The CREATE MODEL statement for Wide-and-Deep models
This document describes the CREATE MODEL
statement for creating
wide-and-deep
models in BigQuery. Wide-and-deep models support
hyperparameter tuning.
You can use wide-and-deep regressor models with the
ML.PREDICT
function
to perform regression, and you can use
wide-and-deep classifier models with the ML.PREDICT
function to
perform classification. You can use
both types of wide-and-deep models with the
ML.DETECT_ANOMALIES
function
to perform anomaly detection.
For information about the supported SQL statements and functions for each model type, see End-to-end user journey for each model.
CREATE MODEL
syntax
{CREATE MODEL | CREATE MODEL IF NOT EXISTS | CREATE OR REPLACE MODEL} model_name OPTIONS(model_option_list) AS query_statement model_option_list: MODEL_TYPE = { 'DNN_LINEAR_COMBINED_CLASSIFIER' | 'DNN_LINEAR_COMBINED_REGRESSOR' } [, LEARN_RATE = float64_value | struct_array ] [, OPTIMIZER = string_value | struct_array ] [, L1_REG = { float64_value | HPARAM_RANGE(range) | HPARAM_CANDIDATES([candidates]) } ] [, L2_REG = { float64_value | HPARAM_RANGE(range) | HPARAM_CANDIDATES([candidates]) } ] [, ACTIVATION_FN = { { 'RELU' | 'RELU6' | 'CRELU' | 'ELU' | 'SELU' | 'SIGMOID' | 'TANH' } | HPARAM_CANDIDATES([candidates]) } ] [, BATCH_SIZE = { int64_value | HPARAM_RANGE(range) | HPARAM_CANDIDATES([candidates]) } ] [, DROPOUT = { float64_value | HPARAM_RANGE(range) | HPARAM_CANDIDATES([candidates]) } ] [, HIDDEN_UNITS = { int_array | HPARAM_CANDIDATES([candidates]) } ] [, INTEGRATED_GRADIENTS_NUM_STEPS = int64_value ] [, TF_VERSION = { '1.15' | '2.8.0' } ] [, AUTO_CLASS_WEIGHTS = { TRUE | FALSE } ] [, CLASS_WEIGHTS = struct_array ] [, ENABLE_GLOBAL_EXPLAIN = { TRUE | FALSE } ] [, EARLY_STOP = { TRUE | FALSE } ] [, MIN_REL_PROGRESS = float64_value ] [, INPUT_LABEL_COLS = string_array ] [, MAX_ITERATIONS = int64_value ] [, WARM_START = { TRUE | FALSE } ] [, DATA_SPLIT_METHOD = { 'AUTO_SPLIT' | 'RANDOM' | 'CUSTOM' | 'SEQ' | 'NO_SPLIT' } ] [, DATA_SPLIT_EVAL_FRACTION = float64_value ] [, DATA_SPLIT_TEST_FRACTION = float64_value ] [, DATA_SPLIT_COL = string_value ] [, NUM_TRIALS = int64_value ] [, MAX_PARALLEL_TRIALS = int64_value ] [, HPARAM_TUNING_ALGORITHM = { 'VIZIER_DEFAULT' | 'RANDOM_SEARCH' | 'GRID_SEARCH' } ] [, HPARAM_TUNING_OBJECTIVES = { 'ROC_AUC' | 'R2_SCORE' | ... } ] [, KMS_KEY_NAME = string_value ]
CREATE MODEL
Creates and trains a new model in the specified dataset. If the model name
exists, CREATE MODEL
returns an error.
CREATE MODEL IF NOT EXISTS
Creates and trains a new model only if the model doesn't exist in the specified dataset.
CREATE OR REPLACE MODEL
Creates and trains a model and replaces an existing model with the same name in the specified dataset.
model_name
The name of the model you're creating or replacing. The model name must be unique in the dataset: no other model or table can have the same name. The model name must follow the same naming rules as a BigQuery table. A model name can:
- Contain up to 1,024 characters
- Contain letters (upper or lower case), numbers, and underscores
model_name
is not case-sensitive.
If you don't have a default project configured, then you must prepend the project ID to the model name in the following format, including backticks:
`[PROJECT_ID].[DATASET].[MODEL]`
For example, `myproject.mydataset.mymodel`.
MODEL_TYPE
MODEL_TYPE = { 'DNN_LINEAR_COMBINED_CLASSIFIER' | 'DNN_LINEAR_COMBINED_REGRESSOR' }
Description
Specifies the model type. This option is required.
LEARN_RATE
Syntax
LEARN_RATE = float64_value | struct_array
Description
The initial learn rate for training.
Arguments
To specify an initial learn rate for both the linear and DNN parts of the model,
provide a FLOAT64
value. For example:
LEARN_RATE = 0.01
To specify different initial learn rates for the linear and DNN parts of the
model, provide an array of STRUCT
values that each contain one STRING
and
one FLOAT64
value. The string identifies the part of the model, and the float
specifies the initial learn rate to use for that part of the model.
For example:
LEARN_RATE = [STRUCT('dnn', 0.001), STRUCT('linear', 0.01)]
The default value is 0.001
for both the linear and DNN parts of the model.
OPTIMIZER
Syntax
OPTIMIZER = string_value | struct_array
Description
Specifies the optimizer for training the model.
Arguments
To specify an optimizer for both the linear and DNN parts of the model,
provide a STRING
value. For example:
OPTIMIZER = 'ADAGRAD'
To specify different optimizers for the linear and DNN parts of the
model, provide an array of STRUCT
values that each contain two STRING
values. The first string identifies the part of the model, and the second string
specifies the optimizer to use for that part of the model. For example:
OPTIMIZER = [STRUCT('dnn', 'ADAGRAD'), STRUCT('linear', 'SGD')]
Use one of the following values for the optimizer string:
ADAGRAD
— Implements the Adagrad algorithmADAM
— Implements the Adam algorithmFTRL
— Implements the FTRL algorithmRMSPROP
— Implements the RMSProp algorithmSGD
— Implements the gradient descent algorithm
The default value of OPTIMIZER
is
[STRUCT('dnn', 'ADAM'), STRUCT('linear', 'FTRL')]
.
L1_REG
Syntax
L1_REG = { float64_value | HPARAM_RANGE(range) | HPARAM_CANDIDATES([candidates]) }
Description
The L1 regularization strength of the OPTIMIZER
. You can only use
this option when OPTIMIZER
is set to one of the following values:
ADAGRAD
— Implements the ProximalAdagradOptimizer algorithmFTRL
— Implements the FtrlOptimizer algorithmSGD
— Implements the ProximalGradientDescentOptimizer algorithm
Arguments
If you aren't running hyperparameter tuning, then you can specify a
FLOAT64
value. The default value is 0
.
If you are running hyperparameter tuning, then you can use one of the following options:
- The
HPARAM_RANGE
keyword and twoFLOAT64
values that define the range to use for the hyperparameter. For example,L1_REG = HPARAM_RANGE(0, 5.0)
. - The
HPARAM_CANDIDATES
keyword and an array ofFLOAT64
values that provide discrete values to use for the hyperparameter. For example,L1_REG = HPARAM_CANDIDATES([0, 1.0, 3.0, 5.0])
.
When running hyperparameter tuning, the valid range is
(0, ∞)
, the default range is (0, 10.0]
, and the scale
type is LOG
.
L2_REG
Syntax
L2_REG = { float64_value | HPARAM_RANGE(range) | HPARAM_CANDIDATES([candidates]) }
Description
The L2 regularization strength of the OPTIMIZER
. You can only
use this option when OPTIMIZER
is set to one of the following values:
ADAGRAD
— Implements the ProximalAdagradOptimizer algorithmFTRL
— Implements the FtrlOptimizer algorithmSGD
— Implements the ProximalGradientDescentOptimizer algorithm
Arguments
If you aren't running hyperparameter tuning, then you can specify a
FLOAT64
value. The default value is 0
.
If you are running hyperparameter tuning, then you can use one of the following options:
- The
HPARAM_RANGE
keyword and twoFLOAT64
values that define the range to use for the hyperparameter. For example,L2_REG = HPARAM_RANGE(1.5, 5.0)
. - The
HPARAM_CANDIDATES
keyword and an array ofFLOAT64
values that provide discrete values to use for the hyperparameter. For example,L2_REG = HPARAM_CANDIDATES([0, 1.0, 3.0, 5.0])
.
When running hyperparameter tuning, the valid range is (0, ∞)
, the
default range is (0, 10.0]
, and the scale type is LOG
.
ACTIVATION_FN
Syntax
ACTIVATION_FN = { { 'RELU' | 'RELU6' | 'CRELU' | 'ELU' | 'SELU' | 'SIGMOID' | 'TANH' } | HPARAM_CANDIDATES([candidates]) }
Description
The activation function of the neural network.
Arguments
This option accepts the following values:
RELU
— Rectified linear. This is the default.RELU6
— Rectified linear 6CRELU
— Concatenated ReLUELU
— Exponential linearSELU
— Scaled exponential linearSIGMOID
— Sigmoid activationTANH
— Tanh activation
If you are running hyperparameter training, then you can provide more than one
value for this option by using HPARAM_CANDIDATES
and specifying an array.
For example, ACTIVATION_FN = HPARAM_CANDIDATES(['RELU', 'RELU6', 'TANH'])
.
BATCH_SIZE
Syntax
BATCH_SIZE = { int64_value | HPARAM_RANGE(range) | HPARAM_CANDIDATES([candidates]) }
Description
The mini batch size of samples that are fed to the neural network.
Arguments
If you aren't running hyperparameter tuning, specify an INT64
value that
is positive and is less than or equal to 8192
. The default value is 32
or the number of samples, whichever is smaller.
If you are running hyperparameter tuning, use one of the following options:
- The
HPARAM_RANGE
keyword and twoFLOAT64
values that define the range to use for the hyperparameter. For example,BATCH_SIZE = HPARAM_RANGE(16, 64)
. - The
HPARAM_CANDIDATES
keyword and an array ofFLOAT64
values that provide discrete values to use for the hyperparameter. For example,BATCH_SIZE = HPARAM_CANDIDATES([32, 64, 256, 1024])
.
When running hyperparameter tuning, the valid range is (0, ∞)
,
the default range is [16, 1024]
, and the scale type is LOG
.
DROPOUT
Syntax
DROPOUT = { float64_value | HPARAM_RANGE(range) | HPARAM_CANDIDATES([candidates]) }
Description
The dropout rate of units in the neural network.
Arguments
If you aren't running hyperparameter tuning, then you can specify a FLOAT64
value that is positive and is less than or equal to 1.0
. The default value is
0
.
If you are running hyperparameter tuning, then you must use one of the following options:
- The
HPARAM_RANGE
keyword and twoFLOAT64
values that define the range to use for the hyperparameter. For example,DROPOUT = HPARAM_RANGE(0, 0.6)
. - The
HPARAM_CANDIDATES
keyword and an array ofFLOAT64
values that provide discrete values to use for the hyperparameter. For example,DROPOUT = HPARAM_CANDIDATES([0.1, 0.3, 0.6])
.
When running hyperparameter tuning, the valid range is [0, 1.0)
, the
default range is [0, 0.8]
, and the scale type is LINEAR
.
HIDDEN_UNITS
Syntax
HIDDEN_UNITS = { int_array | HPARAM_CANDIDATES([candidates]) }
Description
The hidden layers of the neural network.
Arguments
An array of integers that represents the architecture of the hidden layers. If
not specified, BigQuery ML applies a single hidden layer that
contains no more than 128 units. The number of units is calculated as [min(128,
num_samples / (10 * (num_input_units + num_output_units)))]
. The upper bound of
the rule ensures that the model isn't over fitting.
The number in the middle of the array defines the shape of the latent space. For
example, hidden_units=[128, 64, 4, 64, 128]
defines a four-dimensional latent
space.
The number of layers in hidden_units
must be odd, and we recommend that the
sequence be symmetrical.
The following example defines a model architecture that uses three hidden layers with 256, 128, and 64 nodes, respectively.
HIDDEN_UNITS = [256, 128, 64]
If you are running hyperparameter tuning, then you must use the
HPARAM_CANDIDATES
keyword and specify an array in the form
ARRAY<STRUCT<ARRAY<INT64>>>
to provide discrete values to use for the
hyperparameter. Each struct value in the outer array represents a candidate
neural architecture. The array of INT64
values in each struct represents a
hidden layer.
The following example represents a neural architecture search with three candidates, which include a single layer of 8 neurons, two layers of neurons with 8 and 16 in sequence, and three layers of neurons with 16, 32 and 64 in sequence, respectively.
hidden_units=hparam_candidates([struct([8]), struct([8, 16]), struct([16, 32, 64])])
The valid range for the INT64
arrays is [1, ∞)
.
INTEGRATED_GRADIENTS_NUM_STEPS
Syntax
INTEGRATED_GRADIENTS_NUM_STEPS = int64_value
Description
Specifies the number of steps to sample between the example being explained and its baseline for approximating the integral when using integrated gradients attribution methods.
Arguments
An INT64
value. The default value is 50
.
You can only set this option if ENABLE_GLOBAL_EXPLAIN
is TRUE
.
TF_VERSION
Syntax
TF_VERSION = { '1.15' | '2.8.0' }
Description
Specifies the TensorFlow version for model training. The default
value is 1.15
.
Set TF_VERSION
to 2.8.0
to use TensorFlow2 with the Keras API.
AUTO_CLASS_WEIGHTS
Syntax
AUTO_CLASS_WEIGHTS = { TRUE | FALSE }
Description
Determines whether to balance class labels by using weights for each class in inverse proportion to the frequency of that class.
Only use this option with classifier models.
By default, the training data used to create the model is unweighted. If the labels in the training data are imbalanced, the model might learn to predict the most popular class of labels more heavily, which you might not want.
To balance every class, set this option to TRUE
. Balance is
accomplished using the following formula:
total_input_rows / (input_rows_for_class_n * number_of_unique_classes)
Arguments
A BOOL
value. The default value is FALSE
.
CLASS_WEIGHTS
Syntax
CLASS_WEIGHTS = struct_array
Description
The weights to use for each class label. You can't specify this option if
AUTO_CLASS_WEIGHTS
is TRUE
.
Arguments
An ARRAY
of STRUCT
values. Each STRUCT
contains a
STRING
value that specifies the class label and a FLOAT64
value that
specifies the weight for that class label. A weight must be present for every
class label. The weights are not required to add up to 1.
A CLASS_WEIGHTS
value might look like the following example:
CLASS_WEIGHTS = [STRUCT('example_label', .2)]
ENABLE_GLOBAL_EXPLAIN
Syntax
ENABLE_GLOBAL_EXPLAIN = { TRUE | FALSE }
Description
Determines whether to compute global explanations by using explainable AI to evaluate the importance of global features to the model.
Global explanations are computed when you create the model. This option
must be TRUE
if you want to use the
ML.GLOBAL_EXPLAIN
function
to retrieve the global explanations after the model is created.
Arguments
A BOOL
value. The default value is FALSE
.
EARLY_STOP
Syntax
EARLY_STOP = { TRUE | FALSE }
Description
Determines whether training should stop after the first iteration in which the
relative loss improvement is less than the value specified for
MIN_REL_PROGRESS
.
Arguments
A BOOL
value. The default value is TRUE
.
MIN_REL_PROGRESS
Syntax
MIN_REL_PROGRESS = float64_value
Description
The minimum relative loss improvement that is necessary to continue training
when EARLY_STOP
is set to TRUE
. For example, a value of
0.01
specifies that each iteration must reduce the loss by 1% for training
to continue.
Arguments
A FLOAT64
value. The default value is 0.01
.
INPUT_LABEL_COLS
Syntax
INPUT_LABEL_COLS = string_array
Description
The name of the label column in the training data.
Arguments
A one-element ARRAY
of string values. Defaults to label
.
MAX_ITERATIONS
Syntax
MAX_ITERATIONS = int64_value
Description
The maximum number of training iterations, where one iteration represents a single pass of the entire training data.
Arguments
An INT64
value. The default value is 20
.
WARM_START
Syntax
WARM_START = { TRUE | FALSE }
Description
Determines whether to train a model with new training data, new model options, or both. Unless you explicitly override them, the initial options used to train the model are used for the warm start run.
In a warm start run, the iteration numbers are reset to start from zero. Use
the training run or iteration information returned by the
ML.TRAINING_INFO
function
to distinguish the warm start run from the original run.
In a warm start, the values of the MODEL_TYPE
, LABELS
, and HIDDEN_UNITS
options, and the training data schema, must remain the same as they were in
previous training job.
Arguments
A BOOL
value. The default value is FALSE
.
DATA_SPLIT_METHOD
Syntax
DATA_SPLIT_METHOD = { 'AUTO_SPLIT' | 'RANDOM' | 'CUSTOM' | 'SEQ' | 'NO_SPLIT' }
Description
The method used to split input data into training, evaluation, and, if you are running hyperparameter tuning, test data sets. Training data is used to train the model. Evaluation data is used to avoid overfitting by using early stopping. Test data is used to test the hyperparameter tuning trial and record its metrics in the model.
The percentage sizes of the data sets produced by the various arguments for this option are approximate. Larger input data sets come closer to the percentages described than smaller input data sets do.
You can see the model's data split information in the following ways:
- The data split method and percentage are shown in the Training Options section of the model's Details page on the BigQuery page of the Google Cloud console.
- Links to temporary tables that contain the split data are available in the
Model Details section of the model's Details page on the
BigQuery of the Google Cloud console. You can also return
this information from the
DataSplitResult
field in the BigQuery API. These tables are saved for 48 hours. If you need this information for more than 48 hours, then you should export this data or copy it to permanent tables.
Arguments
This option accepts the following values:
*AUTO_SPLIT
: This is the default value. This option splits the data as
follows:
- If there are fewer than 500 rows in the input data, then all rows are used as training data.
If you aren't running hyperparameter tuning, then data is randomized and split as follows:
- If there are between 500 and 50,000 rows in the input data, then 20% of the data is used as evaluation data and 80% is used as training data.
- If there are more than 50,000 rows, then 10,000 rows are used as evaluation data and the remaining rows are used as training data.
If you are running hyperparameter tuning and there are more than 500 rows in the input data, then the data is randomized and split as follows:
- 10% of the data is used as evaluation data
- 10% is used as test data
80% is used as training data
For more information, see Data split.
RANDOM
: Data is randomized before being split into sets. You can use this option with theDATA_SPLIT_EVAL_FRACTION
andDATA_SPLIT_TEST_FRACTION
options to customize the data split. If you don't specify either of those options, data is split in the same way as for theAUTO_SPLIT
option.A random split is deterministic: different training runs produce the same split results if the same underlying training data is used.
CUSTOM
: Split data using the value in a specified column:- If you aren't running hyperparameter tuning, then you must provide the
name of a column of type
BOOL
. Rows with a value ofTRUE
orNULL
are used as evaluation data, rows with a value ofFALSE
are used as training data. - If you are running hyperparameter tuning, then you must provide the name
of a column of type
STRING
. Rows with a value ofTRAIN
are used as training data, rows with a value ofEVAL
are used as evaluation data, and rows with a value ofTEST
are used as test data.
Use the
DATA_SPLIT_COL
option to identify the column that contains the data split information.- If you aren't running hyperparameter tuning, then you must provide the
name of a column of type
SEQ
: Split data sequentially by using the value in a specified column of one of the following types:NUMERIC
BIGNUMERIC
STRING
TIMESTAMP
The data is sorted smallest to largest based on the specified column.
When you aren't running hyperparameter tuning, the first n rows are used as evaluation data, where n is the value specified for
DATA_SPLIT_EVAL_FRACTION
. The remaining rows are used as training data.When you are running hyperparameter tuning, the first n rows are used as evaluation data, where n is the value specified for
DATA_SPLIT_EVAL_FRACTION
. The next m rows are used as test data, where m is the value specified forDATA_SPLIT_TEST_FRACTION
. The remaining rows are used as training data.All rows with split values smaller than the threshold are used as training data. The remaining rows, including
NULLs
, are used as evaluation data.Use the
DATA_SPLIT_COL
option to identify the column that contains the data split information.NO_SPLIT
: No data split; all input data is used as training data.
DATA_SPLIT_EVAL_FRACTION
Syntax
DATA_SPLIT_EVAL_FRACTION = float64_value
Description
The fraction of the data to use as evaluation data. Use when you are
specifying RANDOM
or SEQ
as the value for the
DATA_SPLIT_METHOD
option.
If you are running hyperparameter tuning and you specify a value for this
option, you must also specify a value for
DATA_SPLIT_TEST_FRACTION
. In this
case, the training dataset is
1 - eval_fraction - test_fraction
. For
example, if you specify 20.00
for DATA_SPLIT_EVAL_FRACTION
and 8.0
for
DATA_SPLIT_TEST_FRACTION
, your training dataset is 72% of the input data.
Arguments
A FLOAT64
value. The default is 0.2
. The service maintains the accuracy of
the input value to two decimal places.
DATA_SPLIT_TEST_FRACTION
Syntax
DATA_SPLIT_TEST_FRACTION = float64_value
Description
The fraction of the data to use as test data. Use this option when you are
running hyperparameter tuning and specifying either RANDOM
or SEQ
as
value for the DATA_SPLIT_METHOD
option.
If you specify a value for this option, you must also specify a value for
DATA_SPLIT_EVAL_FRACTION
. In this case,
the training dataset is
1 - eval_fraction - test_fraction
.
For example, if you specify 20.00
for DATA_SPLIT_EVAL_FRACTION
and 8.0
for
DATA_SPLIT_TEST_FRACTION
, your training dataset is 72% of the input data.
Arguments
A FLOAT64
value. The default is 0
. The service maintains the accuracy of
the input value to two decimal places.
DATA_SPLIT_COL
Syntax
DATA_SPLIT_COL = string_value
Description
The name of the column to use to sort input data into the training,
evaluation, or test set. Use when you are specifying CUSTOM
or SEQ
as the
value for the DATA_SPLIT_METHOD
option:
- If you aren't running hyperparameter tuning and you are specifying
SEQ
as the value forDATA_SPLIT_METHOD
, then the data is first sorted smallest to largest based on the specified column. The last n rows are used as evaluation data, where n is the value specified forDATA_SPLIT_EVAL_FRACTION
. The remaining rows are used as training data. - If you aren't running hyperparameter tuning and you are specifying
CUSTOM
as the value forDATA_SPLIT_METHOD
, then you must provide the name of a column of typeBOOL
. Rows with a value ofTRUE
orNULL
are used as evaluation data, rows with a value ofFALSE
are used as training data. - If you are running hyperparameter tuning and you are specifying
SEQ
as the value forDATA_SPLIT_METHOD
, then the data is first sorted smallest to largest based on the specified column. The last n rows are used as evaluation data, where n is the value specified forDATA_SPLIT_EVAL_FRACTION
. The next m rows are used as test data, where m is the value specified forDATA_SPLIT_TEST_FRACTION
. The remaining rows are used as training data. - If you are running hyperparameter tuning and you are specifying
CUSTOM
as the value forDATA_SPLIT_METHOD
, then you must provide the name of a column of typeSTRING
. Rows with a value ofTRAIN
are used as training data, rows with a value ofEVAL
are used as evaluation data, and rows with a value ofTEST
are used as test data.
The column you specify for DATA_SPLIT_COL
can't be used as a feature or
label, and is excluded from features automatically.
Arguments
A STRING
value.
NUM_TRIALS
Syntax
NUM_TRIALS = int64_value
Description
The maximum number of submodels to train. The tuning stops when NUM_TRIALS
submodels are trained, or when the hyperparameter search space is exhausted.
You must specify this option in order to use hyperparameter tuning.
Arguments
An INT64
value between 1
and 100
, inclusive.
MAX_PARALLEL_TRIALS
Syntax
MAX_PARALLEL_TRIALS = int64_value
Description
The maximum number of trials to run at the same time. If you specify a value
for this option, you must also specify a value for
NUM_TRIALS
.
Arguments
An INT64
value between 1
and 5
, inclusive. The default value is 1
.
HPARAM_TUNING_ALGORITHM
Syntax
HPARAM_TUNING_ALGORITHM = { 'VIZIER_DEFAULT' | 'RANDOM_SEARCH' | 'GRID_SEARCH' }
Description
The algorithm used to tune the hyperparameters. If you specify a value
for this option, you must also specify a value for NUM_TRIALS
.
Arguments
Specify one of the following values:
VIZIER_DEFAULT
: Use the default algorithm in Vertex AI Vizier to tune hyperparameters. This algorithm is the most powerful algorithm of those offered. It performs a mixture of advanced search algorithms, including Bayesian optimization with Gaussian processes. It also uses transfer learning to take advantage of previously tuned models. This is the default, and also the recommended approach.RANDOM_SEARCH
: Use random search to explore the search space.GRID_SEARCH
: Use grid search to explore the search space. You can only use this algorithm when every hyperparameter's search space is discrete.
HPARAM_TUNING_OBJECTIVES
Syntax
For DNN_LINEAR_COMBINED_CLASSIFIER
models:
HPARAM_TUNING_OBJECTIVES = { 'PRECISION' | 'RECALL' | 'ACCURACY' | 'F1_SCORE' | 'LOG_LOSS' | 'ROC_AUC' }
For DNN_LINEAR_COMBINED_REGRESSOR
models:
HPARAM_TUNING_OBJECTIVES = { 'MEAN_ABSOLUTE_ERROR' | 'MEAN_SQUARED_ERROR' | 'MEAN_SQUARED_LOG_ERROR' | 'MEDIAN_ABSOLUTE_ERROR' | 'R2_SCORE' | 'EXPLAINED_VARIANCE' }
Description
The hyperparameter tuning objective for the model; only one objective is
supported. If you specify a value for this option, you must also specify a
value for NUM_TRIALS
.
Arguments
The possible objectives are a subset of the
model evaluation metrics
for the model type. If you aren't running hyperparameter tuning, or if you are
and you don't specify an objective, then the default objective
is used. For DNN_LINEAR_COMBINED_CLASSIFIER
models, the default is ROC_AUC
.
For DNN_LINEAR_COMBINED_REGRESSOR
models, the default is R2_SCORE
.
KMS_KEY_NAME
Syntax
KMS_KEY_NAME = string_value
Description
The Cloud Key Management Service customer-managed encryption key (CMEK) to use to encrypt the model.
Arguments
A STRING
value containing the fully-qualified name of the CMEK. For example,
'projects/my_project/locations/my_location/keyRings/my_ring/cryptoKeys/my_key'
Internal parameter defaults
BigQuery ML uses the following default values when building models:
loss_reduction = losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE
batch_norm = False
query_statement
The AS query_statement
clause specifies the
GoogleSQL query used to generate the training data. See the
GoogleSQL query syntax
page for the supported SQL syntax of the query_statement
clause.
All columns referenced by the query_statement
are used as inputs to the model
except for the columns included in INPUT_LABEL_COLS
and
DATA_SPLIT_COL
.
Example
The following example trains a Wide-and-Deep classifier model against 'mytable'
with 'mylabel'
as the label column.
CREATE MODEL `project_id.mydataset.mymodel` OPTIONS(MODEL_TYPE='DNN_LINEAR_COMBINED_CLASSIFIER', ACTIVATION_FN = 'RELU', BATCH_SIZE = 16, DROPOUT = 0.1, EARLY_STOP = FALSE, HIDDEN_UNITS = [128, 128, 128], INPUT_LABEL_COLS = ['mylabel'], LEARN_RATE=0.001, MAX_ITERATIONS = 50, OPTIMIZER = 'ADAGRAD') AS SELECT * FROM `project_id.mydataset.mytable`;
Supported regions
Training Wide-and-Deep models is not supported in all BigQuery ML regions. For a complete list of supported regions and multi-regions, see BigQuery ML locations page.