TransformerEncoderLayer

class torch.nn.TransformerEncoderLayer(d_model, nhead, dim_feedforward=2048, dropout=0.1, activation='relu') [source]

TransformerEncoderLayer is made up of self-attn and feedforward network. This standard encoder layer is based on the paper “Attention Is All You Need”. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems, pages 6000-6010. Users may modify or implement in a different way during application.

Parameters

• d_model – the number of expected features in the input (required).
• nhead – the number of heads in the multiheadattention models (required).
• dim_feedforward – the dimension of the feedforward network model (default=2048).
• dropout – the dropout value (default=0.1).
• activation – the activation function of intermediate layer, relu or gelu (default=relu).

Examples::

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

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

Pass the input through the encoder layer.

Parameters

• src – the sequence to the encoder layer (required).
• src_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.