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1.17.0
User Documentation for Apache MADlib
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1.17.0
User Documentation for Apache MADlib
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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].
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].
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.
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.
You can mini-batch the training and evaluation datasets by using the Preprocessor for Images 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.
You can set up the models and hyperparameters to try with the Setup Model Selection utility to define the unique combinations of model architectures, compile and fit parameters.
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
)
Arguments
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.
TEXT. Name of the table containing model selection parameters to be tried. Here we mean both hyper-parameter tuning and model architecture search.
INTEGER. Number of iterations to train.
BOOLEAN, default: FALSE (i.e., CPU). Determines whether GPUs are to be used for training the neural network. Set to TRUE to use GPUs.
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.
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.
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.
TEXT, default: NULL. Free text string to identify a name, if desired.
Output tables
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':
| mst_key | INTEGER. ID that defines a unique tuple for model architecture-compile parameters-fit parameters, as defined in the 'model_selection_table'. |
|---|---|
| model_weights | BYTEA8. Byte array containing the weights of the neural net. |
| model_arch | TEXT. A JSON representation of the model architecture used in training. |
An info table named <model_output_table>_info is also created, which has the following columns. There is one row per model as per the rows in the 'model_selection_table':
| mst_key | INTEGER. ID that defines a unique tuple for model architecture-compile parameters-fit parameters, as defined in the 'model_selection_table'. |
|---|---|
| model_id | INTEGER. ID that defines model in the 'model_arch_table'. |
| compile_params | Compile parameters passed to Keras. |
| fit_params | Fit parameters passed to Keras. |
| model_type | General identifier for type of model trained. Currently says 'madlib_keras'. |
| model_size | Size of the model in KB. Models are stored in 'bytea' data format which is used for binary strings in PostgreSQL type databases. |
| metrics_elapsed_time | 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. |
| metrics_type | Metric specified in the 'compile_params'. |
| training_metrics_final | Final value of the training metric after all iterations have completed. The metric reported is the one specified in the 'metrics_type' parameter. |
| training_loss_final | Final value of the training loss after all iterations have completed. |
| training_metrics | 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. |
| training_loss | 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. |
| validation_metrics_final | Final value of the validation metric after all iterations have completed. The metric reported is the one specified in the 'metrics_type' parameter. |
| validation_loss_final | Final value of the validation loss after all iterations have completed. |
| validation_metrics | 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. |
| validation_loss | 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. |
A summary table named <model>_summary is also created, which has the following columns:
| source_table | Source table used for training. |
|---|---|
| validation_table | Name of the table containing the validation dataset (if specified). |
| model | Name of the output table containing the model for each model selection tuple. |
| model_info | Name of the output table containing the model performance and other info for each model selection tuple. |
| dependent_varname | Dependent variable column from the original source table in the image preprocessing step. |
| independent_varname | Independent variables column from the original source table in the image preprocessing step. |
| model_arch_table | Name of the table containing the model architecture and (optionally) the initial model weights. |
| num_iterations | Number of iterations of training completed. |
| metrics_compute_frequency | Frequency that per-iteration metrics are computed for the training dataset and validation datasets. |
| warm_start | Indicates whether warm start used or not. |
| name | Name of the training run (free text). |
| description | Description of the training run (free text). |
| start_training_time | Timestamp for start of training. |
| end_training_time | Timestamp for end of training. |
| madlib_version | Version of MADlib used. |
| num_classes | Count of distinct classes values used. |
| class_values | Array of actual class values used. |
| dependent_vartype | Data type of the dependent variable. |
| normalizing_constant | Normalizing constant used from the image preprocessing step. |
| metrics_iters | 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. |
madlib_keras_evaluate(
model_table,
test_table,
output_table,
use_gpus
)
Arguments
TEXT. Name of the table containing the model to use for validation.
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.
| loss | Loss value on evaluation dataset. |
|---|---|
| metric | Metric value on evaluation dataset, where 'metrics_type' below identifies the type of metric. |
| metrics_type | Type of metric used that was used in the training step. |
BOOLEAN, default: FALSE (i.e., CPU). Determines whether GPUs are to be used for training the neural network. Set to TRUE to use GPUs.
madlib_keras_predict(
model_table,
test_table,
id_col,
independent_varname,
output_table,
pred_type,
use_gpus
)
Arguments
TEXT. Name of the table containing the model to use for prediction.
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.
TEXT. Name of the id column in the test data table.
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.
| id | Gives the 'id' for each prediction, corresponding to each row from the test_table. |
|---|---|
| estimated_COL_NAME | (For pred_type='response') The estimated class for classification, where COL_NAME is the name of the column to be predicted from test data. |
| prob_CLASS | (For pred_type='prob' for classification) The probability of a given class. There will be one column for each class in the training data. |
TEXT, default: 'response'. The type of output desired, where 'response' gives the actual prediction and 'prob' gives the probability value for each class.
BOOLEAN, default: FALSE (i.e., CPU). Determines whether GPUs are to be used for prediction/inference. Set to TRUE to use GPUs.
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');
Create a test/validation dataset from the training data:
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;
count ------+ 120
\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;
-[ 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
For the validation dataset:
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;
-[ 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
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()Export the model to JSON:_________________________________________________________________ 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
model1.to_json()
'{"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"}'
Define model architecture with 2 hidden layers: 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()Export the model to JSON: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
model2.to_json()
'{"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"}'
Load into model architecture table:
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
);
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;
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)
This is the name of the model architecture table that corresponds to the model selection table: SELECT * FROM mst_table_summary;
model_arch_table --------------------+ model_arch_library
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
);
View the model summary: SELECT * FROM iris_multi_model_summary;
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}
View results for each model: SELECT * FROM iris_multi_model_info ORDER BY training_metrics_final DESC, training_loss_final;
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)
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;
loss | metric | metrics_type
-------------------+-------------------+--------------
0.103803977370262 | 0.966666638851166 | {accuracy}
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;
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)Count missclassifications:
SELECT COUNT(*) FROM iris_predict JOIN iris_test USING (id) WHERE iris_predict.estimated_class_text != iris_test.class_text;
count
-------+
1
Percent missclassifications:
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;
test_accuracy_percent
-----------------------+
96.67
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
);
View the model summary: SELECT * FROM iris_multi_model_summary;
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}
View results for each model: SELECT * FROM iris_multi_model_info ORDER BY training_metrics_final DESC, training_loss_final;
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)
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;
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)
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;
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}
View results for each model: SELECT * FROM iris_multi_model_info ORDER BY training_metrics_final DESC, training_loss_final;
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)
Note that the loss and accuracy values pick up from where the previous run left off.For an introduction to deep learning foundations, including MLP and CNN, refer to [7].
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].
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.
[1] "Cerebro: Efficient and Reproducible Model Selection on Deep Learning Systems," Supun Nakandala, Yuhao Zhang, and Arun Kumar, ACM SIGMOD 2019 DEEM Workshop, https://adalabucsd.github.io/papers/2019_Cerebro_DEEM.pdf
[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 https://adalabucsd.github.io/papers/TR_2019_Cerebro.pdf
[4] https://www.tensorflow.org/
[5] "Neural Networks for Machine Learning", Lectures 6a and 6b on mini-batch gradient descent, Geoffrey Hinton with Nitish Srivastava and Kevin Swersky, http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf
[6] Deep learning section of Apache MADlib wiki https://cwiki.apache.org/confluence/display/MADLIB/Deep+Learning
[7] Deep Learning, Ian Goodfellow, Yoshua Bengio and Aaron Courville, MIT Press, 2016.
[8] Greenplum Database server configuration parameters https://gpdb.docs.pivotal.io/latest/ref_guide/config_params/guc-list.html
File madlib_keras_fit_multiple_model.sql_in documents training, evaluate and predict functions.
1.8.5