| /* ----------------------------------------------------------------------- *//** |
| * |
| * Licensed to the Apache Software Foundation (ASF) under one |
| * or more contributor license agreements. See the NOTICE file |
| * distributed with this work for additional information |
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| * to you under the Apache License, Version 2.0 (the |
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| * http://www.apache.org/licenses/LICENSE-2.0 |
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| * Unless required by applicable law or agreed to in writing, |
| * software distributed under the License is distributed on an |
| * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| * KIND, either express or implied. See the License for the |
| * specific language governing permissions and limitations |
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| * |
| * |
| * @file madlib_keras_model_selection.sql_in |
| * |
| * @brief SQL functions for model hopper distributed training |
| * @date August 2019 |
| * |
| * |
| *//* ----------------------------------------------------------------------- */ |
| |
| m4_include(`SQLCommon.m4') |
| /** |
| @addtogroup grp_keras_setup_model_selection |
| |
| @brief Utility function to set up a model selection table for model architecture search |
| and hyperparameter tuning. |
| |
| \warning <em> This MADlib method is still in early stage development. |
| Interface and implementation are subject to change. </em> |
| |
| <div class="toc"><b>Contents</b><ul> |
| <li class="level1"><a href="#load_mst_table">Load Model Selection Table</a></li> |
| <li class="level1"><a href="#example">Examples</a></li> |
| <li class="level1"><a href="#notes">Notes</a></li> |
| <li class="level1"><a href="#related">Related Topics</a></li> |
| </ul></div> |
| |
| This utility function sets up a model selection table |
| for use by the multiple model Keras fit feature of MADlib. |
| By model selection we mean both hyperparameter tuning and |
| model architecture search. The table defines the unique combinations |
| of model architectures, compile and fit parameters |
| to run on a massively parallel processing database cluster. |
| |
| @anchor load_mst_table |
| @par Load Model Selection Table |
| |
| <pre class="syntax"> |
| load_model_selection_table( |
| model_arch_table, |
| model_selection_table, |
| model_id_list, |
| compile_params_list, |
| fit_params_list, |
| object_table |
| ) |
| </pre> |
| |
| \b Arguments |
| <dl class="arglist"> |
| <dt>model_arch_table</dt> |
| <dd>VARCHAR. Table containing model architectures and weights. |
| For more information on this table |
| refer to <a href="group__grp__keras__model__arch.html">Load Model</a>. |
| </dd> |
| |
| <dt>model_selection_table</dt> |
| <dd>VARCHAR. Model selection table created by this utility. A summary table |
| named <model_selection_table>_summary is also created. Contents of both output |
| tables are described below. |
| </dd> |
| |
| <dt>model_id_list</dt> |
| <dd>INTEGER[]. Array of model IDs from the 'model_arch_table' to be included |
| in the run combinations. For hyperparameter search, this will typically be |
| one model ID. For model architecture search, this will be the different model IDs |
| that you want to test. |
| </dd> |
| |
| <dt>compile_params_list</dt> |
| <dd>VARCHAR[]. Array of compile parameters to be tested. Each element |
| of the array should consist of a string of compile parameters |
| exactly as it is to be passed to Keras. For custom loss functions or custom metrics, |
| list the custom function name in the usual way, and also provide the name of the |
| table where the serialized objects reside in the parameter 'object_table' |
| below. |
| </dd> |
| |
| <dt>fit_params_list</dt> |
| <dd>VARCHAR[]. Array of fit parameters to be tested. Each element |
| of the array should consist of a string of fit parameters |
| exactly as it is to be passed to Keras. |
| </dd> |
| |
| <dt>object_table (optional)</dt> |
| <dd>VARCHAR, default: NULL. Name of the table containing Python objects in the case that |
| custom loss functions or custom metrics are specified in the |
| parameter 'compile_params_list'. |
| </dd> |
| |
| </dl> |
| |
| <b>Output table</b> |
| <br> |
| The model selection output table contains the following columns: |
| <table class="output"> |
| <tr> |
| <th>mst_key</th> |
| <td>INTEGER. ID that defines a unique tuple for |
| model architecture-compile parameters-fit parameters. |
| </td> |
| </tr> |
| <tr> |
| <th>model_id</th> |
| <td>VARCHAR. Model architecture ID from the 'model_arch_table'. |
| </td> |
| </tr> |
| <tr> |
| <th>compile_params</th> |
| <td>VARCHAR. Keras compile parameters. |
| </td> |
| </tr> |
| <tr> |
| <th>fit_params</th> |
| <td>VARCHAR. Keras fit parameters. |
| </td> |
| </tr> |
| </table> |
| A summary table named <model_selection_table>_summary is |
| also created, which contains the following column: |
| <table class="output"> |
| <tr> |
| <th>model_arch_table</th> |
| <td>VARCHAR. Name of the model architecture table containing the |
| model architecture IDs. |
| </td> |
| </tr> |
| <tr> |
| <th>object_table</th> |
| <td>VARCHAR. Name of the object table containing the serialized |
| Python objects for custom loss functions and custom metrics. |
| If there are none, this field will be blank. |
| </td> |
| </tr> |
| </table> |
| </br> |
| |
| @anchor example |
| @par Examples |
| -# The model selection table works in conjunction with a model architecture table, |
| so we first create a model architecture table with two different models. Use Keras to define |
| a model architecture with 1 hidden layer: |
| <pre class="example"> |
| import keras |
| from keras.models import Sequential |
| from keras.layers import Dense |
| model1 = Sequential() |
| model1.add(Dense(10, activation='relu', input_shape=(4,))) |
| model1.add(Dense(10, activation='relu')) |
| model1.add(Dense(3, activation='softmax')) |
| model1.summary() |
| \verbatim |
| |
| _________________________________________________________________ |
| 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 |
| \endverbatim |
| </pre> |
| Export the model to JSON: |
| <pre class="example"> |
| model1.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> |
| Now use Keras to define |
| a model architecture with 2 hidden layers: |
| <pre class="example"> |
| model2 = Sequential() |
| model2.add(Dense(10, activation='relu', input_shape=(4,))) |
| model2.add(Dense(10, activation='relu')) |
| model2.add(Dense(10, activation='relu')) |
| model2.add(Dense(3, activation='softmax')) |
| model2.summary() |
| \verbatim |
| |
| _________________________________________________________________ |
| Layer (type) Output Shape Param # |
| ================================================================= |
| dense_4 (Dense) (None, 10) 50 |
| _________________________________________________________________ |
| dense_5 (Dense) (None, 10) 110 |
| _________________________________________________________________ |
| dense_6 (Dense) (None, 10) 110 |
| _________________________________________________________________ |
| dense_7 (Dense) (None, 3) 33 |
| ================================================================= |
| Total params: 303 |
| Trainable params: 303 |
| Non-trainable params: 0 |
| \endverbatim |
| </pre> |
| Export the model to JSON: |
| <pre class="example"> |
| model2.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_4", "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_5", "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_6", "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_7", "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> |
| |
| -# Load both models into the 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 |
| 'MLP with 1 hidden layer' -- Descr |
| ); |
| 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_4", "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_5", "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_6", "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_7", "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 |
| 'MLP with 2 hidden layers' -- Descr |
| ); |
| SELECT model_id, name, description FROM model_arch_library ORDER BY model_id; |
| </pre> |
| <pre class="result"> |
| model_id | name | description |
| ----------+--------+-------------------------- |
| 1 | Sophie | MLP with 1 hidden layer |
| 2 | Maria | MLP with 2 hidden layers |
| (2 rows) |
| </pre> |
| |
| -# Load model selection table. Select the model(s) from the model |
| architecture table that you want to run, along with the compile and |
| fit parameters. Unique combinations will be created: |
| <pre class="example"> |
| DROP TABLE IF EXISTS mst_table, mst_table_summary; |
| SELECT madlib.load_model_selection_table('model_arch_library', -- model architecture table |
| 'mst_table', -- model selection table output |
| ARRAY[1,2], -- model ids from model architecture table |
| ARRAY[ -- compile params |
| $$loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']$$, |
| $$loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']$$, |
| $$loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']$$ |
| ], |
| ARRAY[ -- fit params |
| $$batch_size=4,epochs=1$$, |
| $$batch_size=8,epochs=1$$ |
| ] |
| ); |
| SELECT * FROM mst_table ORDER BY mst_key; |
| </pre> |
| <pre class="result"> |
| mst_key | model_id | compile_params | fit_params |
| ---------+---------------+---------------------------------------------------------------------------------+----------------------- |
| 1 | 1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy'] | batch_size=4,epochs=1 |
| 2 | 1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy'] | batch_size=8,epochs=1 |
| 3 | 1 | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=4,epochs=1 |
| 4 | 1 | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=8,epochs=1 |
| 5 | 1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=4,epochs=1 |
| 6 | 1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=8,epochs=1 |
| 7 | 2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy'] | batch_size=4,epochs=1 |
| 8 | 2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy'] | batch_size=8,epochs=1 |
| 9 | 2 | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=4,epochs=1 |
| 10 | 2 | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=8,epochs=1 |
| 11 | 2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=4,epochs=1 |
| 12 | 2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=8,epochs=1 |
| (12 rows) |
| </pre> |
| The name of the model architecture table is stored in the summary table: |
| <pre class="example"> |
| SELECT * FROM mst_table_summary; |
| </pre> |
| <pre class="result"> |
| model_arch_table |
| --------------------+ |
| model_arch_library |
| (1 row) |
| </pre> |
| |
| -# Create model selection table manually. If you would like to |
| have more control over the set of model selection parameters |
| to run, you can manually create the model selection table and |
| the associated summary table. Both must be created since |
| they are needed by the multiple model fit module. |
| For example, let's say we don't want all combinations |
| but only want batch_size=4 for model_id=1 and batch_size=8 for model_id=2: |
| <pre class="example"> |
| DROP TABLE IF EXISTS mst_table_manual; |
| CREATE TABLE mst_table_manual( |
| mst_key serial, |
| model_id integer, |
| compile_params varchar, |
| fit_params varchar |
| ); |
| INSERT INTO mst_table_manual(model_id, compile_params, fit_params) VALUES |
| (1, $$loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']$$, 'batch_size=4,epochs=1'), |
| (1, $$loss='categorical_crossentropy',optimizer='Adam(lr=0.01)',metrics=['accuracy']$$, 'batch_size=4,epochs=1'), |
| (1, $$loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']$$, 'batch_size=4,epochs=1'), |
| (2, $$loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']$$, 'batch_size=8,epochs=1'), |
| (2, $$loss='categorical_crossentropy',optimizer='Adam(lr=0.01)',metrics=['accuracy']$$, 'batch_size=8,epochs=1'), |
| (2, $$loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']$$, 'batch_size=8,epochs=1'); |
| SELECT * FROM mst_table_manual ORDER BY mst_key; |
| </pre> |
| <pre class="result"> |
| mst_key | model_id | compile_params | fit_params |
| ---------+---------------+---------------------------------------------------------------------------------+----------------------- |
| 1 | 1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy'] | batch_size=4,epochs=1 |
| 2 | 1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=4,epochs=1 |
| 3 | 1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=4,epochs=1 |
| 4 | 2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy'] | batch_size=8,epochs=1 |
| 5 | 2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=8,epochs=1 |
| 6 | 2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=8,epochs=1 |
| (6 rows) |
| </pre> |
| Create the summary table which must be named |
| with the model selection output table appended by "_summary": |
| <pre class="example"> |
| DROP TABLE IF EXISTS mst_table_manual_summary; |
| CREATE TABLE mst_table_manual_summary ( |
| model_arch_table varchar |
| ); |
| INSERT INTO mst_table_manual_summary(model_arch_table) VALUES |
| ('model_arch_library'); |
| SELECT * FROM mst_table_manual_summary; |
| </pre> |
| <pre class="result"> |
| model_arch_table |
| --------------------+ |
| model_arch_library |
| (1 row) |
| </pre> |
| |
| -# Generate hyperparameters automatically. You can use other |
| libraries or methods to generate hyperparameters according |
| to the tests that you want to run. For example, let's randomly |
| generate batch size from powers of 2 and learning |
| rate on a log scale. |
| We use psycopg which is a PostgreSQL database adapter |
| for the Python programming language. |
| <pre class="example"> |
| import numpy as np |
| import psycopg2 as p2 |
| conn = p2.connect('postgresql://gpadmin@35.239.240.26:5432/madlib') |
| #conn = p2.connect('postgresql://fmcquillan@localhost:5432/madlib') |
| cur = conn.cursor() |
| \# |
| %sql DROP TABLE IF EXISTS mst_table_auto, mst_table_auto_summary; |
| \# |
| \#compile params |
| learning_rate = np.random.permutation([0.1,0.01,0.001,0.0001])[:3] |
| compile_param1 = "loss='categorical_crossentropy',optimizer='Adam(lr=" + str(learning_rate[0]) + ")',metrics=['accuracy']" |
| compile_param2 = "loss='categorical_crossentropy',optimizer='Adam(lr=" + str(learning_rate[1]) + ")',metrics=['accuracy']" |
| compile_param3 = "loss='categorical_crossentropy',optimizer='Adam(lr=" + str(learning_rate[2]) + ")',metrics=['accuracy']" |
| compile_params = [compile_param1,compile_param2,compile_param3] |
| \# |
| \#fit params |
| batch_size = np.random.permutation([4,8,16,32,64])[:2] |
| fit_param1 = "batch_size=" + str(batch_size[0]) + ",epochs=1" |
| fit_param2 = "batch_size=" + str(batch_size[1]) + ",epochs=1" |
| fit_params = [fit_param1,fit_param2] |
| \# |
| query = "SELECT madlib.load_model_selection_table('model_arch_library', 'mst_table_auto', ARRAY[1,2], %s, %s);" |
| \# |
| cur.execute(query,[compile_params, fit_params]) |
| conn.commit() |
| \# |
| \# review model selection table |
| %sql SELECT * FROM mst_table_auto ORDER BY mst_key; |
| </pre> |
| <pre class="result"> |
| mst_key | model_id | compile_params | fit_params |
| ---------+---------------+----------------------------------------------------------------------------------+----------------------- |
| 1 | 1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.0001)',metrics=['accuracy'] | batch_size=4,epochs=1 |
| 2 | 1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.0001)',metrics=['accuracy'] | batch_size=8,epochs=1 |
| 3 | 1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy'] | batch_size=4,epochs=1 |
| 4 | 1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy'] | batch_size=8,epochs=1 |
| 5 | 1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=4,epochs=1 |
| 6 | 1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=8,epochs=1 |
| 7 | 2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.0001)',metrics=['accuracy'] | batch_size=4,epochs=1 |
| 8 | 2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.0001)',metrics=['accuracy'] | batch_size=8,epochs=1 |
| 9 | 2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy'] | batch_size=4,epochs=1 |
| 10 | 2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy'] | batch_size=8,epochs=1 |
| 11 | 2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=4,epochs=1 |
| 12 | 2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=8,epochs=1 |
| (12 rows) |
| </pre> |
| The name of the model architecture table is stored in the summary table: |
| <pre class="example"> |
| SELECT * FROM mst_table_auto_summary; |
| </pre> |
| <pre class="result"> |
| model_arch_table |
| --------------------+ |
| model_arch_library |
| (1 row) |
| </pre> |
| |
| @anchor notes |
| @par Notes |
| |
| 1. In this method, the same compile and fit parameters are applied to all model architectures |
| when generating combinations. However, you may wish to have different compile and fit parameters |
| for each model. To do so, call 'load_model_selection_table' |
| multiple times - once for each model. Then you can combine the resulting tables using UNION or other means. |
| Note that the 'mst_key' must be unique so you will need to regenerate it in your final combined table. |
| |
| @anchor related |
| @par Related Topics |
| |
| See keras_model_arch_table.sql_in |
| |
| */ |
| |
| CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.load_model_selection_table( |
| model_arch_table VARCHAR, |
| model_selection_table VARCHAR, |
| model_id_list INTEGER[], |
| compile_params_list VARCHAR[], |
| fit_params_list VARCHAR[], |
| object_table VARCHAR |
| ) RETURNS VOID AS $$ |
| PythonFunctionBodyOnly(`deep_learning', `madlib_keras_model_selection') |
| with AOControl(False): |
| mst_loader = madlib_keras_model_selection.MstLoader(**globals()) |
| mst_loader.load() |
| $$ LANGUAGE plpythonu VOLATILE |
| m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `'); |
| |
| CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.load_model_selection_table( |
| model_arch_table VARCHAR, |
| model_selection_table VARCHAR, |
| model_id_list INTEGER[], |
| compile_params_list VARCHAR[], |
| fit_params_list VARCHAR[] |
| ) RETURNS VOID AS $$ |
| SELECT MADLIB_SCHEMA.load_model_selection_table($1, $2, $3, $4, $5, NULL); |
| $$ LANGUAGE sql VOLATILE |
| m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `'); |
