # json.dump(dsl_json, open('./hetero-lr-normal-predict-dsl.json', 'w'), indent=4) # json.dump(conf_json, open('./hetero-lr-normal-predict-conf.json', 'w'), indent=4) # fit model pipeline.fit() # query component summary common_tools.prettify(pipeline.get_component("hetero_lr_0").get_summary()) common_tools.prettify(pipeline.get_component("evaluation_0").get_summary())
def main(config="../../config.yaml", namespace=""): # obtain config if isinstance(config, str): config = load_job_config(config) backend = config.backend work_mode = config.work_mode lr_param = { "name": "hetero_lr_0", "penalty": "L2", "optimizer": "rmsprop", "tol": 0.0001, "alpha": 0.01, "max_iter": 30, "early_stop": "diff", "batch_size": -1, "learning_rate": 0.15, "validation_freqs": 1, "early_stopping_rounds": 3, "metrics": [], "use_first_metric_only": False, "init_param": { "init_method": "zeros" }, "sqn_param": { "update_interval_L": 3, "memory_M": 5, "sample_size": 5000, "random_seed": None }, "cv_param": { "n_splits": 5, "shuffle": False, "random_seed": 103, "need_cv": False } } pipeline = common_tools.make_normal_dsl(config, namespace, lr_param, has_validate=True) # fit model job_parameters = JobParameters(backend=backend, work_mode=work_mode) pipeline.fit(job_parameters) # query component summary common_tools.prettify(pipeline.get_component("hetero_lr_0").get_summary())
def main(config="../../config.yaml", namespace=""): # obtain config if isinstance(config, str): config = load_job_config(config) backend = config.backend work_mode = config.work_mode lr_param = { "name": "hetero_lr_0", "penalty": "L2", "optimizer": "sqn", "tol": 0.0001, "alpha": 1e-05, "max_iter": 30, "early_stop": "diff", "batch_size": 5000, "learning_rate": 0.15, "decay": 0.3, "decay_sqrt": True, "init_param": { "init_method": "zeros" }, "sqn_param": { "update_interval_L": 3, "memory_M": 5, "sample_size": 5000, "random_seed": None }, "cv_param": { "n_splits": 5, "shuffle": False, "random_seed": 103, "need_cv": False } } pipeline = common_tools.make_normal_dsl(config, namespace, lr_param, is_dense=False) # fit model pipeline.fit(backend=backend, work_mode=work_mode) # query component summary common_tools.prettify(pipeline.get_component("hetero_lr_0").get_summary()) common_tools.prettify(pipeline.get_component("evaluation_0").get_summary())
def main(config="../../config.yaml", namespace=""): # obtain config if isinstance(config, str): config = load_job_config(config) lr_param = { "name": "hetero_lr_0", "penalty": "L2", "optimizer": "rmsprop", "tol": 0.0001, "alpha": 0.01, "max_iter": 30, "callback_param": { "callbacks": ["EarlyStopping"], "validation_freqs": 3, "early_stopping_rounds": 3 }, "early_stop": "diff", "batch_size": -1, "learning_rate": 0.15, "init_param": { "init_method": "zeros", "fit_intercept": True }, "encrypt_param": { "key_length": 2048 }, "cv_param": { "n_splits": 5, "shuffle": False, "random_seed": 103, "need_cv": False } } pipeline = common_tools.make_normal_dsl(config, namespace, lr_param, has_validate=True) # fit model pipeline.fit() # query component summary common_tools.prettify(pipeline.get_component("evaluation_0").get_summary())
def main(config="../../config.yaml", namespace=""): # obtain config if isinstance(config, str): config = load_job_config(config) lr_param = { "name": "hetero_lr_0", "penalty": "L2", "optimizer": "nesterov_momentum_sgd", "tol": 0.0001, "alpha": 0.01, "max_iter": 30, "early_stop": "weight_diff", "batch_size": -1, "learning_rate": 0.15, "init_param": { "init_method": "zeros" }, "sqn_param": { "update_interval_L": 3, "memory_M": 5, "sample_size": 5000, "random_seed": None }, "cv_param": { "n_splits": 5, "shuffle": False, "random_seed": 103, "need_cv": False } } pipeline = common_tools.make_normal_dsl(config, namespace, lr_param, is_multi_host=True) # fit model pipeline.fit() # query component summary common_tools.prettify(pipeline.get_component("hetero_lr_0").get_summary())