netmap.model.train_model.create_model_zoo

netmap.model.train_model.create_model_zoo(data_tensor, n_models=10, n_epochs=10000, model_type='ZINBAutoencoder', dropout_rate=0.1, latent_dim=8, hidden_dim=[64])

Creates a set of Autoencoders of the data using the speicified architecture. The architecture of the encoder can be specified using the hidden_dim parameter, the decoder architecture is mirrored. Early stopping is used by default.

Parameters:

  • data_tensor (torch.tensor) – The raw gene expression data

  • n_models (int, optional) – The number of models to compute. Defaults to 10.

  • n_epochs (int, optional) – Maximum number of epochs, if early stopping is not triggered. Defaults to 10000. Use

  • model_type (str, optional) – Model type, one of [ZINBAutoencoder, NegativeBinomialAutoencoder] Defaults to ‘ZINBAutoencoder’.

  • dropout_rate (float, optional) – Dropout rate used during training. Defaults to 0.02.

  • latent_dim (int, optional) – Number of neurons in the latent dimension. Defaults to 8.

  • hidden_dim (list, optional) – Architecture specification, list of ints. Defaults to [128].

Returns:

The list of trained models.

Return type:

Model )list)