transformer预测交通流量

以下是基于Transformer模型预测交通流量的步骤：

1. 定义输入特征和目标：

# 历史交通流序列
history_traffic_flow = ...
# 当前交通流序列
current_traffic_flow = ...
# 目标：预测未来F个时间步的交通流
future_traffic_flow = ...

2. 构建Transformer模型：

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

# 定义Transformer编码器层
class TransformerEncoder(layers.Layer):
    def __init__(self, num_layers, d_model, num_heads, dff, input_vocab_size, maximum_position_encoding, rate=0.1):
        super(TransformerEncoder, self).__init__()
        self.d_model = d_model
        self.num_layers = num_layers
        self.embedding = layers.Embedding(input_vocab_size, d_model)
        self.pos_encoding = positional_encoding(maximum_position_encoding, self.d_model)
        self.enc_layers = [EncoderLayer(d_model, num_heads, dff, rate) for _ in range(num_layers)]
        self.dropout = layers.Dropout(rate)

    def call(self, x, training, mask):
        seq_len = tf.shape(x)[1]
        x = self.embedding(x)
        x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))
        x += self.pos_encoding[:, :seq_len, :]
        x = self.dropout(x, training=training)
        for i in range(self.num_layers):
            x = self.enc_layers[i](x, training, mask)
        return x

# 定义Transformer解码器层
class TransformerDecoder(layers.Layer):
    # 与Encoder类似，省略具体实现

# 定义Transformer模型
class Transformer(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(Transformer, self).__init__()
        self.encoder = TransformerEncoder(num_layers, d_model, num_heads, dff, input_vocab_size, pe_input, rate)
        self.decoder = TransformerDecoder(num_layers, d_model, num_heads, dff, target_vocab_size, pe_target, rate)
        self.final_layer = 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)
        dec_output = self.decoder(tar, enc_output, training, look_ahead_mask, dec_padding_mask)
        final_output = self.final_layer(dec_output)
        return final_output

3. 训练Transformer模型：

# 定义损失函数
loss_object = tf.keras.losses.MeanSquaredError()

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

# 定义评估指标
train_loss = tf.keras.metrics.Mean(name='train_loss')

# 单步训练
def train_step(inp, tar):
    with tf.GradientTape() as tape:
        predictions = transformer(inp, tar, True, None, None, None)
        loss = loss_function(tar, predictions)
    gradients = tape.gradient(loss, transformer.trainable_variables)
    optimizer.apply_gradients(zip(gradients, transformer.trainable_variables))
    train_loss(loss)

# 完整训练过程
for epoch in range(EPOCHS):
    for (batch, (inp, tar)) in enumerate(dataset):
        train_step(inp, tar)

4. 使用Transformer模型进行交通流量预测：

# 使用训练好的模型进行预测
def evaluate(inp_sentence):
    start_token = [tokenizer_pt.vocab_size]
    end_token = [tokenizer_pt.vocab_size + 1]
    inp_sentence = start_token + tokenizer_pt.encode(inp_sentence) + end_token
    encoder_input = tf.expand_dims(inp_sentence, 0)
    decoder_input = [tokenizer_en.vocab_size]
    output = tf.expand_dims(decoder_input, 0)
    for i in range(MAX_LENGTH):
        enc_padding_mask, combined_mask, dec_padding_mask = create_masks(encoder_input, output)
        predictions = transformer(encoder_input, output, False, enc_padding_mask, combined_mask, dec_padding_mask)
        predictions = predictions[:, -1:, :]
        predicted_id = tf.cast(tf.argmax(predictions, axis=-1), tf.int32)
        if predicted_id == tokenizer_en.vocab_size + 1:
            return tf.squeeze(output, axis=0)
        output = tf.concat([output, predicted_id], axis=-1)
    return tf.squeeze(output, axis=0)