Tune a Sequence-to-Sequence 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. You choose the objective metric from the metrics that the algorithm computes. Automatic model tuning searches the hyperparameters chosen 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.

The sequence to sequence algorithm reports three metrics that are computed during training. Choose one of them as an objective to optimize when tuning the hyperparameter values.

Metric Name	Description	Optimization Direction
validation:accuracy	Accuracy computed on the validation dataset.	Maximize
validation:bleu	Bleu score computed on the validation dataset. Because BLEU computation is expensive, you can choose to compute BLEU on a random subsample of the validation dataset to speed up the overall training process. Use the `bleu_sample_size` parameter to specify the subsample.	Maximize
validation:perplexity	Perplexity, is a loss function computed on the validation dataset. Perplexity measures the cross-entropy between an empirical sample and the distribution predicted by a model and so provides a measure of how well a model predicts the sample values, Models that are good at predicting a sample have a low perplexity.	Minimize

You can tune the following hyperparameters for the Amazon SageMaker Sequence to Sequence algorithm. The hyperparameters that have the greatest impact on sequence to sequence objective metrics are: batch_size, optimizer_type, learning_rate, num_layers_encoder, and num_layers_decoder.

Parameter Name	Parameter Type	Recommended Ranges
num_layers_encoder	IntegerParameterRange	[1-10]
num_layers_decoder	IntegerParameterRange	[1-10]
batch_size	CategoricalParameterRange	[16,32,64,128,256,512,1024,2048]
optimizer_type	CategoricalParameterRange	[‘adam’, ‘sgd’, ‘rmsprop’]
weight_init_type	CategoricalParameterRange	[‘xavier’, ‘uniform’]
weight_init_scale	ContinuousParameterRange	For the xavier type: MinValue: 2.0, MaxValue: 3.0 For the uniform type: MinValue: -1.0, MaxValue: 1.0
learning_rate	ContinuousParameterRange	MinValue: 0.00005, MaxValue: 0.2
weight_decay	ContinuousParameterRange	MinValue: 0.0, MaxValue: 0.1
momentum	ContinuousParameterRange	MinValue: 0.5, MaxValue: 0.9
clip_gradient	ContinuousParameterRange	MinValue: 1.0, MaxValue: 5.0
rnn_num_hidden	CategoricalParameterRange	Applicable only to recurrent neural networks (RNNs). [128,256,512,1024,2048]
cnn_num_hidden	CategoricalParameterRange	Applicable only to convolutional neural networks (CNNs). [128,256,512,1024,2048]
num_embed_source	IntegerParameterRange	[256-512]
num_embed_target	IntegerParameterRange	[256-512]
embed_dropout_source	ContinuousParameterRange	MinValue: 0.0, MaxValue: 0.5
embed_dropout_target	ContinuousParameterRange	MinValue: 0.0, MaxValue: 0.5
rnn_decoder_hidden_dropout	ContinuousParameterRange	MinValue: 0.0, MaxValue: 0.5
cnn_hidden_dropout	ContinuousParameterRange	MinValue: 0.0, MaxValue: 0.5
lr_scheduler_type	CategoricalParameterRange	[‘plateau_reduce’, ‘fixed_rate_inv_t’, ‘fixed_rate_inv_sqrt_t’]
plateau_reduce_lr_factor	ContinuousParameterRange	MinValue: 0.1, MaxValue: 0.5
plateau_reduce_lr_threshold	IntegerParameterRange	[1-5]
fixed_rate_lr_half_life	IntegerParameterRange	[10-30]