njayaram2 commented on a change in pull request #399: DL: Enable transfer 
learning
URL: https://github.com/apache/madlib/pull/399#discussion_r288322432
 
 

 ##########
 File path: 
src/ports/postgres/modules/deep_learning/keras_model_arch_table.py_in
 ##########
 @@ -53,88 +53,83 @@ class Format:
            arch = plpy.execute(sql)[0]
 
     """
-    col_names = ('model_id', 'model_arch', 'model_weights', 
'__internal_madlib_id__')
-    col_types = ('SERIAL PRIMARY KEY', 'JSON', 'DOUBLE PRECISION[]', 'TEXT')
-    (MODEL_ID, MODEL_ARCH, MODEL_WEIGHTS, __INTERNAL_MADLIB_ID__) = col_names
-
-@MinWarning("warning")
-def _execute(sql,max_rows=0):
-    return plpy.execute(sql,max_rows)
-
-def load_keras_model(schema_madlib, keras_model_arch_table,
-                     model_arch, **kwargs):
+    col_names = ('model_id', 'model_arch', 'model_weights', 'name', 
'description',
+                 '__internal_madlib_id__')
+    col_types = ('SERIAL PRIMARY KEY', 'JSON', 'bytea', 'TEXT', 'TEXT', 'TEXT')
+    (MODEL_ID, MODEL_ARCH, MODEL_WEIGHTS, NAME, DESCRIPTION,
+     __INTERNAL_MADLIB_ID__) = col_names
+
+def load_keras_model(keras_model_arch_table, model_arch, model_weights,
+                     name, description, **kwargs):
     model_arch_table = quote_ident(keras_model_arch_table)
     if not table_exists(model_arch_table):
-        col_defs = get_col_name_type_sql_string(Format.col_names,
-                                                Format.col_types)
+        col_defs = get_col_name_type_sql_string(ModelArchSchema.col_names,
+                                                ModelArchSchema.col_types)
 
         sql = "CREATE TABLE {model_arch_table} ({col_defs})" \
               .format(**locals())
 
-        _execute(sql)
+        plpy.execute(sql, 0)
         plpy.info("Keras Model Arch: Created new keras model arch table {0}." \
             .format(model_arch_table))
     else:
         missing_cols = columns_missing_from_table(model_arch_table,
-                                                  Format.col_names)
+                                                  ModelArchSchema.col_names)
         if len(missing_cols) > 0:
             plpy.error("Keras Model Arch: Invalid keras model arch table {0},"
                        " missing columns: {1}".format(model_arch_table,
                                                       missing_cols))
 
     unique_str = unique_string(prefix_has_temp=False)
-
-    sql = """INSERT INTO {model_arch_table} ({model_arch_col}, 
{internal_id_col})
-                                    VALUES({model_arch}, '{unique_str}');
-             SELECT {model_id_col}, {model_arch_col}
-                 FROM {model_arch_table} WHERE {internal_id_col} = 
'{unique_str}'
-    """.format(model_arch_table=model_arch_table,
-               model_arch_col=Format.MODEL_ARCH,
-               unique_str=unique_str,
-               model_arch=quote_literal(model_arch),
-               model_id_col=Format.MODEL_ID,
-               internal_id_col=Format.__INTERNAL_MADLIB_ID__)
-    res = _execute(sql,1)
-
-    if len(res) != 1 or res[0][Format.MODEL_ARCH] != model_arch:
+    insert_query = plpy.prepare("INSERT INTO {model_arch_table} "
+                                "VALUES(DEFAULT, $1, $2, $3, $4, 
$5);".format(**locals()),
+                                ModelArchSchema.col_types[1:])
+    insert_res = plpy.execute(insert_query,[model_arch, model_weights, name, 
description,
+                               unique_str], 0)
+
+    select_query = "SELECT {model_id_col}, {model_arch_col} FROM 
{model_arch_table} " \
+                   "WHERE {internal_id_col} = '{unique_str}'".format(
+                    model_id_col=ModelArchSchema.MODEL_ID,
+                    model_arch_col=ModelArchSchema.MODEL_ARCH,
+                    model_arch_table=model_arch_table,
+                    internal_id_col=ModelArchSchema.__INTERNAL_MADLIB_ID__,
+                    unique_str=unique_str)
+    select_res = plpy.execute(select_query,1)
+
+    if len(select_res) != 1 or select_res[0][ModelArchSchema.MODEL_ARCH] != 
model_arch:
         raise Exception("Failed to insert new row in {0} table--try again?"
                        .format(model_arch_table))
     plpy.info("Keras Model Arch: Added model id {0} to {1} table".
-        format(res[0][Format.MODEL_ID], model_arch_table))
+              format(select_res[0][ModelArchSchema.MODEL_ID], 
model_arch_table))
 
-def delete_keras_model(schema_madlib, keras_model_arch_table,
-                       model_id, **kwargs):
+def delete_keras_model(keras_model_arch_table, model_id, **kwargs):
     model_arch_table = quote_ident(keras_model_arch_table)
     input_tbl_valid(model_arch_table, "Keras Model Arch")
 
-    missing_cols = columns_missing_from_table(model_arch_table, 
Format.col_names)
+    missing_cols = columns_missing_from_table(model_arch_table, 
ModelArchSchema.col_names)
     if len(missing_cols) > 0:
         plpy.error("Keras Model Arch: Invalid keras model arch table {0},"
                    " missing columns: {1}".format(model_arch_table, 
missing_cols))
 
     sql = """
            DELETE FROM {model_arch_table} WHERE {model_id_col}={model_id}
-          """.format(model_arch_table=model_arch_table, 
model_id_col=Format.MODEL_ID,
+          """.format(model_arch_table=model_arch_table, 
model_id_col=ModelArchSchema.MODEL_ID,
                      model_id=model_id)
-    res = _execute(sql)
+    res = plpy.execute(sql, 0)
 
 Review comment:
   What is the reason behind specifying the number of rows param in `execute()` 
here and a couple of other places? We should remove it if it's unnecessary.

----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
[email protected]


With regards,
Apache Git Services

Reply via email to