使用tensorflow的transformer代码

以下是使用TensorFlow实现Transformer模型的示例代码：

import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Dropout
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.preprocessing.sequence import pad_sequences

# Define hyperparameters
MAX_LEN = 100
VOCAB_SIZE = 10000
EMBEDDING_DIM = 128
NUM_HEADS = 8
NUM_ENCODER_LAYERS = 6
NUM_DECODER_LAYERS = 6
DENSE_DIM = 512
DROPOUT_RATE = 0.2

# Define input and output shapes
encoder_inputs = Input(shape=(MAX_LEN,))
decoder_inputs = Input(shape=(MAX_LEN - 1,))
decoder_outputs = Input(shape=(MAX_LEN - 1,))

# Define the embedding layers
encoder_embedding = tf.keras.layers.Embedding(VOCAB_SIZE, EMBEDDING_DIM)(encoder_inputs)
decoder_embedding = tf.keras.layers.Embedding(VOCAB_SIZE, EMBEDDING_DIM)(decoder_inputs)

# Define the encoder layers
encoder_output = encoder_embedding
for i in range(NUM_ENCODER_LAYERS):
    multi_head_attention = tf.keras.layers.MultiHeadAttention(NUM_HEADS, EMBEDDING_DIM)
    encoder_output = multi_head_attention([encoder_output, encoder_output])
    encoder_output = tf.keras.layers.BatchNormalization()(encoder_output)
    encoder_output = tf.keras.layers.Dropout(DROPOUT_RATE)(encoder_output)
    feed_forward = tf.keras.layers.Dense(DENSE_DIM, activation='relu')
    encoder_output = feed_forward(encoder_output)
    encoder_output = tf.keras.layers.Dropout(DROPOUT_RATE)(encoder_output)
    encoder_output = tf.keras.layers.BatchNormalization()(encoder_output)

# Define the decoder layers
decoder_output = decoder_embedding
for i in range(NUM_DECODER_LAYERS):
    masked_multi_head_attention = tf.keras.layers.MultiHeadAttention(NUM_HEADS, EMBEDDING_DIM, name='masked_multihead_attention_{}'.format(i))
    decoder_output = masked_multi_head_attention([decoder_output, decoder_output])
    decoder_output = tf.keras.layers.BatchNormalization()(decoder_output)
    decoder_output = tf.keras.layers.Dropout(DROPOUT_RATE)(decoder_output)
    multi_head_attention = tf.keras.layers.MultiHeadAttention(NUM_HEADS, EMBEDDING_DIM, name='multihead_attention_{}'.format(i))
    decoder_output = multi_head_attention([decoder_output, encoder_output])
    decoder_output = tf.keras.layers.BatchNormalization()(decoder_output)
    decoder_output = tf.keras.layers.Dropout(DROPOUT_RATE)(decoder_output)
    feed_forward = tf.keras.layers.Dense(DENSE_DIM, activation='relu')
    decoder_output = feed_forward(decoder_output)
    decoder_output = tf.keras.layers.Dropout(DROPOUT_RATE)(decoder_output)
    decoder_output = tf.keras.layers.BatchNormalization()(decoder_output)

# Define the output layer
outputs = Dense(VOCAB_SIZE, activation='softmax')(decoder_output)

# Define the model
model = Model(inputs=[encoder_inputs, decoder_inputs], outputs=outputs)

# Define the optimizer and loss function
optimizer = Adam(lr=0.001)
loss = 'sparse_categorical_crossentropy'

# Compile the model
model.compile(optimizer=optimizer, loss=loss)

# Train the model
model.fit([encoder_input_data, decoder_input_data], decoder_target_data, batch_size=64, epochs=10)

# Test the model
encoder_inputs_test = pad_sequences(encoder_tokenizer.texts_to_sequences(encoder_text_test), maxlen=MAX_LEN, padding='post')
decoder_inputs_test = pad_sequences(decoder_tokenizer.texts_to_sequences(decoder_text_test), maxlen=MAX_LEN-1, padding='post')
decoder_outputs_test = pad_sequences(decoder_tokenizer.texts_to_sequences(decoder_text_test), maxlen=MAX_LEN-1, padding='post')

model.evaluate([encoder_inputs_test, decoder_inputs_test], decoder_outputs_test)

此代码实现了一个包含6个编码器层和6个解码器层的Transformer模型，使用了多头自注意力机制、遮盖的多头自注意力机制和多头注意力机制。它使用了Adam优化器和稀疏分类交叉熵损失函数进行训练，并在测试集上进行评估。