scvi.nn.FCLayers#
- class scvi.nn.FCLayers(n_in, n_out, n_cat_list=None, n_layers=1, n_hidden=128, dropout_rate=0.1, use_batch_norm=True, use_layer_norm=False, use_activation=True, bias=True, inject_covariates=True, activation_fn=<class 'torch.nn.modules.activation.ReLU'>)[source]#
Bases:
ModuleA helper class to build fully-connected layers for a neural network.
- Parameters:
- n_in :
int The dimensionality of the input
- n_out :
int The dimensionality of the output
- n_cat_list :
Iterable[int] |NoneOptional[Iterable[int]] (default:None) A list containing, for each category of interest, the number of categories. Each category will be included using a one-hot encoding.
- n_layers :
int(default:1) The number of fully-connected hidden layers
- n_hidden :
int(default:128) The number of nodes per hidden layer
- dropout_rate :
float(default:0.1) Dropout rate to apply to each of the hidden layers
- use_batch_norm :
bool(default:True) Whether to have BatchNorm layers or not
- use_layer_norm :
bool(default:False) Whether to have LayerNorm layers or not
- use_activation :
bool(default:True) Whether to have layer activation or not
- bias :
bool(default:True) Whether to learn bias in linear layers or not
- inject_covariates :
bool(default:True) Whether to inject covariates in each layer, or just the first (default).
- activation_fn :
Module(default:<class 'torch.nn.modules.activation.ReLU'>) Which activation function to use
- n_in :
Attributes table#
Methods table#
|
Forward computation on |
|
Helper to determine if covariates should be injected. |
|
Attributes#
T_destination#
alias of TypeVar(‘T_destination’, bound=Dict[str, Any])
.. autoattribute:: FCLayers.T_destination
dump_patches
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
training#
Methods#
forward#
- FCLayers.forward(x, *cat_list)[source]#
Forward computation on
x.- Parameters:
- x : torch.Tensor
tensor of values with shape
(n_in,)- cat_list :
int list of category membership(s) for this sample
- x
- Returns:
py:class:torch.Tensor tensor of shape
(n_out,)