keras实现transformer时间序列预测模型python代码

我可以回答这个问题。以下是一个使用Keras实现Transformer时间序列预测模型的Python代码示例：

import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

# 定义Transformer模型
class TransformerModel(tf.keras.Model):
    def __init__(self, num_layers, d_model, num_heads, dff, input_vocab_size, target_vocab_size, pe_input, pe_target, rate=0.1):
        super(TransformerModel, self).__init__()

        self.encoder = layers.Encoder(num_layers, d_model, num_heads, dff, input_vocab_size, pe_input, rate)

        self.decoder = layers.Decoder(num_layers, d_model, num_heads, dff, target_vocab_size, pe_target, rate)

        self.final_layer = tf.keras.layers.Dense(target_vocab_size)

    def call(self, inp, tar, training, enc_padding_mask, look_ahead_mask, dec_padding_mask):
        enc_output = self.encoder(inp, training, enc_padding_mask)  # (batch_size, inp_seq_len, d_model)

        # dec_output.shape == (batch_size, tar_seq_len, d_model)
        dec_output, attention_weights = self.decoder(tar, enc_output, training, look_ahead_mask, dec_padding_mask)

        final_output = self.final_layer(dec_output)  # (batch_size, tar_seq_len, target_vocab_size)

        return final_output, attention_weights

# 定义损失函数
def loss_function(real, pred):
    loss_object = tf.keras.losses.MeanSquaredError()
    mask = tf.math.logical_not(tf.math.equal(real, 0))
    loss_ = loss_object(real, pred)

    mask = tf.cast(mask, dtype=loss_.dtype)
    loss_ *= mask

    return tf.reduce_mean(loss_)

# 定义学习率
class CustomSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):
    def __init__(self, d_model, warmup_steps=4000):
        super(CustomSchedule, self).__init__()

        self.d_model = d_model
        self.d_model = tf.cast(self.d_model, tf.float32)

        self.warmup_steps = warmup_steps

    def __call__(self, step):
        arg1 = tf.math.rsqrt(step)
        arg2 = step * (self.warmup_steps ** -1.5)

        return tf.math.rsqrt(self.d_model) * tf.math.minimum(arg1, arg2)

# 定义训练步骤
def train_step(inp, tar, targ_real, encoder, decoder, optimizer, tar_inp_padding_mask, look_ahead_mask, tar_padding_mask):
    with tf.GradientTape() as tape:
        predictions, _ = transformer(inp, tar, True, tar_inp_padding_mask, look_ahead_mask, tar_padding_mask)
        loss = loss_function(targ_real, predictions)

    gradients = tape.gradient(loss, transformer.trainable_variables)
    optimizer.apply_gradients(zip(gradients, transformer.trainable_variables))

    return loss

# 定义数据集
def get_dataset():
    # TODO: 从数据集中获取数据
    return inp, tar

# 定义超参数
num_layers = 4
d_model = 128
dff = 512
num_heads = 8
input_vocab_size = 10000
target_vocab_size = 10000
dropout_rate = 0.1

# 初始化Transformer模型
transformer = TransformerModel(num_layers, d_model, num_heads, dff, input_vocab_size, target_vocab_size, pe_input=input_vocab_size, pe_target=target_vocab_size, rate=dropout_rate)

# 定义优化器
learning_rate = CustomSchedule(d_model)
optimizer = tf.keras.optimizers.Adam(learning_rate, beta_1=0.9, beta_2=0.98, epsilon=1e-9)

# 训练模型
EPOCHS = 20
for epoch in range(EPOCHS):
    dataset = get_dataset()
    total_loss = 0

    for (batch, (inp, tar)) in enumerate(dataset):
        tar_real = tar[:, 1:]

        tar_inp_padding_mask, look_ahead_mask, tar_padding_mask = create_masks(inp, tar_real)

        batch_loss = train_step(inp, tar, tar_real, transformer, optimizer, tar_inp_padding_mask, look_ahead_mask, tar_padding_mask)
        total_loss += batch_loss

        if batch % 100 == 0:
            print('Epoch {} Batch {} Loss {:.4f}'.format(epoch + 1, batch, batch_loss.numpy()))

    print('Epoch {} Loss {:.4f}'.format(epoch + 1, total_loss / (batch + 1)))