Reference for built-in NCF algorithm

Beta

This feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms. Pre-GA features are available "as is" and might have limited support. For more information, see the launch stage descriptions.

This page provides detailed reference information about arguments you submit to AI Platform Training when running a training job using the built-in NCF algorithm.

Versioning

The built-in NCF algorithm uses TensorFlow 2.3.

Data format arguments

The following arguments are used for data formatting:

Arguments	Details
train_dataset_path	Cloud Storage path to a TFRecord file. Required Type: String
eval_dataset_path	Cloud Storage path to a TFRecord file. Must have the same format as training_data_path. Required Type: String
job-dir	Cloud Storage path where model, checkpoints and other training artifacts reside. The following directories are created here: model: This contains the trained model Will also contain model training checkpoints Required Type: String

Hyperparameters

Hyperparameter	Details
BASIC PARAMETERS
input_meta_data_path	Google Cloud Storage path to an input metadata schema file Required Type: String
train_epochs	Number of epochs to run training for. Required Type: Int Default: 10
learning_rate	Learning rate used by the Adam optimizer. Required Type:Float Default: 0.001
ADVANCED PARAMETERS
batch_size	Batch size for training. Type: Int Default: 256
eval_batch_size	Batch size for evaluation. Type: Int Default: 256
num_factors	Embedding size of the MF model. Required Type: Int Default: 8
layers	Sizes of the hidden layers for MLP. Format as comma-separated integers. Type: String Default: 64,32,16,6
mf_regularization	Regularization factor for MF embeddings. Type: Float Default: 0.
mlp_regularization	The regularization factor for each MLP layer. Format as comma-separated floats. Must have same number of entries as layers parameter. Type: String Default: 0.,0.,0.,0.
num_neg	Number of negative instances to pair with a positive instance. Type: Int Default: 4
beta1	Beta 1 hyperparameter for the Adam optimizer. Type: Float Default: 0.9
beta2	Beta 2 hyperparameter for the Adam optimizer. Type: Float Default: 0.999
epsilon	Epsilon hyperparameter for the Adam optimizer. Type: Float Default: 0.000000001
hr_threshold	Value of HR evalutation metric at which training should stop. Type: Float Default: None
constructor_type	Strategy used to generate false negatives. Type: Enumeration Options: bisection, materialized
ml_perf	Change model behavior to match MLPerf reference implementations. Type: Boolean Default: False
output_ml_perf_compliance_logging	Output relevant logging for MLPerf compliance (only available if ml_perf is set to True.) Type: Boolean Default: False
keras_use_ctl	Use custom Keras training loop in model training. Type: Boolean Default: False