torch.nn.Linear

torch.nn.Linear(*in_features*, *out_features*, *bias=True*, *device=None*, *dtype=None*)

Parameters

• in_features (int): size of each input sample
• out_features (int): size of each output sample
• bias (bool): If set to False, the layer will not learn an additive bias. Default: True

Shape

• Input
• $(*,H_{in})$ where �닓 means any number of dimensions including none
• $(*,H_{in})= in_features$
• Output
• $(*,H_{out})$ where all but the last dimension are the same shape as the input
• $(*,H_{out})=out_features$

Variables

• weight (torch.Tensor): learnable weights of the module of shape $(out_features, in_features)$
• bias
• learnable bias of the module of shape $(out_features)$
• if bias is True, the values are initialized