This is an automated email from the ASF dual-hosted git repository.
khannaekta pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/madlib.git
The following commit(s) were added to refs/heads/master by this push:
new 96885c8 DL: Split madlib_keras devcheck file
96885c8 is described below
commit 96885c895b6b75456fbe448aa786814bf72421a9
Author: Domino Valdano <dvald...@pivotal.io>
AuthorDate: Mon Jul 29 15:46:04 2019 -0700
DL: Split madlib_keras devcheck file
Prior to this commit, the dev-check test file for deep_learning module
was too big and there was no way of running a subset of the tests for
e.g. predict.
This commit separates madlib_keras.sql_in test file into:
1. Setup files: for data creation used in the tests
2. Specific test files: for testing fit, predict, evaluate and transfer
learning
Additionally, madpack.py is updated to ignore any setup
files(`*.setup.sql_in`) in the test dir and not run it as a test in
dev-check/install-check.
Co-authored-by: Ekta Khanna <ekha...@pivotal.io>
Co-authored-by: Orhan Kislal <okis...@apache.org>
---
src/madpack/madpack.py | 1 +
.../modules/deep_learning/test/madlib_keras.sql_in | 1287 --------------------
.../test/madlib_keras_cifar.setup.sql_in | 152 +++
.../test/madlib_keras_evaluate.sql_in | 61 +
.../deep_learning/test/madlib_keras_fit.sql_in | 379 ++++++
.../test/madlib_keras_iris.setup.sql_in | 266 ++++
.../deep_learning/test/madlib_keras_predict.sql_in | 316 +++++
.../test/madlib_keras_predict_byom.sql_in | 137 +++
.../test/madlib_keras_transfer_learning.sql_in | 116 ++
9 files changed, 1428 insertions(+), 1287 deletions(-)
diff --git a/src/madpack/madpack.py b/src/madpack/madpack.py
index e735526..74fff37 100755
--- a/src/madpack/madpack.py
+++ b/src/madpack/madpack.py
@@ -674,6 +674,7 @@ def _process_py_sql_files_in_modules(modset, args_dict):
# Loop through all SQL files for this module
source_files = glob.glob(mask)
+ source_files = [s for s in source_files if '.setup' not in s]
if calling_operation == INSTALL_DEV_CHECK and madpack_cmd !=
'install-check':
source_files = [s for s in source_files if '.ic' not in s]
diff --git a/src/ports/postgres/modules/deep_learning/test/madlib_keras.sql_in
b/src/ports/postgres/modules/deep_learning/test/madlib_keras.sql_in
deleted file mode 100644
index 28a500e..0000000
--- a/src/ports/postgres/modules/deep_learning/test/madlib_keras.sql_in
+++ /dev/null
@@ -1,1287 +0,0 @@
-/* ---------------------------------------------------------------------*//**
- *
- * 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.
- *
- *//* ---------------------------------------------------------------------*/
-drop table if exists cifar_10_sample;
-create table cifar_10_sample(id INTEGER, y SMALLINT, y_text TEXT, imgpath
TEXT, x REAL[]);
-copy cifar_10_sample from stdin delimiter '|';
-1|0|'cat'|'0/img0.jpg'|{{{202,204,199},{202,204,199},{204,206,201},{206,208,203},{208,210,205},{209,211,206},{210,212,207},{212,214,210},{213,215,212},{215,217,214},{216,218,215},{216,218,215},{215,217,214},{216,218,215},{216,218,215},{216,218,214},{217,219,214},{217,219,214},{218,220,215},{218,219,214},{216,217,212},{217,218,213},{218,219,214},{214,215,209},{213,214,207},{212,213,206},{211,212,205},{209,210,203},{208,209,202},{207,208,200},{205,206,199},{203,204,198}},{{206,208,203},{20
[...]
-2|1|'dog'|'0/img2.jpg'|{{{126,118,110},{122,115,108},{126,119,111},{127,119,109},{130,122,111},{130,122,111},{132,124,113},{133,125,114},{130,122,111},{132,124,113},{134,126,115},{131,123,112},{131,123,112},{134,126,115},{133,125,114},{136,128,117},{137,129,118},{137,129,118},{136,128,117},{131,123,112},{130,122,111},{132,124,113},{132,124,113},{132,124,113},{129,122,110},{127,121,109},{127,121,109},{125,119,107},{124,118,106},{124,118,106},{120,114,102},{117,111,99}},{{122,115,107},{119
[...]
-\.
-
-
--- normalize the indep variable
--- TODO Calling this function makes keras.fit fail with the exception
(investigate later)
--- NOTICE: Releasing segworker groups to finish aborting the transaction.
--- ERROR: could not connect to segment: initialization of segworker group
failed (cdbgang.c:237)
--- update cifar_10_sample_val SET independent_var =
array_scalar_mult(independent_var::real[], (1/255.0)::real);
-
--- Prepare the minibatched data manually instead of calling
--- training_preprocessor_dl since it internally calls array_scalar_mult.
--- Please refer to MADLIB-1326 for more details on the issue.
-
-DROP TABLE IF EXISTS cifar_10_sample_batched;
-CREATE TABLE cifar_10_sample_batched(
- buffer_id smallint,
- dependent_var smallint[],
- dependent_var_text_with_null smallint[],
- independent_var real[]);
-copy cifar_10_sample_batched from stdin delimiter '|';
-0|{{0,1}}|{{0,0,1,0,0}}|{{{{0.494118,0.462745,0.431373},{0.478431,0.45098,0.423529},{0.494118,0.466667,0.435294},{0.498039,0.466667,0.427451},{0.509804,0.478431,0.435294},{0.509804,0.478431,0.435294},{0.517647,0.486275,0.443137},{0.521569,0.490196,0.447059},{0.509804,0.478431,0.435294},{0.517647,0.486275,0.443137},{0.52549,0.494118,0.45098},{0.513726,0.482353,0.439216},{0.513726,0.482353,0.439216},{0.52549,0.494118,0.45098},{0.521569,0.490196,0.447059},{0.533333,0.501961,0.458824},{0.537
[...]
-1|{{1,0}}|{{0,1,0,0,0}}|{{{{0.792157,0.8,0.780392},{0.792157,0.8,0.780392},{0.8,0.807843,0.788235},{0.807843,0.815686,0.796079},{0.815686,0.823529,0.803922},{0.819608,0.827451,0.807843},{0.823529,0.831373,0.811765},{0.831373,0.839216,0.823529},{0.835294,0.843137,0.831373},{0.843137,0.85098,0.839216},{0.847059,0.854902,0.843137},{0.847059,0.854902,0.843137},{0.843137,0.85098,0.839216},{0.847059,0.854902,0.843137},{0.847059,0.854902,0.843137},{0.847059,0.854902,0.839216},{0.85098,0.858824,
[...]
-\.
-
-DROP TABLE IF EXISTS cifar_10_sample_batched_summary;
-CREATE TABLE cifar_10_sample_batched_summary(
- source_table text,
- output_table text,
- dependent_varname text,
- independent_varname text,
- dependent_vartype text,
- class_values smallint[],
- buffer_size integer,
- normalizing_const numeric);
-INSERT INTO cifar_10_sample_batched_summary values (
- 'cifar_10_sample',
- 'cifar_10_sample_batched',
- 'y',
- 'x',
- 'smallint',
- ARRAY[0,1],
- 1,
- 255.0);
-
-drop table if exists cifar_10_sample_val;
-create table cifar_10_sample_val(independent_var REAL[], dependent_var
SMALLINT[], buffer_id SMALLINT);
-copy cifar_10_sample_val from stdin delimiter '|';
-{{{{0.494118,0.462745,0.431373},{0.478431,0.45098,0.423529},{0.494118,0.466667,0.435294},{0.498039,0.466667,0.427451},{0.509804,0.478431,0.435294},{0.509804,0.478431,0.435294},{0.517647,0.486275,0.443137},{0.521569,0.490196,0.447059},{0.509804,0.478431,0.435294},{0.517647,0.486275,0.443137},{0.52549,0.494118,0.45098},{0.513726,0.482353,0.439216},{0.513726,0.482353,0.439216},{0.52549,0.494118,0.45098},{0.521569,0.490196,0.447059},{0.533333,0.501961,0.458824},{0.537255,0.505882,0.462745},{
[...]
-{{{{0.792157,0.8,0.780392},{0.792157,0.8,0.780392},{0.8,0.807843,0.788235},{0.807843,0.815686,0.796079},{0.815686,0.823529,0.803922},{0.819608,0.827451,0.807843},{0.823529,0.831373,0.811765},{0.831373,0.839216,0.823529},{0.835294,0.843137,0.831373},{0.843137,0.85098,0.839216},{0.847059,0.854902,0.843137},{0.847059,0.854902,0.843137},{0.843137,0.85098,0.839216},{0.847059,0.854902,0.843137},{0.847059,0.854902,0.843137},{0.847059,0.854902,0.839216},{0.85098,0.858824,0.839216},{0.85098,0.858
[...]
-\.
-
-DROP TABLE IF EXISTS cifar_10_sample_val_summary;
-CREATE TABLE cifar_10_sample_val_summary AS
- SELECT * FROM cifar_10_sample_batched_summary;
-
---- NOTE: In order to test fit_merge, we need at least 2 rows in the batched
table (1 on each segment).
---- ALSO NOTE: As part of supporting Postgres, an issue was reported JIRA
MADLIB-1326.
---- Once this bug is fixed, we should uncomment these 2 lines, which was used
to generate
---- the 4 tables above (cifar_10_sample{|_val}_batched{|_summary}). Only the
original
----- cifar_10_sample table should be hard-coded, so we don't have to keep
re-generating
----- all of these tables by hand every time something changes.
---- SELECT
minibatch_preprocessor_dl('cifar_10_sample','cifar_10_sample_batched','y','x',
1, 255);
-
-DROP TABLE IF EXISTS model_arch;
-SELECT load_keras_model('model_arch',
- $${
- "class_name": "Sequential",
- "keras_version": "2.1.6",
- "config": [{
- "class_name": "Conv2D", "config": {"kernel_initializer": {"class_name":
"VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed":
null, "mode": "fan_avg"}},
- "name": "conv2d_1",
- "kernel_constraint": null, "bias_regularizer": null, "bias_constraint":
null,
- "dtype": "float32", "activation": "relu", "trainable": true,
- "data_format": "channels_last", "filters": 32, "padding": "valid",
- "strides": [1, 1], "dilation_rate": [1, 1], "kernel_regularizer": null,
- "bias_initializer": {"class_name": "Zeros", "config": {}},
- "batch_input_shape": [null, 32, 32, 3], "use_bias": true,
- "activity_regularizer": null, "kernel_size": [3, 3]}},
- {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_1",
"trainable": true, "data_format": "channels_last", "pool_size": [2, 2],
"padding": "valid", "strides": [2, 2]}},
- {"class_name": "Dropout", "config": {"rate": 0.25, "noise_shape": null,
"trainable": true, "seed": null, "name": "dropout_1"}},
- {"class_name": "Flatten", "config": {"trainable": true, "name":
"flatten_1", "data_format": "channels_last"}},
- {"class_name": "Dense", "config": {"kernel_initializer": {"class_name":
"VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed":
null, "mode": "fan_avg"}}, "name": "dense_1", "kernel_constraint": null,
"bias_regularizer": null, "bias_constraint": null, "activation": "softmax",
"trainable": true, "kernel_regularizer": null, "bias_initializer":
- {"class_name": "Zeros", "config": {}}, "units": 2, "use_bias": true,
"activity_regularizer": null}
- }], "backend": "tensorflow"}$$);
-
--- -- Please do not break up the compile_params string
--- -- It might break the assertion
-DROP TABLE IF EXISTS keras_saved_out, keras_saved_out_summary;
-SELECT madlib_keras_fit(
- 'cifar_10_sample_batched',
- 'keras_saved_out',
- 'model_arch',
- 1,
- $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True),
loss='categorical_crossentropy', metrics=['mae']$$::text,
- $$ batch_size=2, epochs=1, verbose=0 $$::text,
- 3,
- NULL,
- 'cifar_10_sample_val');
-
-SELECT assert(
- model_arch_table = 'model_arch' AND
- model_arch_id = 1 AND
- model_type = 'madlib_keras' AND
- start_training_time < now() AND
- end_training_time > start_training_time AND
- source_table = 'cifar_10_sample_batched' AND
- validation_table = 'cifar_10_sample_val' AND
- model = 'keras_saved_out' AND
- dependent_varname = 'y' AND
- dependent_vartype = 'smallint' AND
- independent_varname = 'x' AND
- normalizing_const = 255.0 AND
- pg_typeof(normalizing_const) = 'real'::regtype AND
- name is NULL AND
- description is NULL AND
- model_size > 0 AND
- madlib_version is NOT NULL AND
- compile_params = $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True),
loss='categorical_crossentropy', metrics=['mae']$$::text AND
- fit_params = $$ batch_size=2, epochs=1, verbose=0 $$::text AND
- num_iterations = 3 AND
- metrics_compute_frequency = 3 AND
- num_classes = 2 AND
- class_values = '{0,1}' AND
- metrics_type = '{mae}' AND
- training_metrics_final >= 0 AND
- training_loss_final >= 0 AND
- array_upper(training_metrics, 1) = 1 AND
- array_upper(training_loss, 1) = 1 AND
- array_upper(metrics_elapsed_time, 1) = 1 AND
- validation_metrics_final >= 0 AND
- validation_loss_final >= 0 AND
- array_upper(validation_metrics, 1) = 1 AND
- array_upper(validation_loss, 1) = 1 ,
- 'Keras model output Summary Validation failed. Actual:' ||
__to_char(summary))
-FROM (SELECT * FROM keras_saved_out_summary) summary;
-
-SELECT assert(
- model_data IS NOT NULL AND
- model_arch IS NOT NULL, 'Keras model output validation failed.
Actual:' || __to_char(k))
-FROM (SELECT * FROM keras_saved_out) k;
-
--- Test that evaluate works as expected:
-DROP TABLE IF EXISTS evaluate_out;
-SELECT madlib_keras_evaluate('keras_saved_out', 'cifar_10_sample_val',
'evaluate_out', 0);
-
-SELECT assert(loss IS NOT NULL AND
- metric IS NOT NULL AND
- metrics_type = '{mae}', 'Evaluate output validation failed. Actual:'
|| __to_char(evaluate_out))
-FROM evaluate_out;
-
--- Test that passing NULL / None instead of 0 for gpus_per_host works
-DROP TABLE IF EXISTS evaluate_out;
-SELECT madlib_keras_evaluate('keras_saved_out', 'cifar_10_sample_val',
'evaluate_out');
-SELECT assert(loss IS NOT NULL AND
- metric IS NOT NULL AND
- metrics_type = '{mae}', 'Evaluate output validation failed. Actual:'
|| __to_char(evaluate_out))
-FROM evaluate_out;
-
--- Test that evaluate errors out correctly if model_arch field missing from
fit output
-DROP TABLE IF EXISTS evaluate_out;
-ALTER TABLE keras_saved_out DROP COLUMN model_arch;
-SELECT assert(trap_error($TRAP$
- SELECT madlib_keras_evaluate('keras_saved_out', 'cifar_10_sample_val',
'evaluate_out');
- $TRAP$) = 1, 'Should error out if model_arch column is missing from
model_table');
-
--- Verify number of iterations for which metrics and loss are computed
-DROP TABLE IF EXISTS keras_saved_out, keras_saved_out_summary;
-SELECT madlib_keras_fit(
- 'cifar_10_sample_batched',
- 'keras_saved_out',
- 'model_arch',
- 1,
- $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True),
loss='categorical_crossentropy', metrics=['accuracy']$$::text,
- $$ batch_size=2, epochs=1, verbose=0 $$::text,
- 7,
- NULL,
- 'cifar_10_sample_val',
- 4);
-SELECT assert(
- num_iterations = 7 AND
- metrics_compute_frequency = 4 AND
- training_metrics_final >= 0 AND
- training_loss_final >= 0 AND
- metrics_type = '{accuracy}' AND
- array_upper(training_metrics, 1) = 2 AND
- array_upper(training_loss, 1) = 2 AND
- array_upper(metrics_elapsed_time, 1) = 2 AND
- validation_metrics_final >= 0 AND
- validation_loss_final >= 0 AND
- array_upper(validation_metrics, 1) = 2 AND
- array_upper(validation_loss, 1) = 2 ,
- 'Keras model output Summary Validation failed. Actual:' ||
__to_char(summary))
-FROM (SELECT * FROM keras_saved_out_summary) summary;
--- Fit with gpus_per_host set to 2 must error out on machines
--- that don't have GPUs. Since Jenkins builds are run on docker containers
--- that don't have GPUs, these queries must error out.
-DROP TABLE IF EXISTS keras_saved_out_gpu, keras_saved_out_gpu_summary;
-SELECT assert(trap_error($TRAP$madlib_keras_fit(
- 'cifar_10_sample_batched',
- 'keras_saved_out_gpu',
- 'model_arch',
- 1,
- $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True),
loss='categorical_crossentropy', metrics=['accuracy']$$::text,
- $$ batch_size=2, epochs=1, verbose=0 $$::text,
- 3,
- 2,
- 'cifar_10_sample_val');$TRAP$) = 1,
- 'Fit with gpus_per_host=2 must error out.');
-
--- Prediction with gpus_per_host set to 2 must error out on machines
--- that don't have GPUs. Since Jenkins builds are run on docker containers
--- that don't have GPUs, these queries must error out.
-
--- IMPORTANT: The following test must be run when we have a valid
--- keras_saved_out model table. Otherwise, it will fail because of a
--- non-existent model table, while we want to trap failure due to
--- gpus_per_host=2
-DROP TABLE IF EXISTS cifar10_predict_gpu;
-SELECT assert(trap_error($TRAP$madlib_keras_predict(
- 'keras_saved_out',
- 'cifar_10_sample',
- 'id',
- 'x',
- 'cifar10_predict_gpu',
- NULL,
- 2);$TRAP$) = 1,
- 'Prediction with gpus_per_host=2 must error out.');
-
--- Test for
- -- Non null name and description columns
- -- Null validation table
-DROP TABLE IF EXISTS keras_out, keras_out_summary;
-SELECT madlib_keras_fit(
- 'cifar_10_sample_batched',
- 'keras_out',
- 'model_arch',
- 1,
- $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True),
loss='categorical_crossentropy', metrics=['accuracy']$$::text,
- $$ batch_size=2, epochs=1, verbose=0 $$::text,
- 2,
- NULL,
- NULL,
- 1,
- NULL,
- 'model name',
- 'model desc');
-
-SELECT assert(
- source_table = 'cifar_10_sample_batched' AND
- model = 'keras_out' AND
- dependent_varname = 'y' AND
- independent_varname = 'x' AND
- model_arch_table = 'model_arch' AND
- model_arch_id = 1 AND
- compile_params = $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True),
loss='categorical_crossentropy', metrics=['accuracy']$$::text AND
- fit_params = $$ batch_size=2, epochs=1, verbose=0 $$::text AND
- num_iterations = 2 AND
- validation_table is NULL AND
- metrics_compute_frequency = 1 AND
- name = 'model name' AND
- description = 'model desc' AND
- model_type = 'madlib_keras' AND
- model_size > 0 AND
- start_training_time < now() AND
- end_training_time > start_training_time AND
- array_upper(metrics_elapsed_time, 1) = 2 AND
- dependent_vartype = 'smallint' AND
- madlib_version is NOT NULL AND
- num_classes = 2 AND
- class_values = '{0,1}' AND
- metrics_type = '{accuracy}' AND
- normalizing_const = 255.0 AND
- training_metrics_final is not NULL AND
- training_loss_final is not NULL AND
- array_upper(training_metrics, 1) = 2 AND
- array_upper(training_loss, 1) = 2 AND
- validation_metrics_final is NULL AND
- validation_loss_final is NULL AND
- validation_metrics is NULL AND
- validation_loss is NULL,
- 'Keras model output Summary Validation failed. Actual:' ||
__to_char(summary))
-FROM (SELECT * FROM keras_out_summary) summary;
-
-SELECT assert(model_data IS NOT NULL , 'Keras model output validation failed')
FROM (SELECT * FROM keras_out) k;
-
--- Validate metrics=NULL works with fit
-DROP TABLE IF EXISTS keras_saved_out, keras_saved_out_summary;
-SELECT madlib_keras_fit(
-'cifar_10_sample_batched',
-'keras_saved_out',
-'model_arch',
-1,
-$$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True),
loss='categorical_crossentropy'$$::text,
-$$ batch_size=2, epochs=1, verbose=0 $$::text,
-1);
-
-SELECT assert(
- metrics_type is NULL AND
- training_metrics IS NULL AND
- array_upper(training_loss, 1) = 1 AND
- array_upper(metrics_elapsed_time, 1) = 1 AND
- validation_metrics_final IS NULL AND
- validation_loss_final >= 0 AND
- validation_metrics IS NULL AND
- array_upper(validation_loss, 1) = 1,
- 'Keras model output Summary Validation failed. Actual:' ||
__to_char(summary))
-FROM (SELECT * FROM keras_saved_out_summary) summary;
-
--- Validate that metrics=NULL works with evaluate
-DROP TABLE IF EXISTS evaluate_out;
-SELECT madlib_keras_evaluate('keras_saved_out', 'cifar_10_sample_val',
'evaluate_out', 0);
-
-SELECT assert(loss IS NOT NULL AND
- metric IS NULL AND
- metrics_type IS NULL, 'Evaluate output validation for NULL metric
failed. Actual:' || __to_char(evaluate_out))
-FROM evaluate_out;
-
--- Validate metrics=[] works with fit
-DROP TABLE IF EXISTS keras_saved_out, keras_saved_out_summary;
-SELECT madlib_keras_fit(
-'cifar_10_sample_batched',
-'keras_saved_out',
-'model_arch',
-1,
-$$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True),
loss='categorical_crossentropy', metrics=[]$$::text,
-$$ batch_size=2, epochs=1, verbose=0 $$::text,
-1);
-
-SELECT assert(
- metrics_type IS NULL AND
- training_metrics IS NULL AND
- array_upper(training_loss, 1) = 1 AND
- array_upper(metrics_elapsed_time, 1) = 1 AND
- validation_metrics_final IS NULL AND
- validation_loss_final >= 0 AND
- validation_metrics IS NULL AND
- array_upper(validation_loss, 1) = 1,
- 'Keras model output Summary Validation failed. Actual:' ||
__to_char(summary))
-FROM (SELECT * FROM keras_saved_out_summary) summary;
-
--- Validate metrics=[] works with evaluate
-DROP TABLE IF EXISTS evaluate_out;
-SELECT madlib_keras_evaluate('keras_saved_out', 'cifar_10_sample_val',
'evaluate_out', 0);
-
-SELECT assert(loss IS NOT NULL AND
- metric IS NULL AND
- metrics_type IS NULL, 'Evaluate output validation for [] metric
failed. Actual:' || __to_char(evaluate_out))
-FROM evaluate_out;
-
-DROP TABLE IF EXISTS cifar10_predict;
-SELECT madlib_keras_predict(
- 'keras_saved_out',
- 'cifar_10_sample',
- 'id',
- 'x',
- 'cifar10_predict',
- NULL,
- 0);
-
--- Validate that prediction output table exists and has correct schema
-SELECT assert(UPPER(pg_typeof(id)::TEXT )= 'INTEGER',
- 'id column should be INTEGER type') FROM cifar10_predict;
-
-SELECT assert(UPPER(pg_typeof(estimated_y)::TEXT) =
- 'SMALLINT', 'prediction column should be SMALLINT type')
-FROM cifar10_predict;
-
--- Validate correct number of rows returned.
-SELECT assert(COUNT(*)=2, 'Output table of madlib_keras_predict should have
two rows')
-FROM cifar10_predict;
-
--- First test that all values are in set of class values; if this breaks, it's
definitely a problem.
-SELECT assert(estimated_y IN (0,1),
- 'Predicted value not in set of defined class values for model')
-FROM cifar10_predict;
-
-DROP TABLE IF EXISTS cifar10_predict;
-SELECT assert(trap_error($TRAP$madlib_keras_predict(
- 'keras_saved_out',
- 'cifar_10_sample_batched',
- 'id',
- 'x',
- 'cifar10_predict',
- NULL,
- 0);$TRAP$) = 1,
- 'Passing batched image table to predict should error out.');
-
--- Compile and fit parameter tests
-DROP TABLE IF EXISTS keras_out, keras_out_summary;
-SELECT madlib_keras_fit(
- 'cifar_10_sample_batched',
- 'keras_out',
- 'model_arch',
- 1,
- $$ optimizer='SGD', loss='categorical_crossentropy',
metrics=['accuracy']$$::text,
- $$ batch_size=2, epochs=1, verbose=0 $$::text,
- 1,
- NULL,
- NULL,
- NULL,
- NULL, 'model name', 'model desc');
-
-DROP TABLE IF EXISTS keras_out, keras_out_summary;
-SELECT madlib_keras_fit(
- 'cifar_10_sample_batched',
- 'keras_out',
- 'model_arch',
- 1,
- $$ optimizer='Adam()', loss='categorical_crossentropy',
metrics=['accuracy']$$::text,
- $$ batch_size=2, epochs=1, verbose=0 $$::text,
- 1,
- NULL,
- NULL,
- NULL,
- NULL, 'model name', 'model desc');
-
-DROP TABLE IF EXISTS keras_out, keras_out_summary;
-SELECT madlib_keras_fit(
- 'cifar_10_sample_batched',
- 'keras_out',
- 'model_arch',
- 1,
- $$ optimizer=Adam(epsilon=None), loss='categorical_crossentropy',
metrics=['accuracy']$$::text,
- $$ batch_size=2, epochs=1, verbose=0 $$::text,
- 1,
- 0,
- NULL,
- NULL,
- NULL, 'model name', 'model desc');
-
-DROP TABLE IF EXISTS keras_out, keras_out_summary;
-SELECT madlib_keras_fit(
- 'cifar_10_sample_batched',
- 'keras_out',
- 'model_arch',
- 1,
- $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True),
metrics=['accuracy'], loss_weights=[2], sample_weight_mode=None,
loss='categorical_crossentropy' $$::text,
- $$ epochs=10, verbose=0, shuffle=True, initial_epoch=1, steps_per_epoch=2
$$::text,
- 1,
- NULL,
- NULL,
- NULL,
- False, 'model name', 'model desc');
-
-DROP TABLE IF EXISTS keras_out, keras_out_summary;
-SELECT madlib_keras_fit(
- 'cifar_10_sample_batched',
- 'keras_out',
- 'model_arch',
- 1,
- $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True),
metrics=['accuracy'], loss_weights=[2], sample_weight_mode=None,
loss='categorical_crossentropy' $$::text,
- NULL,
- 1,
- NULL,
- NULL,
- NULL,
- False, 'model name', 'model desc');
-
--- -- negative test case for passing non numeric y to fit
--- induce failure by passing a non numeric column
-DROP TABLE IF EXISTS cifar_10_sample_val_failure;
-CREATE TABLE cifar_10_sample_val_failure AS SELECT * FROM cifar_10_sample_val;
-ALTER TABLE cifar_10_sample_val_failure rename dependent_var to
dependent_var_original;
-ALTER TABLE cifar_10_sample_val_failure rename buffer_id to dependent_var;
-DROP TABLE IF EXISTS keras_out, keras_out_summary;
-SELECT assert(trap_error($TRAP$madlib_keras_fit(
- 'cifar_10_sample_batched',
- 'keras_out',
- 'model_arch',
- 1,
- $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True),
loss='categorical_crossentropy', metrics=['accuracy']$$::text,
- $$ batch_size=2, epochs=1, verbose=0 $$::text,
- 2,
- NULL,
- 'cifar_10_sample_val_failure');$TRAP$) = 1,
- 'Passing y of type non numeric array to fit should error out.');
-
--- Test with pred_type=prob
-DROP TABLE IF EXISTS cifar10_predict;
-SELECT madlib_keras_predict(
- 'keras_saved_out',
- 'cifar_10_sample',
- 'id',
- 'x',
- 'cifar10_predict',
- 'prob',
- 0);
-
-SELECT assert(UPPER(pg_typeof(prob_0)::TEXT) =
- 'DOUBLE PRECISION', 'column prob_0 should be double precision type')
-FROM cifar10_predict;
-
-SELECT assert(UPPER(pg_typeof(prob_1)::TEXT) =
- 'DOUBLE PRECISION', 'column prob_1 should be double precision type')
-FROM cifar10_predict;
-
-SELECT assert(COUNT(*)=3, 'Predict out table must have exactly three cols.')
-FROM pg_attribute
-WHERE attrelid='cifar10_predict'::regclass AND attnum>0;
-
--- Tests with text class values:
--- Modify input data to have text classes, and mini-batch it.
-DROP TABLE IF EXISTS cifar_10_sample_text_batched;
--- Create a new table using the text based column for dep var.
-CREATE TABLE cifar_10_sample_text_batched AS
- SELECT buffer_id, independent_var, dependent_var_text_with_null AS
dependent_var
- FROM cifar_10_sample_batched;
--- Insert a new row with NULL as the dependent var (one-hot encoded)
-INSERT INTO cifar_10_sample_text_batched(buffer_id, independent_var,
dependent_var)
- SELECT 2, independent_var, ARRAY[[0,1,0,0,0]]
- FROM cifar_10_sample_batched
- WHERE cifar_10_sample_batched.buffer_id=0;
--- Create the necessary summary table for the batched input.
-DROP TABLE IF EXISTS cifar_10_sample_text_batched_summary;
-CREATE TABLE cifar_10_sample_text_batched_summary(
- source_table text,
- output_table text,
- dependent_varname text,
- independent_varname text,
- dependent_vartype text,
- class_values text[],
- buffer_size integer,
- normalizing_const numeric);
-INSERT INTO cifar_10_sample_text_batched_summary values (
- 'cifar_10_sample',
- 'cifar_10_sample_text_batched',
- 'y_text',
- 'x',
- 'text',
- ARRAY[NULL,'cat','dog',NULL,NULL],
- 1,
- 255.0);
-
--- Change model_arch to reflect 5 num_classes
-DROP TABLE IF EXISTS model_arch;
-SELECT load_keras_model('model_arch',
- $${
- "class_name": "Sequential",
- "keras_version": "2.1.6",
- "config": [{
- "class_name": "Conv2D", "config": {"kernel_initializer": {"class_name":
"VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed":
null, "mode": "fan_avg"}},
- "name": "conv2d_1",
- "kernel_constraint": null, "bias_regularizer": null, "bias_constraint":
null,
- "dtype": "float32", "activation": "relu", "trainable": true,
- "data_format": "channels_last", "filters": 32, "padding": "valid",
- "strides": [1, 1], "dilation_rate": [1, 1], "kernel_regularizer": null,
- "bias_initializer": {"class_name": "Zeros", "config": {}},
- "batch_input_shape": [null, 32, 32, 3], "use_bias": true,
- "activity_regularizer": null, "kernel_size": [3, 3]}},
- {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_1",
"trainable": true, "data_format": "channels_last", "pool_size": [2, 2],
"padding": "valid", "strides": [2, 2]}},
- {"class_name": "Dropout", "config": {"rate": 0.25, "noise_shape": null,
"trainable": true, "seed": null, "name": "dropout_1"}},
- {"class_name": "Flatten", "config": {"trainable": true, "name":
"flatten_1", "data_format": "channels_last"}},
- {"class_name": "Dense", "config": {"kernel_initializer": {"class_name":
"VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed":
null, "mode": "fan_avg"}}, "name": "dense_1", "kernel_constraint": null,
"bias_regularizer": null, "bias_constraint": null, "activation": "softmax",
"trainable": true, "kernel_regularizer": null, "bias_initializer":
- {"class_name": "Zeros", "config": {}}, "units": 5, "use_bias": true,
"activity_regularizer": null}
- }], "backend": "tensorflow"}$$);
-
-DROP TABLE IF EXISTS keras_saved_out, keras_saved_out_summary;
-SELECT madlib_keras_fit(
- 'cifar_10_sample_text_batched',
- 'keras_saved_out',
- 'model_arch',
- 1,
- $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True),
loss='categorical_crossentropy', metrics=['accuracy']$$::text,
- $$ batch_size=2, epochs=1, verbose=0 $$::text,
- 3);
--- Assert fit has correct class_values
-SELECT assert(
- dependent_vartype = 'text' AND
- class_values = '{NULL,cat,dog,NULL,NULL}',
- 'Keras model output Summary Validation failed. Actual:' ||
__to_char(summary))
-FROM (SELECT * FROM keras_saved_out_summary) summary;
-
--- Predict with pred_type=prob
-DROP TABLE IF EXISTS cifar_10_sample_text;
-CREATE TABLE cifar_10_sample_text AS
- SELECT id, x, y_text
- FROM cifar_10_sample;
-DROP TABLE IF EXISTS cifar10_predict;
-SELECT madlib_keras_predict(
- 'keras_saved_out',
- 'cifar_10_sample_text',
- 'id',
- 'x',
- 'cifar10_predict',
- 'prob',
- 0);
-
--- Validate the output datatype of newly created prediction columns
--- for prediction type = 'prob' and class_values 'TEXT' with NULL as a valid
--- class_values
-SELECT assert(UPPER(pg_typeof(prob_cat)::TEXT) =
- 'DOUBLE PRECISION', 'column prob_cat should be double precision type')
-FROM cifar10_predict;
-
-SELECT assert(UPPER(pg_typeof(prob_dog)::TEXT) =
- 'DOUBLE PRECISION', 'column prob_dog should be double precision type')
-FROM cifar10_predict;
-
-SELECT assert(UPPER(pg_typeof("prob_NULL")::TEXT) =
- 'DOUBLE PRECISION', 'column prob_NULL should be double precision type')
-FROM cifar10_predict;
-
--- Must have exactly 4 cols (3 for class_values and 1 for id)
-SELECT assert(COUNT(*)=4, 'Predict out table must have exactly four cols.')
-FROM pg_attribute
-WHERE attrelid='cifar10_predict'::regclass AND attnum>0;
-
--- Predict with pred_type=response
-DROP TABLE IF EXISTS cifar10_predict;
-SELECT madlib_keras_predict(
- 'keras_saved_out',
- 'cifar_10_sample_text',
- 'id',
- 'x',
- 'cifar10_predict',
- 'response',
- 0);
-
--- Validate the output datatype of newly created prediction columns
--- for prediction type = 'response' and class_values 'TEXT' with NULL
--- as a valid class_values
-SELECT assert(UPPER(pg_typeof(estimated_y_text)::TEXT) = 'TEXT',
- 'prediction column should be TEXT type')
-FROM cifar10_predict LIMIT 1;
-
-
--- Tests where the assumption is user has one-hot encoded, so class_values
--- in input summary table will be NULL.
-UPDATE keras_saved_out_summary SET class_values=NULL;
-
--- Predict with pred_type=prob
-DROP TABLE IF EXISTS cifar10_predict;
-SELECT madlib_keras_predict(
- 'keras_saved_out',
- 'cifar_10_sample_text',
- 'id',
- 'x',
- 'cifar10_predict',
- 'prob',
- 0);
-
--- Validate the output datatype of newly created prediction column
--- for prediction type = 'response' and class_value = NULL
--- Returns: Array of probabilities for user's one-hot encoded data
-SELECT assert(UPPER(pg_typeof(prob)::TEXT) = 'DOUBLE PRECISION[]',
- 'column prob should be double precision[] type')
-FROM cifar10_predict LIMIT 1;
-
--- Predict with pred_type=response
-DROP TABLE IF EXISTS cifar10_predict;
-SELECT madlib_keras_predict(
- 'keras_saved_out',
- 'cifar_10_sample_text',
- 'id',
- 'x',
- 'cifar10_predict',
- 'response',
- 0);
-
--- Validate the output datatype of newly created prediction column
--- for prediction type = 'response' and class_value = NULL
--- Returns: Index of class value in user's one-hot encoded data with
--- highest probability
-SELECT assert(UPPER(pg_typeof(estimated_y_text)::TEXT) = 'TEXT',
- 'column estimated_y_text should be text type')
-FROM cifar10_predict LIMIT 1;
-
-SELECT assert(
- estimated_y_text IN ('0', '1'),
- 'Predict failure for null class value and response pred_type.')
-FROM cifar10_predict;
-
--- Test predict with INTEGER class_values
--- with NULL as a valid class value
-INSERT INTO cifar_10_sample(id, x, y, imgpath)
-SELECT 3, x, NULL, '0/img3.jpg' FROM cifar_10_sample
-WHERE y = 1;
-INSERT INTO cifar_10_sample(id, x, y, imgpath)
-SELECT 4, x, 4, '0/img4.jpg' FROM cifar_10_sample
-WHERE y = 0;
-INSERT INTO cifar_10_sample(id, x, y, imgpath)
-SELECT 5, x, 5, '0/img5.jpg' FROM cifar_10_sample
-WHERE y = 1;
-
-DROP TABLE IF EXISTS cifar_10_sample_int_batched;
-DROP TABLE IF EXISTS cifar_10_sample_int_batched_summary;
-SELECT
training_preprocessor_dl('cifar_10_sample','cifar_10_sample_int_batched','y','x',
2, 255, 5);
-
-DROP TABLE IF EXISTS keras_saved_out, keras_saved_out_summary;
-SELECT madlib_keras_fit(
- 'cifar_10_sample_int_batched',
- 'keras_saved_out',
- 'model_arch',
- 1,
- $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True),
loss='categorical_crossentropy', metrics=['accuracy']$$::text,
- $$ batch_size=2, epochs=1, verbose=0 $$::text,
- 3);
-
--- Assert fit has correct class_values
-SELECT assert(
- dependent_vartype = 'smallint' AND
- class_values = '{NULL,0,1,4,5}',
- 'Keras model output Summary Validation failed. Actual:' ||
__to_char(summary))
-FROM (SELECT * FROM keras_saved_out_summary) summary;
-
--- Predict with pred_type=prob
-DROP TABLE IF EXISTS cifar10_predict;
-SELECT madlib_keras_predict(
- 'keras_saved_out',
- 'cifar_10_sample',
- 'id',
- 'x',
- 'cifar10_predict',
- 'prob',
- 0);
-
--- Validate the output datatype of newly created prediction column
--- for prediction type = 'prob' and class_values 'INT' with NULL
--- as a valid class_values
-SELECT assert(UPPER(pg_typeof("prob_NULL")::TEXT) =
- 'DOUBLE PRECISION', 'column prob_NULL should be double precision type')
-FROM cifar10_predict;
--- Must have exactly 6 cols (5 for class_values and 1 for id)
-SELECT assert(COUNT(*)=6, 'Predict out table must have exactly six cols.')
-FROM pg_attribute
-WHERE attrelid='cifar10_predict'::regclass AND attnum>0;
-
--- Predict with pred_type=response
-DROP TABLE IF EXISTS cifar10_predict;
-SELECT madlib_keras_predict(
- 'keras_saved_out',
- 'cifar_10_sample',
- 'id',
- 'x',
- 'cifar10_predict',
- 'response',
- 0);
-
--- Validate the output datatype of newly created prediction column
--- for prediction type = 'response' and class_values 'TEXT' with NULL
--- as a valid class_values
--- Returns: class_value with highest probability
-SELECT assert(UPPER(pg_typeof(estimated_y)::TEXT) =
- 'SMALLINT', 'prediction column should be smallint type')
-FROM cifar10_predict;
-
--- Test case with a different input shape (3, 32, 32) instead of (32, 32, 3).
--- Create a new table with image shape 3, 32, 32
-drop table if exists cifar_10_sample_test_shape;
-create table cifar_10_sample_test_shape(id INTEGER, y SMALLINT, x REAL[] );
-copy cifar_10_sample_test_shape from stdin delimiter '|';
-1|0|{{{248,248,250,245,245,246,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245},{247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245},{245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247},{248,248,250,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247
[...]
-\.
-
-DROP TABLE IF EXISTS cifar_10_sample_test_shape_batched;
-DROP TABLE IF EXISTS cifar_10_sample_test_shape_batched_summary;
-SELECT
training_preprocessor_dl('cifar_10_sample_test_shape','cifar_10_sample_test_shape_batched','y','x',
NULL, 255, 3);
-
--- Change model_arch to reflect channels_first
-DROP TABLE IF EXISTS model_arch;
-SELECT load_keras_model('model_arch',
- $${
- "class_name": "Sequential",
- "keras_version": "2.1.6",
- "config": [{
- "class_name": "Conv2D", "config": {"kernel_initializer": {"class_name":
"VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed":
null, "mode": "fan_avg"}},
- "name": "conv2d_1",
- "kernel_constraint": null, "bias_regularizer": null, "bias_constraint":
null,
- "dtype": "float32", "activation": "relu", "trainable": true,
- "data_format": "channels_first", "filters": 32, "padding": "valid",
- "strides": [1, 1], "dilation_rate": [1, 1], "kernel_regularizer": null,
- "bias_initializer": {"class_name": "Zeros", "config": {}},
- "batch_input_shape": [null, 3, 32, 32], "use_bias": true,
- "activity_regularizer": null, "kernel_size": [3, 3]}},
- {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_1",
"trainable": true, "data_format": "channels_first", "pool_size": [2, 2],
"padding": "valid", "strides": [2, 2]}},
- {"class_name": "Dropout", "config": {"rate": 0.25, "noise_shape": null,
"trainable": true, "seed": null, "name": "dropout_1"}},
- {"class_name": "Flatten", "config": {"trainable": true, "name":
"flatten_1", "data_format": "channels_first"}},
- {"class_name": "Dense", "config": {"kernel_initializer": {"class_name":
"VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed":
null, "mode": "fan_avg"}}, "name": "dense_1", "kernel_constraint": null,
"bias_regularizer": null, "bias_constraint": null, "activation": "softmax",
"trainable": true, "kernel_regularizer": null, "bias_initializer":
- {"class_name": "Zeros", "config": {}}, "units": 3, "use_bias": true,
"activity_regularizer": null}
- }], "backend": "tensorflow"}$$);
-
-DROP TABLE IF EXISTS keras_saved_out, keras_saved_out_summary;
-SELECT madlib_keras_fit(
- 'cifar_10_sample_test_shape_batched',
- 'keras_saved_out',
- 'model_arch',
- 1,
- $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True),
loss='categorical_crossentropy', metrics=['accuracy']$$::text,
- $$ batch_size=2, epochs=1, verbose=0 $$::text,
- 3);
-
--- Predict with correctly shaped data, must go thru.
-DROP TABLE IF EXISTS cifar10_predict;
-SELECT madlib_keras_predict(
- 'keras_saved_out',
- 'cifar_10_sample_test_shape',
- 'id',
- 'x',
- 'cifar10_predict',
- 'prob',
- 0);
-
--- Prediction with incorrectly shaped data must error out.
-DROP TABLE IF EXISTS cifar10_predict;
-SELECT assert(trap_error($TRAP$madlib_keras_predict(
- 'keras_saved_out',
- 'cifar_10_sample',
- 'id',
- 'x',
- 'cifar10_predict',
- 'prob',
- 0);$TRAP$) = 1,
- 'Input shape is (32, 32, 3) but model was trained with (3, 32, 32). Should
have failed.');
-
--- Test model_arch is retrieved from model data table and not model
architecture
-DROP TABLE IF EXISTS model_arch;
-DROP TABLE IF EXISTS cifar10_predict;
-SELECT madlib_keras_predict(
- 'keras_saved_out',
- 'cifar_10_sample_test_shape',
- 'id',
- 'x',
- 'cifar10_predict',
- 'prob',
- 0);
-
--------------------- TRANSFER LEARNING and WARM START -----------------
-
-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_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_model_arch;
--- NOTE: The seed is set to 0 for every layer.
-SELECT load_keras_model('iris_model_arch', -- Output table,
-$$
-{
-"class_name": "Sequential",
-"keras_version": "2.1.6",
-"config":
- [{"class_name": "Dense", "config": {"kernel_initializer": {"class_name":
"VarianceScaling",
- "config": {"distribution": "uniform", "scale": 1.0, "seed": 0, "mode":
"fan_avg"}},
- "name": "dense_1", "kernel_constraint": null, "bias_regularizer": null,
- "bias_constraint": null, "dtype": "float32", "activation": "relu",
"trainable": true,
- "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros",
- "config": {}}, "units": 10, "batch_input_shape": [null, 4], "use_bias":
true,
- "activity_regularizer": null}}, {"class_name": "Dense",
- "config": {"kernel_initializer": {"class_name": "VarianceScaling",
- "config": {"distribution": "uniform", "scale": 1.0, "seed": 0, "mode":
"fan_avg"}},
- "name": "dense_2", "kernel_constraint": null, "bias_regularizer": null,
- "bias_constraint": null, "activation": "relu", "trainable": true,
"kernel_regularizer": null,
- "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10,
"use_bias": true,
- "activity_regularizer": null}}, {"class_name": "Dense", "config":
{"kernel_initializer":
- {"class_name": "VarianceScaling", "config": {"distribution": "uniform",
"scale": 1.0,
- "seed": 0, "mode": "fan_avg"}}, "name": "dense_3", "kernel_constraint":
null,
- "bias_regularizer": null, "bias_constraint": null, "activation": "softmax",
- "trainable": true, "kernel_regularizer": null, "bias_initializer":
{"class_name": "Zeros",
- "config": {}}, "units": 3, "use_bias": true, "activity_regularizer":
null}}],
- "backend": "tensorflow"}
-$$
-);
-
-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
- );
-
-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_predict;
-SELECT madlib_keras_predict('iris_model', -- model
- 'iris_test', -- test_table
- 'id', -- id column
- 'attributes', -- independent var
- 'iris_predict' -- output table
- );
-
--- 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 AND
- training_metrics[5]-training_metrics[1] > 0,
- '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;
-
--- 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,
-$$
-{
-"class_name": "Sequential",
-"keras_version": "2.1.6",
-"config":
- [{"class_name": "Dense", "config": {"kernel_initializer": {"class_name":
"VarianceScaling",
- "config": {"distribution": "uniform", "scale": 1.0, "seed": 0, "mode":
"fan_avg"}},
- "name": "dense_1", "kernel_constraint": null, "bias_regularizer": null,
- "bias_constraint": null, "dtype": "float32", "activation": "relu",
- "trainable": false,
- "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros",
- "config": {}}, "units": 10, "batch_input_shape": [null, 4], "use_bias":
true,
- "activity_regularizer": null}}, {"class_name": "Dense",
- "config": {"kernel_initializer": {"class_name": "VarianceScaling",
- "config": {"distribution": "uniform", "scale": 1.0, "seed": 0, "mode":
"fan_avg"}},
- "name": "dense_2", "kernel_constraint": null, "bias_regularizer": null,
- "bias_constraint": null, "activation": "relu",
- "trainable": false,
- "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros",
- "config": {}}, "units": 10, "use_bias": true, "activity_regularizer":
null}},
- {"class_name": "Dense", "config": {"kernel_initializer":
- {"class_name": "VarianceScaling", "config": {"distribution": "uniform",
"scale": 1.0,
- "seed": 0, "mode": "fan_avg"}}, "name": "dense_3", "kernel_constraint":
null,
- "bias_regularizer": null, "bias_constraint": null, "activation": "softmax",
- "trainable": false,
- "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros",
- "config": {}}, "units": 3, "use_bias": true, "activity_regularizer":
null}}],
- "backend": "tensorflow"}
-$$
-);
--- 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_data 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;
-
----------------------- Predict BYOM test --------------------------------
-
--- class_values not NULL, pred_type is response
-DROP TABLE IF EXISTS iris_predict_byom;
-SELECT madlib_keras_predict_byom(
- 'iris_model_arch',
- 2,
- 'iris_test',
- 'id',
- 'attributes',
- 'iris_predict_byom',
- 'response',
- -1,
- ARRAY['Iris-setosa', 'Iris-versicolor',
- 'Iris-virginica']
- );
-
-SELECT assert(
- p0.estimated_class_text = p1.estimated_dependent_var,
- 'Predict byom failure for non null class value and response pred_type.')
-FROM iris_predict AS p0, iris_predict_byom AS p1
-WHERE p0.id=p1.id;
-SELECT assert(UPPER(pg_typeof(estimated_dependent_var)::TEXT) = 'TEXT',
- 'Predict byom failure for non null class value and response pred_type.
- Expeceted estimated_dependent_var to be of type TEXT')
-FROM iris_predict_byom LIMIT 1;
-
--- class_values NULL, pred_type is NULL (response)
-DROP TABLE IF EXISTS iris_predict_byom;
-SELECT madlib_keras_predict_byom(
- 'iris_model_arch',
- 2,
- 'iris_test',
- 'id',
- 'attributes',
- 'iris_predict_byom'
- );
-SELECT assert(
- p1.estimated_dependent_var IN ('0', '1', '2'),
- 'Predict byom failure for null class value and null pred_type.')
-FROM iris_predict_byom AS p1;
-SELECT assert(UPPER(pg_typeof(estimated_dependent_var)::TEXT) = 'TEXT',
- 'Predict byom failure for non null class value and response pred_type.
- Expeceted estimated_dependent_var to be of type TEXT')
-FROM iris_predict_byom LIMIT 1;
-
--- class_values not NULL, pred_type is prob
-DROP TABLE IF EXISTS iris_predict_byom;
-SELECT madlib_keras_predict_byom(
- 'iris_model_arch',
- 2,
- 'iris_test',
- 'id',
- 'attributes',
- 'iris_predict_byom',
- 'prob',
- -1,
- ARRAY['Iris-setosa', 'Iris-versicolor',
- 'Iris-virginica'],
- 1.0
- );
-
-SELECT assert(
- (p1."prob_Iris-setosa" + p1."prob_Iris-virginica" +
p1."prob_Iris-versicolor") - 1 < 1e-6,
- 'Predict byom failure for non null class value and prob pred_type.')
-FROM iris_predict_byom AS p1;
-SELECT assert(UPPER(pg_typeof("prob_Iris-setosa")::TEXT) = 'DOUBLE PRECISION',
- 'Predict byom failure for non null class value and prob pred_type.
- Expeceted "prob_Iris-setosa" to be of type DOUBLE PRECISION')
-FROM iris_predict_byom LIMIT 1;
-
--- class_values NULL, pred_type is prob
-DROP TABLE IF EXISTS iris_predict_byom;
-SELECT madlib_keras_predict_byom(
- 'iris_model_arch',
- 2,
- 'iris_test',
- 'id',
- 'attributes',
- 'iris_predict_byom',
- 'prob',
- 0,
- NULL
- );
-SELECT assert(
- (prob[1] + prob[2] + prob[3]) - 1 < 1e-6,
- 'Predict byom failure for null class value and prob pred_type.')
-FROM iris_predict_byom;
-SELECT assert(UPPER(pg_typeof(prob)::TEXT) = 'DOUBLE PRECISION[]',
- 'Predict byom failure for null class value and prob pred_type.
Expeceted prob to
- be of type DOUBLE PRECISION[]')
-FROM iris_predict_byom LIMIT 1;
diff --git
a/src/ports/postgres/modules/deep_learning/test/madlib_keras_cifar.setup.sql_in
b/src/ports/postgres/modules/deep_learning/test/madlib_keras_cifar.setup.sql_in
new file mode 100644
index 0000000..913921f
--- /dev/null
+++
b/src/ports/postgres/modules/deep_learning/test/madlib_keras_cifar.setup.sql_in
@@ -0,0 +1,152 @@
+/* ---------------------------------------------------------------------*//**
+ *
+ * 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.
+ *
+ *//* ---------------------------------------------------------------------*/
+
+-------------------- CIFAR 10 test input tables -----------------
+
+DROP TABLE IF EXISTS cifar_10_sample;
+CREATE TABLE cifar_10_sample(id INTEGER, y SMALLINT, y_text TEXT, imgpath
TEXT, x REAL[]);
+COPY cifar_10_sample FROM STDIN DELIMITER '|';
+1|0|'cat'|'0/img0.jpg'|{{{202,204,199},{202,204,199},{204,206,201},{206,208,203},{208,210,205},{209,211,206},{210,212,207},{212,214,210},{213,215,212},{215,217,214},{216,218,215},{216,218,215},{215,217,214},{216,218,215},{216,218,215},{216,218,214},{217,219,214},{217,219,214},{218,220,215},{218,219,214},{216,217,212},{217,218,213},{218,219,214},{214,215,209},{213,214,207},{212,213,206},{211,212,205},{209,210,203},{208,209,202},{207,208,200},{205,206,199},{203,204,198}},{{206,208,203},{20
[...]
+2|1|'dog'|'0/img2.jpg'|{{{126,118,110},{122,115,108},{126,119,111},{127,119,109},{130,122,111},{130,122,111},{132,124,113},{133,125,114},{130,122,111},{132,124,113},{134,126,115},{131,123,112},{131,123,112},{134,126,115},{133,125,114},{136,128,117},{137,129,118},{137,129,118},{136,128,117},{131,123,112},{130,122,111},{132,124,113},{132,124,113},{132,124,113},{129,122,110},{127,121,109},{127,121,109},{125,119,107},{124,118,106},{124,118,106},{120,114,102},{117,111,99}},{{122,115,107},{119
[...]
+\.
+
+DROP TABLE IF EXISTS cifar_10_sample_batched;
+DROP TABLE IF EXISTS cifar_10_sample_batched_summary;
+SELECT
training_preprocessor_dl('cifar_10_sample','cifar_10_sample_batched','y','x',
1, 255);
+
+DROP TABLE IF EXISTS cifar_10_sample_val;
+SELECT
validation_preprocessor_dl('cifar_10_sample','cifar_10_sample_val','y','x',
'cifar_10_sample_batched', 1);
+--- NOTE: In order to test fit_merge, we need at least 2 rows in the batched
table (1 on each segment).
+
+-- Text class values.
+DROP TABLE IF EXISTS cifar_10_sample_text_batched;
+-- Create a new table using the text based column for dep var.
+CREATE TABLE cifar_10_sample_text_batched AS
+ SELECT buffer_id, independent_var, dependent_var
+ FROM cifar_10_sample_batched;
+-- Insert a new row with NULL as the dependent var (one-hot encoded)
+UPDATE cifar_10_sample_text_batched set dependent_var = ARRAY[[0,0,1,0,0]]
where buffer_id=0;
+UPDATE cifar_10_sample_text_batched set dependent_var = ARRAY[[0,1,0,0,0]]
where buffer_id=1;
+INSERT INTO cifar_10_sample_text_batched(buffer_id, independent_var,
dependent_var)
+ SELECT 2, independent_var, ARRAY[[0,1,0,0,0]]
+ FROM cifar_10_sample_batched
+ WHERE cifar_10_sample_batched.buffer_id=0;
+-- Create the necessary summary table for the batched input.
+DROP TABLE IF EXISTS cifar_10_sample_text_batched_summary;
+CREATE TABLE cifar_10_sample_text_batched_summary(
+ source_table text,
+ output_table text,
+ dependent_varname text,
+ independent_varname text,
+ dependent_vartype text,
+ class_values text[],
+ buffer_size integer,
+ normalizing_const numeric);
+INSERT INTO cifar_10_sample_text_batched_summary values (
+ 'cifar_10_sample',
+ 'cifar_10_sample_text_batched',
+ 'y_text',
+ 'x',
+ 'text',
+ ARRAY[NULL,'cat','dog',NULL,NULL],
+ 1,
+ 255.0);
+
+DROP TABLE IF EXISTS cifar_10_sample_int_batched;
+DROP TABLE IF EXISTS cifar_10_sample_int_batched_summary;
+SELECT
training_preprocessor_dl('cifar_10_sample','cifar_10_sample_int_batched','y','x',
2, 255, 5);
+
+-- This table is for testing a different input shape (3, 32, 32) instead of
(32, 32, 3).
+-- Create a table with image shape 3, 32, 32
+drop table if exists cifar_10_sample_test_shape;
+create table cifar_10_sample_test_shape(id INTEGER, y SMALLINT, x REAL[] );
+copy cifar_10_sample_test_shape from stdin delimiter '|';
+1|0|{{{248,248,250,245,245,246,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245},{247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245},{245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247},{248,248,250,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247
[...]
+\.
+
+DROP TABLE IF EXISTS cifar_10_sample_test_shape_batched;
+DROP TABLE IF EXISTS cifar_10_sample_test_shape_batched_summary;
+SELECT
training_preprocessor_dl('cifar_10_sample_test_shape','cifar_10_sample_test_shape_batched','y','x',
NULL, 255, 3);
+
+DROP TABLE IF EXISTS model_arch;
+SELECT load_keras_model('model_arch',
+ $${
+ "class_name": "Sequential",
+ "keras_version": "2.1.6",
+ "config": [{
+ "class_name": "Conv2D", "config": {"kernel_initializer": {"class_name":
"VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed":
null, "mode": "fan_avg"}},
+ "name": "conv2d_1",
+ "kernel_constraint": null, "bias_regularizer": null, "bias_constraint":
null,
+ "dtype": "float32", "activation": "relu", "trainable": true,
+ "data_format": "channels_last", "filters": 32, "padding": "valid",
+ "strides": [1, 1], "dilation_rate": [1, 1], "kernel_regularizer": null,
+ "bias_initializer": {"class_name": "Zeros", "config": {}},
+ "batch_input_shape": [null, 32, 32, 3], "use_bias": true,
+ "activity_regularizer": null, "kernel_size": [3, 3]}},
+ {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_1",
"trainable": true, "data_format": "channels_last", "pool_size": [2, 2],
"padding": "valid", "strides": [2, 2]}},
+ {"class_name": "Dropout", "config": {"rate": 0.25, "noise_shape": null,
"trainable": true, "seed": null, "name": "dropout_1"}},
+ {"class_name": "Flatten", "config": {"trainable": true, "name":
"flatten_1", "data_format": "channels_last"}},
+ {"class_name": "Dense", "config": {"kernel_initializer": {"class_name":
"VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed":
null, "mode": "fan_avg"}}, "name": "dense_1", "kernel_constraint": null,
"bias_regularizer": null, "bias_constraint": null, "activation": "softmax",
"trainable": true, "kernel_regularizer": null, "bias_initializer":
+ {"class_name": "Zeros", "config": {}}, "units": 2, "use_bias": true,
"activity_regularizer": null}
+ }], "backend": "tensorflow"}$$);
+
+SELECT load_keras_model('model_arch',
+ $${
+ "class_name": "Sequential",
+ "keras_version": "2.1.6",
+ "config": [{
+ "class_name": "Conv2D", "config": {"kernel_initializer": {"class_name":
"VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed":
null, "mode": "fan_avg"}},
+ "name": "conv2d_1",
+ "kernel_constraint": null, "bias_regularizer": null, "bias_constraint":
null,
+ "dtype": "float32", "activation": "relu", "trainable": true,
+ "data_format": "channels_last", "filters": 32, "padding": "valid",
+ "strides": [1, 1], "dilation_rate": [1, 1], "kernel_regularizer": null,
+ "bias_initializer": {"class_name": "Zeros", "config": {}},
+ "batch_input_shape": [null, 32, 32, 3], "use_bias": true,
+ "activity_regularizer": null, "kernel_size": [3, 3]}},
+ {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_1",
"trainable": true, "data_format": "channels_last", "pool_size": [2, 2],
"padding": "valid", "strides": [2, 2]}},
+ {"class_name": "Dropout", "config": {"rate": 0.25, "noise_shape": null,
"trainable": true, "seed": null, "name": "dropout_1"}},
+ {"class_name": "Flatten", "config": {"trainable": true, "name":
"flatten_1", "data_format": "channels_last"}},
+ {"class_name": "Dense", "config": {"kernel_initializer": {"class_name":
"VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed":
null, "mode": "fan_avg"}}, "name": "dense_1", "kernel_constraint": null,
"bias_regularizer": null, "bias_constraint": null, "activation": "softmax",
"trainable": true, "kernel_regularizer": null, "bias_initializer":
+ {"class_name": "Zeros", "config": {}}, "units": 5, "use_bias": true,
"activity_regularizer": null}
+ }], "backend": "tensorflow"}$$);
+
+SELECT load_keras_model('model_arch',
+ $${
+ "class_name": "Sequential",
+ "keras_version": "2.1.6",
+ "config": [{
+ "class_name": "Conv2D", "config": {"kernel_initializer": {"class_name":
"VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed":
null, "mode": "fan_avg"}},
+ "name": "conv2d_1",
+ "kernel_constraint": null, "bias_regularizer": null, "bias_constraint":
null,
+ "dtype": "float32", "activation": "relu", "trainable": true,
+ "data_format": "channels_first", "filters": 32, "padding": "valid",
+ "strides": [1, 1], "dilation_rate": [1, 1], "kernel_regularizer": null,
+ "bias_initializer": {"class_name": "Zeros", "config": {}},
+ "batch_input_shape": [null, 3, 32, 32], "use_bias": true,
+ "activity_regularizer": null, "kernel_size": [3, 3]}},
+ {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_1",
"trainable": true, "data_format": "channels_first", "pool_size": [2, 2],
"padding": "valid", "strides": [2, 2]}},
+ {"class_name": "Dropout", "config": {"rate": 0.25, "noise_shape": null,
"trainable": true, "seed": null, "name": "dropout_1"}},
+ {"class_name": "Flatten", "config": {"trainable": true, "name":
"flatten_1", "data_format": "channels_first"}},
+ {"class_name": "Dense", "config": {"kernel_initializer": {"class_name":
"VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed":
null, "mode": "fan_avg"}}, "name": "dense_1", "kernel_constraint": null,
"bias_regularizer": null, "bias_constraint": null, "activation": "softmax",
"trainable": true, "kernel_regularizer": null, "bias_initializer":
+ {"class_name": "Zeros", "config": {}}, "units": 3, "use_bias": true,
"activity_regularizer": null}
+ }], "backend": "tensorflow"}$$);
+
diff --git
a/src/ports/postgres/modules/deep_learning/test/madlib_keras_evaluate.sql_in
b/src/ports/postgres/modules/deep_learning/test/madlib_keras_evaluate.sql_in
new file mode 100644
index 0000000..dfb40a0
--- /dev/null
+++ b/src/ports/postgres/modules/deep_learning/test/madlib_keras_evaluate.sql_in
@@ -0,0 +1,61 @@
+/* ---------------------------------------------------------------------*//**
+ *
+ * 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.
+ *
+ *//* ---------------------------------------------------------------------*/
+
+\i m4_regexp(MODULE_PATHNAME,
+ `\(.*\)libmadlib\.so',
+
`\1../../modules/deep_learning/test/madlib_keras_cifar.setup.sql_in'
+)
+
+-- -- Please do not break up the compile_params string
+-- -- It might break the assertion
+DROP TABLE IF EXISTS keras_saved_out, keras_saved_out_summary;
+SELECT madlib_keras_fit(
+ 'cifar_10_sample_batched',
+ 'keras_saved_out',
+ 'model_arch',
+ 1,
+ $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True),
loss='categorical_crossentropy', metrics=['mae']$$::text,
+ $$ batch_size=2, epochs=1, verbose=0 $$::text,
+ 3);
+
+-- Test that evaluate works as expected:
+DROP TABLE IF EXISTS evaluate_out;
+SELECT madlib_keras_evaluate('keras_saved_out', 'cifar_10_sample_val',
'evaluate_out', 0);
+
+SELECT assert(loss IS NOT NULL AND
+ metric IS NOT NULL AND
+ metrics_type = '{mae}', 'Evaluate output validation failed. Actual:'
|| __to_char(evaluate_out))
+FROM evaluate_out;
+
+-- Test that passing NULL / None instead of 0 for gpus_per_host works
+DROP TABLE IF EXISTS evaluate_out;
+SELECT madlib_keras_evaluate('keras_saved_out', 'cifar_10_sample_val',
'evaluate_out');
+SELECT assert(loss IS NOT NULL AND
+ metric IS NOT NULL AND
+ metrics_type = '{mae}', 'Evaluate output validation failed. Actual:'
|| __to_char(evaluate_out))
+FROM evaluate_out;
+
+-- Test that evaluate errors out correctly if model_arch field missing from
fit output
+DROP TABLE IF EXISTS evaluate_out;
+ALTER TABLE keras_saved_out DROP COLUMN model_arch;
+SELECT assert(trap_error($TRAP$
+ SELECT madlib_keras_evaluate('keras_saved_out', 'cifar_10_sample_val',
'evaluate_out');
+ $TRAP$) = 1, 'Should error out if model_arch column is missing from
model_table');
diff --git
a/src/ports/postgres/modules/deep_learning/test/madlib_keras_fit.sql_in
b/src/ports/postgres/modules/deep_learning/test/madlib_keras_fit.sql_in
new file mode 100644
index 0000000..c46f307
--- /dev/null
+++ b/src/ports/postgres/modules/deep_learning/test/madlib_keras_fit.sql_in
@@ -0,0 +1,379 @@
+/* ---------------------------------------------------------------------*//**
+ *
+ * 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.
+ *
+ *//* ---------------------------------------------------------------------*/
+
+\i m4_regexp(MODULE_PATHNAME,
+ `\(.*\)libmadlib\.so',
+
`\1../../modules/deep_learning/test/madlib_keras_cifar.setup.sql_in'
+)
+
+-- -- Please do not break up the compile_params string
+-- -- It might break the assertion
+DROP TABLE IF EXISTS keras_saved_out, keras_saved_out_summary;
+SELECT madlib_keras_fit(
+ 'cifar_10_sample_batched',
+ 'keras_saved_out',
+ 'model_arch',
+ 1,
+ $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True),
loss='categorical_crossentropy', metrics=['mae']$$::text,
+ $$ batch_size=2, epochs=1, verbose=0 $$::text,
+ 3,
+ NULL,
+ 'cifar_10_sample_val');
+
+SELECT assert(
+ model_arch_table = 'model_arch' AND
+ model_arch_id = 1 AND
+ model_type = 'madlib_keras' AND
+ start_training_time < now() AND
+ end_training_time > start_training_time AND
+ source_table = 'cifar_10_sample_batched' AND
+ validation_table = 'cifar_10_sample_val' AND
+ model = 'keras_saved_out' AND
+ dependent_varname = 'y' AND
+ dependent_vartype = 'smallint' AND
+ independent_varname = 'x' AND
+ normalizing_const = 255.0 AND
+ pg_typeof(normalizing_const) = 'real'::regtype AND
+ name is NULL AND
+ description is NULL AND
+ model_size > 0 AND
+ madlib_version is NOT NULL AND
+ compile_params = $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True),
loss='categorical_crossentropy', metrics=['mae']$$::text AND
+ fit_params = $$ batch_size=2, epochs=1, verbose=0 $$::text AND
+ num_iterations = 3 AND
+ metrics_compute_frequency = 3 AND
+ num_classes = 2 AND
+ class_values = '{0,1}' AND
+ metrics_type = '{mae}' AND
+ training_metrics_final >= 0 AND
+ training_loss_final >= 0 AND
+ array_upper(training_metrics, 1) = 1 AND
+ array_upper(training_loss, 1) = 1 AND
+ array_upper(metrics_elapsed_time, 1) = 1 AND
+ validation_metrics_final >= 0 AND
+ validation_loss_final >= 0 AND
+ array_upper(validation_metrics, 1) = 1 AND
+ array_upper(validation_loss, 1) = 1 ,
+ 'Keras model output Summary Validation failed. Actual:' ||
__to_char(summary))
+FROM (SELECT * FROM keras_saved_out_summary) summary;
+
+SELECT assert(
+ model_data IS NOT NULL AND
+ model_arch IS NOT NULL, 'Keras model output validation failed.
Actual:' || __to_char(k))
+FROM (SELECT * FROM keras_saved_out) k;
+
+-- Verify number of iterations for which metrics and loss are computed
+DROP TABLE IF EXISTS keras_saved_out, keras_saved_out_summary;
+SELECT madlib_keras_fit(
+ 'cifar_10_sample_batched',
+ 'keras_saved_out',
+ 'model_arch',
+ 1,
+ $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True),
loss='categorical_crossentropy', metrics=['accuracy']$$::text,
+ $$ batch_size=2, epochs=1, verbose=0 $$::text,
+ 3,
+ NULL,
+ 'cifar_10_sample_val',
+ 2);
+SELECT assert(
+ num_iterations = 3 AND
+ metrics_compute_frequency = 2 AND
+ training_metrics_final >= 0 AND
+ training_loss_final >= 0 AND
+ metrics_type = '{accuracy}' AND
+ array_upper(training_metrics, 1) = 2 AND
+ array_upper(training_loss, 1) = 2 AND
+ array_upper(metrics_elapsed_time, 1) = 2 AND
+ validation_metrics_final >= 0 AND
+ validation_loss_final >= 0 AND
+ array_upper(validation_metrics, 1) = 2 AND
+ array_upper(validation_loss, 1) = 2 ,
+ 'Keras model output Summary Validation failed. Actual:' ||
__to_char(summary))
+FROM (SELECT * FROM keras_saved_out_summary) summary;
+-- Fit with gpus_per_host set to 2 must error out on machines
+-- that don't have GPUs. Since Jenkins builds are run on docker containers
+-- that don't have GPUs, these queries must error out.
+DROP TABLE IF EXISTS keras_saved_out_gpu, keras_saved_out_gpu_summary;
+SELECT assert(trap_error($TRAP$madlib_keras_fit(
+ 'cifar_10_sample_batched',
+ 'keras_saved_out_gpu',
+ 'model_arch',
+ 1,
+ $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True),
loss='categorical_crossentropy', metrics=['accuracy']$$::text,
+ $$ batch_size=2, epochs=1, verbose=0 $$::text,
+ 3,
+ 2,
+ 'cifar_10_sample_val');$TRAP$) = 1,
+ 'Fit with gpus_per_host=2 must error out.');
+
+-- Test for
+ -- Non null name and description columns
+ -- Null validation table
+DROP TABLE IF EXISTS keras_out, keras_out_summary;
+SELECT madlib_keras_fit(
+ 'cifar_10_sample_batched',
+ 'keras_out',
+ 'model_arch',
+ 1,
+ $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True),
loss='categorical_crossentropy', metrics=['accuracy']$$::text,
+ $$ batch_size=2, epochs=1, verbose=0 $$::text,
+ 2,
+ NULL,
+ NULL,
+ 1,
+ NULL,
+ 'model name',
+ 'model desc');
+
+SELECT assert(
+ source_table = 'cifar_10_sample_batched' AND
+ model = 'keras_out' AND
+ dependent_varname = 'y' AND
+ independent_varname = 'x' AND
+ model_arch_table = 'model_arch' AND
+ model_arch_id = 1 AND
+ compile_params = $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True),
loss='categorical_crossentropy', metrics=['accuracy']$$::text AND
+ fit_params = $$ batch_size=2, epochs=1, verbose=0 $$::text AND
+ num_iterations = 2 AND
+ validation_table is NULL AND
+ metrics_compute_frequency = 1 AND
+ name = 'model name' AND
+ description = 'model desc' AND
+ model_type = 'madlib_keras' AND
+ model_size > 0 AND
+ start_training_time < now() AND
+ end_training_time > start_training_time AND
+ array_upper(metrics_elapsed_time, 1) = 2 AND
+ dependent_vartype = 'smallint' AND
+ madlib_version is NOT NULL AND
+ num_classes = 2 AND
+ class_values = '{0,1}' AND
+ metrics_type = '{accuracy}' AND
+ normalizing_const = 255.0 AND
+ training_metrics_final is not NULL AND
+ training_loss_final is not NULL AND
+ array_upper(training_metrics, 1) = 2 AND
+ array_upper(training_loss, 1) = 2 AND
+ validation_metrics_final is NULL AND
+ validation_loss_final is NULL AND
+ validation_metrics is NULL AND
+ validation_loss is NULL,
+ 'Keras model output Summary Validation failed. Actual:' ||
__to_char(summary))
+FROM (SELECT * FROM keras_out_summary) summary;
+
+SELECT assert(model_data IS NOT NULL , 'Keras model output validation failed')
FROM (SELECT * FROM keras_out) k;
+
+-- Validate metrics=NULL works with fit
+DROP TABLE IF EXISTS keras_saved_out, keras_saved_out_summary;
+SELECT madlib_keras_fit(
+'cifar_10_sample_batched',
+'keras_saved_out',
+'model_arch',
+1,
+$$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True),
loss='categorical_crossentropy'$$::text,
+$$ batch_size=2, epochs=1, verbose=0 $$::text,
+1);
+
+SELECT assert(
+ metrics_type is NULL AND
+ training_metrics IS NULL AND
+ array_upper(training_loss, 1) = 1 AND
+ array_upper(metrics_elapsed_time, 1) = 1 AND
+ validation_metrics_final IS NULL AND
+ validation_loss_final >= 0 AND
+ validation_metrics IS NULL AND
+ array_upper(validation_loss, 1) = 1,
+ 'Keras model output Summary Validation failed. Actual:' ||
__to_char(summary))
+FROM (SELECT * FROM keras_saved_out_summary) summary;
+
+-- Validate metrics=[] works with fit
+DROP TABLE IF EXISTS keras_saved_out, keras_saved_out_summary;
+SELECT madlib_keras_fit(
+'cifar_10_sample_batched',
+'keras_saved_out',
+'model_arch',
+1,
+$$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True),
loss='categorical_crossentropy', metrics=[]$$::text,
+$$ batch_size=2, epochs=1, verbose=0 $$::text,
+1);
+
+SELECT assert(
+ metrics_type IS NULL AND
+ training_metrics IS NULL AND
+ array_upper(training_loss, 1) = 1 AND
+ array_upper(metrics_elapsed_time, 1) = 1 AND
+ validation_metrics_final IS NULL AND
+ validation_loss_final >= 0 AND
+ validation_metrics IS NULL AND
+ array_upper(validation_loss, 1) = 1,
+ 'Keras model output Summary Validation failed. Actual:' ||
__to_char(summary))
+FROM (SELECT * FROM keras_saved_out_summary) summary;
+
+-- Compile and fit parameter tests
+DROP TABLE IF EXISTS keras_out, keras_out_summary;
+SELECT madlib_keras_fit(
+ 'cifar_10_sample_batched',
+ 'keras_out',
+ 'model_arch',
+ 1,
+ $$ optimizer='SGD', loss='categorical_crossentropy',
metrics=['accuracy']$$::text,
+ $$ batch_size=2, epochs=1, verbose=0 $$::text,
+ 1,
+ NULL,
+ NULL,
+ NULL,
+ NULL, 'model name', 'model desc');
+
+DROP TABLE IF EXISTS keras_out, keras_out_summary;
+SELECT madlib_keras_fit(
+ 'cifar_10_sample_batched',
+ 'keras_out',
+ 'model_arch',
+ 1,
+ $$ optimizer='Adam()', loss='categorical_crossentropy',
metrics=['accuracy']$$::text,
+ $$ batch_size=2, epochs=1, verbose=0 $$::text,
+ 1,
+ NULL,
+ NULL,
+ NULL,
+ NULL, 'model name', 'model desc');
+
+DROP TABLE IF EXISTS keras_out, keras_out_summary;
+SELECT madlib_keras_fit(
+ 'cifar_10_sample_batched',
+ 'keras_out',
+ 'model_arch',
+ 1,
+ $$ optimizer=Adam(epsilon=None), loss='categorical_crossentropy',
metrics=['accuracy']$$::text,
+ $$ batch_size=2, epochs=1, verbose=0 $$::text,
+ 1,
+ 0,
+ NULL,
+ NULL,
+ NULL, 'model name', 'model desc');
+
+DROP TABLE IF EXISTS keras_out, keras_out_summary;
+SELECT madlib_keras_fit(
+ 'cifar_10_sample_batched',
+ 'keras_out',
+ 'model_arch',
+ 1,
+ $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True),
metrics=['accuracy'], loss_weights=[2], sample_weight_mode=None,
loss='categorical_crossentropy' $$::text,
+ $$ epochs=10, verbose=0, shuffle=True, initial_epoch=1, steps_per_epoch=2
$$::text,
+ 1,
+ NULL,
+ NULL,
+ NULL,
+ False, 'model name', 'model desc');
+
+DROP TABLE IF EXISTS keras_out, keras_out_summary;
+SELECT madlib_keras_fit(
+ 'cifar_10_sample_batched',
+ 'keras_out',
+ 'model_arch',
+ 1,
+ $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True),
metrics=['accuracy'], loss_weights=[2], sample_weight_mode=None,
loss='categorical_crossentropy' $$::text,
+ NULL,
+ 1,
+ NULL,
+ NULL,
+ NULL,
+ False, 'model name', 'model desc');
+
+-- -- negative test case for passing non numeric y to fit
+-- induce failure by passing a non numeric column
+DROP TABLE IF EXISTS cifar_10_sample_val_failure;
+CREATE TABLE cifar_10_sample_val_failure AS SELECT * FROM cifar_10_sample_val;
+ALTER TABLE cifar_10_sample_val_failure rename dependent_var to
dependent_var_original;
+ALTER TABLE cifar_10_sample_val_failure rename buffer_id to dependent_var;
+DROP TABLE IF EXISTS keras_out, keras_out_summary;
+SELECT assert(trap_error($TRAP$madlib_keras_fit(
+ 'cifar_10_sample_batched',
+ 'keras_out',
+ 'model_arch',
+ 1,
+ $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True),
loss='categorical_crossentropy', metrics=['accuracy']$$::text,
+ $$ batch_size=2, epochs=1, verbose=0 $$::text,
+ 2,
+ NULL,
+ 'cifar_10_sample_val_failure');$TRAP$) = 1,
+ 'Passing y of type non numeric array to fit should error out.');
+
+-- Tests with text class values:
+-- Modify input data to have text classes, and mini-batch it.
+DROP TABLE IF EXISTS keras_saved_out, keras_saved_out_summary;
+SELECT madlib_keras_fit(
+ 'cifar_10_sample_text_batched',
+ 'keras_saved_out',
+ 'model_arch',
+ 2,
+ $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True),
loss='categorical_crossentropy', metrics=['accuracy']$$::text,
+ $$ batch_size=2, epochs=1, verbose=0 $$::text,
+ 3);
+-- Assert fit has correct class_values
+SELECT assert(
+ dependent_vartype = 'text' AND
+ class_values = '{NULL,cat,dog,NULL,NULL}',
+ 'Keras model output Summary Validation failed. Actual:' ||
__to_char(summary))
+FROM (SELECT * FROM keras_saved_out_summary) summary;
+
+-- Test with INTEGER class_values
+-- with NULL as a valid class value
+INSERT INTO cifar_10_sample(id, x, y, imgpath)
+SELECT 3, x, NULL, '0/img3.jpg' FROM cifar_10_sample
+WHERE y = 1;
+INSERT INTO cifar_10_sample(id, x, y, imgpath)
+SELECT 4, x, 4, '0/img4.jpg' FROM cifar_10_sample
+WHERE y = 0;
+INSERT INTO cifar_10_sample(id, x, y, imgpath)
+SELECT 5, x, 5, '0/img5.jpg' FROM cifar_10_sample
+WHERE y = 1;
+
+DROP TABLE IF EXISTS cifar_10_sample_int_batched;
+DROP TABLE IF EXISTS cifar_10_sample_int_batched_summary;
+SELECT
training_preprocessor_dl('cifar_10_sample','cifar_10_sample_int_batched','y','x',
2, 255, 5);
+
+DROP TABLE IF EXISTS keras_saved_out, keras_saved_out_summary;
+SELECT madlib_keras_fit(
+ 'cifar_10_sample_int_batched',
+ 'keras_saved_out',
+ 'model_arch',
+ 2,
+ $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True),
loss='categorical_crossentropy', metrics=['accuracy']$$::text,
+ $$ batch_size=2, epochs=1, verbose=0 $$::text,
+ 3);
+
+-- Assert fit has correct class_values
+SELECT assert(
+ dependent_vartype = 'smallint' AND
+ class_values = '{NULL,0,1,4,5}',
+ 'Keras model output Summary Validation failed. Actual:' ||
__to_char(summary))
+FROM (SELECT * FROM keras_saved_out_summary) summary;
+
+DROP TABLE IF EXISTS keras_saved_out, keras_saved_out_summary;
+SELECT madlib_keras_fit(
+ 'cifar_10_sample_test_shape_batched',
+ 'keras_saved_out',
+ 'model_arch',
+ 3,
+ $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True),
loss='categorical_crossentropy', metrics=['accuracy']$$::text,
+ $$ batch_size=2, epochs=1, verbose=0 $$::text,
+ 3);
diff --git
a/src/ports/postgres/modules/deep_learning/test/madlib_keras_iris.setup.sql_in
b/src/ports/postgres/modules/deep_learning/test/madlib_keras_iris.setup.sql_in
new file mode 100644
index 0000000..066adb8
--- /dev/null
+++
b/src/ports/postgres/modules/deep_learning/test/madlib_keras_iris.setup.sql_in
@@ -0,0 +1,266 @@
+/* ---------------------------------------------------------------------*//**
+ *
+ * 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_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_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
+ );
diff --git
a/src/ports/postgres/modules/deep_learning/test/madlib_keras_predict.sql_in
b/src/ports/postgres/modules/deep_learning/test/madlib_keras_predict.sql_in
new file mode 100644
index 0000000..7a27c7b
--- /dev/null
+++ b/src/ports/postgres/modules/deep_learning/test/madlib_keras_predict.sql_in
@@ -0,0 +1,316 @@
+/* ---------------------------------------------------------------------*//**
+ *
+ * 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.
+ *
+ *//* ---------------------------------------------------------------------*/
+
+\i m4_regexp(MODULE_PATHNAME,
+ `\(.*\)libmadlib\.so',
+
`\1../../modules/deep_learning/test/madlib_keras_cifar.setup.sql_in'
+)
+
+-- Please do not break up the compile_params string
+-- It might break the assertion
+DROP TABLE IF EXISTS keras_saved_out, keras_saved_out_summary;
+SELECT madlib_keras_fit(
+ 'cifar_10_sample_batched',
+ 'keras_saved_out',
+ 'model_arch',
+ 1,
+ $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True),
loss='categorical_crossentropy', metrics=['accuracy']$$::text,
+ $$ batch_size=2, epochs=1, verbose=0 $$::text,
+ 3);
+
+-- Prediction with gpus_per_host set to 2 must error out on machines
+-- that don't have GPUs. Since Jenkins builds are run on docker containers
+-- that don't have GPUs, these queries must error out.
+
+-- IMPORTANT: The following test must be run when we have a valid
+-- keras_saved_out model table. Otherwise, it will fail because of a
+-- non-existent model table, while we want to trap failure due to
+-- gpus_per_host=2
+DROP TABLE IF EXISTS cifar10_predict_gpu;
+SELECT assert(trap_error($TRAP$madlib_keras_predict(
+ 'keras_saved_out',
+ 'cifar_10_sample',
+ 'id',
+ 'x',
+ 'cifar10_predict_gpu',
+ NULL,
+ 2);$TRAP$) = 1,
+ 'Prediction with gpus_per_host=2 must error out.');
+
+DROP TABLE IF EXISTS cifar10_predict;
+SELECT madlib_keras_predict(
+ 'keras_saved_out',
+ 'cifar_10_sample',
+ 'id',
+ 'x',
+ 'cifar10_predict',
+ NULL,
+ 0);
+
+-- Validate that prediction output table exists and has correct schema
+SELECT assert(UPPER(pg_typeof(id)::TEXT) = 'INTEGER', 'id column should be
INTEGER type')
+ FROM cifar10_predict;
+
+SELECT assert(UPPER(pg_typeof(estimated_y)::TEXT) =
+ 'SMALLINT', 'prediction column should be SMALLINT type')
+ FROM cifar10_predict;
+
+-- Validate correct number of rows returned.
+SELECT assert(COUNT(*)=2, 'Output table of madlib_keras_predict should have
two rows')
+FROM cifar10_predict;
+
+-- First test that all values are in set of class values; if this breaks, it's
definitely a problem.
+SELECT assert(estimated_y IN (0,1),
+ 'Predicted value not in set of defined class values for model')
+FROM cifar10_predict;
+
+DROP TABLE IF EXISTS cifar10_predict;
+SELECT assert(trap_error($TRAP$madlib_keras_predict(
+ 'keras_saved_out',
+ 'cifar_10_sample_batched',
+ 'id',
+ 'x',
+ 'cifar10_predict',
+ NULL,
+ 0);$TRAP$) = 1,
+ 'Passing batched image table to predict should error out.');
+
+-- Test with pred_type=prob
+DROP TABLE IF EXISTS cifar10_predict;
+SELECT madlib_keras_predict(
+ 'keras_saved_out',
+ 'cifar_10_sample',
+ 'id',
+ 'x',
+ 'cifar10_predict',
+ 'prob',
+ 0);
+
+SELECT assert(UPPER(pg_typeof(prob_0)::TEXT) =
+ 'DOUBLE PRECISION', 'column prob_0 should be double precision type')
+ FROM cifar10_predict;
+
+SELECT assert(UPPER(pg_typeof(prob_1)::TEXT) =
+ 'DOUBLE PRECISION', 'column prob_1 should be double precision type')
+ FROM cifar10_predict;
+
+SELECT assert(COUNT(*)=3, 'Predict out table must have exactly three cols.')
+FROM pg_attribute
+WHERE attrelid='cifar10_predict'::regclass AND attnum>0;
+
+-- Tests with text class values:
+DROP TABLE IF EXISTS keras_saved_out, keras_saved_out_summary;
+SELECT madlib_keras_fit(
+ 'cifar_10_sample_text_batched',
+ 'keras_saved_out',
+ 'model_arch',
+ 2,
+ $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True),
loss='categorical_crossentropy', metrics=['accuracy']$$::text,
+ $$ batch_size=2, epochs=1, verbose=0 $$::text,
+ 3);
+
+-- Predict with pred_type=prob
+DROP TABLE IF EXISTS cifar_10_sample_text;
+CREATE TABLE cifar_10_sample_text AS
+ SELECT id, x, y_text
+ FROM cifar_10_sample;
+DROP TABLE IF EXISTS cifar10_predict;
+SELECT madlib_keras_predict(
+ 'keras_saved_out',
+ 'cifar_10_sample_text',
+ 'id',
+ 'x',
+ 'cifar10_predict',
+ 'prob',
+ 0);
+
+-- Validate the output datatype of newly created prediction columns
+-- for prediction type = 'prob' and class_values 'TEXT' with NULL as a valid
+-- class_values
+SELECT assert(UPPER(pg_typeof(prob_cat)::TEXT) =
+ 'DOUBLE PRECISION', 'column prob_cat should be double precision type')
+FROM cifar10_predict;
+
+SELECT assert(UPPER(pg_typeof(prob_dog)::TEXT) =
+ 'DOUBLE PRECISION', 'column prob_dog should be double precision type')
+FROM cifar10_predict;
+
+SELECT assert(UPPER(pg_typeof("prob_NULL")::TEXT) =
+ 'DOUBLE PRECISION', 'column prob_NULL should be double precision type')
+FROM cifar10_predict;
+
+-- Must have exactly 4 cols (3 for class_values and 1 for id)
+SELECT assert(COUNT(*)=4, 'Predict out table must have exactly four cols.')
+FROM pg_attribute
+WHERE attrelid='cifar10_predict'::regclass AND attnum>0;
+
+-- Predict with pred_type=response
+DROP TABLE IF EXISTS cifar10_predict;
+SELECT madlib_keras_predict(
+ 'keras_saved_out',
+ 'cifar_10_sample_text',
+ 'id',
+ 'x',
+ 'cifar10_predict',
+ 'response',
+ 0);
+
+-- Validate the output datatype of newly created prediction columns
+-- for prediction type = 'response' and class_values 'TEXT' with NULL
+-- as a valid class_values
+SELECT assert(UPPER(pg_typeof(estimated_y_text)::TEXT) =
+ 'TEXT', 'prediction column should be TEXT type')
+FROM cifar10_predict LIMIT 1;
+
+-- Tests where the assumption is user has one-hot encoded, so class_values
+-- in input summary table will be NULL.
+UPDATE keras_saved_out_summary SET class_values=NULL;
+
+-- Predict with pred_type=prob
+DROP TABLE IF EXISTS cifar10_predict;
+SELECT madlib_keras_predict(
+ 'keras_saved_out',
+ 'cifar_10_sample_text',
+ 'id',
+ 'x',
+ 'cifar10_predict',
+ 'prob',
+ 0);
+
+-- Validate the output datatype of newly created prediction column
+-- for prediction type = 'response' and class_value = NULL
+-- Returns: Array of probabilities for user's one-hot encoded data
+SELECT assert(UPPER(pg_typeof(prob)::TEXT) =
+ 'DOUBLE PRECISION[]', 'column prob should be double precision[] type')
+FROM cifar10_predict LIMIT 1;
+
+-- Predict with pred_type=response
+DROP TABLE IF EXISTS cifar10_predict;
+SELECT madlib_keras_predict(
+ 'keras_saved_out',
+ 'cifar_10_sample_text',
+ 'id',
+ 'x',
+ 'cifar10_predict',
+ 'response',
+ 0);
+
+-- Validate the output datatype of newly created prediction column
+-- for prediction type = 'response' and class_value = NULL
+-- Returns: Index of class value in user's one-hot encoded data with
+-- highest probability
+SELECT assert(UPPER(pg_typeof(estimated_y_text)::TEXT) =
+ 'TEXT', 'column estimated_y_text should be text type')
+FROM cifar10_predict LIMIT 1;
+
+-- Test predict with INTEGER class_values
+-- with NULL as a valid class value
+-- Update output_summary table to reflect
+-- class_values {NULL,0,1,4,5} and dependent_vartype is SMALLINT
+UPDATE keras_saved_out_summary
+SET dependent_varname = 'y',
+ class_values = ARRAY[NULL,0,1,4,5]::INTEGER[],
+ dependent_vartype = 'smallint';
+-- Predict with pred_type=prob
+DROP TABLE IF EXISTS cifar10_predict;
+SELECT madlib_keras_predict(
+ 'keras_saved_out',
+ 'cifar_10_sample',
+ 'id',
+ 'x',
+ 'cifar10_predict',
+ 'prob',
+ 0);
+
+-- Validate the output datatype of newly created prediction column
+-- for prediction type = 'prob' and class_values 'INT' with NULL
+-- as a valid class_values
+SELECT assert(UPPER(pg_typeof("prob_NULL")::TEXT) =
+ 'DOUBLE PRECISION', 'column prob_NULL should be double precision type')
+FROM cifar10_predict;
+
+-- Must have exactly 6 cols (5 for class_values and 1 for id)
+SELECT assert(COUNT(*)=6, 'Predict out table must have exactly six cols.')
+FROM pg_attribute
+WHERE attrelid='cifar10_predict'::regclass AND attnum>0;
+
+-- Predict with pred_type=response
+DROP TABLE IF EXISTS cifar10_predict;
+SELECT madlib_keras_predict(
+ 'keras_saved_out',
+ 'cifar_10_sample',
+ 'id',
+ 'x',
+ 'cifar10_predict',
+ 'response',
+ 0);
+
+-- Validate the output datatype of newly created prediction column
+-- for prediction type = 'response' and class_values 'TEXT' with NULL
+-- as a valid class_values
+-- Returns: class_value with highest probability
+SELECT assert(UPPER(pg_typeof(estimated_y)::TEXT) =
+ 'SMALLINT', 'prediction column should be smallint type')
+FROM cifar10_predict;
+
+-- Predict with correctly shaped data, must go thru.
+-- Update output_summary table to reflect
+-- class_values, num_classes and model_arch_id for shaped data
+UPDATE keras_saved_out
+SET model_arch = (SELECT model_arch from model_arch where model_id = 3);
+UPDATE keras_saved_out_summary
+SET model_arch_id = 3,
+ num_classes = 3,
+ class_values = ARRAY[0,NULL,NULL]::INTEGER[];
+
+DROP TABLE IF EXISTS cifar10_predict;
+SELECT madlib_keras_predict(
+ 'keras_saved_out',
+ 'cifar_10_sample_test_shape',
+ 'id',
+ 'x',
+ 'cifar10_predict',
+ 'prob',
+ 0);
+
+-- Prediction with incorrectly shaped data must error out.
+DROP TABLE IF EXISTS cifar10_predict;
+SELECT assert(trap_error($TRAP$madlib_keras_predict(
+ 'keras_saved_out',
+ 'cifar_10_sample',
+ 'id',
+ 'x',
+ 'cifar10_predict',
+ 'prob',
+ 0);$TRAP$) = 1,
+ 'Input shape is (32, 32, 3) but model was trained with (3, 32, 32). Should
have failed.');
+
+-- Test model_arch is retrieved from model data table and not model
architecture
+DROP TABLE IF EXISTS model_arch;
+DROP TABLE IF EXISTS cifar10_predict;
+SELECT madlib_keras_predict(
+ 'keras_saved_out',
+ 'cifar_10_sample_test_shape',
+ 'id',
+ 'x',
+ 'cifar10_predict',
+ 'prob',
+ 0);
diff --git
a/src/ports/postgres/modules/deep_learning/test/madlib_keras_predict_byom.sql_in
b/src/ports/postgres/modules/deep_learning/test/madlib_keras_predict_byom.sql_in
new file mode 100644
index 0000000..0e493aa
--- /dev/null
+++
b/src/ports/postgres/modules/deep_learning/test/madlib_keras_predict_byom.sql_in
@@ -0,0 +1,137 @@
+/* ---------------------------------------------------------------------*//**
+ *
+ * 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.
+ *
+ *//* ---------------------------------------------------------------------*/
+
+\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
+ 3); -- num_iterations
+
+DROP TABLE IF EXISTS iris_predict;
+SELECT madlib_keras_predict('iris_model', -- model
+ 'iris_test', -- test_table
+ 'id', -- id column
+ 'attributes', -- independent var
+ 'iris_predict' -- output table
+ );
+
+-- 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_data from iris_model)
WHERE model_id = 2;
+
+-- class_values not NULL, pred_type is response
+DROP TABLE IF EXISTS iris_predict_byom;
+SELECT madlib_keras_predict_byom(
+ 'iris_model_arch',
+ 2,
+ 'iris_test',
+ 'id',
+ 'attributes',
+ 'iris_predict_byom',
+ 'response',
+ -1,
+ ARRAY['Iris-setosa', 'Iris-versicolor',
+ 'Iris-virginica']
+ );
+
+SELECT assert(
+ p0.estimated_class_text = p1.estimated_dependent_var,
+ 'Predict byom failure for non null class value and response pred_type.')
+FROM iris_predict AS p0, iris_predict_byom AS p1
+WHERE p0.id=p1.id;
+SELECT assert(UPPER(pg_typeof(estimated_dependent_var)::TEXT) = 'TEXT',
+ 'Predict byom failure for non null class value and response pred_type.
+ Expeceted estimated_dependent_var to be of type TEXT')
+FROM iris_predict_byom LIMIT 1;
+
+-- class_values NULL, pred_type is NULL (response)
+DROP TABLE IF EXISTS iris_predict_byom;
+SELECT madlib_keras_predict_byom(
+ 'iris_model_arch',
+ 2,
+ 'iris_test',
+ 'id',
+ 'attributes',
+ 'iris_predict_byom'
+ );
+SELECT assert(
+ p1.estimated_dependent_var IN ('0', '1', '2'),
+ 'Predict byom failure for null class value and null pred_type.')
+FROM iris_predict_byom AS p1;
+SELECT assert(UPPER(pg_typeof(estimated_dependent_var)::TEXT) = 'TEXT',
+ 'Predict byom failure for non null class value and response pred_type.
+ Expeceted estimated_dependent_var to be of type TEXT')
+FROM iris_predict_byom LIMIT 1;
+
+-- class_values not NULL, pred_type is prob
+DROP TABLE IF EXISTS iris_predict_byom;
+SELECT madlib_keras_predict_byom(
+ 'iris_model_arch',
+ 2,
+ 'iris_test',
+ 'id',
+ 'attributes',
+ 'iris_predict_byom',
+ 'prob',
+ -1,
+ ARRAY['Iris-setosa', 'Iris-versicolor',
+ 'Iris-virginica'],
+ 1.0
+ );
+
+SELECT assert(
+ (p1."prob_Iris-setosa" + p1."prob_Iris-virginica" +
p1."prob_Iris-versicolor") - 1 < 1e-6,
+ 'Predict byom failure for non null class value and prob pred_type.')
+FROM iris_predict_byom AS p1;
+SELECT assert(UPPER(pg_typeof("prob_Iris-setosa")::TEXT) = 'DOUBLE PRECISION',
+ 'Predict byom failure for non null class value and prob pred_type.
+ Expeceted "prob_Iris-setosa" to be of type DOUBLE PRECISION')
+FROM iris_predict_byom LIMIT 1;
+
+-- class_values NULL, pred_type is prob
+DROP TABLE IF EXISTS iris_predict_byom;
+SELECT madlib_keras_predict_byom(
+ 'iris_model_arch',
+ 2,
+ 'iris_test',
+ 'id',
+ 'attributes',
+ 'iris_predict_byom',
+ 'prob',
+ 0,
+ NULL
+ );
+SELECT assert(
+ (prob[1] + prob[2] + prob[3]) - 1 < 1e-6,
+ 'Predict byom failure for null class value and prob pred_type.')
+FROM iris_predict_byom;
+SELECT assert(UPPER(pg_typeof(prob)::TEXT) = 'DOUBLE PRECISION[]',
+ 'Predict byom failure for null class value and prob pred_type.
Expeceted prob to
+ be of type DOUBLE PRECISION[]')
+FROM iris_predict_byom LIMIT 1;
\ No newline at end of file
diff --git
a/src/ports/postgres/modules/deep_learning/test/madlib_keras_transfer_learning.sql_in
b/src/ports/postgres/modules/deep_learning/test/madlib_keras_transfer_learning.sql_in
new file mode 100644
index 0000000..33f5886
--- /dev/null
+++
b/src/ports/postgres/modules/deep_learning/test/madlib_keras_transfer_learning.sql_in
@@ -0,0 +1,116 @@
+/* ---------------------------------------------------------------------*//**
+ *
+ * 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.
+ *
+ *//* ---------------------------------------------------------------------*/
+
+\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 AND
+ training_metrics[5]-training_metrics[1] > 0,
+ '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_data 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;
