编写基于TensorFlow的transformer模型代码例子

当然，我很乐意回答你的问题！以下是基于TensorFlow的transformer模型代码例子：

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

# Define vocab size
vocab_size = 5000

# Define input sequence length and output sequence length
max_len = 100

# Define number of attention heads and transformer layers
num_heads = 8
num_layers = 4

# Define embedding size and feedforward size
embedding_size = 256
ffn_size = 512

# Define input token with shape (batch_size, max_len)
input_token = layers.Input(shape=(max_len,))

# Define embedding layer with shape (batch_size, max_len, embedding_size)
embedding_layer = layers.Embedding(vocab_size, embedding_size)(input_token)

# Define position embedding layer with shape (batch_size, max_len, embedding_size)
position_embedding_layer = layers.Embedding(
    input_dim=max_len, output_dim=embedding_size
)(tf.range(start=0, limit=max_len, delta=1))

# Combine embedding layer with position embedding layer
combined_embedding_layer = embedding_layer + position_embedding_layer

# Define transformer layers
for i in range(num_layers):
    # Define multi-head attention layer with input shape (batch_size, seq_length, embedding_size)
    attention_layer = layers.MultiHeadAttention(
        num_heads=num_heads, key_dim=embedding_size
    )
    # Define normalization layers for multi-head attention layer output
    norm_layer_1 = layers.LayerNormalization(
        epsilon=1e-6
    )
    add_and_norm_layer_1 = layers.Add()([combined_embedding_layer, attention_layer(combined_embedding_layer)])
    attention_output_layer = norm_layer_1(add_and_norm_layer_1)
    
    # Define feedforward layer with input shape (batch_size, seq_length, ffn_size)
    ffn_layer = keras.Sequential(
        [
            layers.Dense(ffn_size, activation="relu"),
            layers.Dense(embedding_size),
        ]
    )
    # Define normalization layers for feedforward layer output
    norm_layer_2 = layers.LayerNormalization(
        epsilon=1e-6
    )
    add_and_norm_layer_2 = layers.Add()([attention_output_layer, ffn_layer(attention_output_layer)])
    ffn_output_layer = norm_layer_2(add_and_norm_layer_2)
    # If this is not the last layer, set the combined_embedding_layer to be the current ffn_output_layer
    if i != num_layers-1:
        combined_embedding_layer = ffn_output_layer

# Define output token with shape (batch_size, max_len, vocab_size)
output_token = layers.Dense(vocab_size)(ffn_output_layer)

# Define model with input layer of shape (batch_size, max_len) and output layer of shape (batch_size, max_len, vocab_size)
model = keras.models.Model(inputs=[input_token], outputs=[output_token])

希望对你有所帮助！