This is an automated email from the ASF dual-hosted git repository.
riyer 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 59c7afd Minibatch preprocessor: Improve user doc for image loading
59c7afd is described below
commit 59c7afdbcb2b561cd43d6d57bed03d0bc0fa122e
Author: Frank McQuillan <fmcquil...@pivotal.io>
AuthorDate: Wed Feb 13 14:27:43 2019 -0800
Minibatch preprocessor: Improve user doc for image loading
---
.../utilities/minibatch_preprocessing_dl.sql_in | 355 +++++++++++++--------
1 file changed, 218 insertions(+), 137 deletions(-)
diff --git
a/src/ports/postgres/modules/utilities/minibatch_preprocessing_dl.sql_in
b/src/ports/postgres/modules/utilities/minibatch_preprocessing_dl.sql_in
index 0caca98..6cbe249 100644
--- a/src/ports/postgres/modules/utilities/minibatch_preprocessing_dl.sql_in
+++ b/src/ports/postgres/modules/utilities/minibatch_preprocessing_dl.sql_in
@@ -32,25 +32,29 @@ m4_include(`SQLCommon.m4')
<div class="toc"><b>Contents</b><ul>
<li class="level1"><a href="#minibatch_preprocessor_dl">Mini-Batch
Preprocessor for Deep Learning</a></li>
<li class="level1"><a href="#example">Examples</a></li>
+<li class="level1"><a href="#related">Related Topics</a></li>
</ul></div>
-For Deep Learning based techniques such as Convolutional Neural Nets, the input
-data is mostly images. These images can be represented as an array of numbers
-where all elements are between 0 and 255 in value. It is standard practice
-to divide each of these numbers by 255.0 to normalize the image data.
-minibatch_preprocessor() is for general use-cases, but for deep learning based
-use-cases we provide minibatch_preprocessor_dl() that is light-weight and is
-specific to image datasets. The normalizing constant is parameterized, and can
-be specified based on the kind of image data used.
+For deep learning techniques such as convolutional neural networks, the input
+data is often images. These images can represented as an array of numbers
+with elements between 0 and 255, representing grayscale or RGB channel values
+for each pixel in the image. It is standard practice to divide by 255 to
+normalize the image data. The normalizing constant is parameterized, and can
+be set depending on the format of image data used.
+
+This mini-batch preprocessor is a lightweight version designed specifically
+for image data. A separate more general minibatch_preprocessor() is also
+available for other MADlib modules using non-image input data.
+
<pre class="syntax">
-minibatch_preprocessor_dl(source_table,
- output_table,
- dependent_varname,
- independent_varname,
- buffer_size,
- normalizing_const,
- dependent_offset
- )
+minibatch_preprocessor_dl( source_table,
+ output_table,
+ dependent_varname,
+ independent_varname,
+ buffer_size,
+ normalizing_const,
+ dependent_offset
+ )
</pre>
\b Arguments
@@ -62,9 +66,9 @@ minibatch_preprocessor_dl(source_table,
<dt>output_table</dt>
<dd>TEXT. Name of the output table from the preprocessor which
will be used as input to algorithms that support mini-batching.
- Note that the arrays packed into the output table are randomized
+ Note that the arrays packed into the output table are shuffled
and normalized (by dividing each element in the independent variable array
- by the normalizing_const), so they will not match up in an obvious way with
+ by the "normalizing_const"), so they will not match up in an obvious way with
the rows in the source table.
</dd>
@@ -73,7 +77,7 @@ minibatch_preprocessor_dl(source_table,
</dd>
<dt>independent_varname</dt>
- <dd>TEXT. Name of the independent variable column. The column must be of
+ <dd>TEXT. Name of the independent variable column. The column must be
a numeric array type.
</dd>
@@ -82,19 +86,21 @@ minibatch_preprocessor_dl(source_table,
number of rows from the
source table that are packed into one row of the preprocessor
output table. The default value is computed considering size of
- the source table, number of independent variables, number of groups,
- and number of segments in the database cluster. For larger data sets,
- the computed buffer size will typically be a value in the millions.
+ the source table, number of independent variables,
+ and number of segments in the database cluster.
</dd>
<dt>normalizing_const (optional)</dt>
- <dd>DOUBLE PRECISION, default: 255.0. The normalizing constant to divide
+ <dd>DOUBLE PRECISION, default: 255. The normalizing constant to divide
each value in the independent_varname array by.
</dd>
<dt>dependent_offset (optional)</dt>
<dd>INTEGER, default: NULL. If specified, shifts all dependent
- variable values by this number (should only be used for numeric types).
+ variable values by this number (should only be used for numeric
+ types). For example, can be used to handle the case where you need to
+ convert between class values that start at 0 or 1, which saves
+ you from having to run a separate query on the output table.
</dd>
</dl>
@@ -105,7 +111,7 @@ minibatch_preprocessor_dl(source_table,
<table class="output">
<tr>
<th>buffer_id</th>
- <td>INTEGER. Unique id for packed table.
+ <td>INTEGER. Unique id for each row in the packed table.
</td>
</tr>
<tr>
@@ -142,7 +148,7 @@ A summary table named \<output_table\>_summary is also
created, which has the fo
</tr>
<tr>
<th>dependent_vartype</th>
- <td>Type of the dependent varialbe from the source table.</td>
+ <td>Type of the dependent variable from the source table.</td>
</tr>
<tr>
<th>buffer_size</th>
@@ -152,160 +158,235 @@ A summary table named \<output_table\>_summary is also
created, which has the fo
@anchor example
@par Examples
--# Create an input data set based on the well known iris data set:
+-# Create an artificial 2x2 resolution color image data set with 3 possible
classifications:
<pre class="example">
-DROP TABLE IF EXISTS iris_data;
-CREATE TABLE iris_data(
- id serial,
- attributes numeric[],
- class_text varchar,
- class integer,
- state varchar
+DROP TABLE IF EXISTS image_data;
+CREATE TABLE image_data AS (
+SELECT ARRAY[
+ (random() * 256)::integer, -- R values
+ (random() * 256)::integer,
+ (random() * 256)::integer,
+ (random() * 256)::integer,
+ (random() * 256)::integer, -- G values
+ (random() * 256)::integer,
+ (random() * 256)::integer,
+ (random() * 256)::integer,
+ (random() * 256)::integer, -- B values
+ (random() * 256)::integer,
+ (random() * 256)::integer,
+ (random() * 256)::integer
+ ] as rgb, ('{cat,dog,bird}'::text[])[ceil(random()*3)] as species
+FROM generate_series(1, 52)
);
-INSERT INTO iris_data(id, attributes, class_text, class, state) VALUES
-(1,ARRAY[5.0,3.2,1.2,0.2],'Iris_setosa',1,'Alaska'),
-(2,ARRAY[5.5,3.5,1.3,0.2],'Iris_setosa',1,'Alaska'),
-(3,ARRAY[4.9,3.1,1.5,0.1],'Iris_setosa',1,'Alaska'),
-(4,ARRAY[4.4,3.0,1.3,0.2],'Iris_setosa',1,'Alaska'),
-(5,ARRAY[5.1,3.4,1.5,0.2],'Iris_setosa',1,'Alaska'),
-(6,ARRAY[5.0,3.5,1.3,0.3],'Iris_setosa',1,'Alaska'),
-(7,ARRAY[4.5,2.3,1.3,0.3],'Iris_setosa',1,'Alaska'),
-(8,ARRAY[4.4,3.2,1.3,0.2],'Iris_setosa',1,'Alaska'),
-(9,ARRAY[5.0,3.5,1.6,0.6],'Iris_setosa',1,'Alaska'),
-(10,ARRAY[5.1,3.8,1.9,0.4],'Iris_setosa',1,'Alaska'),
-(11,ARRAY[4.8,3.0,1.4,0.3],'Iris_setosa',1,'Alaska'),
-(12,ARRAY[5.1,3.8,1.6,0.2],'Iris_setosa',1,'Alaska'),
-(13,ARRAY[5.7,2.8,4.5,1.3],'Iris_versicolor',2,'Alaska'),
-(14,ARRAY[6.3,3.3,4.7,1.6],'Iris_versicolor',2,'Alaska'),
-(15,ARRAY[4.9,2.4,3.3,1.0],'Iris_versicolor',2,'Alaska'),
-(16,ARRAY[6.6,2.9,4.6,1.3],'Iris_versicolor',2,'Alaska'),
-(17,ARRAY[5.2,2.7,3.9,1.4],'Iris_versicolor',2,'Alaska'),
-(18,ARRAY[5.0,2.0,3.5,1.0],'Iris_versicolor',2,'Alaska'),
-(19,ARRAY[5.9,3.0,4.2,1.5],'Iris_versicolor',2,'Alaska'),
-(20,ARRAY[6.0,2.2,4.0,1.0],'Iris_versicolor',2,'Alaska'),
-(21,ARRAY[6.1,2.9,4.7,1.4],'Iris_versicolor',2,'Alaska'),
-(22,ARRAY[5.6,2.9,3.6,1.3],'Iris_versicolor',2,'Alaska'),
-(23,ARRAY[6.7,3.1,4.4,1.4],'Iris_versicolor',2,'Alaska'),
-(24,ARRAY[5.6,3.0,4.5,1.5],'Iris_versicolor',2,'Alaska'),
-(25,ARRAY[5.8,2.7,4.1,1.0],'Iris_versicolor',2,'Alaska'),
-(26,ARRAY[6.2,2.2,4.5,1.5],'Iris_versicolor',2,'Alaska'),
-(27,ARRAY[5.6,2.5,3.9,1.1],'Iris_versicolor',2,'Alaska'),
-(28,ARRAY[5.0,3.4,1.5,0.2],'Iris_setosa',1,'Tennessee'),
-(29,ARRAY[4.4,2.9,1.4,0.2],'Iris_setosa',1,'Tennessee'),
-(30,ARRAY[4.9,3.1,1.5,0.1],'Iris_setosa',1,'Tennessee'),
-(31,ARRAY[5.4,3.7,1.5,0.2],'Iris_setosa',1,'Tennessee'),
-(32,ARRAY[4.8,3.4,1.6,0.2],'Iris_setosa',1,'Tennessee'),
-(33,ARRAY[4.8,3.0,1.4,0.1],'Iris_setosa',1,'Tennessee'),
-(34,ARRAY[4.3,3.0,1.1,0.1],'Iris_setosa',1,'Tennessee'),
-(35,ARRAY[5.8,4.0,1.2,0.2],'Iris_setosa',1,'Tennessee'),
-(36,ARRAY[5.7,4.4,1.5,0.4],'Iris_setosa',1,'Tennessee'),
-(37,ARRAY[5.4,3.9,1.3,0.4],'Iris_setosa',1,'Tennessee'),
-(38,ARRAY[6.0,2.9,4.5,1.5],'Iris_versicolor',2,'Tennessee'),
-(39,ARRAY[5.7,2.6,3.5,1.0],'Iris_versicolor',2,'Tennessee'),
-(40,ARRAY[5.5,2.4,3.8,1.1],'Iris_versicolor',2,'Tennessee'),
-(41,ARRAY[5.5,2.4,3.7,1.0],'Iris_versicolor',2,'Tennessee'),
-(42,ARRAY[5.8,2.7,3.9,1.2],'Iris_versicolor',2,'Tennessee'),
-(43,ARRAY[6.0,2.7,5.1,1.6],'Iris_versicolor',2,'Tennessee'),
-(44,ARRAY[5.4,3.0,4.5,1.5],'Iris_versicolor',2,'Tennessee'),
-(45,ARRAY[6.0,3.4,4.5,1.6],'Iris_versicolor',2,'Tennessee'),
-(46,ARRAY[6.7,3.1,4.7,1.5],'Iris_versicolor',2,'Tennessee'),
-(47,ARRAY[6.3,2.3,4.4,1.3],'Iris_versicolor',2,'Tennessee'),
-(48,ARRAY[5.6,3.0,4.1,1.3],'Iris_versicolor',2,'Tennessee'),
-(49,ARRAY[5.5,2.5,4.0,1.3],'Iris_versicolor',2,'Tennessee'),
-(50,ARRAY[5.5,2.6,4.4,1.2],'Iris_versicolor',2,'Tennessee'),
-(51,ARRAY[6.1,3.0,4.6,1.4],'Iris_versicolor',2,'Tennessee'),
-(52,ARRAY[5.8,2.6,4.0,1.2],'Iris_versicolor',2,'Tennessee');
+SELECT * FROM image_data;
+</pre>
+<pre class="result">
+ rgb | species
+--------------------------------------------------+---------
+ {177,194,185,175,43,16,205,92,164,130,204,100} | cat
+ {128,51,73,226,104,194,73,190,98,23,98,101} | cat
+ {237,14,81,38,116,2,232,45,177,19,61,126} | dog
+ {225,0,59,69,29,187,120,102,157,224,40,230} | dog
+ {138,253,256,239,51,237,253,132,19,113,134,251} | dog
+ {55,14,219,182,125,189,182,184,2,211,115,122} | cat
+ {16,90,96,246,248,234,243,248,217,39,229,215} | dog
+ {247,72,49,242,230,104,256,193,30,125,126,212} | cat
+ {128,167,168,250,223,184,84,63,174,76,42,161} | cat
+ {3,200,41,218,114,32,33,163,18,8,11,18} | bird
+ {41,143,71,253,196,200,163,108,194,130,35,22} | bird
+ {209,98,235,114,167,238,58,207,200,172,240,233} | cat
+ {2,241,90,21,186,130,164,1,127,104,201,34} | bird
+ {139,164,247,161,102,200,3,82,58,170,64,115} | dog
+ {8,31,105,241,110,107,226,200,128,156,74,36} | dog
+ {200,140,103,234,95,241,143,86,146,245,30,149} | cat
+ {87,63,134,203,185,142,234,34,127,88,141,97} | cat
+ {13,254,105,49,155,50,189,2,28,28,243,171} | dog
+ {133,160,144,26,231,231,90,109,178,19,252,156} | cat
+ {123,244,194,221,20,208,218,125,1,117,175,190} | dog
+ {204,218,106,119,75,239,23,219,9,254,194,99} | dog
+ {116,118,103,16,171,226,5,109,191,25,61,153} | dog
+ {62,14,69,252,133,17,213,239,136,32,222,159} | bird
+ {231,157,189,74,9,49,192,112,66,107,83,70} | bird
+ {18,95,22,171,245,84,185,58,79,62,75,36} | cat
+ {211,69,11,114,64,242,15,254,60,24,47,252} | dog
+ {31,171,230,197,167,228,220,138,123,199,79,75} | cat
+ {83,238,149,1,213,101,150,168,179,155,255,49} | dog
+ {123,157,77,177,69,108,92,43,49,3,15,13} | dog
+ {138,212,221,212,219,48,194,113,49,152,213,199} | cat
+ {136,98,62,185,256,185,86,77,106,155,185,198} | bird
+ {235,202,213,247,87,95,203,52,51,167,100,245} | cat
+ {149,141,236,93,204,117,191,10,46,190,195,133} | cat
+ {45,32,196,244,230,175,189,187,166,21,26,113} | cat
+ {77,24,173,66,47,66,207,28,159,155,145,94} | dog
+ {191,28,104,67,39,150,99,235,137,73,154,71} | cat
+ {65,92,30,178,164,107,202,81,173,249,147,124} | cat
+ {50,23,166,144,188,101,172,36,168,211,186,11} | bird
+ {67,84,88,138,88,153,229,117,75,138,224,21} | bird
+ {140,14,110,8,36,160,31,201,48,219,46,219} | bird
+ {214,174,184,224,108,251,52,196,133,139,93,106} | cat
+ {168,244,225,189,207,109,203,61,117,239,221,148} | bird
+ {13,111,230,232,109,188,150,37,157,2,32,209} | bird
+ {165,92,36,15,93,203,4,61,136,210,171,84} | cat
+ {32,66,236,180,251,249,34,225,225,144,157,119} | bird
+ {58,121,213,11,64,122,103,99,137,195,47,141} | cat
+ {183,95,171,11,111,203,77,91,127,72,83,162} | cat
+ {52,49,199,172,230,1,37,187,11,101,52,114} | bird
+ {190,53,247,75,54,174,170,226,185,24,173,6} | dog
+ {44,78,198,206,119,251,255,62,166,229,63,204} | dog
+ {75,49,212,189,249,146,242,240,221,40,159,134} | cat
+ {88,159,183,94,18,228,172,216,178,35,211,177} | dog
+(52 rows)
</pre>
--# Run the preprocessor:
+-# Run the preprocessor for image data:
<pre class="example">
-DROP TABLE IF EXISTS iris_data_packed, iris_data_packed_summary;
-SELECT madlib.minibatch_preprocessor_dl('iris_data', -- Source table
- 'iris_data_packed', -- Output table
- 'class_text', -- Dependent variable
- 'attributes', -- Independent
variables
- NULL, -- buffer size
- 2 -- normalizing
constant
- );
+DROP TABLE IF EXISTS image_data_packed, image_data_packed_summary;
+SELECT madlib.minibatch_preprocessor_dl('image_data', -- Source table
+ 'image_data_packed', -- Output table
+ 'species', -- Dependent
variable
+ 'rgb' -- Independent
variable
+ );
</pre>
For small datasets like in this example, buffer size is mainly
determined by the number of segments in the database.
-This example is run on a Greenplum database with 3 segments,
-so there are 3 rows with a buffer size of 18.
+This example is run on a Greenplum database with 2 segments,
+so there are 2 rows with a buffer size of 26.
For PostgresSQL, there would be only one row with a buffer
size of 52 since it is a single node database.
For larger data sets, other factors go into
computing buffers size besides number of segments.
-Also, note that the dependent variable has
-been one-hot encoded since it is categorical.
Here is a sample of the packed output table:
<pre class="example">
\\x on
-SELECT * FROM iris_data_packed;
+SELECT * FROM image_data_packed ORDER BY buffer_id;
</pre>
<pre class="result">
-[ RECORD 1 ]-------+-------------------------------------
-independent_varname |
{{2.55,1.7,0.75,0.1},{2.55,1.9,0.95,0.2},{2.9,1.35,1.95,0.6},{3.35,1.55,2.2,0.7},{2.85,1.3,1.75,0.5},{3.1,1.1,2.25,0.75},...}}
-dependent_varname |
{Iris_versicolor,Iris_versicolor,Iris_versicolor,Iris_versicolor,Iris_setosa,Iris_setosa,...}
-buffer_id | 0
--[ RECORD 2 ]-------+-------------------------------------
-independent_varname |
{{2.4,1.5,0.7,0.15},{2.2,1.6,0.65,0.1},{2.8,1.45,1.8,0.65},{2.9,1.3,2,0.6},{2.2,1.45,0.7,0.1},{2.85,1.4,2.25,0.65},{2.8,1.25,1.95,0.55},...}}
-dependent_varname |
{Iris_setosa,Iris_setosa,Iris_versicolor,Iris_versicolor,Iris_setosa,Iris_versicolor,...}
-buffer_id | 1
+independent_var |
{{0.501961,0.2,0.286275,0.886275,0.407843,0.760784,0.286275,0.745098,0.384314,0.0901961,0.384314,0.396078},...}}
+dependent_var |
{cat,dog,cat,cat,bird,cat,dog,cat,dog,dog,dog,bird,dog,dog,cat,cat,cat,cat,bird,bird,bird,cat,cat,bird,dog,dog}
+buffer_id | 0
-[ RECORD 2 ]-------+-------------------------------------
-independent_varname |
{{2.4,1.7,0.8,0.1},{2.9,1.35,2.05,0.5},{3.15,1.65,2.35,0.8},{2.7,1.95,0.65,0.2},{2.75,1.3,2.2,0.6},{3,1.1,2,0.5},{2.55,1.9,0.8,0.1},...}}
-dependent_varname |
{Iris_versicolor,Iris_setosa,Iris_versicolor,Iris_versicolor,Iris_setosa,Iris_setosa,...}
-buffer_id | 2
+independent_var |
{{0.694118,0.760784,0.72549,0.686275,0.168627,0.0627451,0.803922,0.360784,0.643137,0.509804,0.8,0.392157},...}}
+dependent_var |
{cat,dog,dog,dog,cat,bird,bird,dog,cat,cat,dog,bird,cat,cat,dog,bird,cat,dog,cat,bird,cat,bird,bird,cat,dog,cat}
+buffer_id | 1
</pre>
Review the output summary table:
<pre class="example">
\\x on
-SELECT * FROM iris_data_packed_summary;
+SELECT * FROM image_data_packed_summary;
</pre>
<pre class="result">
-[ RECORD 1 ]-------+------------------
-source_table | iris_data
-output_table | iris_data_packed
-dependent_varname | class_text
-independent_varname | attributes
-dependent_vartype | character varying
-buffer_size | 18
+source_table | image_data
+output_table | image_data_packed
+dependent_varname | species
+independent_varname | rgb
+dependent_vartype | text
+buffer_size | 26
</pre>
-# Generally the default buffer size will work well,
but if you have occasion to change it:
<pre class="example">
-DROP TABLE IF EXISTS iris_data_packed, iris_data_packed_summary;
-SELECT madlib.minibatch_preprocessor_dl('iris_data', -- Source table
- 'iris_data_packed', -- Output table
- 'class_text', -- Dependent variable
- 'attributes', -- Independent
variables
- 10 -- Buffer size
- );
-</pre>
-Review the number of buffers in the output table:
-<pre class="example">
-SELECT COUNT(*) FROM iris_data_packed;
+DROP TABLE IF EXISTS image_data_packed, image_data_packed_summary;
+SELECT madlib.minibatch_preprocessor_dl('image_data', -- Source table
+ 'image_data_packed', -- Output table
+ 'species', -- Dependent
variable
+ 'rgb', -- Independent
variable
+ 10 -- Buffer size
+ );
+SELECT COUNT(*) FROM image_data_packed;
</pre>
<pre class="result">
--[ RECORD 1 ]
-count | 6
+ count
++-------
+ 6
</pre>
Review the output summary table:
<pre class="example">
\\x on
-SELECT * FROM iris_data_packed_summary;
+SELECT * FROM image_data_packed_summary;
</pre>
<pre class="result">
-[ RECORD 1 ]-------+------------------
-source_table | iris_data
-output_table | iris_data_packed
-dependent_varname | class_text
-independent_varname | attributes
-dependent_vartype | character varying
+source_table | image_data
+output_table | image_data_packed
+dependent_varname | species
+independent_varname | rgb
+dependent_vartype | text
buffer_size | 10
</pre>
+-# Shift integer dependent variable. Let's say our input data has class levels
+starting from 1, but we want them to start from 0 after preprocesssing.
+Create a new image data set with class values as 1, 2 or 3:
+<pre class="example">
+DROP TABLE IF EXISTS image_data_2;
+CREATE TABLE image_data_2 AS (
+SELECT ARRAY[
+ (random() * 256)::integer, -- R values
+ (random() * 256)::integer,
+ (random() * 256)::integer,
+ (random() * 256)::integer,
+ (random() * 256)::integer, -- G values
+ (random() * 256)::integer,
+ (random() * 256)::integer,
+ (random() * 256)::integer,
+ (random() * 256)::integer, -- B values
+ (random() * 256)::integer,
+ (random() * 256)::integer,
+ (random() * 256)::integer
+ ] as rgb, ('{1,2,3}'::integer[])[ceil(random()*3)] as species
+FROM generate_series(1, 52)
+);
+SELECT * FROM image_data_2 LIMIT 10;
+</pre>
+<pre class="result">
+ rgb | species
+------------------------------------------------+---------
+ {103,220,183,64,237,22,86,152,10,170,81,68} | 3
+ {144,143,30,189,98,99,200,212,163,185,227,161} | 3
+ {252,208,241,132,65,89,96,247,153,77,13,239} | 3
+ {23,153,55,92,146,199,235,176,131,77,19,75} | 1
+ {182,5,4,86,250,0,38,235,132,103,67,228} | 2
+ {220,50,107,203,23,131,100,78,222,246,21,201} | 2
+ {152,22,185,227,55,110,232,59,197,226,59,235} | 3
+ {192,82,16,164,176,236,214,27,183,237,158,27} | 1
+ {124,17,80,69,183,233,91,111,204,146,221,180} | 3
+ {162,150,8,141,98,200,222,114,108,142,95,66} | 2
+...
+</pre>
+Now run the preprocessor with a dependent variable offset of -1:
+<pre class="example">
+DROP TABLE IF EXISTS image_data_packed_2, image_data_packed_2_summary;
+SELECT madlib.minibatch_preprocessor_dl('image_data_2', -- Source table
+ 'image_data_packed_2', -- Output table
+ 'species', -- Dependent
variable
+ 'rgb', -- Independent
variable
+ NULL, -- Buffer size
+ 255, -- Normalizing
constant
+ -1 -- Dependent
variable offset
+ );
+SELECT * FROM image_data_packed_2 ORDER BY buffer_id;
+</pre>
+<pre class="result">
+-[ RECORD 1 ]-------+-------------------------------------
+independent_var |
{{0.564706,0.560784,0.117647,0.741176,0.384314,0.388235,0.784314,0.831373,0.639216,0.72549,0.890196,0.631373},...}}
+dependent_var | {2,0,1,0,1,1,1,0,1,0,2,0,1,0,1,0,2,0,2,0,2,1,0,1,0,0}
+buffer_id | 0
+-[ RECORD 2 ]-------+-------------------------------------
+independent_var |
{{0.403922,0.862745,0.717647,0.25098,0.929412,0.0862745,0.337255,0.596078,0.0392157,0.666667,0.317647,0.266667},...}}
+dependent_var | {2,2,1,2,2,1,1,2,0,0,0,1,1,1,0,2,2,1,2,1,1,0,2,1,2,2}
+buffer_id | 1
+</pre>
+
+@anchor related
+@par Related Topics
+
+minibatch_preprocessing_dl.sql_in
+
+minibatch_preprocessing.sql_in
+
*/
CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.minibatch_preprocessor_dl(
