不使用Attention类，使用tensorflow.keras.layers里边的API

好的，使用TensorFlow的API来实现一个简单的Transformer模型，不使用Attention类，可以参考以下代码：

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

def create_model(input_vocab_size, output_vocab_size, max_length, d_model, num_heads, dff, dropout_rate):
    # Encoder Input
    encoder_input = Input(shape=(max_length,), name='encoder_input')
    # Decoder Input
    decoder_input = Input(shape=(max_length,), name='decoder_input')

    # Embedding layers
    encoder_embedding = Dense(d_model, name='encoder_embedding')
    decoder_embedding = Dense(d_model, name='decoder_embedding')
    # Scale embedding by sqrt(d_model)
    encoder_embedding_scaled = tf.math.sqrt(tf.cast(d_model, tf.float32)) * encoder_embedding(encoder_input)
    decoder_embedding_scaled = tf.math.sqrt(tf.cast(d_model, tf.float32)) * decoder_embedding(decoder_input)

    # Positional Encoding
    def positional_encoding(inputs):
        position = tf.range(0, max_length, dtype=tf.float32)[:, tf.newaxis]
        div_term = tf.exp(tf.range(0, d_model, 2, dtype=tf.float32) * -(tf.math.log(10000.0) / d_model))
        pos_encoding = tf.concat([tf.sin(position * div_term), tf.cos(position * div_term)], axis=-1)
        return inputs + pos_encoding

    # Add Positional Encoding
    encoder_embedding_scaled = positional_encoding(encoder_embedding_scaled)
    decoder_embedding_scaled = positional_encoding(decoder_embedding_scaled)

    # Encoder
    encoder_outputs = encoder_embedding_scaled
    for i in range(2):
        # Multi-Head Attention
        attention = tf.keras.layers.MultiHeadAttention(num_heads=num_heads, key_dim=d_model)
        attention_output = attention(encoder_outputs, encoder_outputs)
        # Add & Normalize
        attention_output = LayerNormalization(epsilon=1e-6)(encoder_outputs + attention_output)
        # Feed Forward
        ffn = tf.keras.Sequential([Dense(dff, activation='relu'), Dense(d_model)])
        ffn_output = ffn(attention_output)
        # Add & Normalize
        encoder_outputs = LayerNormalization(epsilon=1e-6)(attention_output + ffn_output)

    # Decoder
    decoder_outputs = decoder_embedding_scaled
    for i in range(2):
        # Masked Multi-Head Attention
        masked_attention = tf.keras.layers.MultiHeadAttention(num_heads=num_heads, key_dim=d_model)
        masked_attention_output = masked_attention(decoder_outputs, decoder_outputs, look_ahead_mask=True)
        # Add & Normalize
        masked_attention_output = LayerNormalization(epsilon=1e-6)(decoder_outputs + masked_attention_output)
        # Multi-Head Attention
        attention = tf.keras.layers.MultiHeadAttention(num_heads=num_heads, key_dim=d_model)
        attention_output = attention(encoder_outputs, masked_attention_output)
        # Add & Normalize
        attention_output = LayerNormalization(epsilon=1e-6)(masked_attention_output + attention_output)
        # Feed Forward
        ffn = tf.keras.Sequential([Dense(dff, activation='relu'), Dense(d_model)])
        ffn_output = ffn(attention_output)
        # Add & Normalize
        decoder_outputs = LayerNormalization(epsilon=1e-6)(attention_output + ffn_output)

    # Output layer
    output = Dense(output_vocab_size, activation='softmax', name='decoder_output')(decoder_outputs)

    # Define model
    model = Model(inputs=[encoder_input, decoder_input], outputs=output)

    return model

上述代码中，我们使用了Dense层来进行嵌入，而不是使用Embedding层。我们也没有使用Attention层，而是使用MultiHeadAttention类。同时，我们使用了LayerNormalization层来对模型进行归一化处理。在Transformer的Encoder和Decoder中，我们分别使用了两个MultiHeadAttention层和一个Feed Forward Network层，并且在每个层中都使用了LayerNormalization层。最后，我们使用Dense层来进行输出。