import sqlflow_models from sqlflow_submitter.tensorflow.estimator_example import ( datasource, feature_column_names, feature_columns, feature_metas, label_meta, select, validate_select) from sqlflow_submitter.tensorflow.predict import pred from sqlflow_submitter.tensorflow.train import train if __name__ == "__main__": train(datasource=datasource, estimator=sqlflow_models.DNNClassifier, select=select, validate_select=validate_select, feature_columns=feature_columns, feature_column_names=feature_column_names, feature_metas=feature_metas, label_meta=label_meta, model_params={ "n_classes": 3, "hidden_units": [10, 20] }, save="mymodel_keras", batch_size=1, epochs=3, verbose=0) pred(datasource=datasource, estimator=sqlflow_models.DNNClassifier, select=select, result_table="iris.predict", feature_columns=feature_columns, feature_column_names=feature_column_names, feature_metas=feature_metas, label_meta=label_meta,
from estimator_example import (datasource, feature_column_names, feature_columns, feature_metas, label_meta, select_binary, validate_select_binary) from sqlflow_submitter.tensorflow.evaluate import evaluate from sqlflow_submitter.tensorflow.train import train if __name__ == "__main__": # Test evaluation on an estimator model train(datasource=datasource, estimator_string="tf.estimator.DNNClassifier", select="SELECT * FROM iris.train where class!=2", validation_select="SELECT * FROM iris.test where class!=2", feature_columns=feature_columns, feature_column_names=feature_column_names, feature_metas=feature_metas, label_meta=label_meta, model_params={ "n_classes": 2, "hidden_units": [128, 32] }, save="bin_model", batch_size=10, epoch=20, verbose=0) # FIXME(typhoonzero): need to re-create result table: iris.evaluate_result? evaluate( datasource=datasource, estimator_string="tf.estimator.DNNClassifier", select="SELECT * FROM iris.test where class!=2", result_table="", feature_columns=feature_columns, feature_column_names=feature_column_names,
from sqlflow_submitter.tensorflow.estimator_example import ( datasource, feature_column_names, feature_column_names_map, feature_columns, feature_metas, label_meta, select, validate_select) from sqlflow_submitter.tensorflow.predict import pred from sqlflow_submitter.tensorflow.train import train if __name__ == "__main__": train(datasource=datasource, estimator_string="sqlflow_models.DNNClassifier", select=select, validation_select=validate_select, feature_columns=feature_columns, feature_column_names=feature_column_names, feature_metas=feature_metas, label_meta=label_meta, model_params={ "n_classes": 3, "hidden_units": [10, 20] }, validation_metrics=["CategoricalAccuracy"], save="mymodel_keras", batch_size=1, epoch=3, verbose=0) pred(datasource=datasource, estimator_string="sqlflow_models.DNNClassifier", select=select, result_table="iris.predict", feature_columns=feature_columns, feature_column_names=feature_column_names, feature_column_names_map=feature_column_names_map,
feature_columns, feature_metas, label_meta, select_binary, validate_select_binary) from sqlflow_submitter.tensorflow.explain import explain from sqlflow_submitter.tensorflow.predict import pred from sqlflow_submitter.tensorflow.train import train if __name__ == "__main__": train(datasource=datasource, estimator=tf.estimator.BoostedTreesClassifier, select="SELECT * FROM iris.train where class!=2", validate_select="SELECT * FROM iris.test where class!=2", feature_columns=feature_columns, feature_column_names=feature_column_names, feature_metas=feature_metas, label_meta=label_meta, model_params={ "n_batches_per_layer": 1, "n_classes": 2, "n_trees": 50, "center_bias": True }, save="btmodel", batch_size=100, epochs=20, verbose=0) explain(datasource=datasource, estimator_cls=tf.estimator.BoostedTreesClassifier, select="SELECT * FROM iris.test where class!=2", feature_columns=feature_columns, feature_column_names=feature_column_names, feature_metas=feature_metas, label_meta=label_meta,
select_binary, validate_select_binary) from sqlflow_submitter.tensorflow.explain import explain from sqlflow_submitter.tensorflow.predict import pred from sqlflow_submitter.tensorflow.train import train if __name__ == "__main__": # Train and explain BoostedTreesClassifier train(datasource=datasource, estimator_string="tf.estimator.BoostedTreesClassifier", select="SELECT * FROM iris.train where class!=2", validation_select="SELECT * FROM iris.test where class!=2", feature_columns=feature_columns, feature_column_names=feature_column_names, feature_metas=feature_metas, label_meta=label_meta, model_params={ "n_batches_per_layer": 1, "n_classes": 2, "n_trees": 50, "center_bias": True }, save="btmodel", batch_size=100, epoch=20, verbose=0) explain(datasource=datasource, estimator_string="tf.estimator.BoostedTreesClassifier", select="SELECT * FROM iris.test where class!=2", feature_columns=feature_columns, feature_column_names=feature_column_names, feature_metas=feature_metas,
"dtype": "int64", "delimiter": "", "shape": [], "is_sparse": "false" == "true" } if __name__ == "__main__": # tf.python.training.basic_session_run_hooks.LoggingTensorHook = sqlflow_submitter.tensorflow.train.PrintTensorsHook train(datasource=datasource, estimator=tf.estimator.DNNClassifier, select=select, validation_select=validate_select, feature_columns=feature_columns, feature_column_names=feature_column_names, feature_metas=feature_metas, label_meta=label_meta, model_params={ "n_classes": 3, "hidden_units": [10, 20] }, save="mymodel", batch_size=1, epoch=3, verbose=0) train(datasource=datasource, estimator=tf.estimator.DNNClassifier, select=select_binary, validation_select=validate_select_binary, feature_columns=feature_columns, feature_column_names=feature_column_names, feature_metas=feature_metas, label_meta=label_meta,