Keras实现Transformer

# 导入所需的库
import numpy as np
from keras.models import Model
from keras.layers import Input, Dense, Embedding, MultiHeadAttention
from keras.optimizers import Adam

# 定义模型参数
vocab_size = 10000  # 词汇表大小
embedding_dim = 256  # 嵌入维度
num_heads = 8  # 多头注意力的头数
max_seq_length = 128  # 序列的最大长度

# 定义输入层
input_layer = Input(shape=(max_seq_length,))

# 定义嵌入层
embedding_layer = Embedding(vocab_size, embedding_dim)(input_layer)

# 定义多头注意力层
attention_layer = MultiHeadAttention(num_heads=num_heads, key_dim=embedding_dim)(embedding_layer, embedding_layer)

# 定义前馈神经网络层
ffn_layer = Dense(embedding_dim, activation='relu')(attention_layer)
ffn_layer = Dense(embedding_dim)(ffn_layer)

# 定义输出层
output_layer = Dense(vocab_size, activation='softmax')(ffn_layer)

# 构建模型
model = Model(inputs=input_layer, outputs=output_layer)

# 编译模型
model.compile(optimizer=Adam(learning_rate=0.001), loss='categorical_crossentropy', metrics=['accuracy'])

# 打印模型结构
model.summary()