transformer预测交通流量
时间: 2023-12-20 07:05:51 浏览: 158
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:
```python
# 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:
```python
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:
```python
# 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:
```python
# 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)
```
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