Sequence-to-Sequence Hyperparameters

Parameter Name	Description
`batch_size`	Mini batch size for gradient descent. Optional Valid values: positive integer Default value: 64
`beam_size`	Length of the beam for beam search. Used during training for computing `bleu` and used during inference. Optional Valid values: positive integer Default value: 5
`bleu_sample_size`	Number of instances to pick from validation dataset to decode and compute `bleu` score during training. Set to -1 to use full validation set (if `bleu` is chosen as `optimized_metric`). Optional Valid values: integer Default value: 0
`bucket_width`	Returns (source,target) buckets up to (`max_seq_len_source`, `max_seq_len_target`). The longer side of the data uses steps of `bucket_width` while the shorter side uses steps scaled down by the average target/source length ratio. If one sided reaches its maximum length before the other, width of extra buckets on that side is fixed to that side of `max_len`. Optional Valid values: positive integer Default value: 10
`bucketing_enabled`	Set to `false` to disable bucketing, unroll to maximum length. Optional Valid values: `true` or `false` Default value: `true`
`checkpoint_frequency_num_batches`	Checkpoint and evaluate every x batches. This checkpointing hyperparameter is passed to the SageMaker's seq2seq algorithm for early stopping and retrieving the best model. The algorithm's checkpointing runs locally in the algorithm's training container and is not compatible with SageMaker checkpointing. The algorithm temporarily saves checkpoints to a local path and stores the best model artifact to the model output path in S3 after the training job has stopped. Optional Valid values: positive integer Default value: 1000
`checkpoint_threshold`	Maximum number of checkpoints model is allowed to not improve in `optimized_metric` on validation dataset before training is stopped. This checkpointing hyperparameter is passed to the SageMaker's seq2seq algorithm for early stopping and retrieving the best model. The algorithm's checkpointing runs locally in the algorithm's training container and is not compatible with SageMaker checkpointing. The algorithm temporarily saves checkpoints to a local path and stores the best model artifact to the model output path in S3 after the training job has stopped. Optional Valid values: positive integer Default value: 3
`clip_gradient`	Clip absolute gradient values greater than this. Set to negative to disable. Optional Valid values: float Default value: 1
`cnn_activation_type`	The `cnn` activation type to be used. Optional Valid values: String. One of `glu`, `relu`, `softrelu`, `sigmoid`, or `tanh`. Default value: `glu`
`cnn_hidden_dropout`	Dropout probability for dropout between convolutional layers. Optional Valid values: Float. Range in [0,1]. Default value: 0
`cnn_kernel_width_decoder`	Kernel width for the `cnn` decoder. Optional Valid values: positive integer Default value: 5
`cnn_kernel_width_encoder`	Kernel width for the `cnn` encoder. Optional Valid values: positive integer Default value: 3
`cnn_num_hidden`	Number of `cnn` hidden units for encoder and decoder. Optional Valid values: positive integer Default value: 512
`decoder_type`	Decoder type. Optional Valid values: String. Either `rnn` or `cnn`. Default value: rnn
`embed_dropout_source`	Dropout probability for source side embeddings. Optional Valid values: Float. Range in [0,1]. Default value: 0
`embed_dropout_target`	Dropout probability for target side embeddings. Optional Valid values: Float. Range in [0,1]. Default value: 0
`encoder_type`	Encoder type. The `rnn` architecture is based on attention mechanism by Bahdanau et al. and cnn architecture is based on Gehring et al. Optional Valid values: String. Either `rnn` or `cnn`. Default value: `rnn`
`fixed_rate_lr_half_life`	Half life for learning rate in terms of number of checkpoints for `fixed_rate_`* schedulers. Optional Valid values: positive integer Default value: 10
`learning_rate`	Initial learning rate. Optional Valid values: float Default value: 0.0003
`loss_type`	Loss function for training. Optional Valid values: String. `cross-entropy` Default value: `cross-entropy`
`lr_scheduler_type`	Learning rate scheduler type. `plateau_reduce` means reduce the learning rate whenever `optimized_metric` on `validation_accuracy` plateaus. `inv_t` is inverse time decay. `learning_rate`/(1+`decay_rate`t) Optional* Valid values: String. One of `plateau_reduce`, `fixed_rate_inv_t`, or `fixed_rate_inv_sqrt_t`. Default value: `plateau_reduce`
`max_num_batches`	Maximum number of updates/batches to process. -1 for infinite. Optional Valid values: integer Default value: -1
`max_num_epochs`	Maximum number of epochs to pass through training data before fitting is stopped. Training continues until this number of epochs even if validation accuracy is not improving if this parameter is passed. Ignored if not passed. Optional Valid values: Positive integer and less than or equal to max_num_epochs. Default value: none
`max_seq_len_source`	Maximum length for the source sequence length. Sequences longer than this length are truncated to this length. Optional Valid values: positive integer Default value: 100
`max_seq_len_target`	Maximum length for the target sequence length. Sequences longer than this length are truncated to this length. Optional Valid values: positive integer Default value: 100
`min_num_epochs`	Minimum number of epochs the training must run before it is stopped via `early_stopping` conditions. Optional Valid values: positive integer Default value: 0
`momentum`	Momentum constant used for `sgd`. Don't pass this parameter if you are using `adam` or `rmsprop`. Optional Valid values: float Default value: none
`num_embed_source`	Embedding size for source tokens. Optional Valid values: positive integer Default value: 512
`num_embed_target`	Embedding size for target tokens. Optional Valid values: positive integer Default value: 512
`num_layers_decoder`	Number of layers for Decoder rnn or cnn. Optional Valid values: positive integer Default value: 1
`num_layers_encoder`	Number of layers for Encoder `rnn` or `cnn`. Optional Valid values: positive integer Default value: 1
`optimized_metric`	Metrics to optimize with early stopping. Optional Valid values: String. One of `perplexity`, `accuracy`, or `bleu`. Default value: `perplexity`
`optimizer_type`	Optimizer to choose from. Optional Valid values: String. One of `adam`, `sgd`, or `rmsprop`. Default value: `adam`
`plateau_reduce_lr_factor`	Factor to multiply learning rate with (for `plateau_reduce`). Optional Valid values: float Default value: 0.5
`plateau_reduce_lr_threshold`	For `plateau_reduce` scheduler, multiply learning rate with reduce factor if `optimized_metric` didn't improve for this many checkpoints. Optional Valid values: positive integer Default value: 3
`rnn_attention_in_upper_layers`	Pass the attention to upper layers of rnn, like Google NMT paper. Only applicable if more than one layer is used. Optional Valid values: boolean (`true` or `false`) Default value: `true`
`rnn_attention_num_hidden`	Number of hidden units for attention layers. defaults to `rnn_num_hidden`. Optional Valid values: positive integer Default value: `rnn_num_hidden`
`rnn_attention_type`	Attention model for encoders. `mlp` refers to concat and bilinear refers to general from the Luong et al. paper. Optional Valid values: String. One of `dot`, `fixed`, `mlp`, or `bilinear`. Default value: `mlp`
`rnn_cell_type`	Specific type of `rnn` architecture. Optional Valid values: String. Either `lstm` or `gru`. Default value: `lstm`
`rnn_decoder_state_init`	How to initialize `rnn` decoder states from encoders. Optional Valid values: String. One of `last`, `avg`, or `zero`. Default value: `last`
`rnn_first_residual_layer`	First rnn layer to have a residual connection, only applicable if number of layers in encoder or decoder is more than 1. Optional Valid values: positive integer Default value: 2
`rnn_num_hidden`	The number of rnn hidden units for encoder and decoder. This must be a multiple of 2 because the algorithm uses bi-directional Long Term Short Term Memory (LSTM) by default. Optional Valid values: positive even integer Default value: 1024
`rnn_residual_connections`	Add residual connection to stacked rnn. Number of layers should be more than 1. Optional Valid values: boolean (`true` or `false`) Default value: `false`
`rnn_decoder_hidden_dropout`	Dropout probability for hidden state that combines the context with the rnn hidden state in the decoder. Optional Valid values: Float. Range in [0,1]. Default value: 0
`training_metric`	Metrics to track on training on validation data. Optional Valid values: String. Either `perplexity` or `accuracy`. Default value: `perplexity`
`weight_decay`	Weight decay constant. Optional Valid values: float Default value: 0
`weight_init_scale`	Weight initialization scale (for `uniform` and `xavier` initialization). Optional Valid values: float Default value: 2.34
`weight_init_type`	Type of weight initialization. Optional Valid values: String. Either `uniform` or `xavier`. Default value: `xavier`
`xavier_factor_type`	Xavier factor type. Optional Valid values: String. One of `in`, `out`, or `avg`. Default value: `in`

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How It Works

Model Tuning