"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, result_col_name=label_meta["feature_name"], feature_metas=feature_metas, model_params={ "n_classes": 3, "hidden_units": [10, 20] }, save="mymodel_keras", batch_size=1) os.unlink("mymodel_keras") train(datasource=datasource, estimator_string="sqlflow_models.RawDNNClassifier", select=select, validation_select=validate_select, feature_columns=feature_columns, feature_column_names=feature_column_names,
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, model_params={ "n_classes": 3, "hidden_units": [10, 20] }, save="mymodel_keras", batch_size=1)
select=select_binary, validate_select=validate_select_binary, 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": [10, 20] }, save="mymodel_binary", batch_size=1, epochs=3, verbose=1) pred(datasource=datasource, estimator=tf.estimator.DNNClassifier, select=select, result_table="iris.predict", feature_columns=feature_columns, feature_column_names=feature_column_names, result_col_name=label_meta["feature_name"], feature_metas=feature_metas, model_params={ "n_classes": 3, "hidden_units": [10, 20] }, save="mymodel", batch_size=1) shutil.rmtree("mymodel") shutil.rmtree("mymodel_binary")