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Posted to dev@madlib.apache.org by GitBox <gi...@apache.org> on 2019/05/29 17:29:55 UTC

[GitHub] [madlib] kaknikhil commented on a change in pull request #399: DL: Enable transfer learning

kaknikhil commented on a change in pull request #399: DL: Enable transfer learning
URL: https://github.com/apache/madlib/pull/399#discussion_r288683731
 
 

 ##########
 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:
 
 Review comment:
   This was existing code that we chose not to change but we also thought of deleting this check. I will go ahead and delete it now. 

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