基于tensorflow的transformer模型代码

Transformer是一种非常流行的神经网络模型，用于自然语言处理任务，如机器翻译、文本摘要等。TensorFlow是一个非常流行的深度学习框架，提供了丰富的API和工具，可以方便地实现Transformer模型。

下面是一个基于TensorFlow实现Transformer模型的代码示例：

import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Dropout, LayerNormalization
from tensorflow.keras.layers import MultiHeadAttention, Embedding, Flatten
from tensorflow.keras.models import Model

def transformer_model(vocab_size, max_seq_len, num_heads, embedding_dim, dense_dim, dropout_rate):
    # Input layer
    inputs = Input(shape=(max_seq_len,), dtype=tf.int32)

    # Embedding layer
    embeddings = Embedding(input_dim=vocab_size, output_dim=embedding_dim)(inputs)

    # Positional encoding
    pos_encoding = positional_encoding(max_seq_len, embedding_dim)
    embeddings = embeddings * tf.math.sqrt(tf.cast(embedding_dim, tf.float32))
    embeddings = embeddings + pos_encoding

    # Dropout layer
    embeddings = Dropout(rate=dropout_rate)(embeddings)

    # Transformer blocks
    for i in range(num_heads):
        # Multi-head attention layer
        attn_output = MultiHeadAttention(num_heads=num_heads, key_dim=embedding_dim)(embeddings, embeddings)

        # Add & normalize layer
        attn_output = LayerNormalization(epsilon=1e-6)(embeddings + attn_output)

        # Feedforward layer
        dense_output = Dense(units=dense_dim, activation='relu')(attn_output)
        dense_output = Dense(units=embedding_dim)(dense_output)

        # Add & normalize layer
        dense_output = LayerNormalization(epsilon=1e-6)(attn_output + dense_output)

        # Dropout layer
        dense_output = Dropout(rate=dropout_rate)(dense_output)

        # Residual connection
        embeddings = dense_output + embeddings

    # Flatten layer
    flatten = Flatten()(embeddings)

    # Output layer
    outputs = Dense(units=vocab_size, activation='softmax')(flatten)

    # Model
    model = Model(inputs=inputs, outputs=outputs)

    return model

def positional_encoding(max_seq_len, embedding_dim):
    pos_encoding = np.zeros((max_seq_len, embedding_dim))
    for pos in range(max_seq_len):
        for i in range(embedding_dim):
            if i % 2 == 0:
                pos_encoding[pos, i] = np.sin(pos / (10000 ** (i / embedding_dim)))
            else:
                pos_encoding[pos, i] = np.cos(pos / (10000 ** ((i - 1) / embedding_dim)))
    return tf.constant(pos_encoding, dtype=tf.float32)

这个代码实现了一个Transformer模型，包括输入层、嵌入层、位置编码、多头注意力层、前馈层、残差连接、层归一化等组件。你可以根据自己的需求修改模型的超参数，如词汇表大小、最大序列长度、注意力头数、嵌入维度、前馈层维度、dropout率等。