transformer 流量预测 代码

以下是一个基于Transformer的流量预测代码的示例：

import pandas as pd
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers, Model, Input
from sklearn.preprocessing import MinMaxScaler

# 加载数据
data = pd.read_csv('data.csv')
train_data = data.iloc[:800, :]
test_data = data.iloc[800:, :]

# 数据预处理
scaler = MinMaxScaler()
train_data_scaled = scaler.fit_transform(train_data)
test_data_scaled = scaler.transform(test_data)

# 准备训练数据
look_back = 24  # 每个样本包含的历史数据数量
batch_size = 32  # 每个训练批次的数据量
train_data_X, train_data_Y = [], []
for i in range(look_back, len(train_data_scaled)):
    train_data_X.append(train_data_scaled[i-look_back:i, :])
    train_data_Y.append(train_data_scaled[i, 0])
train_data_X, train_data_Y = np.array(train_data_X), np.array(train_data_Y)
train_data_X = np.reshape(train_data_X, (train_data_X.shape[0], look_back, train_data_X.shape[2]))

# 准备测试数据
test_data_X, test_data_Y = [], []
for i in range(look_back, len(test_data_scaled)):
    test_data_X.append(test_data_scaled[i-look_back:i, :])
    test_data_Y.append(test_data_scaled[i, 0])
test_data_X, test_data_Y = np.array(test_data_X), np.array(test_data_Y)
test_data_X = np.reshape(test_data_X, (test_data_X.shape[0], look_back, test_data_X.shape[2]))

# 定义Transformer模型
def transformer_model(input_shape):
    inputs = Input(shape=input_shape)
    encoder_outputs = layers.MultiHeadAttention(num_heads=8, key_dim=64)(inputs, inputs)
    encoder_outputs = layers.Dropout(0.1)(encoder_outputs)
    encoder_outputs = layers.LayerNormalization(epsilon=1e-6)(inputs + encoder_outputs)
    encoder_outputs = layers.Dense(64, activation='relu')(encoder_outputs)
    encoder_outputs = layers.Dense(32, activation='relu')(encoder_outputs)
    outputs = layers.Dense(1)(encoder_outputs)
    model = Model(inputs=inputs, outputs=outputs)
    return model

model = transformer_model((look_back, train_data_X.shape[2]))
model.compile(optimizer='adam', loss='mse')

# 训练模型
model.fit(train_data_X, train_data_Y, epochs=50, batch_size=batch_size, verbose=2)

# 测试模型
test_loss = model.evaluate(test_data_X, test_data_Y, verbose=0)
print(f'Test loss: {test_loss}')

# 预测
predictions = model.predict(test_data_X)
predictions = scaler.inverse_transform(predictions)
test_data_Y = scaler.inverse_transform(test_data_Y.reshape(-1, 1))

# 可视化预测结果
import matplotlib.pyplot as plt
plt.plot(test_data_Y, label='True')
plt.plot(predictions, label='Predicted')
plt.legend()
plt.show()

这段代码实现了一个简单的Transformer模型，用于预测流量数据。模型采用了Multi-Head Attention、Dropout、Layer Normalization和全连接层等技术，可以有效地处理序列数据。在训练和测试过程中，使用了批量训练和评估方式，以提高效率。最终，使用了逆归一化技术将预测结果转换为实际的流量数据，并可视化了预测结果。