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<title>MADlib: Run Model Selection</title>
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<div class="header">
  <div class="headertitle">
<div class="title">Run Model Selection<div class="ingroups"><a class="el" href="group__grp__early__stage.html">Early Stage Development</a> &raquo; <a class="el" href="group__grp__dl.html">Deep Learning</a> &raquo; <a class="el" href="group__grp__model__selection.html">Model Selection for DL</a></div></div>  </div>
</div><!--header-->
<div class="contents">
<div class="toc"><b>Contents</b><ul>
<li class="level1">
<a href="#keras_fit">Fit</a> </li>
<li class="level1">
<a href="#keras_evaluate">Evaluate</a> </li>
<li class="level1">
<a href="#keras_predict">Predict</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="#background">Technical Background</a> </li>
<li class="level1">
<a href="#literature">Literature</a> </li>
<li class="level1">
<a href="#related">Related Topics</a> </li>
</ul>
</div><dl class="section warning"><dt>Warning</dt><dd><em> This MADlib method is still in early stage development. Interface and implementation are subject to change. </em></dd></dl>
<p>This module allows you to explore network architectures and hyperparameters by training many models a time across the database cluster. The aim is to support efficient empirical comparison of multiple training configurations. This process is called model selection, and the implementation here is based on a parallel execution strategy called model hopper parallelism (MOP) [1,2].</p>
<p>Models are designed in Keras [3], which is a high-level neural network API written in Python. It can run on top of different backends and the one that is currently supported by MADlib is TensorFlow [4].</p>
<p>The main use case is image classification using sequential models, which are made up of a linear stack of layers. This includes multilayer perceptrons (MLPs) and convolutional neural networks (CNNs). Regression is not currently supported.</p>
<p>Before doing model selection in MADlib you will need to run the mini-batch preprocessor, and create a table with the various models and hyperparameters to try.</p>
<p>You can mini-batch the training and evaluation datasets by using the <a href="group__grp__input__preprocessor__dl.html">Preprocessor for Images</a> which is a utility that prepares image data for use by models that support mini-batch as an optimization option. This is a one-time operation and you would only need to re-run the preprocessor if your input data has changed. The advantage of using mini-batching is that it can perform better than stochastic gradient descent because it uses more than one training example at a time, typically resulting faster and smoother convergence [5]. The input preprocessor also sets the distribution rules for the training data. For example, you may only want to train models on segments that reside on hosts that are GPU enabled.</p>
<p>You can set up the models and hyperparameters to try with the <a href="group__grp__keras__setup__model__selection.html">Setup Model Selection</a> utility to define the unique combinations of model architectures, compile and fit parameters.</p>
<dl class="section note"><dt>Note</dt><dd>If '<a class="el" href="madlib__keras__fit__multiple__model_8sql__in.html#ab62d8e8a4e8d221b7baa4da6a214c286">madlib_keras_fit_multiple_model()</a>' is running on GPDB 5 and some versions of GPDB 6, the database will keep adding to the disk space (in proportion to model size) and will only release the disk space once the fit multiple query has completed execution. This is not the case for GPDB 6.5.0+ where disk space is released during the fit multiple query.</dd>
<dd>
CUDA GPU memory cannot be released until the process holding it is terminated. When a MADlib deep learning function is called with GPUs, Greenplum internally creates a process (called a slice) which calls TensorFlow to do the computation. This process holds the GPU memory until one of the following two things happen: query finishes and user logs out of the Postgres client/session; or, query finishes and user waits for the timeout set by gp_vmem_idle_resource_timeout. The default value for this timeout is 18 sec [8]. So the recommendation is: log out/reconnect to the session after every GPU query; or wait for gp_vmem_idle_resource_timeout before you run another GPU query (you can also set it to a lower value).</dd></dl>
<p><a class="anchor" id="keras_fit"></a></p><dl class="section user"><dt>Fit</dt><dd>The fit (training) function has the following format:</dd></dl>
<pre class="syntax">
madlib_keras_fit_multiple_model(
    source_table,
    model_output_table,
    model_selection_table,
    num_iterations,
    use_gpus,
    validation_table,
    metrics_compute_frequency,
    warm_start,
    name,
    description
    )
</pre><p><b>Arguments</b> </p><dl class="arglist">
<dt>source_table </dt>
<dd><p class="startdd">TEXT. Name of the table containing the training data. This is the name of the output table from the image preprocessor. Independent and dependent variables are specified in the preprocessor step which is why you do not need to explictly state them here as part of the fit function.</p>
<p class="enddd"></p>
</dd>
<dt>model_output_table </dt>
<dd>TEXT. Name of the output table containing the multiple models created. <dl class="section note"><dt>Note</dt><dd>pg_temp is not allowed as an output table schema for fit multiple. Details of output tables are shown below. </dd></dl>
</dd>
<dt>model_selection_table </dt>
<dd><p class="startdd">TEXT. Name of the table containing model selection parameters to be tried. Here we mean both hyper-parameter tuning and model architecture search. </p>
<p class="enddd"></p>
</dd>
<dt>num_iterations </dt>
<dd><p class="startdd">INTEGER. Number of iterations to train.</p>
<dl class="section note"><dt>Note</dt><dd>This parameter is different than the number of passes over the dataset, which is commonly referred to as the number of epochs. Since MADlib operates in a distributed system, the number of epochs is actually equal to this parameter 'num_iterations' X 'epochs' as specified in the Keras fit parameter. </dd></dl>
</dd>
<dt>use_gpus (optional) </dt>
<dd><p class="startdd">BOOLEAN, default: FALSE (i.e., CPU). Determines whether GPUs are to be used for training the neural network. Set to TRUE to use GPUs.</p>
<dl class="section note"><dt>Note</dt><dd>This parameter must not conflict with how the distribution rules are set in the preprocessor function. For example, if you set a distribution rule to use certain segments on hosts that do not have GPUs attached, you will get an error if you set ‘use_gpus’ to TRUE. Also, we have seen some memory related issues when segments share GPU resources. For example, if you have 1 GPU per segment host and your cluster has 4 segments per segment host, it means that all 4 segments will share the same GPU on each host. The current recommended configuration is 1 GPU per segment. </dd></dl>
</dd>
<dt>validation_table (optional) </dt>
<dd><p class="startdd">TEXT, default: none. Name of the table containing the validation dataset. Note that the validation dataset must be preprocessed in the same way as the training dataset, so this is the name of the output table from running the image preprocessor on the validation dataset. Using a validation dataset can mean a longer training time, depending on its size. This can be controlled using the 'metrics_compute_frequency' parameter described below.</p>
<p class="enddd"></p>
</dd>
<dt>metrics_compute_frequency (optional) </dt>
<dd><p class="startdd">INTEGER, default: once at the end of training after 'num_iterations'. Frequency to compute per-iteration metrics for the training dataset and validation dataset (if specified). There can be considerable cost to computing metrics every iteration, especially if the training dataset is large. This parameter is a way of controlling the frequency of those computations. For example, if you specify 5, then metrics will be computed every 5 iterations as well as at the end of training after 'num_iterations'. If you use the default, metrics will be computed only once after 'num_iterations' have completed. </p>
<p class="enddd"></p>
</dd>
<dt>warm_start (optional) </dt>
<dd><p class="startdd">BOOLEAN, default: FALSE. Initalize weights with the coefficients from the last call to the fit function. If set to TRUE, weights will be initialized from the model table generated by the previous training run.</p>
<dl class="section note"><dt>Note</dt><dd>The warm start feature works based on the name of the model output table from a previous training run. When using warm start, do not drop the model output table or the model output summary table before calling the fit function, since these are needed to obtain the weights from the previous run. If you are not using warm start, the model output table and the model output table summary must be dropped in the usual way before calling the training function. </dd></dl>
</dd>
<dt>name (optional) </dt>
<dd><p class="startdd">TEXT, default: NULL. Free text string to identify a name, if desired. </p>
<p class="enddd"></p>
</dd>
<dt>description (optional) </dt>
<dd>TEXT, default: NULL. Free text string to provide a description, if desired.  </dd>
</dl>
<p><b>Output tables</b> <br />
 The model output table produced by fit contains the following columns. There is one row per model as per the rows in the 'model_selection_table': </p><table class="output">
<tr>
<th>mst_key </th><td>INTEGER. ID that defines a unique tuple for model architecture-compile parameters-fit parameters, as defined in the 'model_selection_table'.  </td></tr>
<tr>
<th>model_weights </th><td>BYTEA8. Byte array containing the weights of the neural net.  </td></tr>
<tr>
<th>model_arch </th><td>TEXT. A JSON representation of the model architecture used in training.  </td></tr>
</table>
<p>An info table named &lt;model_output_table&gt;_info is also created, which has the following columns. There is one row per model as per the rows in the 'model_selection_table': </p><table class="output">
<tr>
<th>mst_key </th><td>INTEGER. ID that defines a unique tuple for model architecture-compile parameters-fit parameters, as defined in the 'model_selection_table'.  </td></tr>
<tr>
<th>model_id </th><td>INTEGER. ID that defines model in the 'model_arch_table'.  </td></tr>
<tr>
<th>compile_params </th><td>Compile parameters passed to Keras.  </td></tr>
<tr>
<th>fit_params </th><td>Fit parameters passed to Keras.  </td></tr>
<tr>
<th>model_type </th><td>General identifier for type of model trained. Currently says 'madlib_keras'.  </td></tr>
<tr>
<th>model_size </th><td>Size of the model in KB. Models are stored in 'bytea' data format which is used for binary strings in PostgreSQL type databases.  </td></tr>
<tr>
<th>metrics_elapsed_time </th><td>Array of elapsed time for metric computations as per the 'metrics_compute_frequency' parameter. Useful for drawing a curve showing loss, accuracy or other metrics as a function of time. For example, if 'metrics_compute_frequency=5' this would be an array of elapsed time for every 5th iteration, plus the last iteration.  </td></tr>
<tr>
<th>metrics_type </th><td>Metric specified in the 'compile_params'.  </td></tr>
<tr>
<th>training_metrics_final </th><td>Final value of the training metric after all iterations have completed. The metric reported is the one specified in the 'metrics_type' parameter.  </td></tr>
<tr>
<th>training_loss_final </th><td>Final value of the training loss after all iterations have completed.  </td></tr>
<tr>
<th>training_metrics </th><td>Array of training metrics as per the 'metrics_compute_frequency' parameter. For example, if 'metrics_compute_frequency=5' this would be an array of metrics for every 5th iteration, plus the last iteration.  </td></tr>
<tr>
<th>training_loss </th><td>Array of training losses as per the 'metrics_compute_frequency' parameter. For example, if 'metrics_compute_frequency=5' this would be an array of losses for every 5th iteration, plus the last iteration.  </td></tr>
<tr>
<th>validation_metrics_final </th><td>Final value of the validation metric after all iterations have completed. The metric reported is the one specified in the 'metrics_type' parameter.  </td></tr>
<tr>
<th>validation_loss_final </th><td>Final value of the validation loss after all iterations have completed.  </td></tr>
<tr>
<th>validation_metrics </th><td>Array of validation metrics as per the 'metrics_compute_frequency' parameter. For example, if 'metrics_compute_frequency=5' this would be an array of metrics for every 5th iteration, plus the last iteration.  </td></tr>
<tr>
<th>validation_loss </th><td><p class="starttd">Array of validation losses as per the 'metrics_compute_frequency' parameter. For example, if 'metrics_compute_frequency=5' this would be an array of losses for every 5th iteration, plus the last iteration. </p>
<p class="endtd"></p>
</td></tr>
</table>
<p>A summary table named &lt;model&gt;_summary is also created, which has the following columns: </p><table class="output">
<tr>
<th>source_table </th><td>Source table used for training.  </td></tr>
<tr>
<th>validation_table </th><td>Name of the table containing the validation dataset (if specified).  </td></tr>
<tr>
<th>model </th><td>Name of the output table containing the model for each model selection tuple.  </td></tr>
<tr>
<th>model_info </th><td>Name of the output table containing the model performance and other info for each model selection tuple.  </td></tr>
<tr>
<th>dependent_varname </th><td>Dependent variable column from the original source table in the image preprocessing step.  </td></tr>
<tr>
<th>independent_varname </th><td>Independent variables column from the original source table in the image preprocessing step.  </td></tr>
<tr>
<th>model_arch_table </th><td>Name of the table containing the model architecture and (optionally) the initial model weights.  </td></tr>
<tr>
<th>num_iterations </th><td>Number of iterations of training completed.  </td></tr>
<tr>
<th>metrics_compute_frequency </th><td>Frequency that per-iteration metrics are computed for the training dataset and validation datasets.  </td></tr>
<tr>
<th>warm_start </th><td>Indicates whether warm start used or not.  </td></tr>
<tr>
<th>name </th><td>Name of the training run (free text).  </td></tr>
<tr>
<th>description </th><td>Description of the training run (free text).  </td></tr>
<tr>
<th>start_training_time </th><td>Timestamp for start of training.  </td></tr>
<tr>
<th>end_training_time </th><td>Timestamp for end of training.  </td></tr>
<tr>
<th>madlib_version </th><td>Version of MADlib used.  </td></tr>
<tr>
<th>num_classes </th><td>Count of distinct classes values used.  </td></tr>
<tr>
<th>class_values </th><td>Array of actual class values used.  </td></tr>
<tr>
<th>dependent_vartype </th><td>Data type of the dependent variable.  </td></tr>
<tr>
<th>normalizing_constant </th><td>Normalizing constant used from the image preprocessing step.  </td></tr>
<tr>
<th>metrics_iters </th><td>Array indicating the iterations for which metrics are calculated, as derived from the parameters 'num_iterations' and 'metrics_compute_frequency'. For example, if 'num_iterations=5' and 'metrics_compute_frequency=2', then 'metrics_iters' value would be {2,4,5} indicating that metrics were computed at iterations 2, 4 and 5 (at the end). If 'num_iterations=5' and 'metrics_compute_frequency=1', then 'metrics_iters' value would be {1,2,3,4,5} indicating that metrics were computed at every iteration.  </td></tr>
</table>
<p><a class="anchor" id="keras_evaluate"></a></p><dl class="section user"><dt>Evaluate</dt><dd>The evaluation function has the following format:</dd></dl>
<pre class="syntax">
madlib_keras_evaluate(
    model_table,
    test_table,
    output_table,
    use_gpus
    )
</pre><p><b>Arguments</b> </p><dl class="arglist">
<dt>model_table </dt>
<dd><p class="startdd">TEXT. Name of the table containing the model to use for validation. </p>
<p class="enddd"></p>
</dd>
<dt>test_table </dt>
<dd><p class="startdd">TEXT. Name of the table containing the evaluation dataset. Note that test/validation data must be preprocessed in the same way as the training dataset, so this is the name of the output table from the image preprocessor. Independent and dependent variables are specified in the preprocessor step which is why you do not need to explictly state them here as part of the fit function.</p>
<p class="enddd"></p>
</dd>
<dt>output_table </dt>
<dd>TEXT. Name of table that validation output will be written to. Table contains: <table class="output">
<tr>
<th>loss </th><td>Loss value on evaluation dataset.  </td></tr>
<tr>
<th>metric </th><td>Metric value on evaluation dataset, where 'metrics_type' below identifies the type of metric.  </td></tr>
<tr>
<th>metrics_type </th><td><p class="starttd">Type of metric used that was used in the training step. </p>
<p class="endtd"></p>
</td></tr>
</table>
</dd>
<dt>use_gpus (optional) </dt>
<dd><p class="startdd">BOOLEAN, default: FALSE (i.e., CPU). Determines whether GPUs are to be used for training the neural network. Set to TRUE to use GPUs.</p>
<dl class="section note"><dt>Note</dt><dd>This parameter must not conflict with how the distribution rules are set in the preprocessor function. For example, if you set a distribution rule to use certain segments on hosts that do not have GPUs attached, you will get an error if you set ‘use_gpus’ to TRUE. Also, we have seen some memory related issues when segments share GPU resources. For example, if you have 1 GPU per segment host and your cluster has 4 segments per segment host, it means that all 4 segments will share the same GPU on each host. The current recommended configuration is 1 GPU per segment.  </dd></dl>
</dd>
</dl>
<p><a class="anchor" id="keras_predict"></a></p><dl class="section user"><dt>Predict</dt><dd>The prediction function has the following format: <pre class="syntax">
madlib_keras_predict(
    model_table,
    test_table,
    id_col,
    independent_varname,
    output_table,
    pred_type,
    use_gpus
    )
</pre></dd></dl>
<p><b>Arguments</b> </p><dl class="arglist">
<dt>model_table </dt>
<dd><p class="startdd">TEXT. Name of the table containing the model to use for prediction. </p>
<p class="enddd"></p>
</dd>
<dt>test_table </dt>
<dd><p class="startdd">TEXT. Name of the table containing the dataset to predict on. Note that test data is not preprocessed (unlike fit and evaluate) so put one test image per row for prediction. Also see the comment below for the 'independent_varname' parameter regarding normalization.</p>
<p></p>
<p class="enddd"></p>
</dd>
<dt>id_col </dt>
<dd><p class="startdd">TEXT. Name of the id column in the test data table. </p>
<p class="enddd"></p>
</dd>
<dt>independent_varname </dt>
<dd><p class="startdd">TEXT. Column with independent variables in the test table. If a 'normalizing_const' is specified when preprocessing the training dataset, this same normalization will be applied to the independent variables used in predict. </p>
<p class="enddd"></p>
</dd>
<dt>output_table </dt>
<dd>TEXT. Name of the table that prediction output will be written to. Table contains: <table class="output">
<tr>
<th>id </th><td>Gives the 'id' for each prediction, corresponding to each row from the test_table.  </td></tr>
<tr>
<th>estimated_COL_NAME </th><td>(For pred_type='response') The estimated class for classification, where COL_NAME is the name of the column to be predicted from test data.   </td></tr>
<tr>
<th>prob_CLASS </th><td><p class="starttd">(For pred_type='prob' for classification) The probability of a given class. There will be one column for each class in the training data.  </p>
<p class="endtd"></p>
</td></tr>
</table>
</dd>
<dt>pred_type (optional) </dt>
<dd><p class="startdd">TEXT, default: 'response'. The type of output desired, where 'response' gives the actual prediction and 'prob' gives the probability value for each class. </p>
<p class="enddd"></p>
</dd>
<dt>use_gpus (optional) </dt>
<dd><p class="startdd">BOOLEAN, default: FALSE (i.e., CPU). Determines whether GPUs are to be used for prediction/inference. Set to TRUE to use GPUs.</p>
<dl class="section note"><dt>Note</dt><dd>The prediction function uses the whole cluster. If you are using GPUs, it requires that GPUs are attached to all hosts, and that there are the same number of GPUs on each host (homogeneous cluster). This is different from the fit() and evaluate() functions that support GPUs on only some of the hosts (heterogeneous cluster). Therefore, if you have GPUs only on some of the hosts, or an uneven numbers of GPUs per host, then set this parameter to FALSE to use CPUs.  </dd></dl>
</dd>
</dl>
<p><a class="anchor" id="example"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
<dl class="section note"><dt>Note</dt><dd>Deep learning works best on very large datasets, but that is not convenient for a quick introduction to the syntax. So in this example we use an MLP on the well known iris data set from <a href="https://archive.ics.uci.edu/ml/datasets/iris">https://archive.ics.uci.edu/ml/datasets/iris</a>. For more realistic examples with images please refer to the deep learning notebooks at <a href="https://github.com/apache/madlib-site/tree/asf-site/community-artifacts">https://github.com/apache/madlib-site/tree/asf-site/community-artifacts</a>.</dd></dl>
<h4>Classification</h4>
<ol type="1">
<li>Create an input data set. <pre class="example">
DROP TABLE IF EXISTS iris_data;
CREATE TABLE iris_data(
    id serial,
    attributes numeric[],
    class_text varchar
);
INSERT INTO iris_data(id, attributes, class_text) VALUES
(1,ARRAY[5.1,3.5,1.4,0.2],'Iris-setosa'),
(2,ARRAY[4.9,3.0,1.4,0.2],'Iris-setosa'),
(3,ARRAY[4.7,3.2,1.3,0.2],'Iris-setosa'),
(4,ARRAY[4.6,3.1,1.5,0.2],'Iris-setosa'),
(5,ARRAY[5.0,3.6,1.4,0.2],'Iris-setosa'),
(6,ARRAY[5.4,3.9,1.7,0.4],'Iris-setosa'),
(7,ARRAY[4.6,3.4,1.4,0.3],'Iris-setosa'),
(8,ARRAY[5.0,3.4,1.5,0.2],'Iris-setosa'),
(9,ARRAY[4.4,2.9,1.4,0.2],'Iris-setosa'),
(10,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa'),
(11,ARRAY[5.4,3.7,1.5,0.2],'Iris-setosa'),
(12,ARRAY[4.8,3.4,1.6,0.2],'Iris-setosa'),
(13,ARRAY[4.8,3.0,1.4,0.1],'Iris-setosa'),
(14,ARRAY[4.3,3.0,1.1,0.1],'Iris-setosa'),
(15,ARRAY[5.8,4.0,1.2,0.2],'Iris-setosa'),
(16,ARRAY[5.7,4.4,1.5,0.4],'Iris-setosa'),
(17,ARRAY[5.4,3.9,1.3,0.4],'Iris-setosa'),
(18,ARRAY[5.1,3.5,1.4,0.3],'Iris-setosa'),
(19,ARRAY[5.7,3.8,1.7,0.3],'Iris-setosa'),
(20,ARRAY[5.1,3.8,1.5,0.3],'Iris-setosa'),
(21,ARRAY[5.4,3.4,1.7,0.2],'Iris-setosa'),
(22,ARRAY[5.1,3.7,1.5,0.4],'Iris-setosa'),
(23,ARRAY[4.6,3.6,1.0,0.2],'Iris-setosa'),
(24,ARRAY[5.1,3.3,1.7,0.5],'Iris-setosa'),
(25,ARRAY[4.8,3.4,1.9,0.2],'Iris-setosa'),
(26,ARRAY[5.0,3.0,1.6,0.2],'Iris-setosa'),
(27,ARRAY[5.0,3.4,1.6,0.4],'Iris-setosa'),
(28,ARRAY[5.2,3.5,1.5,0.2],'Iris-setosa'),
(29,ARRAY[5.2,3.4,1.4,0.2],'Iris-setosa'),
(30,ARRAY[4.7,3.2,1.6,0.2],'Iris-setosa'),
(31,ARRAY[4.8,3.1,1.6,0.2],'Iris-setosa'),
(32,ARRAY[5.4,3.4,1.5,0.4],'Iris-setosa'),
(33,ARRAY[5.2,4.1,1.5,0.1],'Iris-setosa'),
(34,ARRAY[5.5,4.2,1.4,0.2],'Iris-setosa'),
(35,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa'),
(36,ARRAY[5.0,3.2,1.2,0.2],'Iris-setosa'),
(37,ARRAY[5.5,3.5,1.3,0.2],'Iris-setosa'),
(38,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa'),
(39,ARRAY[4.4,3.0,1.3,0.2],'Iris-setosa'),
(40,ARRAY[5.1,3.4,1.5,0.2],'Iris-setosa'),
(41,ARRAY[5.0,3.5,1.3,0.3],'Iris-setosa'),
(42,ARRAY[4.5,2.3,1.3,0.3],'Iris-setosa'),
(43,ARRAY[4.4,3.2,1.3,0.2],'Iris-setosa'),
(44,ARRAY[5.0,3.5,1.6,0.6],'Iris-setosa'),
(45,ARRAY[5.1,3.8,1.9,0.4],'Iris-setosa'),
(46,ARRAY[4.8,3.0,1.4,0.3],'Iris-setosa'),
(47,ARRAY[5.1,3.8,1.6,0.2],'Iris-setosa'),
(48,ARRAY[4.6,3.2,1.4,0.2],'Iris-setosa'),
(49,ARRAY[5.3,3.7,1.5,0.2],'Iris-setosa'),
(50,ARRAY[5.0,3.3,1.4,0.2],'Iris-setosa'),
(51,ARRAY[7.0,3.2,4.7,1.4],'Iris-versicolor'),
(52,ARRAY[6.4,3.2,4.5,1.5],'Iris-versicolor'),
(53,ARRAY[6.9,3.1,4.9,1.5],'Iris-versicolor'),
(54,ARRAY[5.5,2.3,4.0,1.3],'Iris-versicolor'),
(55,ARRAY[6.5,2.8,4.6,1.5],'Iris-versicolor'),
(56,ARRAY[5.7,2.8,4.5,1.3],'Iris-versicolor'),
(57,ARRAY[6.3,3.3,4.7,1.6],'Iris-versicolor'),
(58,ARRAY[4.9,2.4,3.3,1.0],'Iris-versicolor'),
(59,ARRAY[6.6,2.9,4.6,1.3],'Iris-versicolor'),
(60,ARRAY[5.2,2.7,3.9,1.4],'Iris-versicolor'),
(61,ARRAY[5.0,2.0,3.5,1.0],'Iris-versicolor'),
(62,ARRAY[5.9,3.0,4.2,1.5],'Iris-versicolor'),
(63,ARRAY[6.0,2.2,4.0,1.0],'Iris-versicolor'),
(64,ARRAY[6.1,2.9,4.7,1.4],'Iris-versicolor'),
(65,ARRAY[5.6,2.9,3.6,1.3],'Iris-versicolor'),
(66,ARRAY[6.7,3.1,4.4,1.4],'Iris-versicolor'),
(67,ARRAY[5.6,3.0,4.5,1.5],'Iris-versicolor'),
(68,ARRAY[5.8,2.7,4.1,1.0],'Iris-versicolor'),
(69,ARRAY[6.2,2.2,4.5,1.5],'Iris-versicolor'),
(70,ARRAY[5.6,2.5,3.9,1.1],'Iris-versicolor'),
(71,ARRAY[5.9,3.2,4.8,1.8],'Iris-versicolor'),
(72,ARRAY[6.1,2.8,4.0,1.3],'Iris-versicolor'),
(73,ARRAY[6.3,2.5,4.9,1.5],'Iris-versicolor'),
(74,ARRAY[6.1,2.8,4.7,1.2],'Iris-versicolor'),
(75,ARRAY[6.4,2.9,4.3,1.3],'Iris-versicolor'),
(76,ARRAY[6.6,3.0,4.4,1.4],'Iris-versicolor'),
(77,ARRAY[6.8,2.8,4.8,1.4],'Iris-versicolor'),
(78,ARRAY[6.7,3.0,5.0,1.7],'Iris-versicolor'),
(79,ARRAY[6.0,2.9,4.5,1.5],'Iris-versicolor'),
(80,ARRAY[5.7,2.6,3.5,1.0],'Iris-versicolor'),
(81,ARRAY[5.5,2.4,3.8,1.1],'Iris-versicolor'),
(82,ARRAY[5.5,2.4,3.7,1.0],'Iris-versicolor'),
(83,ARRAY[5.8,2.7,3.9,1.2],'Iris-versicolor'),
(84,ARRAY[6.0,2.7,5.1,1.6],'Iris-versicolor'),
(85,ARRAY[5.4,3.0,4.5,1.5],'Iris-versicolor'),
(86,ARRAY[6.0,3.4,4.5,1.6],'Iris-versicolor'),
(87,ARRAY[6.7,3.1,4.7,1.5],'Iris-versicolor'),
(88,ARRAY[6.3,2.3,4.4,1.3],'Iris-versicolor'),
(89,ARRAY[5.6,3.0,4.1,1.3],'Iris-versicolor'),
(90,ARRAY[5.5,2.5,4.0,1.3],'Iris-versicolor'),
(91,ARRAY[5.5,2.6,4.4,1.2],'Iris-versicolor'),
(92,ARRAY[6.1,3.0,4.6,1.4],'Iris-versicolor'),
(93,ARRAY[5.8,2.6,4.0,1.2],'Iris-versicolor'),
(94,ARRAY[5.0,2.3,3.3,1.0],'Iris-versicolor'),
(95,ARRAY[5.6,2.7,4.2,1.3],'Iris-versicolor'),
(96,ARRAY[5.7,3.0,4.2,1.2],'Iris-versicolor'),
(97,ARRAY[5.7,2.9,4.2,1.3],'Iris-versicolor'),
(98,ARRAY[6.2,2.9,4.3,1.3],'Iris-versicolor'),
(99,ARRAY[5.1,2.5,3.0,1.1],'Iris-versicolor'),
(100,ARRAY[5.7,2.8,4.1,1.3],'Iris-versicolor'),
(101,ARRAY[6.3,3.3,6.0,2.5],'Iris-virginica'),
(102,ARRAY[5.8,2.7,5.1,1.9],'Iris-virginica'),
(103,ARRAY[7.1,3.0,5.9,2.1],'Iris-virginica'),
(104,ARRAY[6.3,2.9,5.6,1.8],'Iris-virginica'),
(105,ARRAY[6.5,3.0,5.8,2.2],'Iris-virginica'),
(106,ARRAY[7.6,3.0,6.6,2.1],'Iris-virginica'),
(107,ARRAY[4.9,2.5,4.5,1.7],'Iris-virginica'),
(108,ARRAY[7.3,2.9,6.3,1.8],'Iris-virginica'),
(109,ARRAY[6.7,2.5,5.8,1.8],'Iris-virginica'),
(110,ARRAY[7.2,3.6,6.1,2.5],'Iris-virginica'),
(111,ARRAY[6.5,3.2,5.1,2.0],'Iris-virginica'),
(112,ARRAY[6.4,2.7,5.3,1.9],'Iris-virginica'),
(113,ARRAY[6.8,3.0,5.5,2.1],'Iris-virginica'),
(114,ARRAY[5.7,2.5,5.0,2.0],'Iris-virginica'),
(115,ARRAY[5.8,2.8,5.1,2.4],'Iris-virginica'),
(116,ARRAY[6.4,3.2,5.3,2.3],'Iris-virginica'),
(117,ARRAY[6.5,3.0,5.5,1.8],'Iris-virginica'),
(118,ARRAY[7.7,3.8,6.7,2.2],'Iris-virginica'),
(119,ARRAY[7.7,2.6,6.9,2.3],'Iris-virginica'),
(120,ARRAY[6.0,2.2,5.0,1.5],'Iris-virginica'),
(121,ARRAY[6.9,3.2,5.7,2.3],'Iris-virginica'),
(122,ARRAY[5.6,2.8,4.9,2.0],'Iris-virginica'),
(123,ARRAY[7.7,2.8,6.7,2.0],'Iris-virginica'),
(124,ARRAY[6.3,2.7,4.9,1.8],'Iris-virginica'),
(125,ARRAY[6.7,3.3,5.7,2.1],'Iris-virginica'),
(126,ARRAY[7.2,3.2,6.0,1.8],'Iris-virginica'),
(127,ARRAY[6.2,2.8,4.8,1.8],'Iris-virginica'),
(128,ARRAY[6.1,3.0,4.9,1.8],'Iris-virginica'),
(129,ARRAY[6.4,2.8,5.6,2.1],'Iris-virginica'),
(130,ARRAY[7.2,3.0,5.8,1.6],'Iris-virginica'),
(131,ARRAY[7.4,2.8,6.1,1.9],'Iris-virginica'),
(132,ARRAY[7.9,3.8,6.4,2.0],'Iris-virginica'),
(133,ARRAY[6.4,2.8,5.6,2.2],'Iris-virginica'),
(134,ARRAY[6.3,2.8,5.1,1.5],'Iris-virginica'),
(135,ARRAY[6.1,2.6,5.6,1.4],'Iris-virginica'),
(136,ARRAY[7.7,3.0,6.1,2.3],'Iris-virginica'),
(137,ARRAY[6.3,3.4,5.6,2.4],'Iris-virginica'),
(138,ARRAY[6.4,3.1,5.5,1.8],'Iris-virginica'),
(139,ARRAY[6.0,3.0,4.8,1.8],'Iris-virginica'),
(140,ARRAY[6.9,3.1,5.4,2.1],'Iris-virginica'),
(141,ARRAY[6.7,3.1,5.6,2.4],'Iris-virginica'),
(142,ARRAY[6.9,3.1,5.1,2.3],'Iris-virginica'),
(143,ARRAY[5.8,2.7,5.1,1.9],'Iris-virginica'),
(144,ARRAY[6.8,3.2,5.9,2.3],'Iris-virginica'),
(145,ARRAY[6.7,3.3,5.7,2.5],'Iris-virginica'),
(146,ARRAY[6.7,3.0,5.2,2.3],'Iris-virginica'),
(147,ARRAY[6.3,2.5,5.0,1.9],'Iris-virginica'),
(148,ARRAY[6.5,3.0,5.2,2.0],'Iris-virginica'),
(149,ARRAY[6.2,3.4,5.4,2.3],'Iris-virginica'),
(150,ARRAY[5.9,3.0,5.1,1.8],'Iris-virginica');
</pre> Create a test/validation dataset from the training data: <pre class="example">
DROP TABLE IF EXISTS iris_train, iris_test;
-- Set seed so results are reproducible
SELECT setseed(0);
SELECT madlib.train_test_split('iris_data',     -- Source table
                               'iris',          -- Output table root name
                                0.8,            -- Train proportion
                                NULL,           -- Test proportion (0.2)
                                NULL,           -- Strata definition
                                NULL,           -- Output all columns
                                NULL,           -- Sample without replacement
                                TRUE            -- Separate output tables
                              );
SELECT COUNT(*) FROM iris_train;
</pre> <pre class="result">
 count
------+
   120
</pre></li>
<li>Call the preprocessor for deep learning. For the training dataset: <pre class="example">
\x on
DROP TABLE IF EXISTS iris_train_packed, iris_train_packed_summary;
SELECT madlib.training_preprocessor_dl('iris_train',         -- Source table
                                       'iris_train_packed',  -- Output table
                                       'class_text',         -- Dependent variable
                                       'attributes'          -- Independent variable
                                        );
SELECT * FROM iris_train_packed_summary;
</pre> <pre class="result">
-[ RECORD 1 ]-------+---------------------------------------------
source_table        | iris_train
output_table        | iris_train_packed
dependent_varname   | class_text
independent_varname | attributes
dependent_vartype   | character varying
class_values        | {Iris-setosa,Iris-versicolor,Iris-virginica}
buffer_size         | 60
normalizing_const   | 1.0
num_classes         | 3
</pre> For the validation dataset: <pre class="example">
DROP TABLE IF EXISTS iris_test_packed, iris_test_packed_summary;
SELECT madlib.validation_preprocessor_dl('iris_test',          -- Source table
                                         'iris_test_packed',   -- Output table
                                         'class_text',         -- Dependent variable
                                         'attributes',         -- Independent variable
                                         'iris_train_packed'   -- From training preprocessor step
                                          );
SELECT * FROM iris_test_packed_summary;
</pre> <pre class="result">
-[ RECORD 1 ]-------+---------------------------------------------
source_table        | iris_test
output_table        | iris_test_packed
dependent_varname   | class_text
independent_varname | attributes
dependent_vartype   | character varying
class_values        | {Iris-setosa,Iris-versicolor,Iris-virginica}
buffer_size         | 15
normalizing_const   | 1.0
num_classes         | 3
</pre></li>
<li>Define and load model architecture. Use Keras to define the 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()
<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">
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> Define 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()
<pre class="fragment">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
</pre>
</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 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
                               '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
);
</pre></li>
<li>Define model selection tuples and load. Select the model(s) from the model architecture table that you want to run, along with the compile and fit parameters. Combinations will be created for the set of model selection parameters will be loaded: <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> This is the name of the model architecture table that corresponds to the model selection table: <pre class="example">
SELECT * FROM mst_table_summary;
</pre> <pre class="result">
  model_arch_table
--------------------+
 model_arch_library
</pre></li>
<li>Train multiple models. <pre class="example">
DROP TABLE IF EXISTS iris_multi_model, iris_multi_model_summary, iris_multi_model_info;
SELECT madlib.madlib_keras_fit_multiple_model('iris_train_packed',    -- source_table
                                              'iris_multi_model',     -- model_output_table
                                              'mst_table',            -- model_selection_table
                                              10,                     -- num_iterations
                                              FALSE                   -- use gpus
                                             );
</pre> View the model summary: <pre class="example">
SELECT * FROM iris_multi_model_summary;
</pre> <pre class="result">
source_table              | iris_train_packed
validation_table          |
model                     | iris_multi_model
model_info                | iris_multi_model_info
dependent_varname         | class_text
independent_varname       | attributes
model_arch_table          | model_arch_library
num_iterations            | 10
metrics_compute_frequency | 10
warm_start                | f
name                      |
description               |
start_training_time       | 2019-12-16 18:54:33.826414
end_training_time         | 2019-12-16 18:56:19.106321
madlib_version            | 1.17.0
num_classes               | 3
class_values              | {Iris-setosa,Iris-versicolor,Iris-virginica}
dependent_vartype         | character varying
normalizing_const         | 1
metrics_iters             | {10}
</pre> View results for each model: <pre class="example">
SELECT * FROM iris_multi_model_info ORDER BY training_metrics_final DESC, training_loss_final;
</pre> <pre class="result">
 mst_key | model_id |                                 compile_params                                  |      fit_params       |  model_type  |  model_size  | metrics_elapsed_time | metrics_type | training_metrics_final | training_loss_final |  training_metrics   |    training_loss    | validation_metrics_final | validation_loss_final | validation_metrics | validation_loss
---------+----------+---------------------------------------------------------------------------------+-----------------------+--------------+--------------+----------------------+--------------+------------------------+---------------------+---------------------+---------------------+--------------------------+-----------------------+--------------------+-----------------
       9 |        2 | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=4,epochs=1 | madlib_keras | 1.2197265625 | {119.42963886261}    | {accuracy}   |         0.983333349228 |       0.07286978513 | {0.983333349227905} | {0.072869785130024} |                          |                       |                    |
      10 |        2 | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=8,epochs=1 | madlib_keras | 1.2197265625 | {118.485460996628}   | {accuracy}   |         0.975000023842 |     0.0798489004374 | {0.975000023841858} | {0.079848900437355} |                          |                       |                    |
       4 |        1 | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=8,epochs=1 | madlib_keras | 0.7900390625 | {118.707404851913}   | {accuracy}   |         0.975000023842 |      0.143356323242 | {0.975000023841858} | {0.143356323242188} |                          |                       |                    |
      11 |        2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=4,epochs=1 | madlib_keras | 1.2197265625 | {118.224883794785}   | {accuracy}   |         0.958333313465 |      0.636615753174 | {0.958333313465118} | {0.636615753173828} |                          |                       |                    |
       2 |        1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']   | batch_size=8,epochs=1 | madlib_keras | 0.7900390625 | {117.732690811157}   | {accuracy}   |         0.925000011921 |      0.161811202765 | {0.925000011920929} | {0.161811202764511} |                          |                       |                    |
       5 |        1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=4,epochs=1 | madlib_keras | 0.7900390625 | {120.357484817505}   | {accuracy}   |         0.833333313465 |        0.5542948246 | {0.833333313465118} | {0.55429482460022}  |                          |                       |                    |
       3 |        1 | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=4,epochs=1 | madlib_keras | 0.7900390625 | {118.928852796555}   | {accuracy}   |         0.824999988079 |      0.301002770662 | {0.824999988079071} | {0.301002770662308} |                          |                       |                    |
       6 |        1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=8,epochs=1 | madlib_keras | 0.7900390625 | {120.566634893417}   | {accuracy}   |         0.816666662693 |      0.875298440456 | {0.816666662693024} | {0.87529844045639}  |                          |                       |                    |
      12 |        2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=8,epochs=1 | madlib_keras | 1.2197265625 | {119.182703018188}   | {accuracy}   |         0.774999976158 |      0.785651266575 | {0.774999976158142} | {0.78565126657486}  |                          |                       |                    |
       1 |        1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']   | batch_size=4,epochs=1 | madlib_keras | 0.7900390625 | {119.643137931824}   | {accuracy}   |         0.508333325386 |      0.762569189072 | {0.508333325386047} | {0.762569189071655} |                          |                       |                    |
       7 |        2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']   | batch_size=4,epochs=1 | madlib_keras | 1.2197265625 | {120.15305685997}    | {accuracy}   |         0.333333343267 |       1.09794270992 | {0.333333343267441} | {1.09794270992279}  |                          |                       |                    |
       8 |        2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']   | batch_size=8,epochs=1 | madlib_keras | 1.2197265625 | {119.911739826202}   | {accuracy}   |         0.333333343267 |       1.10344016552 | {0.333333343267441} | {1.10344016551971}  |                          |                       |                    |
(12 rows)
</pre></li>
<li>Evaluate. Now run evaluate using models we built above: <pre class="example">
DROP TABLE IF EXISTS iris_validate;
SELECT madlib.madlib_keras_evaluate('iris_multi_model',  -- model
                                    'iris_test_packed',  -- test table
                                    'iris_validate',     -- output table
                                     NULL,               -- use gpus
                                     3                   -- mst_key to use
                                   );
SELECT * FROM iris_validate;
</pre> <pre class="result">
       loss        |      metric       | metrics_type
-------------------+-------------------+--------------
 0.103803977370262 | 0.966666638851166 | {accuracy}
</pre></li>
<li>Predict. Now predict using one of the models we built. We will use the validation data set for prediction as well, which is not usual but serves to show the syntax. The prediction is in the estimated_class_text column: <pre class="example">
DROP TABLE IF EXISTS iris_predict;
SELECT madlib.madlib_keras_predict('iris_multi_model', -- model
                                   'iris_test',        -- test_table
                                   'id',               -- id column
                                   'attributes',       -- independent var
                                   'iris_predict',     -- output table
                                    'response',        -- prediction type
                                    FALSE,             -- use gpus
                                    3                  -- mst_key to use
                                   );
SELECT * FROM iris_predict ORDER BY id;
</pre> <pre class="result">
 id  | estimated_class_text
-----+----------------------
   9 | Iris-setosa
  18 | Iris-setosa
  22 | Iris-setosa
  26 | Iris-setosa
  35 | Iris-setosa
  38 | Iris-setosa
  42 | Iris-setosa
  43 | Iris-setosa
  45 | Iris-setosa
  46 | Iris-setosa
  50 | Iris-setosa
  53 | Iris-versicolor
  60 | Iris-versicolor
  68 | Iris-versicolor
  77 | Iris-versicolor
  78 | Iris-versicolor
  79 | Iris-versicolor
  81 | Iris-versicolor
  82 | Iris-versicolor
  85 | Iris-virginica
  95 | Iris-versicolor
  97 | Iris-versicolor
  98 | Iris-versicolor
 113 | Iris-virginica
 117 | Iris-virginica
 118 | Iris-virginica
 127 | Iris-virginica
 136 | Iris-virginica
 143 | Iris-virginica
 145 | Iris-virginica
(30 rows)
</pre> Count missclassifications: <pre class="example">
SELECT COUNT(*) FROM iris_predict JOIN iris_test USING (id)
WHERE iris_predict.estimated_class_text != iris_test.class_text;
</pre> <pre class="result">
 count
-------+
     1
</pre> Percent missclassifications: <pre class="example">
SELECT round(count(*)*100/(150*0.2),2) as test_accuracy_percent from
    (select iris_test.class_text as actual, iris_predict.estimated_class_text as estimated
     from iris_predict inner join iris_test
     on iris_test.id=iris_predict.id) q
WHERE q.actual=q.estimated;
</pre> <pre class="result">
 test_accuracy_percent
-----------------------+
                 96.67
</pre></li>
</ol>
<h4>Classification with Other Parameters</h4>
<ol type="1">
<li>Validation dataset. Now use a validation dataset and compute metrics every 3rd iteration using the 'metrics_compute_frequency' parameter. This can help reduce run time if you do not need metrics computed at every iteration. <pre class="example">
DROP TABLE IF EXISTS iris_multi_model, iris_multi_model_summary, iris_multi_model_info;
SELECT madlib.madlib_keras_fit_multiple_model('iris_train_packed',    -- source_table
                                              'iris_multi_model',     -- model_output_table
                                              'mst_table',            -- model_selection_table
                                               10,                     -- num_iterations
                                               FALSE,                 -- use gpus
                                              'iris_test_packed',     -- validation dataset
                                               3,                     -- metrics compute frequency
                                               FALSE,                 -- warm start
                                              'Sophie L.',            -- name
                                              'Model selection for iris dataset'  -- description
                                             );
</pre> View the model summary: <pre class="example">
SELECT * FROM iris_multi_model_summary;
</pre> <pre class="result">
source_table              | iris_train_packed
validation_table          | iris_test_packed
model                     | iris_multi_model
model_info                | iris_multi_model_info
dependent_varname         | class_text
independent_varname       | attributes
model_arch_table          | model_arch_library
num_iterations            | 10
metrics_compute_frequency | 3
warm_start                | f
name                      | Sophie L.
description               | Model selection for iris dataset
start_training_time       | 2019-12-16 19:28:16.219137
end_training_time         | 2019-12-16 19:30:19.238692
madlib_version            | 1.17.0
num_classes               | 3
class_values              | {Iris-setosa,Iris-versicolor,Iris-virginica}
dependent_vartype         | character varying
normalizing_const         | 1
metrics_iters             | {3,6,9,10}
</pre> View results for each model: <pre class="example">
SELECT * FROM iris_multi_model_info ORDER BY training_metrics_final DESC, training_loss_final;
</pre> <pre class="result">
 mst_key | model_id |                                 compile_params                                  |      fit_params       |  model_type  |  model_size  |                         metrics_elapsed_time                          | metrics_type | training_metrics_final | training_loss_final |                             training_metrics                              |                               training_loss                               | validation_metrics_final | validation_loss_final |                            validation_metrics                             |                              validation_loss
---------+----------+---------------------------------------------------------------------------------+-----------------------+--------------+--------------+-----------------------------------------------------------------------+--------------+------------------------+---------------------+---------------------------------------------------------------------------+---------------------------------------------------------------------------+--------------------------+-----------------------+---------------------------------------------------------------------------+---------------------------------------------------------------------------
       4 |        1 | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=8,epochs=1 | madlib_keras | 0.7900390625 | {37.0420558452606,78.2046208381653,116.242669820786,134.287139892578} | {accuracy}   |         0.975000023842 |      0.165132269263 | {0.75,0.958333313465118,0.958333313465118,0.975000023841858}              | {0.618549585342407,0.319452553987503,0.223872095346451,0.165132269263268} |           0.966666638851 |        0.213689729571 | {0.733333349227905,0.933333337306976,0.933333337306976,0.966666638851166} | {0.683791160583496,0.370491921901703,0.255890935659409,0.213689729571342}
       2 |        1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']   | batch_size=8,epochs=1 | madlib_keras | 0.7900390625 | {36.3931469917297,77.5780539512634,115.430645942688,133.599857807159} | {accuracy}   |         0.966666638851 |      0.277698725462 | {0.591666638851166,0.966666638851166,0.666666686534882,0.966666638851166} | {0.634598553180695,0.334936827421188,0.615665555000305,0.27769872546196}  |           0.966666638851 |         0.34405490756 | {0.5,0.966666638851166,0.566666662693024,0.966666638851166}               | {0.643225967884064,0.41021603345871,0.805291295051575,0.344054907560349}
      10 |        2 | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=8,epochs=1 | madlib_keras | 1.2197265625 | {36.8482949733734,78.0155048370361,115.83317399025,134.079672813416}  | {accuracy}   |         0.958333313465 |      0.122385449708 | {0.883333325386047,0.941666662693024,0.858333349227905,0.958333313465118} | {0.291894346475601,0.146935686469078,0.270052850246429,0.122385449707508} |           0.933333337307 |        0.181496843696 | {0.766666650772095,0.866666674613953,0.899999976158142,0.933333337306976} | {0.395013928413391,0.245234906673431,0.301119148731232,0.181496843695641}
       3 |        1 | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=4,epochs=1 | madlib_keras | 0.7900390625 | {37.2318170070648,78.3925468921661,116.45490694046,134.491376876831}  | {accuracy}   |         0.941666662693 |      0.193545326591 | {0.966666638851166,0.941666662693024,0.941666662693024,0.941666662693024} | {0.39665362238884,0.213271111249924,0.190151125192642,0.193545326590538}  |           0.933333337307 |        0.151459023356 | {1,0.966666638851166,0.933333337306976,0.933333337306976}                 | {0.464315593242645,0.198051139712334,0.138570576906204,0.151459023356438}
       9 |        2 | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=4,epochs=1 | madlib_keras | 1.2197265625 | {37.6678929328918,78.820240020752,116.939878940582,134.959810972214}  | {accuracy}   |         0.925000011921 |      0.192344605923 | {0.824999988079071,0.774999976158142,0.966666638851166,0.925000011920929} | {0.434513121843338,0.326292037963867,0.131333693861961,0.192344605922699} |           0.899999976158 |        0.209528595209 | {0.800000011920929,0.766666650772095,0.966666638851166,0.899999976158142} | {0.52033931016922,0.344535797834396,0.170280396938324,0.209528595209122}
       8 |        2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']   | batch_size=8,epochs=1 | madlib_keras | 1.2197265625 | {38.0689258575439,79.4995639324188,117.36315202713,135.380483865738}  | {accuracy}   |         0.866666674614 |      0.390509605408 | {0.691666662693024,0.691666662693024,0.633333325386047,0.866666674613953} | {0.490214675664902,0.444783747196198,0.627961099147797,0.390509605407715} |           0.933333337307 |        0.376114845276 | {0.566666662693024,0.566666662693024,0.533333361148834,0.933333337306976} | {0.575542628765106,0.54660427570343,0.785183191299438,0.376114845275879}
       5 |        1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=4,epochs=1 | madlib_keras | 0.7900390625 | {38.474328994751,79.9709329605103,117.766183853149,135.803887844086}  | {accuracy}   |         0.841666638851 |      0.576696753502 | {0.616666674613953,0.699999988079071,0.758333325386047,0.841666638851166} | {0.90448260307312,0.750164151191711,0.616493880748749,0.576696753501892}  |           0.899999976158 |        0.631914675236 | {0.666666686534882,0.699999988079071,0.733333349227905,0.899999976158142} | {0.871200919151306,0.780709445476532,0.665971457958221,0.631914675235748}
      11 |        2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=4,epochs=1 | madlib_keras | 1.2197265625 | {36.6214678287506,77.7987759113312,115.631717920303,133.83836388588}  | {accuracy}   |         0.758333325386 |      0.881635427475 | {0.308333337306976,0.316666662693024,0.75,0.758333325386047}              | {1.12997460365295,1.02749967575073,0.923768699169159,0.881635427474976}   |           0.766666650772 |        0.878168046474 | {0.433333337306976,0.433333337306976,0.766666650772095,0.766666650772095} | {1.07487094402313,0.974115014076233,0.916269063949585,0.878168046474457}
       7 |        2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']   | batch_size=4,epochs=1 | madlib_keras | 1.2197265625 | {38.2849600315094,79.7524738311768,117.580325841904,135.606695890427} | {accuracy}   |         0.691666662693 |      0.444524824619 | {0.908333361148834,0.391666680574417,0.691666662693024,0.691666662693024} | {0.335082054138184,2.02327847480774,0.444351017475128,0.444524824619293}  |           0.566666662693 |        0.539750337601 | {0.800000011920929,0.266666680574417,0.566666662693024,0.566666662693024} | {0.433189332485199,2.3276960849762,0.534160375595093,0.539750337600708}
       6 |        1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=8,epochs=1 | madlib_keras | 0.7900390625 | {38.6593668460846,80.1789360046387,117.957875013351,135.995815992355} | {accuracy}   |         0.683333337307 |      0.841839790344 | {0.316666662693024,0.366666674613953,0.666666686534882,0.683333337306976} | {1.07646071910858,0.963329672813416,0.87216705083847,0.841839790344238}   |           0.666666686535 |        0.840192914009 | {0.433333337306976,0.533333361148834,0.666666686534882,0.666666686534882} | {1.02845978736877,0.941896677017212,0.861787617206573,0.840192914009094}
       1 |        1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']   | batch_size=4,epochs=1 | madlib_keras | 0.7900390625 | {37.8553328514099,79.2480089664459,117.139881849289,135.155915975571} | {accuracy}   |         0.358333319426 |       1.11013436317 | {0.358333319425583,0.333333343267441,0.333333343267441,0.358333319425583} | {1.10554325580597,1.11694586277008,1.09756696224213,1.11013436317444}     |           0.233333334327 |         1.17629003525 | {0.233333334326744,0.333333343267441,0.333333343267441,0.233333334326744} | {1.16081762313843,1.14324629306793,1.11625325679779,1.1762900352478}
      12 |        2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=8,epochs=1 | madlib_keras | 1.2197265625 | {37.4500079154968,78.6058378219604,116.700626850128,134.72905087471}  | {accuracy}   |         0.308333337307 |       1.06241953373 | {0.150000005960464,0.333333343267441,0.333333343267441,0.308333337306976} | {1.13338851928711,1.09694564342499,1.07030868530273,1.06241953372955}     |           0.433333337307 |         1.03659796715 | {0.16666667163372,0.333333343267441,0.433333337306976,0.433333337306976}  | {1.06262242794037,1.07252764701843,1.05843663215637,1.03659796714783}
(12 rows)
</pre></li>
<li>Predict probabilities for each class: <pre class="example">
DROP TABLE IF EXISTS iris_predict;
SELECT madlib.madlib_keras_predict('iris_multi_model', -- model
                                   'iris_test',        -- test_table
                                   'id',               -- id column
                                   'attributes',       -- independent var
                                   'iris_predict',     -- output table
                                    'prob',            -- prediction type
                                    FALSE,             -- use gpus
                                    3                  -- mst_key to use
                                   );
SELECT * FROM iris_predict ORDER BY id;
</pre> <pre class="result">
 id  | prob_Iris-setosa | prob_Iris-versicolor | prob_Iris-virginica
-----+------------------+----------------------+---------------------
   9 |       0.99931216 |        0.00068789057 |       6.2587335e-10
  18 |       0.99984336 |        0.00015656587 |        7.969957e-12
  22 |        0.9998497 |        0.00015029701 |       6.4133347e-12
  26 |        0.9995004 |        0.00049964694 |       2.2795305e-10
  35 |       0.99964666 |        0.00035332117 |       9.4490485e-11
  38 |       0.99964666 |        0.00035332117 |       9.4490485e-11
  42 |        0.9985154 |         0.0014845316 |        5.293262e-09
  43 |       0.99964476 |         0.0003552362 |        9.701174e-11
  45 |        0.9997311 |        0.00026883607 |        3.076166e-11
  46 |        0.9995486 |        0.00045140853 |       1.6814435e-10
  50 |        0.9997856 |        0.00021441824 |       2.1316622e-11
  53 |     9.837335e-06 |           0.97109175 |         0.028898403
  60 |    0.00014028326 |           0.96552837 |         0.034331344
  68 |    0.00087942625 |            0.9883348 |         0.010785843
  77 |      6.08114e-06 |           0.94356424 |         0.056429718
  78 |     7.116364e-07 |            0.8596206 |          0.14037873
  79 |    1.3918722e-05 |           0.94052655 |          0.05945957
  81 |    0.00045687397 |            0.9794796 |         0.020063542
  82 |     0.0015463434 |           0.98768973 |         0.010763981
  85 |    1.0929693e-05 |           0.87866926 |         0.121319845
  95 |    6.3600986e-05 |           0.95264935 |         0.047287125
  97 |    0.00020298029 |             0.981617 |         0.018180028
  98 |    0.00019721613 |           0.98902065 |          0.01078211
 113 |    1.0388683e-09 |           0.23626474 |           0.7637353
 117 |     4.598902e-09 |           0.25669694 |           0.7433031
 118 |    3.7139156e-11 |           0.13193987 |           0.8680601
 127 |    2.1297862e-07 |             0.670349 |          0.32965073
 136 |    7.1760774e-12 |           0.07074605 |            0.929254
 143 |    1.2568385e-09 |          0.113820426 |           0.8861796
 145 |      6.17019e-11 |          0.117578305 |          0.88242173
(30 rows)
</pre></li>
<li>Warm start. Next, use the warm_start parameter to continue learning, using the coefficients from the run above. Note that we don't drop the model table or model summary table: <pre class="example">
SELECT madlib.madlib_keras_fit_multiple_model('iris_train_packed',    -- source_table
                                              'iris_multi_model',     -- model_output_table
                                              'mst_table',            -- model_selection_table
                                               3,                     -- num_iterations
                                               FALSE,                 -- use gpus
                                              'iris_test_packed',     -- validation dataset
                                               1,                     -- metrics compute frequency
                                               TRUE,                  -- warm start
                                              'Sophie L.',            -- name
                                              'Simple MLP for iris dataset'  -- description
                                             );
SELECT * FROM iris_multi_model_summary;
</pre> <pre class="result">
source_table              | iris_train_packed
validation_table          | iris_test_packed
model                     | iris_multi_model
model_info                | iris_multi_model_info
dependent_varname         | class_text
independent_varname       | attributes
model_arch_table          | model_arch_library
num_iterations            | 3
metrics_compute_frequency | 1
warm_start                | t
name                      | Sophie L.
description               | Simple MLP for iris dataset
start_training_time       | 2019-12-16 20:07:41.488587
end_training_time         | 2019-12-16 20:08:27.20651
madlib_version            | 1.17.0
num_classes               | 3
class_values              | {Iris-setosa,Iris-versicolor,Iris-virginica}
dependent_vartype         | character varying
normalizing_const         | 1
metrics_iters             | {1,2,3}
</pre> View results for each model: <pre class="example">
SELECT * FROM iris_multi_model_info ORDER BY training_metrics_final DESC, training_loss_final;
</pre> <pre class="result">
 mst_key | model_id |                                 compile_params                                  |      fit_params       |  model_type  |  model_size  |                 metrics_elapsed_time                 | metrics_type | training_metrics_final | training_loss_final |                    training_metrics                     |                      training_loss                       | validation_metrics_final | validation_loss_final |                   validation_metrics                    |                     validation_loss
---------+----------+---------------------------------------------------------------------------------+-----------------------+--------------+--------------+------------------------------------------------------+--------------+------------------------+---------------------+---------------------------------------------------------+----------------------------------------------------------+--------------------------+-----------------------+---------------------------------------------------------+---------------------------------------------------------
       5 |        1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=4,epochs=1 | madlib_keras | 0.7900390625 | {19.451014995575,37.2563629150391,54.7182998657227}  | {accuracy}   |         0.975000023842 |      0.490673750639 | {0.958333313465118,0.691666662693024,0.975000023841858} | {0.541427075862885,0.517450392246246,0.490673750638962}  |           0.933333337307 |        0.557333409786 | {0.933333337306976,0.666666686534882,0.933333337306976} | {0.60710871219635,0.570206344127655,0.557333409786224}
       9 |        2 | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=4,epochs=1 | madlib_keras | 0.7900390625 | {18.2973220348358,36.3793680667877,54.0178129673004} | {accuracy}   |         0.966666638851 |     0.0894369781017 | {0.966666638851166,0.966666638851166,0.966666638851166} | {0.133233144879341,0.111788973212242,0.0894369781017303} |           0.899999976158 |        0.195293620229 | {0.933333337306976,0.966666638851166,0.899999976158142} | {0.156044512987137,0.132803827524185,0.195293620228767}
       4 |        1 | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=8,epochs=1 | madlib_keras | 1.2197265625 | {17.6080539226532,35.6788699626923,53.3836889266968} | {accuracy}   |         0.966666638851 |      0.147051945329 | {0.908333361148834,0.958333313465118,0.966666638851166} | {0.225205257534981,0.168186634778976,0.147051945328712}  |           0.866666674614 |        0.250319689512 | {0.899999976158142,0.933333337306976,0.866666674613953} | {0.23467344045639,0.182851999998093,0.250319689512253}
       8 |        2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']   | batch_size=8,epochs=1 | madlib_keras | 1.2197265625 | {18.7529940605164,36.8255958557129,54.3704080581665} | {accuracy}   |         0.966666638851 |      0.244641214609 | {0.691666662693024,0.891666650772095,0.966666638851166} | {0.939713299274445,0.462556451559067,0.244641214609146}  |           0.966666638851 |        0.298279434443 | {0.566666662693024,0.966666638851166,0.966666638851166} | {1.30671143531799,0.412235885858536,0.29827943444252}
      10 |        2 | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=8,epochs=1 | madlib_keras | 0.7900390625 | {17.4004180431366,35.4556438922882,53.1877279281616} | {accuracy}   |         0.958333313465 |      0.123381219804 | {0.949999988079071,0.766666650772095,0.958333313465118} | {0.0919980704784393,0.576169073581696,0.123381219804287} |           0.933333337307 |        0.203262642026 | {0.866666674613953,0.766666650772095,0.933333337306976} | {0.199721112847328,0.959742486476898,0.203262642025948}
       3 |        1 | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=4,epochs=1 | madlib_keras | 1.2197265625 | {17.81547498703,35.8978669643402,53.5737180709839}   | {accuracy}   |         0.933333337307 |      0.150664463639 | {0.941666662693024,0.925000011920929,0.933333337306976} | {0.117781177163124,0.163000836968422,0.150664463639259}  |           0.833333313465 |        0.365329563618 | {0.866666674613953,0.833333313465118,0.833333313465118} | {0.249404579401016,0.375173389911652,0.365329563617706}
       6 |        1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=8,epochs=1 | madlib_keras | 1.2197265625 | {19.686233997345,37.4543249607086,54.8708770275116}  | {accuracy}   |         0.858333349228 |      0.743227303028 | {0.675000011920929,0.708333313465118,0.858333349227905} | {0.808507084846497,0.774080872535706,0.743227303028107}  |           0.966666638851 |        0.770158529282 | {0.666666686534882,0.666666686534882,0.966666638851166} | {0.808504283428192,0.791898012161255,0.770158529281616}
      11 |        2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=4,epochs=1 | madlib_keras | 1.2197265625 | {17.1583528518677,35.0312390327454,52.96133685112}   | {accuracy}   |         0.816666662693 |      0.739802956581 | {0.774999976158142,0.816666662693024,0.816666662693024} | {0.83727890253067,0.792884111404419,0.739802956581116}   |           0.800000011921 |        0.758302807808 | {0.766666650772095,0.800000011920929,0.800000011920929} | {0.837629973888397,0.801746726036072,0.758302807807922}
       2 |        1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']   | batch_size=8,epochs=1 | madlib_keras | 1.2197265625 | {16.9146749973297,34.794900894165,52.7328250408173}  | {accuracy}   |         0.808333337307 |      0.303489625454 | {0.683333337306976,0.966666638851166,0.808333337306976} | {1.05107569694519,0.189959138631821,0.303489625453949}   |           0.866666674614 |        0.285375326872 | {0.666666686534882,0.966666638851166,0.866666674613953} | {1.01942157745361,0.238933652639389,0.285375326871872}
      12 |        2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=8,epochs=1 | madlib_keras | 0.7900390625 | {18.0590150356293,36.1394078731537,53.7930529117584} | {accuracy}   |         0.699999988079 |       1.02253305912 | {0.550000011920929,0.691666662693024,0.699999988079071} | {1.0493084192276,1.03803598880768,1.02253305912018}      |           0.666666686535 |         1.02013540268 | {0.633333325386047,0.600000023841858,0.666666686534882} | {1.03952574729919,1.03439521789551,1.02013540267944}
       7 |        2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']   | batch_size=4,epochs=1 | madlib_keras | 1.2197265625 | {19.2141709327698,37.0566499233246,54.5629329681396} | {accuracy}   |         0.691666662693 |      0.448221176863 | {0.691666662693024,0.691666662693024,0.691666662693024} | {0.447027385234833,0.444605946540833,0.448221176862717}  |           0.566666662693 |        0.555035352707 | {0.566666662693024,0.566666662693024,0.566666662693024} | {0.551217257976532,0.540408432483673,0.555035352706909}
       1 |        1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']   | batch_size=4,epochs=1 | madlib_keras | 1.2197265625 | {18.501914024353,36.5938439369202,54.194118976593}   | {accuracy}   |         0.358333319426 |       1.09730923176 | {0.333333343267441,0.333333343267441,0.358333319425583} | {1.09999334812164,1.10405397415161,1.09730923175812}     |           0.233333334327 |         1.12532019615 | {0.333333343267441,0.333333343267441,0.233333334326744} | {1.12446486949921,1.13782525062561,1.12532019615173}
(12 rows)
</pre> Note that the loss and accuracy values pick up from where the previous run left off.</li>
</ol>
<p><a class="anchor" id="notes"></a></p><dl class="section user"><dt>Notes</dt><dd></dd></dl>
<ol type="1">
<li>Refer to the deep learning section of the Apache MADlib wiki [6] for important information including supported libraries and versions.</li>
<li>Classification is currently supported, not regression.</li>
<li>Reminder about the distinction between warm start and transfer learning. Warm start uses model state (weights) from the model output table from a previous training run - set the 'warm_start' parameter to TRUE in the fit function. Transfer learning uses initial model state (weights) stored in the 'model_arch_table' - in this case set the 'warm_start' parameter to FALSE in the fit function.</li>
<li>Here are some more details on how warm start works. These details are mostly applicable when implementing autoML algorithms on top of MADlib's model selection. In short, the 'model_selection_table' dictates which models get trained and output to the 'model_output_table' and associated summary and info tables. When 'warm_start' is TRUE, models are built for each 'mst_key' in the 'model_selection_table'. If there are prior runs for an 'mst_key' then the weights from that run will be used. If there are no prior runs for an 'mst_key' then random initialization will be used. For example, let's say we start with 'mst_keys' of 1, 2, 3, and 4 in the 'model_selection_table'. We run fit once to get model and info tables for 1, 2, 3, and 4. Then we modify the 'model_selection_table' as part of an autoML scheme, in which we remove the 'mst_key' for 1 and add a new 'mst_key' for 5. Next we run fit with warm start. The result will be models created for 'mst_keys' of 2, 3, 4, and 5. Warm start will be used for 2, 3, and 4 (using prior run) and random initialization will be used for 5 (no prior run). The 'mst_key' of 1 will be dropped.</li>
<li>The 'num_iterations' parameter and the Keras fit parameter 'epochs' can substantially affect accuracy and run-time. In general, increasing the number of 'epochs' for a fixed 'num_iterations' will speed up training, but may result in lower accuracy. It's best to do some experimentation to find out what works for your models and dataset.</li>
</ol>
<p><a class="anchor" id="background"></a></p><dl class="section user"><dt>Technical Background</dt><dd></dd></dl>
<p>For an introduction to deep learning foundations, including MLP and CNN, refer to [7].</p>
<p>This module trains many models a time across the database cluster in order to explore network architectures and hyperparameters. It uses model hopper parallelism (MOP) and has high convergence efficiency since it does not do model averaging [2].</p>
<p>On the effect of database cluster size: as the database cluster size increases, it will be faster to train a set of models, as long as you have at least as many model selection tuples as segments. This is because model state is "hopped" from segment to segment and training takes place in parallel [1,2]. If you have fewer model selection tuples to train than segments, then some segments may not be busy 100% of the time so speedup will not necessarily increase on a larger cluster. Inference (predict) is an embarrassingly parallel operation so inference runtimes will be proportionally faster as the number of segments increases.</p>
<p><a class="anchor" id="literature"></a></p><dl class="section user"><dt>Literature</dt><dd></dd></dl>
<p><a class="anchor" id="mlp-lit-1"></a>[1] "Cerebro: Efficient and Reproducible Model Selection on Deep Learning Systems," Supun Nakandala, Yuhao Zhang, and Arun Kumar, ACM SIGMOD 2019 DEEM Workshop, <a href="https://adalabucsd.github.io/papers/2019_Cerebro_DEEM.pdf">https://adalabucsd.github.io/papers/2019_Cerebro_DEEM.pdf</a></p>
<p>[2] "Resource-Efficient and Reproducible Model Selection on Deep Learning Systems," Supun Nakandala, Yuhao Zhang, and Arun Kumar, Technical Report, Computer Science and Engineering, University of California, San Diego <a href="https://adalabucsd.github.io/papers/TR_2019_Cerebro.pdf">https://adalabucsd.github.io/papers/TR_2019_Cerebro.pdf</a></p>
<p>[3] <a href="https://keras.io/">https://keras.io/</a></p>
<p>[4] <a href="https://www.tensorflow.org/">https://www.tensorflow.org/</a></p>
<p>[5] "Neural Networks for Machine Learning", Lectures 6a and 6b on mini-batch gradient descent, Geoffrey Hinton with Nitish Srivastava and Kevin Swersky, <a href="http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf">http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf</a></p>
<p>[6] Deep learning section of Apache MADlib wiki <a href="https://cwiki.apache.org/confluence/display/MADLIB/Deep+Learning">https://cwiki.apache.org/confluence/display/MADLIB/Deep+Learning</a></p>
<p>[7] Deep Learning, Ian Goodfellow, Yoshua Bengio and Aaron Courville, MIT Press, 2016.</p>
<p>[8] Greenplum Database server configuration parameters <a href="https://gpdb.docs.pivotal.io/latest/ref_guide/config_params/guc-list.html">https://gpdb.docs.pivotal.io/latest/ref_guide/config_params/guc-list.html</a></p>
<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd></dd></dl>
<p>File <a class="el" href="madlib__keras__fit__multiple__model_8sql__in.html">madlib_keras_fit_multiple_model.sql_in</a> documents training, evaluate and predict functions. </p>
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