| /* ---------------------------------------------------------------------*//** |
| * |
| * Licensed to the Apache Software Foundation (ASF) under one |
| * or more contributor license agreements. See the NOTICE file |
| * distributed with this work for additional information |
| * regarding copyright ownership. The ASF licenses this file |
| * to you under the Apache License, Version 2.0 (the |
| * "License"); you may not use this file except in compliance |
| * with the License. You may obtain a copy of the License at |
| * |
| * http://www.apache.org/licenses/LICENSE-2.0 |
| * |
| * Unless required by applicable law or agreed to in writing, |
| * software distributed under the License is distributed on an |
| * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| * KIND, either express or implied. See the License for the |
| * specific language governing permissions and limitations |
| * under the License. |
| * |
| *//* ---------------------------------------------------------------------*/ |
| |
| -------------------- IRIS test input tables ----------------- |
| |
| DROP TABLE IF EXISTS iris_data; |
| CREATE TABLE iris_data( |
| id serial, |
| attributes numeric[], |
| class_text varchar |
| ); |
| INSERT INTO iris_data(id, attributes, class_text) VALUES |
| (1,ARRAY[5.1,3.5,1.4,0.2],'Iris-setosa'), |
| (2,ARRAY[4.9,3.0,1.4,0.2],'Iris-setosa'), |
| (3,ARRAY[4.7,3.2,1.3,0.2],'Iris-setosa'), |
| (4,ARRAY[4.6,3.1,1.5,0.2],'Iris-setosa'), |
| (5,ARRAY[5.0,3.6,1.4,0.2],'Iris-setosa'), |
| (6,ARRAY[5.4,3.9,1.7,0.4],'Iris-setosa'), |
| (7,ARRAY[4.6,3.4,1.4,0.3],'Iris-setosa'), |
| (8,ARRAY[5.0,3.4,1.5,0.2],'Iris-setosa'), |
| (9,ARRAY[4.4,2.9,1.4,0.2],'Iris-setosa'), |
| (10,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa'), |
| (11,ARRAY[5.4,3.7,1.5,0.2],'Iris-setosa'), |
| (12,ARRAY[4.8,3.4,1.6,0.2],'Iris-setosa'), |
| (13,ARRAY[4.8,3.0,1.4,0.1],'Iris-setosa'), |
| (14,ARRAY[4.3,3.0,1.1,0.1],'Iris-setosa'), |
| (15,ARRAY[5.8,4.0,1.2,0.2],'Iris-setosa'), |
| (16,ARRAY[5.7,4.4,1.5,0.4],'Iris-setosa'), |
| (17,ARRAY[5.4,3.9,1.3,0.4],'Iris-setosa'), |
| (18,ARRAY[5.1,3.5,1.4,0.3],'Iris-setosa'), |
| (19,ARRAY[5.7,3.8,1.7,0.3],'Iris-setosa'), |
| (20,ARRAY[5.1,3.8,1.5,0.3],'Iris-setosa'), |
| (21,ARRAY[5.4,3.4,1.7,0.2],'Iris-setosa'), |
| (22,ARRAY[5.1,3.7,1.5,0.4],'Iris-setosa'), |
| (23,ARRAY[4.6,3.6,1.0,0.2],'Iris-setosa'), |
| (24,ARRAY[5.1,3.3,1.7,0.5],'Iris-setosa'), |
| (25,ARRAY[4.8,3.4,1.9,0.2],'Iris-setosa'), |
| (26,ARRAY[5.0,3.0,1.6,0.2],'Iris-setosa'), |
| (27,ARRAY[5.0,3.4,1.6,0.4],'Iris-setosa'), |
| (28,ARRAY[5.2,3.5,1.5,0.2],'Iris-setosa'), |
| (29,ARRAY[5.2,3.4,1.4,0.2],'Iris-setosa'), |
| (30,ARRAY[4.7,3.2,1.6,0.2],'Iris-setosa'), |
| (31,ARRAY[4.8,3.1,1.6,0.2],'Iris-setosa'), |
| (32,ARRAY[5.4,3.4,1.5,0.4],'Iris-setosa'), |
| (33,ARRAY[5.2,4.1,1.5,0.1],'Iris-setosa'), |
| (34,ARRAY[5.5,4.2,1.4,0.2],'Iris-setosa'), |
| (35,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa'), |
| (36,ARRAY[5.0,3.2,1.2,0.2],'Iris-setosa'), |
| (37,ARRAY[5.5,3.5,1.3,0.2],'Iris-setosa'), |
| (38,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa'), |
| (39,ARRAY[4.4,3.0,1.3,0.2],'Iris-setosa'), |
| (40,ARRAY[5.1,3.4,1.5,0.2],'Iris-setosa'), |
| (41,ARRAY[5.0,3.5,1.3,0.3],'Iris-setosa'), |
| (42,ARRAY[4.5,2.3,1.3,0.3],'Iris-setosa'), |
| (43,ARRAY[4.4,3.2,1.3,0.2],'Iris-setosa'), |
| (44,ARRAY[5.0,3.5,1.6,0.6],'Iris-setosa'), |
| (45,ARRAY[5.1,3.8,1.9,0.4],'Iris-setosa'), |
| (46,ARRAY[4.8,3.0,1.4,0.3],'Iris-setosa'), |
| (47,ARRAY[5.1,3.8,1.6,0.2],'Iris-setosa'), |
| (48,ARRAY[4.6,3.2,1.4,0.2],'Iris-setosa'), |
| (49,ARRAY[5.3,3.7,1.5,0.2],'Iris-setosa'), |
| (50,ARRAY[5.0,3.3,1.4,0.2],'Iris-setosa'), |
| (51,ARRAY[7.0,3.2,4.7,1.4],'Iris-versicolor'), |
| (52,ARRAY[6.4,3.2,4.5,1.5],'Iris-versicolor'), |
| (53,ARRAY[6.9,3.1,4.9,1.5],'Iris-versicolor'), |
| (54,ARRAY[5.5,2.3,4.0,1.3],'Iris-versicolor'), |
| (55,ARRAY[6.5,2.8,4.6,1.5],'Iris-versicolor'), |
| (56,ARRAY[5.7,2.8,4.5,1.3],'Iris-versicolor'), |
| (57,ARRAY[6.3,3.3,4.7,1.6],'Iris-versicolor'), |
| (58,ARRAY[4.9,2.4,3.3,1.0],'Iris-versicolor'), |
| (59,ARRAY[6.6,2.9,4.6,1.3],'Iris-versicolor'), |
| (60,ARRAY[5.2,2.7,3.9,1.4],'Iris-versicolor'), |
| (61,ARRAY[5.0,2.0,3.5,1.0],'Iris-versicolor'), |
| (62,ARRAY[5.9,3.0,4.2,1.5],'Iris-versicolor'), |
| (63,ARRAY[6.0,2.2,4.0,1.0],'Iris-versicolor'), |
| (64,ARRAY[6.1,2.9,4.7,1.4],'Iris-versicolor'), |
| (65,ARRAY[5.6,2.9,3.6,1.3],'Iris-versicolor'), |
| (66,ARRAY[6.7,3.1,4.4,1.4],'Iris-versicolor'), |
| (67,ARRAY[5.6,3.0,4.5,1.5],'Iris-versicolor'), |
| (68,ARRAY[5.8,2.7,4.1,1.0],'Iris-versicolor'), |
| (69,ARRAY[6.2,2.2,4.5,1.5],'Iris-versicolor'), |
| (70,ARRAY[5.6,2.5,3.9,1.1],'Iris-versicolor'), |
| (71,ARRAY[5.9,3.2,4.8,1.8],'Iris-versicolor'), |
| (72,ARRAY[6.1,2.8,4.0,1.3],'Iris-versicolor'), |
| (73,ARRAY[6.3,2.5,4.9,1.5],'Iris-versicolor'), |
| (74,ARRAY[6.1,2.8,4.7,1.2],'Iris-versicolor'), |
| (75,ARRAY[6.4,2.9,4.3,1.3],'Iris-versicolor'), |
| (76,ARRAY[6.6,3.0,4.4,1.4],'Iris-versicolor'), |
| (77,ARRAY[6.8,2.8,4.8,1.4],'Iris-versicolor'), |
| (78,ARRAY[6.7,3.0,5.0,1.7],'Iris-versicolor'), |
| (79,ARRAY[6.0,2.9,4.5,1.5],'Iris-versicolor'), |
| (80,ARRAY[5.7,2.6,3.5,1.0],'Iris-versicolor'), |
| (81,ARRAY[5.5,2.4,3.8,1.1],'Iris-versicolor'), |
| (82,ARRAY[5.5,2.4,3.7,1.0],'Iris-versicolor'), |
| (83,ARRAY[5.8,2.7,3.9,1.2],'Iris-versicolor'), |
| (84,ARRAY[6.0,2.7,5.1,1.6],'Iris-versicolor'), |
| (85,ARRAY[5.4,3.0,4.5,1.5],'Iris-versicolor'), |
| (86,ARRAY[6.0,3.4,4.5,1.6],'Iris-versicolor'), |
| (87,ARRAY[6.7,3.1,4.7,1.5],'Iris-versicolor'), |
| (88,ARRAY[6.3,2.3,4.4,1.3],'Iris-versicolor'), |
| (89,ARRAY[5.6,3.0,4.1,1.3],'Iris-versicolor'), |
| (90,ARRAY[5.5,2.5,4.0,1.3],'Iris-versicolor'), |
| (91,ARRAY[5.5,2.6,4.4,1.2],'Iris-versicolor'), |
| (92,ARRAY[6.1,3.0,4.6,1.4],'Iris-versicolor'), |
| (93,ARRAY[5.8,2.6,4.0,1.2],'Iris-versicolor'), |
| (94,ARRAY[5.0,2.3,3.3,1.0],'Iris-versicolor'), |
| (95,ARRAY[5.6,2.7,4.2,1.3],'Iris-versicolor'), |
| (96,ARRAY[5.7,3.0,4.2,1.2],'Iris-versicolor'), |
| (97,ARRAY[5.7,2.9,4.2,1.3],'Iris-versicolor'), |
| (98,ARRAY[6.2,2.9,4.3,1.3],'Iris-versicolor'), |
| (99,ARRAY[5.1,2.5,3.0,1.1],'Iris-versicolor'), |
| (100,ARRAY[5.7,2.8,4.1,1.3],'Iris-versicolor'), |
| (101,ARRAY[6.3,3.3,6.0,2.5],'Iris-virginica'), |
| (102,ARRAY[5.8,2.7,5.1,1.9],'Iris-virginica'), |
| (103,ARRAY[7.1,3.0,5.9,2.1],'Iris-virginica'), |
| (104,ARRAY[6.3,2.9,5.6,1.8],'Iris-virginica'), |
| (105,ARRAY[6.5,3.0,5.8,2.2],'Iris-virginica'), |
| (106,ARRAY[7.6,3.0,6.6,2.1],'Iris-virginica'), |
| (107,ARRAY[4.9,2.5,4.5,1.7],'Iris-virginica'), |
| (108,ARRAY[7.3,2.9,6.3,1.8],'Iris-virginica'), |
| (109,ARRAY[6.7,2.5,5.8,1.8],'Iris-virginica'), |
| (110,ARRAY[7.2,3.6,6.1,2.5],'Iris-virginica'), |
| (111,ARRAY[6.5,3.2,5.1,2.0],'Iris-virginica'), |
| (112,ARRAY[6.4,2.7,5.3,1.9],'Iris-virginica'), |
| (113,ARRAY[6.8,3.0,5.5,2.1],'Iris-virginica'), |
| (114,ARRAY[5.7,2.5,5.0,2.0],'Iris-virginica'), |
| (115,ARRAY[5.8,2.8,5.1,2.4],'Iris-virginica'), |
| (116,ARRAY[6.4,3.2,5.3,2.3],'Iris-virginica'), |
| (117,ARRAY[6.5,3.0,5.5,1.8],'Iris-virginica'), |
| (118,ARRAY[7.7,3.8,6.7,2.2],'Iris-virginica'), |
| (119,ARRAY[7.7,2.6,6.9,2.3],'Iris-virginica'), |
| (120,ARRAY[6.0,2.2,5.0,1.5],'Iris-virginica'), |
| (121,ARRAY[6.9,3.2,5.7,2.3],'Iris-virginica'), |
| (122,ARRAY[5.6,2.8,4.9,2.0],'Iris-virginica'), |
| (123,ARRAY[7.7,2.8,6.7,2.0],'Iris-virginica'), |
| (124,ARRAY[6.3,2.7,4.9,1.8],'Iris-virginica'), |
| (125,ARRAY[6.7,3.3,5.7,2.1],'Iris-virginica'), |
| (126,ARRAY[7.2,3.2,6.0,1.8],'Iris-virginica'), |
| (127,ARRAY[6.2,2.8,4.8,1.8],'Iris-virginica'), |
| (128,ARRAY[6.1,3.0,4.9,1.8],'Iris-virginica'), |
| (129,ARRAY[6.4,2.8,5.6,2.1],'Iris-virginica'), |
| (130,ARRAY[7.2,3.0,5.8,1.6],'Iris-virginica'), |
| (131,ARRAY[7.4,2.8,6.1,1.9],'Iris-virginica'), |
| (132,ARRAY[7.9,3.8,6.4,2.0],'Iris-virginica'), |
| (133,ARRAY[6.4,2.8,5.6,2.2],'Iris-virginica'), |
| (134,ARRAY[6.3,2.8,5.1,1.5],'Iris-virginica'), |
| (135,ARRAY[6.1,2.6,5.6,1.4],'Iris-virginica'), |
| (136,ARRAY[7.7,3.0,6.1,2.3],'Iris-virginica'), |
| (137,ARRAY[6.3,3.4,5.6,2.4],'Iris-virginica'), |
| (138,ARRAY[6.4,3.1,5.5,1.8],'Iris-virginica'), |
| (139,ARRAY[6.0,3.0,4.8,1.8],'Iris-virginica'), |
| (140,ARRAY[6.9,3.1,5.4,2.1],'Iris-virginica'), |
| (141,ARRAY[6.7,3.1,5.6,2.4],'Iris-virginica'), |
| (142,ARRAY[6.9,3.1,5.1,2.3],'Iris-virginica'), |
| (143,ARRAY[5.8,2.7,5.1,1.9],'Iris-virginica'), |
| (144,ARRAY[6.8,3.2,5.9,2.3],'Iris-virginica'), |
| (145,ARRAY[6.7,3.3,5.7,2.5],'Iris-virginica'), |
| (146,ARRAY[6.7,3.0,5.2,2.3],'Iris-virginica'), |
| (147,ARRAY[6.3,2.5,5.0,1.9],'Iris-virginica'), |
| (148,ARRAY[6.5,3.0,5.2,2.0],'Iris-virginica'), |
| (149,ARRAY[6.2,3.4,5.4,2.3],'Iris-virginica'), |
| (150,ARRAY[5.9,3.0,5.1,1.8],'Iris-virginica'); |
| |
| |
| DROP TABLE IF EXISTS iris_data_one_hot_encoded; |
| CREATE TABLE iris_data_one_hot_encoded as select id, attributes, ARRAY[class_text is not distinct from 'Iris-setosa', class_text is not distinct from 'Iris-versicolor', class_text is not distinct from 'Iris-virginica']::int[] as class_one_hot_encoded |
| from iris_data; |
| |
| |
| DROP TABLE IF EXISTS iris_data_packed, iris_data_packed_summary; |
| SELECT training_preprocessor_dl('iris_data', -- Source table |
| 'iris_data_packed', -- Output table |
| 'class_text', -- Dependent variable |
| 'attributes' -- Independent variable |
| ); |
| |
| DROP TABLE IF EXISTS iris_data_one_hot_encoded_packed, iris_data_one_hot_encoded_packed_summary; |
| SELECT training_preprocessor_dl('iris_data_one_hot_encoded', -- Source table |
| 'iris_data_one_hot_encoded_packed', -- Output table |
| 'class_one_hot_encoded', -- Dependent variable |
| 'attributes' -- Independent variable |
| ); |
| |
| DROP TABLE IF EXISTS iris_data_val, iris_data_val_summary; |
| SELECT validation_preprocessor_dl('iris_data', -- Source table |
| 'iris_data_val', -- Output table |
| 'class_text', -- Dependent variable |
| 'attributes', -- Independent variable |
| 'iris_data_packed'-- Training preprocessed table |
| ); |
| |
| DROP TABLE IF EXISTS iris_data_one_hot_encoded_val, iris_data_one_hot_encoded_val_summary; |
| SELECT validation_preprocessor_dl('iris_data_one_hot_encoded', -- Source table |
| 'iris_data_one_hot_encoded_val', -- Output table |
| 'class_one_hot_encoded', -- Dependent variable |
| 'attributes', -- Independent variable |
| 'iris_data_one_hot_encoded_packed' -- Training preprocessed table |
| ); |
| |
| DROP TABLE IF EXISTS iris_model_arch; |
| -- NOTE: The seed is set to 0 for every layer. |
| SELECT load_keras_model('iris_model_arch', -- Output table, |
| $$ |
| { |
| "class_name": "Sequential", |
| "keras_version": "2.1.6", |
| "config": |
| [{"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", |
| "config": {"distribution": "uniform", "scale": 1.0, "seed": 0, "mode": "fan_avg"}}, |
| "name": "dense_1", "kernel_constraint": null, "bias_regularizer": null, |
| "bias_constraint": null, "dtype": "float32", "activation": "relu", "trainable": true, |
| "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", |
| "config": {}}, "units": 10, "batch_input_shape": [null, 4], "use_bias": true, |
| "activity_regularizer": null}}, {"class_name": "Dense", |
| "config": {"kernel_initializer": {"class_name": "VarianceScaling", |
| "config": {"distribution": "uniform", "scale": 1.0, "seed": 0, "mode": "fan_avg"}}, |
| "name": "dense_2", "kernel_constraint": null, "bias_regularizer": null, |
| "bias_constraint": null, "activation": "relu", "trainable": true, "kernel_regularizer": null, |
| "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "use_bias": true, |
| "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": |
| {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, |
| "seed": 0, "mode": "fan_avg"}}, "name": "dense_3", "kernel_constraint": null, |
| "bias_regularizer": null, "bias_constraint": null, "activation": "softmax", |
| "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", |
| "config": {}}, "units": 3, "use_bias": true, "activity_regularizer": null}}], |
| "backend": "tensorflow"} |
| $$ |
| ); |
| |
| -- Duplicate the architecture, but note that trainable is set to FALSE. |
| -- This is to ensure we don't learn anything new, that would help us |
| -- deterministically assert the accuracy and loss after transfer learning |
| -- and warm start. |
| SELECT load_keras_model('iris_model_arch', -- Output table, |
| $$ |
| { |
| "class_name": "Sequential", |
| "keras_version": "2.1.6", |
| "config": |
| [{"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", |
| "config": {"distribution": "uniform", "scale": 1.0, "seed": 0, "mode": "fan_avg"}}, |
| "name": "dense_1", "kernel_constraint": null, "bias_regularizer": null, |
| "bias_constraint": null, "dtype": "float32", "activation": "relu", |
| "trainable": false, |
| "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", |
| "config": {}}, "units": 10, "batch_input_shape": [null, 4], "use_bias": true, |
| "activity_regularizer": null}}, {"class_name": "Dense", |
| "config": {"kernel_initializer": {"class_name": "VarianceScaling", |
| "config": {"distribution": "uniform", "scale": 1.0, "seed": 0, "mode": "fan_avg"}}, |
| "name": "dense_2", "kernel_constraint": null, "bias_regularizer": null, |
| "bias_constraint": null, "activation": "relu", |
| "trainable": false, |
| "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", |
| "config": {}}, "units": 10, "use_bias": true, "activity_regularizer": null}}, |
| {"class_name": "Dense", "config": {"kernel_initializer": |
| {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, |
| "seed": 0, "mode": "fan_avg"}}, "name": "dense_3", "kernel_constraint": null, |
| "bias_regularizer": null, "bias_constraint": null, "activation": "softmax", |
| "trainable": false, |
| "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", |
| "config": {}}, "units": 3, "use_bias": true, "activity_regularizer": null}}], |
| "backend": "tensorflow"} |
| $$ |
| ); |
| |
| DROP TABLE IF EXISTS iris_train, iris_test; |
| -- Set seed so results are reproducible |
| SELECT setseed(0); |
| SELECT train_test_split('iris_data', -- Source table |
| 'iris', -- Output table root name |
| 0.8, -- Train proportion |
| NULL, -- Test proportion (0.2) |
| NULL, -- Strata definition |
| NULL, -- Output all columns |
| NULL, -- Sample without replacement |
| TRUE -- Separate output tables |
| ); |
| |
| DROP TABLE IF EXISTS iris_data_15buf_packed, iris_data_15buf_packed_summary; |
| SELECT training_preprocessor_dl('iris_test', -- Source table |
| 'iris_data_15buf_packed', -- Output table |
| 'class_text', -- Dependent variable |
| 'attributes', -- Independent variable |
| 2 -- buffer_size (15 buffers) |
| ); |
| |
| |
| -- Assuming that there are only 3 segments, we want to distribute the data on < 3 segs |
| DROP TABLE IF EXISTS segments_to_use; |
| CREATE TABLE segments_to_use (dbid INTEGER, notes TEXT); |
| INSERT INTO segments_to_use VALUES (2, 'GPU segment'); |
| INSERT INTO segments_to_use VALUES (3, 'GPU segment'); |
| |
| DROP TABLE IF EXISTS iris_data_2seg_packed, iris_data_2seg_packed_summary; |
| SELECT training_preprocessor_dl('iris_data', -- Source table |
| 'iris_data_2seg_packed', -- Output table |
| 'class_text', -- Dependent variable |
| 'attributes', -- Independent variable |
| NULL, |
| NULL, |
| NULL, |
| 'segments_to_use' |
| ); |
| |
| -- Create multi io dataset |
| |
| DROP TABLE IF EXISTS iris_mult; |
| CREATE TABLE iris_mult AS |
| SELECT id, attributes, array_square(attributes) AS attributes2, |
| class_text AS class_text, class_text AS class_text2 |
| FROM iris_data; |
| |
| SELECT load_keras_model('iris_model_arch', -- Output table, |
| $$ |
| {"class_name": "Model", "keras_version": "2.2.4-tf", "config": {"layers": [{"class_name": "InputLayer", "config": {"dtype": "float32", "batch_input_shape": [null, 4], "name": "input_1", "sparse": false}, "inbound_nodes": [], "name": "input_1"}, {"class_name": "InputLayer", "config": {"dtype": "float32", "batch_input_shape": [null, 4], "name": "input_2", "sparse": false}, "inbound_nodes": [], "name": "input_2"}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"dtype": "float32", "distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_in"}}, "name": "dense", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "dtype": "float32", "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {"dtype": "float32"}}, "units": 10, "use_bias": true, "activity_regularizer": null}, "inbound_nodes": [[["input_1", 0, 0, {}]]], "name": "dense"}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"dtype": "float32", "distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_in"}}, "name": "dense_2", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "dtype": "float32", "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {"dtype": "float32"}}, "units": 10, "use_bias": true, "activity_regularizer": null}, "inbound_nodes": [[["input_2", 0, 0, {}]]], "name": "dense_2"}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"dtype": "float32", "distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_in"}}, "name": "dense_1", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "dtype": "float32", "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {"dtype": "float32"}}, "units": 10, "use_bias": true, "activity_regularizer": null}, "inbound_nodes": [[["dense", 0, 0, {}]]], "name": "dense_1"}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"dtype": "float32", "distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_in"}}, "name": "dense_3", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "dtype": "float32", "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {"dtype": "float32"}}, "units": 10, "use_bias": true, "activity_regularizer": null}, "inbound_nodes": [[["dense_2", 0, 0, {}]]], "name": "dense_3"}, {"class_name": "Concatenate", "config": {"dtype": "float32", "trainable": true, "name": "concatenate", "axis": -1}, "inbound_nodes": [[["dense_1", 0, 0, {}], ["dense_3", 0, 0, {}]]], "name": "concatenate"}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"dtype": "float32", "distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_in"}}, "name": "dense_4", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "dtype": "float32", "activation": "softmax", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {"dtype": "float32"}}, "units": 3, "use_bias": true, "activity_regularizer": null}, "inbound_nodes": [[["concatenate", 0, 0, {}]]], "name": "dense_4"}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"dtype": "float32", "distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_in"}}, "name": "dense_5", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "dtype": "float32", "activation": "softmax", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {"dtype": "float32"}}, "units": 3, "use_bias": true, "activity_regularizer": null}, "inbound_nodes": [[["dense_3", 0, 0, {}]]], "name": "dense_5"}], "input_layers": [["input_1", 0, 0], ["input_2", 0, 0]], "output_layers": [["dense_4", 0, 0], ["dense_5", 0, 0]], "name": "model"}, "backend": "tensorflow"} |
| $$::json, NULL, |
| 'Sophie', |
| 'A simple model' |
| ); |
| |
| DROP TABLE IF EXISTS iris_mult_packed, iris_mult_packed_summary; |
| SELECT training_preprocessor_dl('iris_mult', |
| 'iris_mult_packed', |
| 'class_text, class_text2', |
| 'attributes, attributes2', |
| NULL, |
| 255 |
| ); |
| |
| DROP TABLE IF EXISTS iris_mult_val, iris_mult_val_summary; |
| SELECT validation_preprocessor_dl('iris_mult', -- Source table |
| 'iris_mult_val', -- Output table |
| 'class_text, class_text2', -- Dependent variable |
| 'attributes, attributes2', -- Independent variable |
| 'iris_mult_packed'-- Training preprocessed table |
| ); |
