用python 写一个用TensorFlow 实现的trasfomer

下面是一个使用 TensorFlow 实现 Transformer 模型的代码示例：

import tensorflow as tf

class Transformer(tf.keras.Model):
    def __init__(self, num_layers, d_model, num_heads, dff, input_vocab_size, 
                 target_vocab_size, pe_input, pe_target, rate=0.1):
        super(Transformer, self).__init__()

        self.encoder = Encoder(num_layers, d_model, num_heads, dff, 
                               input_vocab_size, pe_input, rate)

        self.decoder = Decoder(num_layers, d_model, num_heads, dff, 
                               target_vocab_size, pe_target, rate)

        self.final_layer = tf.keras.layers.Dense(target_vocab_size)
    
    def call(self, inp, tar, training, enc_padding_mask, 
             look_ahead_mask, dec_padding_mask):

        enc_output = self.encoder(inp, training, enc_padding_mask)  # (batch_size, inp_seq_len, d_model)
        
        dec_output, attention_weights = self.decoder(
            tar, enc_output, training, look_ahead_mask, dec_padding_mask)
        
        final_output = self.final_layer(dec_output)  # (batch_size, tar_seq_len, target_vocab_size)
        
        return final_output, attention_weights

class Encoder(tf.keras.layers.Layer):
    def __init__(self, num_layers, d_model, num_heads, dff, input_vocab_size, 
                 maximum_position_encoding, rate=0.1):
        super(Encoder, self).__init__()

        self.d_model = d_model
        self.num_layers = num_layers
        
        self.embedding = tf.keras.layers.Embedding(input_vocab_size, d_model)
        self.pos_encoding = positional_encoding(maximum_position_encoding, 
                                                 self.d_model)
        
        self.enc_layers = [EncoderLayer(d_model, num_heads, dff, rate) 
                           for _ in range(num_layers)]
  
        self.dropout = tf.keras.layers.Dropout(rate)
        
    def call(self, x, training, mask):

        seq_len = tf.shape(x)[1]
        
        x = self.embedding(x)  # (batch_size, input_seq_len, d_model)
        x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))
        x += self.pos_encoding[:, :seq_len, :]

        x = self.dropout(x, training=training)