TransformerEncoder

class torch.nn.TransformerEncoder(encoder_layer, num_layers, norm=None) [source]

TransformerEncoder is a stack of N encoder layers

Parameters

• encoder_layer – an instance of the TransformerEncoderLayer() class (required).
• num_layers – the number of sub-encoder-layers in the encoder (required).
• norm – the layer normalization component (optional).

Examples::

>>> encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8)
>>> transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=6)
>>> src = torch.rand(10, 32, 512)
>>> out = transformer_encoder(src)

forward(src: torch.Tensor, mask: Optional[torch.Tensor] = None, src_key_padding_mask: Optional[torch.Tensor] = None) → torch.Tensor [source]

Pass the input through the encoder layers in turn.

Parameters

• src – the sequence to the encoder (required).
• mask – the mask for the src sequence (optional).
• src_key_padding_mask – the mask for the src keys per batch (optional).

Shape:

see the docs in Transformer class.