用tensorflow2写一个informer
时间: 2024-06-09 11:07:33 浏览: 18
Informer是一种用于时间序列预测的神经网络模型,其主要特点是具有自注意力机制和Encoder-Decoder结构。以下是使用TensorFlow 2.x编写Informer模型的示例代码:
首先,我们需要导入必要的库和模块:
```
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Model
```
接下来,我们定义Informer模型的类:
```
class Informer(Model):
def __init__(self, enc_in, dec_in, c_out, seq_len, label_len, pred_len, factor=5, d_model=512, n_heads=8, e_layers=2, d_layers=1):
super(Informer, self).__init__()
self.pred_len = pred_len
self.attn = Auto_Attention(d_model, n_heads)
# Encoder
self.enc_embedding = keras.Sequential([
layers.Dense(d_model, activation='relu'),
layers.Reshape((-1, d_model))
])
self.enc_pos_embedding = PositionalEmbedding(d_model)
self.enc_layers = [EncoderLayer(d_model, n_heads) for _ in range(e_layers)]
self.enc_dropout = layers.Dropout(0.1)
# Decoder
self.dec_embedding = keras.Sequential([
layers.Dense(d_model, activation='relu'),
layers.Reshape((-1, d_model))
])
self.dec_pos_embedding = PositionalEmbedding(d_model)
self.dec_layers = [DecoderLayer(d_model, n_heads) for _ in range(d_layers)]
self.dec_dropout = layers.Dropout(0.1)
# Output
self.fc = keras.Sequential([
layers.Dense(c_out)
])
# Encoder Input
self.enc_in = enc_in
self.enc_inp = layers.Input(shape=(seq_len, enc_in))
# Decoder Input
self.dec_in = dec_in
self.dec_inp = layers.Input(shape=(self.pred_len, dec_in))
# Encoder
enc = self.enc_embedding(self.enc_inp)
enc = self.enc_pos_embedding(enc)
for i in range(e_layers):
enc = self.enc_layers[i](enc)
enc = self.enc_dropout(enc)
# Decoder
dec = self.dec_embedding(self.dec_inp)
dec = self.dec_pos_embedding(dec)
for i in range(d_layers):
dec = self.dec_layers[i](dec, enc, self.attn)
dec = self.dec_dropout(dec)
# Output
self.fc_out = self.fc(dec)
# Combine Encoder and Decoder
self.model = Model(inputs=[self.enc_inp, self.dec_inp], outputs=self.fc_out)
def call(self, inputs):
enc_inp, dec_inp = inputs
enc = self.enc_embedding(enc_inp)
enc = self.enc_pos_embedding(enc)
for i in range(len(self.enc_layers)):
enc = self.enc_layers[i](enc)
enc = self.enc_dropout(enc)
dec = self.dec_embedding(dec_inp)
dec = self.dec_pos_embedding(dec)
for i in range(len(self.dec_layers)):
dec = self.dec_layers[i](dec, enc, self.attn)
dec = self.dec_dropout(dec)
fc_out = self.fc(dec)
return fc_out
```
在上述代码中,我们定义了Informer模型的各个组件,包括自注意力机制、编码器、解码器和输出层。我们还实现了模型的前向传递函数。
接下来,我们定义模型的各个组件:
```
class Auto_Attention(layers.Layer):
def __init__(self, d_model, n_heads):
super(Auto_Attention, self).__init__()
self.n_heads = n_heads
self.d_model = d_model
self.qw = layers.Dense(d_model)
self.kw = layers.Dense(d_model)
self.vw = layers.Dense(d_model)
self.fc = layers.Dense(d_model)
def call(self, x):
q = self.qw(x)
k = self.kw(x)
v = self.vw(x)
q_ = tf.concat(tf.split(q, self.n_heads, axis=2), axis=0)
k_ = tf.concat(tf.split(k, self.n_heads, axis=2), axis=0)
v_ = tf.concat(tf.split(v, self.n_heads, axis=2), axis=0)
output = tf.matmul(q_, k_, transpose_b=True)
output = output / (self.d_model // self.n_heads) ** 0.5
output = keras.activations.softmax(output)
output = tf.matmul(output, v_)
output = tf.concat(tf.split(output, self.n_heads, axis=0), axis=2)
output = self.fc(output)
return output
class EncoderLayer(layers.Layer):
def __init__(self, d_model, n_heads):
super(EncoderLayer, self).__init__()
self.attn = Auto_Attention(d_model, n_heads)
self.ffn = keras.Sequential([
layers.Dense(d_model, activation='relu'),
layers.Dense(d_model)
])
self.layernorm1 = layers.LayerNormalization()
self.layernorm2 = layers.LayerNormalization()
def call(self, x):
attn_output = self.attn(x)
x = self.layernorm1(x + attn_output)
ffn_output = self.ffn(x)
x = self.layernorm2(x + ffn_output)
return x
class DecoderLayer(layers.Layer):
def __init__(self, d_model, n_heads):
super(DecoderLayer, self).__init__()
self.attn1 = Auto_Attention(d_model, n_heads)
self.attn2 = Auto_Attention(d_model, n_heads)
self.ffn = keras.Sequential([
layers.Dense(d_model, activation='relu'),
layers.Dense(d_model)
])
self.layernorm1 = layers.LayerNormalization()
self.layernorm2 = layers.LayerNormalization()
self.layernorm3 = layers.LayerNormalization()
def call(self, x, enc_output, attn):
attn1_output = self.attn1(x)
x = self.layernorm1(x + attn1_output)
attn2_output = self.attn2(x, enc_output)
x = self.layernorm2(x + attn2_output)
ffn_output = self.ffn(x)
x = self.layernorm3(x + ffn_output)
return x
class PositionalEmbedding(layers.Layer):
def __init__(self, d_model):
super(PositionalEmbedding, self).__init__()
self.d_model = d_model
def get_angles(self, pos, i, d_model):
angle_rates = 1 / tf.pow(10000, (2 * (i // 2)) / tf.cast(d_model, tf.float32))
return pos * angle_rates
def call(self, x):
seq_len = tf.shape(x)[1]
pos = tf.range(start=0, limit=seq_len, dtype=tf.float32)
angle_rads = self.get_angles(
pos=tf.expand_dims(pos, axis=1),
i=tf.expand_dims(tf.range(0, self.d_model, 2), axis=0),
d_model=self.d_model)
sines = tf.math.sin(angle_rads[:, 0::2])
cosines = tf.math.cos(angle_rads[:, 1::2])
pos_encoding = tf.concat([sines, cosines], axis=-1)
pos_encoding = tf.expand_dims(pos_encoding, axis=0)
return x + tf.repeat(pos_encoding, repeats=tf.shape(x)[0], axis=0)
```
上述代码中,我们定义了自注意力机制、编码器、解码器和位置嵌入层的各个组件。
最后,我们可以实例化Informer模型并进行训练和预测:
```
# Instantiate the model
model = Informer(enc_in=1, dec_in=1, c_out=1, seq_len=24, label_len=12, pred_len=12)
# Compile the model
model.compile(loss='mse', optimizer='adam')
# Train the model
model.fit(x_train, y_train, epochs=100)
# Predict
y_pred = model.predict(x_test)
```