用tensorflow写 transformer代码

好的，下面是使用TensorFlow 2.0实现Transformer的代码示例，主要使用了tf.keras.layers和tf.data来构建和训练模型：

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

# 定义超参数
max_seq_len = 50 # 最大序列长度
vocab_size = 10000 # 词汇表大小
embedding_dim = 128 # 词嵌入维度
num_heads = 8 # 多头注意力头数
num_encoder_layers = 2 # 编码器层数
num_decoder_layers = 2 # 解码器层数
dff = 512 # 前馈网络隐藏层维度
dropout_rate = 0.1 # dropout率

# 定义输入层
encoder_inputs = Input(shape=(max_seq_len,), name='encoder_inputs')
decoder_inputs = Input(shape=(max_seq_len,), name='decoder_inputs')

# 定义词嵌入层
embedding_layer = Embedding(vocab_size, embedding_dim)

# 编码器
def encoder_layer(units, dff, num_heads, dropout_rate):
    inputs = Input(shape=(None, embedding_dim), name='encoder_inputs')
    attention = MultiHeadAttention(num_heads=num_heads, key_dim=embedding_dim)(inputs, inputs)
    attention = Dropout(dropout_rate)(attention)
    attention = LayerNormalization(epsilon=1e-6)(inputs + attention)
    outputs = Dense(units, activation='relu')(attention)
    outputs = Dense(dff, activation='relu')(outputs)
    outputs = Dropout(dropout_rate)(outputs)
    outputs = LayerNormalization(epsilon=1e-6)(attention + outputs)
    return Model(inputs=inputs, outputs=outputs)

# 解码器
def decoder_layer(units, dff, num_heads, dropout_rate):
    inputs = Input(shape=(None, embedding_dim), name='decoder_inputs')
    attention1 = MultiHeadAttention(num_heads=num_heads, key_dim=embedding_dim)(inputs, inputs)
    attention1 = Dropout(dropout_rate)(attention1)
    attention1 = LayerNormalization(epsilon=1e-6)(inputs + attention1)
    attention2 = MultiHeadAttention(num_heads=num_heads, key_dim=embedding_dim)(attention1, encoder_outputs)
    attention2 = Dropout(dropout_rate)(attention2)
    attention2 = LayerNormalization(epsilon=1e-6)(attention1 + attention2)
    outputs = Dense(units, activation='relu')(attention2)
    outputs = Dense(dff, activation='relu')(outputs)
    outputs = Dropout(dropout_rate)(outputs)
    outputs = LayerNormalization(epsilon=1e-6)(attention2 + outputs)
    return Model(inputs=[inputs, encoder_outputs], outputs=outputs)

# 定义编码器
encoder_outputs = embedding_layer(encoder_inputs)
for i in range(num_encoder_layers):
    encoder_outputs = encoder_layer(embedding_dim, dff, num_heads, dropout_rate)(encoder_outputs)

# 定义解码器
decoder_outputs = embedding_layer(decoder_inputs)
for i in range(num_decoder_layers):
    decoder_outputs = decoder_layer(embedding_dim, dff, num_heads, dropout_rate)(decoder_outputs, encoder_outputs)

# 定义输出层
outputs = Dense(vocab_size, activation='softmax')(decoder_outputs)

# 定义模型
model = Model(inputs=[encoder_inputs, decoder_inputs], outputs=outputs)

# 定义损失函数
def loss_function(y_true, y_pred):
    mask = tf.math.logical_not(tf.math.equal(y_true, 0))
    loss_ = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)(y_true, y_pred)
    mask = tf.cast(mask, dtype=loss_.dtype)
    loss_ *= mask
    return tf.reduce_mean(loss_)

# 定义学习率
learning_rate = CustomSchedule(embedding_dim)

# 定义优化器
optimizer = Adam(learning_rate, beta_1=0.9, beta_2=0.98, epsilon=1e-9)

# 编译模型
model.compile(optimizer=optimizer, loss=loss_function)

# 训练模型
model.fit(train_dataset, epochs=num_epochs)

以上是一个简单的Transformer模型实现，其中还需要定义一些其他的辅助函数和数据预处理部分。这里只是提供了一个大致的代码框架，实际应用中还需要根据具体任务进行调整和优化。