keras实现Transformer回归模型

以下是使用Keras实现Transformer回归模型的步骤：

1.导入所需的库和模块

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.layers.experimental.preprocessing import Normalization

2.准备数据集

# 假设我们有一个形状为(1000, 10)的数据集
x = tf.random.normal(shape=(1000, 10))
y = tf.random.normal(shape=(1000, 1))

3.构建Transformer模型

# 定义一个Transformer层
class Transformer(layers.Layer):
    def __init__(self, embed_dim, num_heads, ff_dim, rate=0.1):
        super(Transformer, self).__init__()
        self.att = layers.MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim)
        self.ffn = keras.Sequential(
            [layers.Dense(ff_dim, activation="relu"), layers.Dense(embed_dim),]
        )
        self.layernorm1 = layers.LayerNormalization(epsilon=1e-6)
        self.layernorm2 = layers.LayerNormalization(epsilon=1e-6)
        self.dropout1 = layers.Dropout(rate)
        self.dropout2 = layers.Dropout(rate)

    def call(self, inputs, training):
        attn_output = self.att(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)

# 定义一个序列模型
def TransformerRegressor():
    # 定义输入层
    inputs = layers.Input(shape=(10,))
    # 定义归一化层
    norm_layer = Normalization()
    norm_layer.adapt(x)
    x = norm_layer(inputs)
    # 定义Transformer层
    transformer_block = Transformer(embed_dim=32, num_heads=2, ff_dim=64)
    x = transformer_block(x)
    # 定义输出层
    outputs = layers.Dense(1)(x)
    # 定义模型
    model = keras.Model(inputs=inputs, outputs=outputs)
    return model

# 实例化模型
model = TransformerRegressor()

4.编译和训练模型

# 编译模型
model.compile(optimizer="adam", loss="mse")
# 训练模型
model.fit(x, y, epochs=10, batch_size=32)

5.使用模型进行预测

# 使用模型进行预测
predictions = model.predict(x)