请用python编写在tensorflow框架下的tcn与注意力机制融合进行预测回归的代码
时间: 2023-02-23 08:25:31 浏览: 44
你可以以下的代码作为参考:
```python
import tensorflow as tf
from tensorflow.keras import layers
class TemporalConvNet(layers.Layer):
def __init__(self, num_channels, kernel_size=2, dropout=0.2, **kwargs):
super(TemporalConvNet, self).__init__(**kwargs)
self.layers = []
for i in range(len(num_channels)):
dilation_size = 2 ** i
in_channels = num_channels[i-1] if i > 0 else 1
out_channels = num_channels[i]
self.layers.append(layers.Conv1D(filters=out_channels, kernel_size=kernel_size, dilation_rate=dilation_size, activation='relu', padding='causal'))
self.layers.append(layers.BatchNormalization())
self.layers.append(layers.Dropout(dropout))
def call(self, inputs):
x = inputs
for layer in self.layers:
x = layer(x)
return x
class AttentionLayer(layers.Layer):
def __init__(self, **kwargs):
super(AttentionLayer, self).__init__(**kwargs)
def build(self, input_shape):
self.W = self.add_weight(name='W', shape=(input_shape[-1], input_shape[-1]), initializer='glorot_uniform', trainable=True)
self.b = self.add_weight(name='b', shape=(input_shape[-1],), initializer='zeros', trainable=True)
self.u = self.add_weight(name='u', shape=(input_shape[-1],), initializer='glorot_uniform', trainable=True)
super(AttentionLayer, self).build(input_shape)
def call(self, inputs):
uit = tf.tensordot(inputs, self.W, axes=1)
uit = tf.nn.tanh(uit + self.b)
ait = tf.tensordot(uit, self.u, axes=1)
ait = tf.nn.softmax(ait, axis=1)
weighted_input = inputs * ait[..., tf.newaxis]
return tf.reduce_sum(weighted_input, axis=1)
def create_model(input_shape, num_channels, kernel_size, dropout):
inputs = layers.Input(shape=input_shape)
tcn = TemporalConvNet(num_channels, kernel_size, dropout)
x = tcn(inputs)
x = AttentionLayer()(x)
x = layers.Dense(1, activation='linear')(x)
model = tf.keras.Model(inputs, x)
return model
model = create_model(input_shape=(None, 1