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# Create the TransformerEncoder with 6 layers transformer_encoder = nn.TransformerEncoder( encoder_layer=encoder_layer, num_layers=6 ) # Dummy input: batch_size=3, sequence_length=10, feature_dim=512 src = torch.rand(10, 3, 512) # Shape: (seq_len, batch, d_model) # Forward pass output = transformer_encoder(src) print(output.shape) # Output shape: (10, 3, 512) ``` ## Key Points - The input tensor must have shape `(seq_len, batch, d_model)` — note that the batch dimension is second. - `d_model` should match the dimensionality of your embeddings or features. - You can customize `nhead`, `dim_feedforward`, and `dropout` based on your model requirements. - For better performance, consider using `LayerNorm` at the end via the `norm` parameter. ## Practical Tips - Use `torch.nn.TransformerEncoderLayer` with `batch_first=False` for standard sequence processing. - If you're working with variable-length sequences, consider padding and masking techniques. - Combine with `torch.nn.TransformerDecoder` for full Transformer architectures (e.g., in translation tasks). ## Conclusion The `torch.nn.TransformerEncoder` is a powerful tool for encoding sequential data in modern deep learning applications. By stacking multiple layers of self-attention and feed-forward networks, it enables models to understand complex patterns in text and other sequential inputs. Mastering this component is essential for building state-of-the-art NLP systems using PyTorch. > **Note**: Always refer to the official (https://pytorch.org/docs/stable/generated/torch.nn.TransformerEncoder.html) for the latest updates and advanced configurations.