Amazon SageMaker
Developer Guide

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Tune an Object2Vec Model

Automatic model tuning, also known as hyperparameter tuning, finds the best version of a model by running many jobs that test a range of hyperparameters on your dataset. You choose the tunable hyperparameters, a range of values for each, and an objective metric. For the objective metric, you use one of the metrics that the algorithm computes. Automatic model tuning searches the chosen hyperparameters to find the combination of values that result in the model that optimizes the objective metric.

For more information about model tuning, see Automatic Model Tuning.

Metrics Computed by the Object2Vec Algorithm

The Object2Vec algorithm has both classification and regression metrics. The output_layer type determines which metric you can use for automatic model tuning.

Regressor Metrics Computed by the Object2Vec Algorithm

The algorithm reports a mean squared error regressor metric, which is computed during testing and validation. When tuning the model for regression tasks, choose this metric as the objective.

Metric Name Description Optimization Direction
test:mean_squared_error

Root Mean Square Error

Minimize

validation:mean_squared_error

Root Mean Square Error

Minimize

Classification Metrics Computed by the Object2Vec Algorithm

The Object2Vec algorithm reports accuracy and cross-entropy classification metrics, which are computed during test and validation. When tuning the model for classification tasks, choose one of these as the objective.

Metric Name Description Optimization Direction
test:accuracy

Accuracy

Maximize

test:cross_entropy

Cross-entropy

Minimize

validation:accuracy

Accuracy

Maximize

validation:cross_entropy

Cross-entropy

Minimize

Tunable Object2Vec Hyperparameters

You can tune the following hyperparameters for the Object2Vec algorithm.

Hyperparameter Name Hyperparameter Type Recommended Ranges and Values
dropout

ContinuousParameterRange

MinValue: 0.0, MaxValue: 1.0

early_stopping_patience

IntegerParameterRange

MinValue: 1, MaxValue: 5

early_stopping_tolerance

ContinuousParameterRange

MinValue: 0.001, MaxValue: 0.1

enc_dim

IntegerParameterRange

MinValue: 4, MaxValue: 4096

enc0_cnn_filter_width

IntegerParameterRange

MinValue: 1, MaxValue: 5

enc0_layers

IntegerParameterRange

MinValue: 1, MaxValue: 4

enc0_token_embedding_dim

IntegerParameterRange

MinValue: 5, MaxValue: 300

enc1_cnn_filter_width

IntegerParameterRange

MinValue: 1, MaxValue: 5

enc1_layers

IntegerParameterRange

MinValue: 1, MaxValue: 4

enc1_token_embedding_dim

IntegerParameterRange

MinValue: 5, MaxValue: 300

epochs

IntegerParameterRange

MinValue: 4, MaxValue: 20

learning_rate

ContinuousParameterRange

MinValue: 1e-6, MaxValue: 1.0

mini_batch_size

IntegerParameterRange

MinValue: 1, MaxValue: 8192

mlp_activation

CategoricalParameterRanges

[tanh, relu, linear]

mlp_dim

IntegerParameterRange

MinValue: 16, MaxValue: 1024

mlp_layers

IntegerParameterRange

MinValue: 1, MaxValue: 4

optimizer CategoricalParameterRanges

[adagrad, adam, rmsprop, sgd, adadelta]

weight_decay

ContinuousParameterRange

MinValue: 0.0, MaxValue: 1.0