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* 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 Generate configurations for model architecture search and hyperparameter tuning.
* @date August 2020
*
*
*//* ----------------------------------------------------------------------- */
m4_include(`SQLCommon.m4')
/**
@addtogroup grp_keras_setup_model_selection
@brief Generate configurations for model architecture search and hyperparameter tuning.
<div class="toc"><b>Contents</b><ul>
<li class="level1"><a href="#gen_mst_configs">Generate Model Configurations</a></li>
<li class="level1"><a href="#load_mst_table">Load Model Selection Table [Deprecated]</a></li>
<li class="level1"><a href="#example">Examples</a></li>
<li class="level1"><a href="#related">Related Topics</a></li>
</ul></div>
This module generates model configurations using grid search or random search.
Once the configurations are defined, they can be used by the fit function
in <a href="group__grp__keras__run__model__selection.html">Train Model Configurations</a>.
By model configurations we mean both hyperparameters and
model architectures.
The output table from this module
defines the combinations of model architectures,
compile and fit parameters to be trained in parallel.
<!-- --------------------------------------START-------------------------------------------------------------- -->
@anchor gen_mst_configs
@par Generate Model Configurations
<pre class="syntax">
generate_model_configs(
model_arch_table,
model_selection_table,
model_id_list,
compile_params_grid,
fit_params_grid,
search_type,
num_configs,
random_state,
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 the module <a href="group__grp__keras__model__arch.html">Define Model Architectures</a>.
</dd>
<dt>model_selection_table</dt>
<dd>VARCHAR. Model selection table created by this module. If this table already
exists, it will be appended to. 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 compare.
</dd>
<dt>compile_params_grid</dt>
<dd>VARCHAR. String representation of a Python dictionary
of compile parameters to be tested. Each entry
of the dictionary should consist of keys as compile parameter names,
and values as a Python list of compile parameter values to be passed to Keras.
Also, optimizer parameters are a nested dictionary to allow different
optimizer types to have different parameters or ranges of parameters.
Here is an example:
<pre class="example">
$$
{'loss': ['categorical_crossentropy'],
'optimizer_params_list': [
{'optimizer': ['SGD'], 'lr': [0.0001, 0.001, 'log'], 'momentum': [0.95, 0.99, 'log_near_one']},
{'optimizer': ['Adam'], 'lr': [0.01, 0.1, 'log'], 'decay': [1e-6, 1e-4, 'log']}],
'metrics': ['accuracy']
}
$$
</pre>
The following types of sampling are supported: 'linear', 'log' and 'log_near_one'.
The 'log_near_one' sampling is useful for exponentially weighted average types of parameters like momentum,
which are very sensitive to changes near 1. It has the effect of producing more values near 1
than regular log-based sampling.
In the case of grid search, omit the sampling type and just put the grid points in the list.
@note
- Custom loss functions and custom metrics can be used as defined in
<a href="group__grp__custom__function.html">Define Custom Functions.</a>
List the custom function name and provide the name of the table where the
serialized Python objects reside using the parameter 'object_table' below.
- The following loss function is
not supported: <em>sparse_categorical_crossentropy</em>.
The following metrics are not
supported: <em>sparse_categorical_accuracy, sparse_top_k_categorical_accuracy</em>.
- The Keras accuracy parameter <em>top_k_categorical_accuracy</em> returns top 5 accuracy by
default. If you want a different top k value, use the helper function
<a href="group__grp__custom__function.html#top_k_function">Top k Accuracy Function</a>
to create a custom
Python function to compute the top k accuracy that you want.
</dd>
<dt>fit_params_grid</dt>
<dd>VARCHAR. String representation of a Python dictionary
of fit parameters to be tested. Each entry
of the dictionary should consist of keys as fit parameter names,
and values as a Python list of fit parameter values
to be passed to Keras. Here is an example:
<pre class="example">
$$
{'batch_size': [32, 64, 128, 256],
'epochs': [10, 20, 30]
}
$$
</pre>
@note
Callbacks are not currently supported except for TensorBoard
which you can specify in the usual way,
e.g., 'callbacks': ['[TensorBoard(log_dir="/tmp/logs/fit")]']
</dd>
<dt>search_type</dt>
<dd>VARCHAR, 'grid' or 'random'. Search strategy
for generating model configurations. For grid search, will generate
all combinations of model IDs + compile params + fit params. For
random search, specify the number of configs you want to
generate using the 'num_configs' parameter below.
Note that you can also use short forms
for the 'grid' or 'random' keywords, e.g.,'rand' or 'r' instead
of writing out 'random' in full.
</dd>
<dt>num_configs (optional)</dt>
<dd>INTEGER, default: NULL. Number of model configs to generate.
Only applies when search_type='random'.
</dd>
<dt>random_state (optional)</dt>
<dd>INTEGER, default: NULL. Pseudo random number generator
state used for random uniform sampling from lists of possible
values. Pass an integer for reproducible output across multiple
function calls. Only applies when search_type='random'.
</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 'compile_params_grid'.
Note that this table has to be created by
the <a href="group__grp__custom__function.html">Define Custom Functions</a> method. It is
not allowed to pass a schema name, since it will be automatically pulled from
this functions associated madlib schema.
</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>
<!-- --------------------------------------END-------------------------------------------------------------- -->
@anchor load_mst_table
@par Load Model Selection Table [Deprecated]
This method is deprecated and replaced by the method 'generate_model_configs' described above.
<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">Define Model Architectures</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 the same as described above for the method 'generate_model_configs'.
</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. Callbacks are not currently supported
except for TensorBoard which you can specify in the usual way,
e.g., callbacks=[TensorBoard(log_dir="/tmp/logs/fit")]
</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>
@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">
from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.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>
-# Generate model configurations using grid search. The output table for grid search
contains the unique combinations of model architectures, compile and fit parameters.
<pre class="example">
DROP TABLE IF EXISTS mst_table, mst_table_summary;
SELECT madlib.generate_model_configs(
'model_arch_library', -- model architecture table
'mst_table', -- model selection table output
ARRAY[1,2], -- model ids from model architecture table
$$
{'loss': ['categorical_crossentropy'],
'optimizer_params_list': [ {'optimizer': ['Adam', 'SGD'], 'lr': [0.001, 0.01]} ],
'metrics': ['accuracy']}
$$, -- compile_param_grid
$$
{ 'batch_size': [64, 128],
'epochs': [10]
}
$$, -- fit_param_grid
'grid' -- search_type
);
SELECT * FROM mst_table ORDER BY mst_key;
</pre>
<pre class="result">
mst_key | model_id | compile_params | fit_params
---------+----------+---------------------------------------------------------------------------------+--------------------------
1 | 1 | optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
2 | 1 | optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
3 | 1 | optimizer='SGD(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
4 | 1 | optimizer='SGD(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
5 | 1 | optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
6 | 1 | optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
7 | 1 | optimizer='SGD(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
8 | 1 | optimizer='SGD(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
9 | 2 | optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
10 | 2 | optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
11 | 2 | optimizer='SGD(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
12 | 2 | optimizer='SGD(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
13 | 2 | optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
14 | 2 | optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
15 | 2 | optimizer='SGD(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
16 | 2 | optimizer='SGD(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
(16 rows)
</pre>
Note that above uses the same learning rate for the two optimizers. If you wanted to
use different learning rates and different parameters for different optimizers (common):
<pre class="example">
DROP TABLE IF EXISTS mst_table, mst_table_summary;
SELECT madlib.generate_model_configs(
'model_arch_library', -- model architecture table
'mst_table', -- model selection table output
ARRAY[1,2], -- model ids from model architecture table
$$
{'loss': ['categorical_crossentropy'],
'optimizer_params_list': [
{'optimizer': ['SGD']},
{'optimizer': ['SGD'], 'lr': [0.0001, 0.001], 'momentum': [0.95]},
{'optimizer': ['Adam'], 'lr': [0.01, 0.1], 'decay': [1e-4]}],
'metrics': ['accuracy']}
$$, -- compile_param_grid
$$
{ 'batch_size': [64, 128],
'epochs': [10]
}
$$, -- fit_param_grid
'grid' -- search_type
);
SELECT * FROM mst_table ORDER BY mst_key;
</pre>
<pre class="result">
mst_key | model_id | compile_params | fit_params
---------+----------+-----------------------------------------------------------------------------------------------+--------------------------
1 | 1 | optimizer='SGD()',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
2 | 1 | optimizer='SGD()',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
3 | 2 | optimizer='SGD()',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
4 | 2 | optimizer='SGD()',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
5 | 1 | optimizer='SGD(lr=0.0001,momentum=0.95)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
6 | 1 | optimizer='SGD(lr=0.0001,momentum=0.95)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
7 | 1 | optimizer='SGD(lr=0.001,momentum=0.95)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
8 | 1 | optimizer='SGD(lr=0.001,momentum=0.95)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
9 | 2 | optimizer='SGD(lr=0.0001,momentum=0.95)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
10 | 2 | optimizer='SGD(lr=0.0001,momentum=0.95)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
11 | 2 | optimizer='SGD(lr=0.001,momentum=0.95)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
12 | 2 | optimizer='SGD(lr=0.001,momentum=0.95)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
13 | 1 | optimizer='Adam(lr=0.01,decay=0.0001)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
14 | 1 | optimizer='Adam(lr=0.01,decay=0.0001)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
15 | 1 | optimizer='Adam(lr=0.1,decay=0.0001)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
16 | 1 | optimizer='Adam(lr=0.1,decay=0.0001)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
17 | 2 | optimizer='Adam(lr=0.01,decay=0.0001)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
18 | 2 | optimizer='Adam(lr=0.01,decay=0.0001)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
19 | 2 | optimizer='Adam(lr=0.1,decay=0.0001)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
20 | 2 | optimizer='Adam(lr=0.1,decay=0.0001)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
(20 rows)
</pre>
-# Generate model configurations using random search. The output table for random search
contains the specified number of model architectures, compile and fit parameters,
sampled from the specified distributions.
<pre class="example">
DROP TABLE IF EXISTS mst_table, mst_table_summary;
SELECT madlib.generate_model_configs(
'model_arch_library', -- model architecture table
'mst_table', -- model selection table output
ARRAY[1,2], -- model ids from model architecture table
$$
{'loss': ['categorical_crossentropy'],
'optimizer_params_list': [
{'optimizer': ['SGD'], 'lr': [0.0001, 0.001, 'log'], 'momentum': [0.95, 0.99, 'log_near_one']},
{'optimizer': ['Adam'], 'lr': [0.01, 0.1, 'log'], 'decay': [1e-6, 1e-4, 'log']}],
'metrics': ['accuracy']}
$$, -- compile_param_grid
$$
{ 'batch_size': [64, 128],
'epochs': [10]
}
$$, -- fit_param_grid
'random', -- search_type
20
);
SELECT * FROM mst_table ORDER BY mst_key;
</pre>
<pre class="result">
mst_key | model_id | compile_params | fit_params
---------+----------+-----------------------------------------------------------------------------------------------------------------------------+--------------------------
1 | 1 | optimizer='SGD(lr=0.000195784477708685,momentum=0.9768159513291526)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
2 | 2 | optimizer='SGD(lr=0.0002499200066875511,momentum=0.9807877269510826)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
3 | 1 | optimizer='SGD(lr=0.0009097798285407916,momentum=0.9706029152411938)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
4 | 1 | optimizer='SGD(lr=0.0001272842475986666,momentum=0.9858583458057799)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
5 | 1 | optimizer='SGD(lr=0.0001367874444015989,momentum=0.9772674033475668)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
6 | 2 | optimizer='SGD(lr=0.0002233708561319785,momentum=0.9743315606145182)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
7 | 1 | optimizer='SGD(lr=0.0009066689970530365,momentum=0.9835897505288803)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
8 | 1 | optimizer='SGD(lr=0.0007589416356572876,momentum=0.958751411608181)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
9 | 2 | optimizer='Adam(lr=0.057814228170084386,decay=1.0641718595377929e-06)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
10 | 1 | optimizer='Adam(lr=0.01927466297833838,decay=1.039476442716842e-06)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
11 | 2 | optimizer='Adam(lr=0.014718555287257804,decay=9.947768661882175e-05)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
12 | 1 | optimizer='Adam(lr=0.010397686133595378,decay=2.5730580994358942e-05)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
13 | 1 | optimizer='SGD(lr=0.0008624562426613621,momentum=0.989134963527059)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
14 | 2 | optimizer='SGD(lr=0.00010555974470031461,momentum=0.980489419269402)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
15 | 2 | optimizer='Adam(lr=0.05041699703418617,decay=4.685540619995589e-05)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
16 | 1 | optimizer='Adam(lr=0.034295140601304126,decay=1.6034699865163222e-06)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
17 | 1 | optimizer='Adam(lr=0.06888969005355218,decay=1.6318109152382423e-05)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
18 | 2 | optimizer='SGD(lr=0.0008225712651952847,momentum=0.9819748008695103)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
19 | 1 | optimizer='Adam(lr=0.0819110285922332,decay=1.6912312124827899e-06)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
20 | 1 | optimizer='Adam(lr=0.011688026325555774,decay=2.9315437856404027e-05)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
(20 rows)
</pre>
-# Incremental loading for more complex combinations. If it is easier to generate the model configurations
incrementally rather than all at once, you can do that by not dropping the model selection table and associated
summary table, in which case the new model configurations will be appended to the existing table. Here we combine two
of the previous examples in to a single output table:
<pre class="example">
DROP TABLE IF EXISTS mst_table, mst_table_summary;
SELECT madlib.generate_model_configs(
'model_arch_library', -- model architecture table
'mst_table', -- model selection table output
ARRAY[1,2], -- model ids from model architecture table
$$
{'loss': ['categorical_crossentropy'],
'optimizer_params_list': [ {'optimizer': ['Adam', 'SGD'], 'lr': [0.001, 0.01]} ],
'metrics': ['accuracy']}
$$, -- compile_param_grid
$$
{ 'batch_size': [64, 128],
'epochs': [10]
}
$$, -- fit_param_grid
'grid' -- search_type
);
</pre>
Now add to the existing table and note that mst_key continues where it left off:
<pre class="example">
SELECT madlib.generate_model_configs(
'model_arch_library', -- model architecture table
'mst_table', -- model selection table output
ARRAY[1,2], -- model ids from model architecture table
$$
{'loss': ['categorical_crossentropy'],
'optimizer_params_list': [
{'optimizer': ['SGD'], 'lr': [0.0001, 0.001, 'log'], 'momentum': [0.95, 0.99, 'log_near_one']},
{'optimizer': ['Adam'], 'lr': [0.01, 0.1, 'log'], 'decay': [1e-6, 1e-4, 'log']}],
'metrics': ['accuracy']}
$$, -- compile_param_grid
$$
{ 'batch_size': [64, 128],
'epochs': [10]
}
$$, -- fit_param_grid
'random', -- search_type
20
);
SELECT * FROM mst_table ORDER BY mst_key;
</pre>
<pre class="result">
mst_key | model_id | compile_params | fit_params
---------+----------+-----------------------------------------------------------------------------------------------------------------------------+--------------------------
1 | 1 | optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
2 | 1 | optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
3 | 1 | optimizer='SGD(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
4 | 1 | optimizer='SGD(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
5 | 1 | optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
6 | 1 | optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
7 | 1 | optimizer='SGD(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
8 | 1 | optimizer='SGD(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
9 | 2 | optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
10 | 2 | optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
11 | 2 | optimizer='SGD(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
12 | 2 | optimizer='SGD(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
13 | 2 | optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
14 | 2 | optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
15 | 2 | optimizer='SGD(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
16 | 2 | optimizer='SGD(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
17 | 2 | optimizer='SGD(lr=0.00013996842804647915,momentum=0.9677072493281305)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
18 | 1 | optimizer='Adam(lr=0.04252873277972123,decay=9.503983307511243e-05)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
19 | 2 | optimizer='Adam(lr=0.06666969394323848,decay=1.5626668941131748e-05)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
20 | 2 | optimizer='SGD(lr=0.00016137313867804707,momentum=0.954293112127019)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
21 | 2 | optimizer='Adam(lr=0.019443570245321506,decay=1.2882524497407873e-06)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
22 | 2 | optimizer='Adam(lr=0.06302317748060839,decay=6.238009849562074e-05)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
23 | 2 | optimizer='SGD(lr=0.00010890482493011119,momentum=0.9826239169968034)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
24 | 1 | optimizer='SGD(lr=0.0009201966766121783,momentum=0.9896730563556151)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
25 | 2 | optimizer='SGD(lr=0.00028961522836420906,momentum=0.9859394149216544)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
26 | 1 | optimizer='SGD(lr=0.0001503249757866609,momentum=0.9777816636354879)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
27 | 2 | optimizer='SGD(lr=0.0008405326172626768,momentum=0.9538686498263182)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
28 | 1 | optimizer='SGD(lr=0.00011926989091387571,momentum=0.9876746918399469)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
29 | 1 | optimizer='Adam(lr=0.018794361633022855,decay=9.387826286694454e-06)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
30 | 2 | optimizer='SGD(lr=0.0009692977025027591,momentum=0.9878758592330659)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
31 | 2 | optimizer='SGD(lr=0.0006671929498585603,momentum=0.9786502962872058)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
32 | 2 | optimizer='Adam(lr=0.03948766165185474,decay=3.056584635386748e-06)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
33 | 2 | optimizer='Adam(lr=0.020343961099103417,decay=1.183810228780669e-05)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
34 | 1 | optimizer='Adam(lr=0.016854644990148417,decay=3.561117893117444e-06)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
35 | 2 | optimizer='SGD(lr=0.0004620089560788749,momentum=0.9887310587871919)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
36 | 1 | optimizer='SGD(lr=0.0002493912675066962,momentum=0.9892077270385708)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
(36 rows)
</pre>
-# Create model selection table manually.
If you want more control over the content of the model selection table,
you could use grid or random search to generate a large number of combinations,
then SELECT a subset of rows for training. Alternatively, you could 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>
-# Custom loss functions and custom metrics.
Let's say we have a table 'custom_function_table' that contains a custom loss
function called 'my_custom_loss' and a custom accuracy function
called 'my_custom_accuracy' based
on <a href="group__grp__custom__function.html">Define Custom Functions.</a>
Generate the model configurations with:
<pre class="example">
DROP TABLE IF EXISTS mst_table, mst_table_summary;
SELECT madlib.generate_model_configs(
'model_arch_library', -- model architecture table
'mst_table', -- model selection table output
ARRAY[1,2], -- model ids from model architecture table
$$
{'loss': ['my_custom_loss'],
'optimizer_params_list': [ {'optimizer': ['Adam', 'SGD'], 'lr': [0.001, 0.01]} ],
'metrics': ['my_custom_accuracy']}
$$, -- compile_param_grid
$$
{ 'batch_size': [64, 128],
'epochs': [10]
}
$$, -- fit_param_grid
'grid', -- search_type
NULL, -- num_configs
NULL, -- random_state
'custom_function_table' -- table with custom functions
);
SELECT * FROM mst_table ORDER BY mst_key;
</pre>
<pre class="result">
mst_key | model_id | compile_params | fit_params
---------+----------+---------------------------------------------------------------------------------+--------------------------
1 | 1 | optimizer='Adam(lr=0.001)',metrics=['my_custom_accuracy'],loss='my_custom_loss' | epochs=10,batch_size=64
2 | 1 | optimizer='Adam(lr=0.001)',metrics=['my_custom_accuracy'],loss='my_custom_loss' | epochs=10,batch_size=128
3 | 1 | optimizer='SGD(lr=0.001)',metrics=['my_custom_accuracy'],loss='my_custom_loss' | epochs=10,batch_size=64
4 | 1 | optimizer='SGD(lr=0.001)',metrics=['my_custom_accuracy'],loss='my_custom_loss' | epochs=10,batch_size=128
5 | 1 | optimizer='Adam(lr=0.01)',metrics=['my_custom_accuracy'],loss='my_custom_loss' | epochs=10,batch_size=64
6 | 1 | optimizer='Adam(lr=0.01)',metrics=['my_custom_accuracy'],loss='my_custom_loss' | epochs=10,batch_size=128
7 | 1 | optimizer='SGD(lr=0.01)',metrics=['my_custom_accuracy'],loss='my_custom_loss' | epochs=10,batch_size=64
8 | 1 | optimizer='SGD(lr=0.01)',metrics=['my_custom_accuracy'],loss='my_custom_loss' | epochs=10,batch_size=128
9 | 2 | optimizer='Adam(lr=0.001)',metrics=['my_custom_accuracy'],loss='my_custom_loss' | epochs=10,batch_size=64
10 | 2 | optimizer='Adam(lr=0.001)',metrics=['my_custom_accuracy'],loss='my_custom_loss' | epochs=10,batch_size=128
11 | 2 | optimizer='SGD(lr=0.001)',metrics=['my_custom_accuracy'],loss='my_custom_loss' | epochs=10,batch_size=64
12 | 2 | optimizer='SGD(lr=0.001)',metrics=['my_custom_accuracy'],loss='my_custom_loss' | epochs=10,batch_size=128
13 | 2 | optimizer='Adam(lr=0.01)',metrics=['my_custom_accuracy'],loss='my_custom_loss' | epochs=10,batch_size=64
14 | 2 | optimizer='Adam(lr=0.01)',metrics=['my_custom_accuracy'],loss='my_custom_loss' | epochs=10,batch_size=128
15 | 2 | optimizer='SGD(lr=0.01)',metrics=['my_custom_accuracy'],loss='my_custom_loss' | epochs=10,batch_size=64
16 | 2 | optimizer='SGD(lr=0.01)',metrics=['my_custom_accuracy'],loss='my_custom_loss' | epochs=10,batch_size=128
(16 rows)
</pre>
Similarly, if you created a custom top k categorical accuracy function 'top_3_accuracy'
in <a href="group__grp__custom__function.html">Define Custom Functions</a>
you can generate the model configurations as:
<pre class="example">
DROP TABLE IF EXISTS mst_table, mst_table_summary;
SELECT madlib.generate_model_configs(
'model_arch_library', -- model architecture table
'mst_table', -- model selection table output
ARRAY[1,2], -- model ids from model architecture table
$$
{'loss': ['categorical_crossentropy'],
'optimizer_params_list': [ {'optimizer': ['Adam', 'SGD'], 'lr': [0.001, 0.01]} ],
'metrics': ['top_3_accuracy']}
$$, -- compile_param_grid
$$
{ 'batch_size': [64, 128],
'epochs': [10]
}
$$, -- fit_param_grid
'grid', -- search_type
NULL, -- num_configs
NULL, -- random_state
'custom_function_table' -- table with custom functions
);
SELECT * FROM mst_table ORDER BY mst_key;
</pre>
<pre class="result">
mst_key | model_id | compile_params | fit_params
---------+----------+---------------------------------------------------------------------------------+--------------------------
1 | 1 | optimizer='Adam(lr=0.001)',metrics=['top_3_accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
2 | 1 | optimizer='Adam(lr=0.001)',metrics=['top_3_accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
3 | 1 | optimizer='SGD(lr=0.001)',metrics=['top_3_accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
4 | 1 | optimizer='SGD(lr=0.001)',metrics=['top_3_accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
5 | 1 | optimizer='Adam(lr=0.01)',metrics=['top_3_accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
6 | 1 | optimizer='Adam(lr=0.01)',metrics=['top_3_accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
7 | 1 | optimizer='SGD(lr=0.01)',metrics=['top_3_accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
8 | 1 | optimizer='SGD(lr=0.01)',metrics=['top_3_accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
9 | 2 | optimizer='Adam(lr=0.001)',metrics=['top_3_accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
10 | 2 | optimizer='Adam(lr=0.001)',metrics=['top_3_accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
11 | 2 | optimizer='SGD(lr=0.001)',metrics=['top_3_accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
12 | 2 | optimizer='SGD(lr=0.001)',metrics=['top_3_accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
13 | 2 | optimizer='Adam(lr=0.01)',metrics=['top_3_accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
14 | 2 | optimizer='Adam(lr=0.01)',metrics=['top_3_accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
15 | 2 | optimizer='SGD(lr=0.01)',metrics=['top_3_accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
16 | 2 | optimizer='SGD(lr=0.01)',metrics=['top_3_accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
(16 rows)
</pre>
-# <b>[Deprecated]</b> Load model selection table. This method is replaced
by the 'generate_model_configs' method described above.
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>
@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 $$
PythonFunctionBodyOnly(`deep_learning', `madlib_keras_model_selection')
mst_loader = madlib_keras_model_selection.MstLoader(**globals())
mst_loader.load()
$$ 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', `');
/*
--------------------------------------------------------------------------
*/
CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.generate_model_configs(
model_arch_table VARCHAR,
model_selection_table VARCHAR,
model_id_list INTEGER[],
compile_params_grid VARCHAR,
fit_params_grid VARCHAR,
search_type VARCHAR DEFAULT 'grid',
num_configs INTEGER DEFAULT NULL,
random_state INTEGER DEFAULT NULL,
object_table VARCHAR DEFAULT NULL
) RETURNS VOID AS $$
PythonFunctionBodyOnly(`deep_learning', `madlib_keras_model_selection')
mst_loader = madlib_keras_model_selection.MstSearch(**globals())
mst_loader.load()
$$ LANGUAGE plpythonu VOLATILE
m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
/*
--------------------------------------------------------------------------
*/
CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.generate_model_configs(
model_arch_table VARCHAR,
model_selection_table VARCHAR,
model_id_list INTEGER[],
compile_params_grid VARCHAR,
fit_params_grid VARCHAR,
search_type VARCHAR DEFAULT 'grid',
num_configs INTEGER DEFAULT NULL,
random_state INTEGER DEFAULT NULL,
object_table VARCHAR DEFAULT NULL
) RETURNS VOID AS $$
PythonFunctionBodyOnly(`deep_learning', `madlib_keras_model_selection')
mst_loader = madlib_keras_model_selection.MstSearch(**globals())
mst_loader.load()
$$ LANGUAGE plpythonu VOLATILE
m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');