transformer文本分类代码

以下是一个基于Transformer的文本分类代码示例：

首先，我们需要导入所需的库和模块：

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
from tensorflow.keras.datasets import imdb
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.layers import LayerNormalization, MultiHeadAttention, Embedding, Flatten

接下来，我们需要准备IMDB数据集和一些超参数：

# 超参数
vocab_size = 5000
maxlen = 200
embedding_dims = 32
num_heads = 8
ff_dim = 64
dropout_rate = 0.1
batch_size = 32
epochs = 10

# 加载IMDB数据集
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=vocab_size)
x_train = pad_sequences(x_train, maxlen=maxlen)
x_test = pad_sequences(x_test, maxlen=maxlen)

# 将分类标签进行one-hot编码
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)

接下来，我们创建Transformer的层：

class Transformer(Layer):
    def __init__(self, embed_dim, num_heads, ff_dim, rate=0.1):
        super(Transformer, self).__init__()
        self.mha = MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim)
        self.ffn = Sequential([
            Dense(ff_dim, activation='relu'),
            Dense(embed_dim)
        ])
        self.layernorm1 = LayerNormalization(epsilon=1e-6)
        self.layernorm2 = LayerNormalization(epsilon=1e-6)
        self.dropout1 = Dropout(rate)
        self.dropout2 = Dropout(rate)

    def call(self, inputs, training=True):
        attn_output = self.mha(inputs, inputs, inputs)
        attn_output = self.dropout1(attn_output, training=training)
        out1 = self.layernorm1(inputs + attn_output)
        ffn_output = self.ffn(out1)
        ffn_output = self.dropout2(ffn_output, training=training)
        return self.layernorm2(out1 + ffn_output)

然后，我们定义模型结构：

inputs = Input(shape=(maxlen,))
embedding = Embedding(vocab_size, embedding_dims)(inputs)
transformer_block = Transformer(embedding_dims, num_heads, ff_dim)
x = transformer_block(embedding)
x = Flatten()(x)
x = Dense(64, activation='relu')(x)
x = Dropout(dropout_rate)(x)
outputs = Dense(2, activation='softmax')(x)
model = Model(inputs=inputs, outputs=outputs)

接下来，我们编译和训练模型：

model.compile(optimizer=Adam(lr=1e-4), loss='categorical_crossentropy', metrics=['accuracy'])
es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=3)
history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1, callbacks=[es])

最后，我们可以评估模型并进行预测：

_, acc = model.evaluate(x_test, y_test, verbose=0)
print('Test Accuracy: %.2f%%' % (acc*100))

这就是一个基于Transformer的文本分类代码示例。