scvi.external.scbasset.ScBassetModule#
- class scvi.external.scbasset.ScBassetModule(n_cells, batch_ids=None, n_filters_init=288, n_repeat_blocks_tower=6, filters_mult=1.122, n_filters_pre_bottleneck=256, n_bottleneck_layer=32, batch_norm=True, dropout=0.0, l2_reg_cell_embedding=0.0)[source]#
Bases:
BaseModuleClass
PyTorch implementation of ScBasset [Yuan and Kelley, 2022].
Original implementation in Keras: https://github.com/calico/scBasset
- Parameters:
n_cells (int) – Number of cells to predict region accessibility
batch_ids (Optional[ndarray]) – Array of (n_cells,) with batch ids for each cell
n_filters_init (int) – Number of filters for the initial conv layer
n_repeat_blocks_tower (int) – Number of layers in the convolutional tower
filters_mult (float) – Proportion by which the number of filters should inrease in the convolutional tower
n_bottleneck_layer (int) – Size of the bottleneck layer
batch_norm (bool) – Whether to apply batch norm across model layers
dropout (float) – Dropout rate across layers, by default we do not do it for convolutional layers but we do it for the dense layers
l2_reg_cell_embedding (float) – L2 regularization for the cell embedding layer
n_filters_pre_bottleneck (int) –
Attributes table#
Methods table#
|
Generative method for the model. |
|
Inference method for the model. |
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Loss function for the model. |
Attributes#
training
Methods#
generative
inference
loss