Keras写得transformer时间序列预测代码

以下一个使用Keras编写的Transformer时间序列预测代码示例：

import numpy as np
from keras.models import Model
from keras.layers import Input, Dense, Dropout, LayerNormalization, Add
from keras.layers import MultiHeadAttention, Flatten, Embedding

# 构建Transformer模型
def build_transformer_model(input_shape):
    inputs = Input(shape=input_shape)
    x = Embedding(input_dim=input_shape[0], output_dim=input_shape[1])(inputs)
    x = Dropout(0.1)(x)

    # 多头注意力层
    multi_head_att = MultiHeadAttention(num_heads=8, key_dim=64)
    att_out = multi_head_att(x, x)
    att_out = Dropout(0.1)(att_out)
    att_out = Add()([att_out, x])
    att_out = LayerNormalization()(att_out)

    # Feedforward层
    ff = Dense(units=2048, activation='relu')(att_out)
    ff = Dense(units=input_shape[1])(ff)
    ff = Dropout(0.1)(ff)
    ff = Add()([ff, att_out])
    ff = LayerNormalization()(ff)

    # 展平层
    flatten = Flatten()(ff)

    # 输出层
    outputs = Dense(units=1, activation='linear')(flatten)

    model = Model(inputs=inputs, outputs=outputs)
    return model

# 构建训练数据
def build_data(n, sequence_length):
    x = np.zeros((n, sequence_length), dtype=np.int32)
    y = np.zeros((n, 1), dtype=np.float32)
    for i in range(n):
        x[i] = np.arange(i, i+sequence_length)
        y[i] = np.sin(i+sequence_length)
    return x, y

# 训练模型
x_train, y_train = build_data(10000, 64)
model = build_transformer_model((100, 64))
model.compile(optimizer='adam', loss='mse')
model.fit(x_train, y_train, epochs=10, batch_size=32)

# 预测
x_test, y_test = build_data(100, 64)
y_pred = model.predict(x_test)
print("Predicted Values:", y_pred[:, 0])
print("True Values:", y_test[:, 0])

这个代码构建了一个Transformer模型，用于预测一个正弦函数。您可以根据需要更改输入形状和模型超参数，以适应您的数据集和任务。