帮我实现一个transformer的结构，基于tensorflow，用于时序预测

好的，我可以帮您实现一个基于 TensorFlow 的 Transformer 结构，用于时序预测。以下是代码示例：

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

class TransformerBlock(layers.Layer):
    def __init__(self, embed_dim, num_heads, ff_dim, rate=0.1):
        super(TransformerBlock, 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)

class TransformerEncoder(layers.Layer):
    def __init__(self, num_layers, embed_dim, num_heads, ff_dim, input_shape, rate=0.1):
        super(TransformerEncoder, self).__init__()
        self.embed_dim = embed_dim
        self.num_layers = num_layers
        self.pos_emb = layers.Embedding(input_shape, embed_dim)
        self.transformer_blocks = [TransformerBlock(embed_dim, num_heads, ff_dim, rate) for _ in range(num_layers)]
        self.dropout = layers.Dropout(rate)

    def call(self, inputs, training):
        input_shape = tf.shape(inputs)
        positions = tf.range(start=0, limit=input_shape[-2], delta=1)
        positions = self.pos_emb(positions)
        x = inputs + positions
        x = self.dropout(x, training=training)
        for i in range(self.num_layers):
            x = self.transformer_blocks[i](x, training)
        return x

class Transformer(keras.Model):
    def __init__(self, num_layers, embed_dim, num_heads, ff_dim, input_shape, output_shape, rate=0.1):
        super(Transformer, self).__init__()
        self.encoder = TransformerEncoder(num_layers, embed_dim, num_heads, ff_dim, input_shape, rate)
        self.flatten = layers.Flatten()
        self.dense = layers.Dense(output_shape)

    def call(self, inputs, training):
        x = self.encoder(inputs, training)
        x = self.flatten(x)
        x = self.dense(x)
        return x

您可以根据需要调整参数，例如 num_layers、embed_dim、num_heads、ff_dim 等。同时，您需要准备好输入数据和输出数据，然后使用以下代码进行模型训练：

model = Transformer(num_layers=2, embed_dim=32, num_heads=4, ff_dim=64, input_shape=(10, 5), output_shape=1)
model.compile(optimizer="adam", loss="mse")
model.fit(x_train, y_train, epochs=10, batch_size=32)

其中，x_train 和 y_train 分别是输入数据和输出数据，可以根据实际情况进行调整。