Attention Mechanism:

• Attention in Sequence-to-Sequence Models: Adding attention to sequence-to-sequence models allows the model to focus on different parts of the input sequence when generating output tokens.

pythonCopy codefrom tensorflow.keras import layers, models

# Define attention mechanism
def attention(hidden_states):
    score = layers.Dense(1)(hidden_states)
    attention_weights = layers.Softmax()(score)
    context_vector = layers.Dot(axes=1)([attention_weights, hidden_states])
    return context_vector

# Apply attention in a seq2seq model
decoder_lstm_outputs = layers.LSTM(256, return_sequences=True)(decoder_inputs)
context_vector = attention(decoder_lstm_outputs)
concat_output = layers.Concatenate()([context_vector, decoder_lstm_outputs])
decoder_dense = layers.Dense(output_dim, activation='softmax')(concat_output)