给我transformer信号识别的代码

由于Transformer模型通常用于自然语言处理任务，例如文本分类、机器翻译等，因此在这里提供一个基于Transformer模型的文本分类代码示例：

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.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences

# 定义超参数
MAX_SEQUENCE_LENGTH = 100
MAX_NUM_WORDS = 20000
EMBEDDING_DIM = 100
NUM_CLASSES = 2
BATCH_SIZE = 32
EPOCHS = 10

# 加载数据集
train_texts = [...] # 训练集文本
train_labels = [...] # 训练集标签
test_texts = [...] # 测试集文本
test_labels = [...] # 测试集标签

# 对文本进行预处理
tokenizer = Tokenizer(num_words=MAX_NUM_WORDS)
tokenizer.fit_on_texts(train_texts)
train_sequences = tokenizer.texts_to_sequences(train_texts)
test_sequences = tokenizer.texts_to_sequences(test_texts)
train_data = pad_sequences(train_sequences, maxlen=MAX_SEQUENCE_LENGTH)
test_data = pad_sequences(test_sequences, maxlen=MAX_SEQUENCE_LENGTH)

# 构建Transformer模型
inputs = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')
embedding_layer = tf.keras.layers.Embedding(MAX_NUM_WORDS, EMBEDDING_DIM)(inputs)
transformer_layer = tf.keras.layers.MultiHeadAttention(num_heads=8, key_dim=EMBEDDING_DIM)(embedding_layer, embedding_layer)
transformer_layer = tf.keras.layers.Dropout(0.1)(transformer_layer)
transformer_layer = tf.keras.layers.LayerNormalization()(transformer_layer)
flatten_layer = tf.keras.layers.Flatten()(transformer_layer)
outputs = Dense(NUM_CLASSES, activation='softmax')(flatten_layer)
model = Model(inputs=inputs, outputs=outputs)

# 编译模型
model.compile(optimizer=Adam(learning_rate=1e-4), loss='categorical_crossentropy', metrics=['accuracy'])

# 训练模型
model.fit(train_data, train_labels, batch_size=BATCH_SIZE, epochs=EPOCHS, validation_data=(test_data, test_labels))

需要注意的是，由于Transformer模型的计算量较大，因此在实际应用时可能需要进行优化，例如使用分布式训练、混合精度训练等技术。此外，还需要根据具体任务进行模型的调整和优化。