src/ports/postgres/modules/deep_learning/test/madlib_keras_transfer_learning.sql_in

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 * 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
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 * Unless required by applicable law or agreed to in writing,
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 * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
 * KIND, either express or implied.  See the License for the
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 *//* ---------------------------------------------------------------------*/

m4_include(`SQLCommon.m4')

\i m4_regexp(MODULE_PATHNAME,
             `\(.*\)libmadlib\.so',
             `\1../../modules/deep_learning/test/madlib_keras_iris.setup.sql_in'
)

DROP TABLE IF EXISTS iris_model, iris_model_summary;
SELECT madlib_keras_fit('iris_data_packed',   -- source table
                        'iris_model',          -- model output table
                        'iris_model_arch',  -- model arch table
                         1,                    -- model arch id
                         $$ loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] $$,  -- compile_params
                         $$ batch_size=5, epochs=3 $$,  -- fit_params
                         5,                    -- num_iterations
                         NULL, NULL,
                         1 -- metrics_compute_frequency
                        );

-- Test that our code is indeed learning something and not broken. The loss
-- from the first iteration should be less than the 5th, while the accuracy
-- must be greater.
SELECT assert(
  array_upper(training_loss, 1) = 5 AND
  array_upper(training_metrics, 1) = 5,
  'metrics compute frequency must be 1.')
FROM iris_model_summary;

SELECT assert(
  training_loss[5]-training_loss[1] < 0.1 AND
  training_metrics[5]-training_metrics[1] > -0.1,
    'The loss and accuracy should have improved with more iterations.'
)
FROM iris_model_summary;

-- Make a copy of the loss and metrics array, to compare it with runs after
-- warm start and transfer learning.
DROP TABLE IF EXISTS iris_model_first_run;
CREATE TABLE iris_model_first_run AS
SELECT training_loss_final, training_metrics_final
FROM iris_model_summary;

-- Copy weights that were learnt from the previous run, for transfer
-- learning. Copy it now, because using warm_start will overwrite it.
UPDATE iris_model_arch set model_weights = (select model_weights from iris_model)  WHERE model_id = 2;

-- Warm start test
SELECT madlib_keras_fit('iris_data_packed',   -- source table
                       'iris_model',          -- model output table
                       'iris_model_arch',  -- model arch table
                        2,                    -- model arch id
                        $$ loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] $$,  -- compile_params
                        $$ batch_size=5, epochs=3 $$,  -- fit_params
                        2,                    -- num_iterations,
                        NULL, NULL, 1,
                        true -- warm start
                      );

SELECT assert(
  array_upper(training_loss, 1) = 2 AND
  array_upper(training_metrics, 1) = 2,
  'metrics compute frequency must be 1.')
FROM iris_model_summary;

SELECT assert(
  abs(first.training_loss_final-second.training_loss[1]) < 1e-6 AND
  abs(first.training_loss_final-second.training_loss[2]) < 1e-6 AND
  abs(first.training_metrics_final-second.training_metrics[1]) < 1e-10 AND
  abs(first.training_metrics_final-second.training_metrics[2]) < 1e-10,
  'warm start test failed because training loss and metrics don''t match the expected value from the previous run of keras fit.')
FROM iris_model_first_run AS first, iris_model_summary AS second;

-- Transfer learning test
DROP TABLE IF EXISTS iris_model_transfer, iris_model_transfer_summary;
SELECT madlib_keras_fit('iris_data_packed',   -- source table
                       'iris_model_transfer',          -- model output table
                       'iris_model_arch',  -- model arch table
                        2,                    -- model arch id
                        $$ loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] $$,  -- compile_params
                        $$ batch_size=5, epochs=3 $$,  -- fit_params
                        2,
                        NULL, NULL, 1
                      );

SELECT assert(
  array_upper(training_loss, 1) = 2 AND
  array_upper(training_metrics, 1) = 2,
  'metrics compute frequency must be 1.')
FROM iris_model_transfer_summary;

SELECT assert(
  abs(first.training_loss_final-second.training_loss[1]) < 1e-6 AND
  abs(first.training_loss_final-second.training_loss[2]) < 1e-6 AND
  abs(first.training_metrics_final-second.training_metrics[1]) < 1e-10 AND
  abs(first.training_metrics_final-second.training_metrics[2]) < 1e-10,
  'Transfer learning test failed because training loss and metrics don''t match the expected value.')
FROM iris_model_first_run AS first, iris_model_transfer_summary AS second;

-- Rerun the iris setup to discard the changes
\i m4_regexp(MODULE_PATHNAME,
             `\(.*\)libmadlib\.so',
             `\1../../modules/deep_learning/test/madlib_keras_iris.setup.sql_in'
)

m4_changequote(`<!', `!>')
m4_ifdef(<!__POSTGRESQL__!>, <!!>, <!

DROP TABLE IF EXISTS mst_table, mst_table_summary;
SELECT load_model_selection_table(
    'iris_model_arch',
    'mst_table',
    ARRAY[1,2],
    ARRAY[
        $$loss='categorical_crossentropy', optimizer='Adam(lr=0.001)',metrics=['accuracy']$$
    ],
    ARRAY[
        $$batch_size=5,epochs=1$$
    ]
);

DROP TABLE if exists iris_multiple_model, iris_multiple_model_summary, iris_multiple_model_info;
SELECT setseed(0);
SELECT madlib_keras_fit_multiple_model(
  'iris_data_packed',
  'iris_multiple_model',
  'mst_table',
  3,
  FALSE, NULL, 1
);

DROP TABLE IF EXISTS iris_model_first_run;
CREATE TABLE iris_model_first_run AS
SELECT mst_key, model_id, training_loss, training_metrics,
    training_loss_final, training_metrics_final
FROM iris_multiple_model_info;

-- warm start for fit multiple model
SELECT madlib_keras_fit_multiple_model(
  'iris_data_packed',
  'iris_multiple_model',
  'mst_table',
  3,
  FALSE,
  NULL, 1,
  TRUE -- warm_start
);

SELECT assert(
  array_upper(training_loss, 1) = 3 AND
  array_upper(training_metrics, 1) = 3,
  'metrics compute frequency must be 1.')
FROM iris_multiple_model_info;

SELECT assert(
  abs(first.training_loss_final-second.training_loss[1]) < 1e-6 AND
  abs(first.training_loss_final-second.training_loss[2]) < 1e-6 AND
  abs(first.training_metrics_final-second.training_metrics[1]) < 1e-10 AND
  abs(first.training_metrics_final-second.training_metrics[2]) < 1e-10,
  'warm start test failed because training loss and metrics don''t match the expected value from the previous run of keras fit.')
FROM iris_model_first_run AS first, iris_multiple_model_info AS second
WHERE first.mst_key = second.mst_key AND first.model_id = 2;

-- warm start with different mst tables
DROP TABLE IF EXISTS mst_table, mst_table_summary;
SELECT load_model_selection_table(
    'iris_model_arch',
    'mst_table',
    ARRAY[1],
    ARRAY[
        $$loss='categorical_crossentropy', optimizer='Adam(lr=0.001)',metrics=['accuracy']$$
    ],
    ARRAY[
        $$batch_size=5,epochs=1$$,
        $$batch_size=10,epochs=1$$,
        $$batch_size=15,epochs=1$$,
        $$batch_size=20,epochs=1$$
    ]
);

DROP TABLE if exists iris_multiple_model, iris_multiple_model_summary, iris_multiple_model_info;
SELECT setseed(0);
SELECT madlib_keras_fit_multiple_model(
  'iris_data_packed',
  'iris_multiple_model',
  'mst_table',
  3,
  FALSE, NULL, 1
);

DROP TABLE IF EXISTS iris_model_first_run;
CREATE TABLE iris_model_first_run AS
SELECT mst_key, model_id, training_loss, training_metrics,
    training_loss_final, training_metrics_final
FROM iris_multiple_model_info;

DELETE FROM mst_table WHERE mst_key = 4;

SELECT madlib_keras_fit_multiple_model(
  'iris_data_packed',
  'iris_multiple_model',
  'mst_table',
  3,
  FALSE, NULL, 1,
  TRUE);


SELECT assert(
  abs(first.training_loss_final-second.training_loss_final) < 1e-6,
  'The loss should not change for mst_key 4 since it has been removed from mst_table')
FROM iris_model_first_run AS first, iris_multiple_model_info AS second
WHERE first.mst_key = second.mst_key AND second.mst_key = 4;

INSERT INTO mst_table SELECT 4 AS mst_key, model_id, compile_params,
    'batch_size=8, epochs=1' FROM mst_table WHERE mst_key = 1;

INSERT INTO mst_table SELECT 5 AS mst_key, model_id, compile_params,
    'batch_size=18, epochs=1' FROM mst_table WHERE mst_key = 1;

SELECT assert(trap_error($TRAP$madlib_keras_fit_multiple_model(
  'iris_data_packed',
  'iris_multiple_model',
  'mst_table',
  3,
  FALSE,
  NULL, 1,
  TRUE -- warm_start
);$TRAP$) = 1, 'Warm start with extra mst keys should fail.');

-- Transfer learning tests

-- Load the same arch again so that we can compare transfer learning results
SELECT load_keras_model('iris_model_arch',  -- Output table,
$$
{
"class_name": "Sequential",
"keras_version": "2.1.6",
"config":
    [{"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling",
    "config": {"distribution": "uniform", "scale": 1.0, "seed": 0, "mode": "fan_avg"}},
    "name": "dense_1", "kernel_constraint": null, "bias_regularizer": null,
    "bias_constraint": null, "dtype": "float32", "activation": "relu", "trainable": true,
    "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros",
    "config": {}}, "units": 10, "batch_input_shape": [null, 4], "use_bias": true,
    "activity_regularizer": null}}, {"class_name": "Dense",
    "config": {"kernel_initializer": {"class_name": "VarianceScaling",
    "config": {"distribution": "uniform", "scale": 1.0, "seed": 0, "mode": "fan_avg"}},
    "name": "dense_2", "kernel_constraint": null, "bias_regularizer": null,
    "bias_constraint": null, "activation": "relu", "trainable": true, "kernel_regularizer": null,
    "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "use_bias": true,
    "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer":
    {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0,
    "seed": 0, "mode": "fan_avg"}}, "name": "dense_3", "kernel_constraint": null,
    "bias_regularizer": null, "bias_constraint": null, "activation": "softmax",
    "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros",
    "config": {}}, "units": 3, "use_bias": true, "activity_regularizer": null}}],
    "backend": "tensorflow"}
$$
);

DROP TABLE IF EXISTS mst_table, mst_table_summary;
SELECT load_model_selection_table(
    'iris_model_arch',
    'mst_table',
    ARRAY[1,3],
    ARRAY[
        $$loss='categorical_crossentropy',optimizer='Adam(lr=0.01)',metrics=['accuracy']$$,
        $$loss='categorical_crossentropy', optimizer='Adam(lr=0.001)',metrics=['accuracy']$$
    ],
    ARRAY[
        $$batch_size=5,epochs=1$$
    ]
);

DROP TABLE if exists iris_multiple_model, iris_multiple_model_summary, iris_multiple_model_info;
SELECT setseed(0);
SELECT madlib_keras_fit_multiple_model(
  'iris_data_packed',
  'iris_multiple_model',
  'mst_table',
  3,
  FALSE, NULL, 1
);

UPDATE iris_model_arch
SET model_weights = (SELECT model_weights FROM iris_multiple_model WHERE mst_key=1)
WHERE model_id = 1;

DROP TABLE IF EXISTS iris_model_first_run;
CREATE TABLE iris_model_first_run AS
SELECT mst_key, model_id, training_loss, training_metrics,
    training_loss_final, training_metrics_final
FROM iris_multiple_model_info;

DROP TABLE if exists iris_multiple_model, iris_multiple_model_summary, iris_multiple_model_info;
SELECT madlib_keras_fit_multiple_model(
  'iris_data_packed',
  'iris_multiple_model',
  'mst_table',
  3,
  FALSE, NULL, 1
);

SELECT assert(
  (first.training_loss_final-second.training_loss_final) > 1e-6,
  'Transfer learning test failed because training loss and metrics don''t match the expected value.')
FROM iris_model_first_run AS first, iris_multiple_model_info AS second
WHERE first.mst_key = second.mst_key AND first.model_id = 1;
!>)