TransformerEncoder

class torch.nn.TransformerEncoder(encoder_layer, num_layers, norm=None, enable_nested_tensor=False)

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).

• enable_nested_tensor – if True, input will automatically convert to nested tensor (and convert back on output). This will improve the overall performance of TransformerEncoder when padding rate is high. Default: False (disabled).

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, mask=None, src_key_padding_mask=None)

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.