ncem.estimators.EstimatorCVAEncem.init_model

EstimatorCVAEncem.init_model(optimizer: str = 'adam', learning_rate: float = 0.0001, latent_dim: int = 8, intermediate_dim_enc: int = 128, intermediate_dim_dec: int = 128, depth_enc: int = 1, depth_dec: int = 1, dropout_rate: float = 0.1, l2_coef: float = 0.0, l1_coef: float = 0.0, cond_depth: int = 1, cond_dim: int = 8, cond_dropout_rate: float = 0.1, cond_activation: str = 'relu', cond_l2_reg: float = 0.0, cond_use_bias: bool = False, n_eval_nodes_per_graph: int = 32, use_domain: bool = False, use_batch_norm: bool = False, scale_node_size: bool = True, transform_input: bool = False, beta: float = 0.01, max_beta: float = 1.0, pre_warm_up: int = 0, output_layer: str = 'gaussian', **kwargs)[source]

Initialize a ModelCVAEncem object.

Parameters

  • optimizer (str) – Optimizer.

  • learning_rate (float) – Learning rate.

  • latent_dim (int) – Latent dimension.

  • dropout_rate (float) – Dropout rate.

  • l2_coef (float) – l2 regularization coefficient.

  • l1_coef (float) – l1 regularization coefficient.

  • intermediate_dim_enc (int) – Encoder intermediate dimension.

  • depth_enc (int) – Encoder depth.

  • intermediate_dim_dec (int) – Decoder intermediate dimension.

  • depth_dec (int) – Decoder depth.

  • cond_depth (int) – Graph conditional depth.

  • cond_dim (int) – Graph conditional dimension.

  • cond_dropout_rate (float) – Graph conditional dropout rate.

  • cond_activation (str) – Graph conditional activation.

  • cond_l2_reg (float) – Graph conditional l2 regularization coefficient.

  • cond_use_bias (bool) – Graph conditional use bias.

  • n_eval_nodes_per_graph (int) – Number of nodes per graph.

  • use_domain (bool) – Whether to use domain information.

  • use_batch_norm (bool) – Whether to use batch normalization.

  • scale_node_size (bool) – Whether to scale output layer by node sizes.

  • transform_input (bool) – Whether to transform input.

  • beta (float) – Beta used in BetaScheduler.

  • max_beta (float) – Maximal beta used in BetaScheduler.

  • pre_warm_up (int) – Number of epochs in pre warm up.

  • output_layer (str) – Output layer.

  • kwargs – Arbitrary keyword arguments.