Initially, each word in the input sequence is represented as a vector. This can be done using techniques like word embeddings, where each word is mapped to a dense vector representation in a high-dimensional space. These embeddings capture semantic relationships between words, allowing the model to understand the context of the input.
The input layer of the MLP consists of neurons equal to the dimensionality of the word embeddings. So, if each word is represented as a 100-dimensional vector, then the input layer of the MLP would have 100 neurons.
3. Hidden Layers
The hidden layers of the MLP are responsible for learning the patterns and relationships in the input data. Each hidden layer consists of a number of neurons, and the depth (number of hidden layers) and width (number of neurons in each layer) of these layers can vary depending on the complexity of the task and the available computational resources.
The output layer of the MLP represents the prediction of the next word in the sequence. The number of neurons in the output layer is equal to the size of the vocabulary, i.e., the total number of unique words in the training dataset. Each neuron in the output layer corresponds to a word in the vocabulary, and the output values represent the likelihood or probability of each word being the next word in the sequence.
Typically, each neuron in the hidden layers and the output layer of the MLP is associated with an activation function, such as the ReLU (Rectified Linear Unit) function for the hidden layers and the softmax function for the output layer. These activation functions introduce non-linearity into the model, allowing it to learn complex patterns in the data and produce probabilistic predictions over the vocabulary.
During the training process, the parameters of the MLP (weights and biases) are learned using an optimization algorithm such as stochastic gradient descent (SGD) or Adam. The model is trained to minimize a loss function, such as categorical cross-entropy, which measures the difference between the predicted probabilities and the true labels (the actual next words in the training data).
By training on a diverse corpus of text data and leveraging techniques like word embeddings and deep learning architectures such as MLPs, the model can learn to generate coherent and contextually relevant predictions for the next word in a sequence. This approach has various practical applications in natural language processing tasks such as autocomplete suggestions, language generation, and text completion systems.