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new 7eeb29c clarify example in user docs for loading model arch
7eeb29c is described below
commit 7eeb29c6827ff6968e9533536d6f32f8bc6de3c8
Author: Frank McQuillan
AuthorDate: Thu Mar 4 15:41:33 2021 -0800
clarify example in user docs for loading model arch
---
.../deep_learning/keras_model_arch_table.sql_in| 46 +-
1 file changed, 28 insertions(+), 18 deletions(-)
diff --git
a/src/ports/postgres/modules/deep_learning/keras_model_arch_table.sql_in
b/src/ports/postgres/modules/deep_learning/keras_model_arch_table.sql_in
index ee30f94..0c099e0 100644
--- a/src/ports/postgres/modules/deep_learning/keras_model_arch_table.sql_in
+++ b/src/ports/postgres/modules/deep_learning/keras_model_arch_table.sql_in
@@ -237,8 +237,15 @@ output table 'iris_model' from a previous run
of 'madlib_keras_fit()' :
UPDATE model_arch_library SET model_weights = model_weights FROM iris_model
WHERE model_id = 2;
+SELECT model_id, name, description, (model_weights IS NOT NULL) AS
has_model_weights FROM model_arch_library ORDER BY model_id;
-To load weights from Keras using a PL/Python function,
+
+ model_id | name | description | has_model_weights
+--++-+---
+1 | Sophie | A simple model | f
+2 | Maria | Also a simple model | t
+
+-# To load weights from Keras using a PL/Python function,
we need to flatten then serialize the weights to store
as a PostgreSQL binary data type. Byte format is more
efficient on space and memory compared to a numeric array.
@@ -273,15 +280,16 @@ plpy.execute(load_query, [model.to_json(), weights_bytea])
$$ language plpythonu;
-- Call load function
SELECT load_weights();
--- Check weights loaded OK
-SELECT COUNT(*) FROM model_arch_library WHERE model_weights IS NOT NULL;
+SELECT model_id, name, description, (model_weights IS NOT NULL) AS
has_model_weights FROM model_arch_library ORDER BY model_id;
- count
+
- 1
+ model_id | name | description | has_model_weights
+--++-+---
+1 | Sophie | A simple model | f
+2 | Maria | Also a simple model | t
+3 | Ella | Model x | t
-Load weights from Keras using psycopg2. (Psycopg is a PostgreSQL database
adapter for the
+-# Load weights from Keras using psycopg2. (Psycopg is a PostgreSQL database
adapter for the
Python programming language.) As above we need to flatten then serialize the
weights to store as a
PostgreSQL binary data type. Note that the psycopg2.Binary function used
below will increase the size of the
Python object for the weights, so if your model is large it might be better to
use a PL/Python function as above.
@@ -310,27 +318,29 @@ weights_bytea = psycopg2.Binary(weights1d.tostring())
query = "SELECT madlib.load_keras_model('model_arch_library', %s,%s)"
cur.execute(query,[model.to_json(),weights_bytea])
conn.commit()
-
-From SQL check if weights loaded OK:
-
-SELECT COUNT(*) FROM model_arch_library WHERE model_weights IS NOT NULL;
+SELECT model_id, name, description, (model_weights IS NOT NULL) AS
has_model_weights FROM model_arch_library ORDER BY model_id;
- count
+
- 2
+ model_id | name | description | has_model_weights
+--++-+---
+1 | Sophie | A simple model | f
+2 | Maria | Also a simple model | t
+3 | Ella | Model x | t
+4 | Grace | Model y | t
-# Delete one of the models:
SELECT madlib.delete_keras_model('model_arch_library', -- Output table
1 -- Model id
);
-SELECT COUNT(*) FROM model_arch_library;
+SELECT model_id, name, description, (model_weights IS NOT NULL) AS
has_model_weights FROM model_arch_library ORDER BY model_id;
- count
+
- 2
+ model_id | name | description | has_model_weights
+--+---+-+---
+2 | Maria | Also a simple model | t
+3 | Ella | Model x | t
+4 | Grace | Model y | t
@anchor related
