SequenzausgabeLayer

SequenzausgabeLayer, or sequence output layer, is a neural network component responsible for converting a sequence of hidden representations into a sequence of output tokens or distributions. It is commonly used as the final projection step in decoders of sequence-to-sequence models, providing the bridge between learned features and token predictions.

In typical implementations, the input to the SequenzausgabeLayer has shape [batch_size, sequence_length, hidden_size]. The layer applies a time-distributed linear transformation to produce [batch_size, sequence_length, vocabulary_size], optionally followed by a softmax to yield token probability distributions for each position. A shared embedding matrix can be used for weight tying, linking input representations to the output vocabulary.

Variants of the layer differ in architecture and integration. In transformer-based models, the sequence output layer is usually a linear projection onto the vocabulary followed by a softmax. In recurrent architectures, it may be a dense layer applied at each time step. Some designs reuse the same parameters as the input embedding layer to improve efficiency and consistency. Additional refinements include normalization, temperature scaling, or conditioning on attention-derived context.

Training and inference commonly involve cross-entropy loss computed per position, with strategies such as teacher forcing during training and greedy decoding or beam search during inference. The SequenzausgabeLayer is central to tasks like machine translation, language modeling, speech recognition, and other sequence generation problems.

Challenges associated with this layer include handling padding masks, balancing efficiency with model capacity, and ensuring proper weight tying when using shared embeddings.