Transformer Neural Networks
Transformer Neural Networks are non-recurrent models used for processing sequential data such as text. There is also research and development under way to apply them to additional areas such as image recognition.
Key aspects of Transformers include:
Scope of Token Relations - using a recurrent mechanism, one token, such as a word, can be related to only a small number of other elements; attention mechanisms can relate an individual token to all other tokens in even a very large collection of tokens
Accuracy - the ability to relate all tokens in a grouping to all other tokens leads to higher accuracy
Training Generalization - models can be trained on generalized applications and then training enhanced for specific applications
Training Efficiency - parallel processing can be used at a large scale to enhance training efficiency and speed
Attention Mechanisms - play an important role
An Example of Transformer Generated Text
ChatGPT generates text based on text input. Below is an example of ChatGPT, a Transformer, explaining Transformers:
Question:
write a page on how transformer neural networks function
Answer:
A transformer neural network is a type of deep learning architecture that is commonly used in natural language processing tasks, such as language translation and text summarization. The transformer was introduced in a 2017 paper by Google researchers, "Attention Is All You Need."
The key innovation of the transformer is the use of self-attention mechanisms, which allow the model to weigh the importance of different parts of the input when making predictions. This is in contrast to traditional recurrent neural networks (RNNs), which process the input sequentially and maintain an internal hidden state.
The transformer model consists of an encoder and a decoder, both of which are made up of multiple layers of self-attention and feed-forward neural networks. The encoder takes in the input sequence and produces a set of "key," "value," and "query" vectors for each position in the sequence. These vectors are then used by the decoder to generate the output sequence.
The self-attention mechanism works by computing a dot product between the query vector and the key vector for each position in the input sequence. The dot product is then scaled by the square root of the dimension of the key vector, and a softmax function is applied to the resulting values to obtain the attention weights. These weights indicate the importance of each position in the input when making the prediction for the current position in the output sequence.
In addition to the self-attention mechanism, the transformer also includes a feed-forward neural network with a rectified linear unit (ReLU) activation function, which is applied to each position in the sequence independently. This allows the model to learn non-linear relationships between the input and output.
Finally, the transformer uses multi-head attention, which allows the model to attend to different parts of the input at different positions in the output sequence. This helps the model to better capture the dependencies between the input and output.
Overall, the transformer neural network is a powerful deep learning architecture that has shown to be very effective in a wide range of natural language processing tasks. The self-attention mechanism allows the model to weigh the importance of different parts of the input, and the feed-forward neural network allows the model to learn non-linear relationships between the input and output. The multi-head attention mechanism further improves the model's ability to capture dependencies between the input and output.
Transformer Architecture Example
The diagram below depicts a Transformer developed by Google and the University of Toronto:
Inputs - such as text, documents, corpus of words
Embedding - such as Word2vec, convert words to numbers using using models trained on a corpus of text
Position Encoding - add encoding for the position of words in a text to the encoding for the word itself
Encoder - stack of identical layers allowing parallel processing that map an input sequence of symbol representations (x1, ..., xn) to a sequence
of representations (z1, ..., zn)
Decoder - stack of identical layers allowing parallel processing that use the representations (z1, ..., zn), generate an output sequence (y1, ..., ym) of symbols one element at a time; the Decoder outputs logit floating point numbers with values from minus infinity to plus infinity
Multi-Head Attention - relates tokens, such as words, to each other regardless of their distance between one another in a group, such as a document or group of documents
Masking - indicates which elements of a matrix or vector should and should not be used; used to mask out future positions in the Decoder
Add - performs matrix addition
Norm - layer normalization; also see Normalization
Feed Forward - feed forward neural network
Outputs Shifted Right - to process time movement
Softmax - is a function that adjusts values to a range between zero and one
Output Probabilities - the probability (between 0 and 1) that an output is correct