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 <title>MADlib: Define Model Architectures</title>
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 <div class="header">
   <div class="headertitle">
 <div class="title">Define Model Architectures<div class="ingroups"><a class="el" href="group__grp__dl.html">Deep Learning</a> &raquo; <a class="el" href="group__grp__model__prep.html">Model Preparation</a></div></div>  </div>
 </div><!--header-->
 <div class="contents">
 <div class="toc"><b>Contents</b><ul>
 <li class="level1">
 <a href="#load_keras_model">Load Model</a> </li>
 <li class="level1">
 <a href="#delete_keras_model">Delete Model</a> </li>
 <li class="level1">
 <a href="#example">Examples</a> </li>
 <li class="level1">
 <a href="#related">Related Topics</a> </li>
 </ul>
 </div><p>This function loads model architectures and weights into a table for use by deep learning algorithms.</p>
 <p>Model architecture is in JSON form and model weights are in the form of PostgreSQL binary data types (bytea). If the output table already exists, a new row is inserted into the table so it can act as a repository for multiple model architectures and weights.</p>
 <p>There is also a function to delete a model from the table.</p>
 <p>MADlib's deep learning methods are designed to use the TensorFlow package and its built in Keras functions. To ensure consistency, please use tensorflow.keras objects (models, layers, etc.) instead of importing Keras and using its objects.</p>
 <p><a class="anchor" id="load_keras_model"></a></p><dl class="section user"><dt>Load Model</dt><dd></dd></dl>
 <pre class="syntax">
 load_keras_model(
     keras_model_arch_table,
     model_arch,
     model_weights,
     name,
     description
     )
 </pre><p> <b>Arguments</b> </p><dl class="arglist">
 <dt>keras_model_arch_table </dt>
 <dd><p class="startdd">VARCHAR. Output table to load keras model architecture and weights. </p>
 <p class="enddd"></p>
 </dd>
 <dt>model_arch </dt>
 <dd><p class="startdd">JSON. JSON of the model architecture to load. </p><dl class="section note"><dt>Note</dt><dd>Please note that every input layer must have the 'input_shape' stated explicitly in the model architecture. MADlib has this requirement because, in some cases, the JSON representation may not have the input shape by default and it has to be read from the JSON for fit() type functions.</dd></dl>
 <p class="enddd"></p>
 </dd>
 <dt>model_weights (optional) </dt>
 <dd><p class="startdd">bytea. Model weights to load as a PostgreSQL binary data type. </p>
 <p class="enddd"></p>
 </dd>
 <dt>name (optional) </dt>
 <dd><p class="startdd">TEXT, default: NULL. Free text string to provide a name, if desired. </p>
 <p class="enddd"></p>
 </dd>
 <dt>description (optional) </dt>
 <dd><p class="startdd">TEXT, default: NULL. Free text string to provide a description, if desired. </p>
 <p class="enddd"></p>
 </dd>
 </dl>
 <p><b>Output table</b> <br />
  The output table contains the following columns: </p><table class="output">
 <tr>
 <th>model_id </th><td>SERIAL PRIMARY KEY. Model ID.   </td></tr>
 <tr>
 <th>model_arch </th><td>JSON. JSON blob of the model architecture.   </td></tr>
 <tr>
 <th>model_weights </th><td>BYTEA. Weights of the model which may be used for warm start or transfer learning. Weights are stored as a PostgreSQL binary data type.   </td></tr>
 <tr>
 <th>name </th><td>TEXT. Name of model (free text).   </td></tr>
 <tr>
 <th>description </th><td>TEXT. Description of model (free text).   </td></tr>
 <tr>
 <th>__internal_madlib_id__ </th><td>TEXT. Unique id for model arch. This is an id used internally be MADlib.   </td></tr>
 </table>
 <p><a class="anchor" id="delete_keras_model"></a></p><dl class="section user"><dt>Delete Model</dt><dd></dd></dl>
 <pre class="syntax">
 delete_keras_model(
     keras_model_arch_table
     model_id
 )
 </pre><p> <b>Arguments</b> </p><dl class="arglist">
 <dt>keras_model_arch_table </dt>
 <dd><p class="startdd">VARCHAR. Table containing model architectures and weights. </p>
 <p class="enddd"></p>
 </dd>
 <dt>model_id </dt>
 <dd>INTEGER. The id of the model to be deleted.  </dd>
 </dl>
 <p><a class="anchor" id="example"></a></p><dl class="section user"><dt>Examples</dt><dd><ol type="1">
 <li>Define model architecture. Use tensorflow.keras to define the model architecture: <pre class="example">
 from tensorflow import keras
 from tensorflow.keras.models import Sequential
 from tensorflow.keras.layers import Dense
 model_simple = Sequential()
 model_simple.add(Dense(10, activation='relu', input_shape=(4,)))
 model_simple.add(Dense(10, activation='relu'))
 model_simple.add(Dense(3, activation='softmax'))
 model_simple.summary()
 <pre class="fragment">_________________________________________________________________
 Layer (type)                 Output Shape              Param #
 =================================================================
 dense_1 (Dense)              (None, 10)                50
 _________________________________________________________________
 dense_2 (Dense)              (None, 10)                110
 _________________________________________________________________
 dense_3 (Dense)              (None, 3)                 33
 =================================================================
 Total params: 193
 Trainable params: 193
 Non-trainable params: 0
 </pre>
 </pre> Export the model to JSON: <pre class="example">
 model_simple.to_json()
 </pre> <pre class="result">
 '{"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": null, "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": null, "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": null, "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"}'
 </pre></li>
 <li>Load into model architecture table: <pre class="example">
 DROP TABLE IF EXISTS model_arch_library;
 SELECT madlib.load_keras_model('model_arch_library',  -- Output table,
 $$
 {"class_name": "Sequential", "keras_version": "2.1.6", "config": [{"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_1", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "dtype": "float32", "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "batch_input_shape": [null, 4], "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_2", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_3", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "softmax", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 3, "use_bias": true, "activity_regularizer": null}}], "backend": "tensorflow"}
 $$
 ::json,  -- JSON blob
                                NULL,                  -- Weights
                                'Sophie',              -- Name
                                'A simple model'       -- Descr
 );
 SELECT COUNT(*) FROM model_arch_library;
 </pre> <pre class="result">
  count
 -------+
      1
 </pre> Load another model architecture: <pre class="example">
 SELECT madlib.load_keras_model('model_arch_library',  -- Output table,
 $$
 {"class_name": "Sequential", "keras_version": "2.1.6", "config": [{"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_1", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "dtype": "float32", "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "batch_input_shape": [null, 4], "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_2", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_3", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "softmax", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 3, "use_bias": true, "activity_regularizer": null}}], "backend": "tensorflow"}
 $$
 ::json,  -- JSON blob
                                NULL,                  -- Weights
                                'Maria',               -- Name
                                'Also a simple model'  -- Descr
 );
 SELECT COUNT(*) FROM model_arch_library;
 </pre> <pre class="result">
  count
 -------+
      2
 </pre></li>
 <li>Load model weights. To load weights from previous MADlib run, use UPDATE to load directly into the table. For example, if 'model_weights' are the weights in the output table 'iris_model' from a previous run of '<a class="el" href="madlib__keras_8sql__in.html#a544c395da869ee39bef3555de77c4195">madlib_keras_fit()</a>' : <pre class="example">
 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;
 </pre> <pre class="result">
  model_id |  name  |     description     | has_model_weights
 ----------+--------+---------------------+-------------------
         1 | Sophie | A simple model      | f
         2 | Maria  | Also a simple model | t
 </pre></li>
 <li>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. The model weights will be de-serialized when passed to Keras functions. <pre class="example">
 CREATE OR REPLACE FUNCTION load_weights() RETURNS VOID AS
 $$
 from tensorflow.keras.layers import *
 from tensorflow.keras import Sequential
 import numpy as np
 import plpy
 #
 # create model
 model = Sequential()
 model.add(Dense(10, activation='relu', input_shape=(4,)))
 model.add(Dense(10, activation='relu'))
 model.add(Dense(3, activation='softmax'))
 #
 # get weights, flatten and serialize
 weights = model.get_weights()
 weights_flat = [w.flatten() for w in weights]
 weights1d =  np.concatenate(weights_flat).ravel()
 weights_bytea = weights1d.tostring()
 #
 # load query
 load_query = plpy.prepare("""SELECT madlib.load_keras_model(
                         'model_arch_library',
                         $1, $2)
                     """, ['json','bytea'])
 plpy.execute(load_query, [model.to_json(), weights_bytea])
 $$ language plpythonu;
 -- Call load function
 SELECT load_weights();
 SELECT model_id, name, description, (model_weights IS NOT NULL) AS has_model_weights FROM model_arch_library ORDER BY model_id;
 </pre> <pre class="result">
  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
 </pre></li>
 <li>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. <pre class="example">
 import psycopg2
 import psycopg2 as p2
 conn = p2.connect('postgresql://gpadmin@35.239.240.26:5432/madlib')
 cur = conn.cursor()
 from tensorflow.keras.layers import *
 from tensorflow.keras import Sequential
 import numpy as np
 #
 # create model
 model = Sequential()
 model.add(Dense(10, activation='relu', input_shape=(4,)))
 model.add(Dense(10, activation='relu'))
 model.add(Dense(3, activation='softmax'))
 #
 # get weights, flatten and serialize
 weights = model.get_weights()
 weights_flat = [w.flatten() for w in weights]
 weights1d =  np.concatenate(weights_flat).ravel()
 weights_bytea = psycopg2.Binary(weights1d.tostring())
 #
 # load query
 query = "SELECT madlib.load_keras_model('model_arch_library', %s,%s)"
 cur.execute(query,[model.to_json(),weights_bytea])
 conn.commit()
 SELECT model_id, name, description, (model_weights IS NOT NULL) AS has_model_weights FROM model_arch_library ORDER BY model_id;
 </pre> <pre class="result">
  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
 </pre></li>
 <li>Delete one of the models: <pre class="example">
 SELECT madlib.delete_keras_model('model_arch_library',   -- Output table
                                   1                      -- Model id
                                 );
 SELECT model_id, name, description, (model_weights IS NOT NULL) AS has_model_weights FROM model_arch_library ORDER BY model_id;
 </pre> <pre class="result">
  model_id | name  |     description     | has_model_weights
 ----------+-------+---------------------+-------------------
         2 | Maria | Also a simple model | t
         3 | Ella  | Model x             | t
         4 | Grace | Model y             | t
 </pre></li>
 </ol>
 </dd></dl>
 <p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd></dd></dl>
 <p>See <a class="el" href="keras__model__arch__table_8sql__in.html">keras_model_arch_table.sql_in</a> </p>
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	<div class="title">Define Model Architectures<div class="ingroups"><a class="el" href="group__grp__dl.html">Deep Learning</a> » <a class="el" href="group__grp__model__prep.html">Model Preparation</a></div></div> </div>
	</div><!--header-->
	<div class="contents">
	<div class="toc"><b>Contents</b><ul>
	<li class="level1">
	<a href="#load_keras_model">Load Model</a> </li>
	<li class="level1">
	<a href="#delete_keras_model">Delete Model</a> </li>
	<li class="level1">
	<a href="#example">Examples</a> </li>
	<li class="level1">
	<a href="#related">Related Topics</a> </li>
	</ul>
	</div><p>This function loads model architectures and weights into a table for use by deep learning algorithms.</p>
	<p>Model architecture is in JSON form and model weights are in the form of PostgreSQL binary data types (bytea). If the output table already exists, a new row is inserted into the table so it can act as a repository for multiple model architectures and weights.</p>
	<p>There is also a function to delete a model from the table.</p>
	<p>MADlib's deep learning methods are designed to use the TensorFlow package and its built in Keras functions. To ensure consistency, please use tensorflow.keras objects (models, layers, etc.) instead of importing Keras and using its objects.</p>
	<p><a class="anchor" id="load_keras_model"></a></p><dl class="section user"><dt>Load Model</dt><dd></dd></dl>
	<pre class="syntax">
	load_keras_model(
	keras_model_arch_table,
	model_arch,
	model_weights,
	name,
	description
	)
	</pre><p> <b>Arguments</b> </p><dl class="arglist">
	<dt>keras_model_arch_table </dt>
	<dd><p class="startdd">VARCHAR. Output table to load keras model architecture and weights. </p>
	<p class="enddd"></p>
	</dd>
	<dt>model_arch </dt>
	<dd><p class="startdd">JSON. JSON of the model architecture to load. </p><dl class="section note"><dt>Note</dt><dd>Please note that every input layer must have the 'input_shape' stated explicitly in the model architecture. MADlib has this requirement because, in some cases, the JSON representation may not have the input shape by default and it has to be read from the JSON for fit() type functions.</dd></dl>
	<p class="enddd"></p>
	</dd>
	<dt>model_weights (optional) </dt>
	<dd><p class="startdd">bytea. Model weights to load as a PostgreSQL binary data type. </p>
	<p class="enddd"></p>
	</dd>
	<dt>name (optional) </dt>
	<dd><p class="startdd">TEXT, default: NULL. Free text string to provide a name, if desired. </p>
	<p class="enddd"></p>
	</dd>
	<dt>description (optional) </dt>
	<dd><p class="startdd">TEXT, default: NULL. Free text string to provide a description, if desired. </p>
	<p class="enddd"></p>
	</dd>
	</dl>
	<p><b>Output table</b> <br />
	The output table contains the following columns: </p><table class="output">
	<tr>
	<th>model_id </th><td>SERIAL PRIMARY KEY. Model ID. </td></tr>
	<tr>
	<th>model_arch </th><td>JSON. JSON blob of the model architecture. </td></tr>
	<tr>
	<th>model_weights </th><td>BYTEA. Weights of the model which may be used for warm start or transfer learning. Weights are stored as a PostgreSQL binary data type. </td></tr>
	<tr>
	<th>name </th><td>TEXT. Name of model (free text). </td></tr>
	<tr>
	<th>description </th><td>TEXT. Description of model (free text). </td></tr>
	<tr>
	<th>__internal_madlib_id__ </th><td>TEXT. Unique id for model arch. This is an id used internally be MADlib. </td></tr>
	</table>
	<p><a class="anchor" id="delete_keras_model"></a></p><dl class="section user"><dt>Delete Model</dt><dd></dd></dl>
	<pre class="syntax">
	delete_keras_model(
	keras_model_arch_table
	model_id
	)
	</pre><p> <b>Arguments</b> </p><dl class="arglist">
	<dt>keras_model_arch_table </dt>
	<dd><p class="startdd">VARCHAR. Table containing model architectures and weights. </p>
	<p class="enddd"></p>
	</dd>
	<dt>model_id </dt>
	<dd>INTEGER. The id of the model to be deleted. </dd>
	</dl>
	<p><a class="anchor" id="example"></a></p><dl class="section user"><dt>Examples</dt><dd><ol type="1">
	<li>Define model architecture. Use tensorflow.keras to define the model architecture: <pre class="example">
	from tensorflow import keras
	from tensorflow.keras.models import Sequential
	from tensorflow.keras.layers import Dense
	model_simple = Sequential()
	model_simple.add(Dense(10, activation='relu', input_shape=(4,)))
	model_simple.add(Dense(10, activation='relu'))
	model_simple.add(Dense(3, activation='softmax'))
	model_simple.summary()
	<pre class="fragment">_________________________________________________________________
	Layer (type) Output Shape Param #
	=================================================================
	dense_1 (Dense) (None, 10) 50
	_________________________________________________________________
	dense_2 (Dense) (None, 10) 110
	_________________________________________________________________
	dense_3 (Dense) (None, 3) 33
	=================================================================
	Total params: 193
	Trainable params: 193
	Non-trainable params: 0
	</pre>
	</pre> Export the model to JSON: <pre class="example">
	model_simple.to_json()
	</pre> <pre class="result">
	'{"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": null, "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": null, "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": null, "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"}'
	</pre></li>
	<li>Load into model architecture table: <pre class="example">
	DROP TABLE IF EXISTS model_arch_library;
	SELECT madlib.load_keras_model('model_arch_library', -- Output table,
	$$
	{"class_name": "Sequential", "keras_version": "2.1.6", "config": [{"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_1", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "dtype": "float32", "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "batch_input_shape": [null, 4], "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_2", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_3", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "softmax", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 3, "use_bias": true, "activity_regularizer": null}}], "backend": "tensorflow"}
	$$
	::json, -- JSON blob
	NULL, -- Weights
	'Sophie', -- Name
	'A simple model' -- Descr
	);
	SELECT COUNT(*) FROM model_arch_library;
	</pre> <pre class="result">
	count
	-------+
	1
	</pre> Load another model architecture: <pre class="example">
	SELECT madlib.load_keras_model('model_arch_library', -- Output table,
	$$
	{"class_name": "Sequential", "keras_version": "2.1.6", "config": [{"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_1", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "dtype": "float32", "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "batch_input_shape": [null, 4], "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_2", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_3", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "softmax", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 3, "use_bias": true, "activity_regularizer": null}}], "backend": "tensorflow"}
	$$
	::json, -- JSON blob
	NULL, -- Weights
	'Maria', -- Name
	'Also a simple model' -- Descr
	);
	SELECT COUNT(*) FROM model_arch_library;
	</pre> <pre class="result">
	count
	-------+
	2
	</pre></li>
	<li>Load model weights. To load weights from previous MADlib run, use UPDATE to load directly into the table. For example, if 'model_weights' are the weights in the output table 'iris_model' from a previous run of '<a class="el" href="madlib__keras_8sql__in.html#a544c395da869ee39bef3555de77c4195">madlib_keras_fit()</a>' : <pre class="example">
	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;
	</pre> <pre class="result">
	model_id \| name \| description \| has_model_weights
	----------+--------+---------------------+-------------------
	1 \| Sophie \| A simple model \| f
	2 \| Maria \| Also a simple model \| t
	</pre></li>
	<li>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. The model weights will be de-serialized when passed to Keras functions. <pre class="example">
	CREATE OR REPLACE FUNCTION load_weights() RETURNS VOID AS
	$$
	from tensorflow.keras.layers import *
	from tensorflow.keras import Sequential
	import numpy as np
	import plpy
	#
	# create model
	model = Sequential()
	model.add(Dense(10, activation='relu', input_shape=(4,)))
	model.add(Dense(10, activation='relu'))
	model.add(Dense(3, activation='softmax'))
	#
	# get weights, flatten and serialize
	weights = model.get_weights()
	weights_flat = [w.flatten() for w in weights]
	weights1d = np.concatenate(weights_flat).ravel()
	weights_bytea = weights1d.tostring()
	#
	# load query
	load_query = plpy.prepare("""SELECT madlib.load_keras_model(
	'model_arch_library',
	$1, $2)
	""", ['json','bytea'])
	plpy.execute(load_query, [model.to_json(), weights_bytea])
	$$ language plpythonu;
	-- Call load function
	SELECT load_weights();
	SELECT model_id, name, description, (model_weights IS NOT NULL) AS has_model_weights FROM model_arch_library ORDER BY model_id;
	</pre> <pre class="result">
	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
	</pre></li>
	<li>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. <pre class="example">
	import psycopg2
	import psycopg2 as p2
	conn = p2.connect('postgresql://gpadmin@35.239.240.26:5432/madlib')
	cur = conn.cursor()
	from tensorflow.keras.layers import *
	from tensorflow.keras import Sequential
	import numpy as np
	#
	# create model
	model = Sequential()
	model.add(Dense(10, activation='relu', input_shape=(4,)))
	model.add(Dense(10, activation='relu'))
	model.add(Dense(3, activation='softmax'))
	#
	# get weights, flatten and serialize
	weights = model.get_weights()
	weights_flat = [w.flatten() for w in weights]
	weights1d = np.concatenate(weights_flat).ravel()
	weights_bytea = psycopg2.Binary(weights1d.tostring())
	#
	# load query
	query = "SELECT madlib.load_keras_model('model_arch_library', %s,%s)"
	cur.execute(query,[model.to_json(),weights_bytea])
	conn.commit()
	SELECT model_id, name, description, (model_weights IS NOT NULL) AS has_model_weights FROM model_arch_library ORDER BY model_id;
	</pre> <pre class="result">
	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
	</pre></li>
	<li>Delete one of the models: <pre class="example">
	SELECT madlib.delete_keras_model('model_arch_library', -- Output table
	1 -- Model id
	);
	SELECT model_id, name, description, (model_weights IS NOT NULL) AS has_model_weights FROM model_arch_library ORDER BY model_id;
	</pre> <pre class="result">
	model_id \| name \| description \| has_model_weights
	----------+-------+---------------------+-------------------
	2 \| Maria \| Also a simple model \| t
	3 \| Ella \| Model x \| t
	4 \| Grace \| Model y \| t
	</pre></li>
	</ol>
	</dd></dl>
	<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd></dd></dl>
	<p>See <a class="el" href="keras__model__arch__table_8sql__in.html">keras_model_arch_table.sql_in</a> </p>
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