scvi.module.Classifier

class scvi.module.Classifier(n_input, n_hidden=128, n_labels=5, n_layers=1, dropout_rate=0.1, logits=False, use_batch_norm=True, use_layer_norm=False, activation_fn=<class 'torch.nn.modules.activation.ReLU'>, **kwargs)

Bases: Module, SupervisedModuleClass

Basic fully-connected NN classifier.

Parameters:

• n_input (int) – Number of input dimensions

• n_hidden (int (default: 128)) – Number of nodes in hidden layer(s). If 0, the classifier only consists of a single linear layer.

• n_labels (int (default: 5)) – Numput of outputs dimensions

• n_layers (int (default: 1)) – Number of hidden layers. If 0, the classifier only consists of a single linear layer.

• dropout_rate (float (default: 0.1)) – dropout_rate for nodes

• logits (bool (default: False)) – Return logits or not

• use_batch_norm (bool (default: True)) – Whether to use batch norm in layers

• use_layer_norm (bool (default: False)) – Whether to use layer norm in layers

• activation_fn (Module (default: <class 'torch.nn.modules.activation.ReLU'>)) – Valid activation function from torch.nn

• **kwargs – Keyword arguments passed into FCLayers.

Attributes table

training

Methods table

forward(x)

Forward computation.

Attributes

Classifier.training: bool

Methods

Classifier.forward(x)

Forward computation.