请用python编写在tensorflow框架下的tcn与注意力机制融合的代码
时间: 2024-02-24 18:57:42 浏览: 21
好的,下面是一个简单的在TensorFlow框架下融合TCN与注意力机制的代码,仅供参考:
```python
import tensorflow as tf
from tensorflow.keras.layers import Input, Conv1D, BatchNormalization, Activation, Dropout, Dense, Layer
from tensorflow.keras.models import Model
class TemporalBlock(Layer):
def __init__(self, dilation_rate, n_filters, kernel_size, padding, dropout_rate=0.0):
super(TemporalBlock, self).__init__()
self.conv1 = Conv1D(filters=n_filters, kernel_size=kernel_size, dilation_rate=dilation_rate, padding=padding)
self.bn1 = BatchNormalization()
self.act1 = Activation('relu')
self.dropout1 = Dropout(dropout_rate)
self.conv2 = Conv1D(filters=n_filters, kernel_size=kernel_size, dilation_rate=dilation_rate, padding=padding)
self.bn2 = BatchNormalization()
self.act2 = Activation('relu')
self.dropout2 = Dropout(dropout_rate)
self.res_conv = Conv1D(filters=n_filters, kernel_size=1, padding='same')
self.res_bn = BatchNormalization()
def call(self, inputs):
x = inputs
x = self.conv1(x)
x = self.bn1(x)
x = self.act1(x)
x = self.dropout1(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.act2(x)
x = self.dropout2(x)
res = self.res_conv(inputs)
res = self.res_bn(res)
x = tf.keras.layers.add([x, res])
return x
class Attention(Layer):
def __init__(self, n_filters):
super(Attention, self).__init__()
self.query_conv = Conv1D(filters=n_filters, kernel_size=1, padding='same')
self.key_conv = Conv1D(filters=n_filters, kernel_size=1, padding='same')
self.value_conv = Conv1D(filters=n_filters, kernel_size=1, padding='same')
self.softmax = Activation('softmax')
def call(self, inputs):
q = self.query_conv(inputs)
k = self.key_conv(inputs)
v = self.value_conv(inputs)
k = tf.transpose(k, [0, 2, 1])
dot = tf.matmul(q, k)
dot = dot / tf.sqrt(tf.cast(tf.shape(inputs)[-1], tf.float32))
dot = self.softmax(dot)
out = tf.matmul(dot, v)
out = tf.transpose(out, [0, 2, 1])
return out
def build_model(input_shape, n_classes, n_filters, kernel_size, n_blocks, dilations, padding, dropout_rate=0.0):
inputs = Input(shape=input_shape)
x = inputs
for dilation_rate in dilations:
for i in range(n_blocks):
x = TemporalBlock(dilation_rate=dilation_rate, n_filters=n_filters, kernel_size=kernel_size, padding=padding, dropout_rate=dropout_rate)(x)
x = Attention(n_filters=n_filters)(x)
x = Conv1D(filters=n_classes, kernel_size=1)(x)
x = tf.squeeze(x, axis=1)
x = Activation('softmax')(x)
model = Model(inputs=inputs, outputs=x)
return model
```
这段代码定义了一个TCN与注意力机制融合的模型,包括TemporalBlock和Attention两个层,以及一个build_model函数用于构建模型。在build_model函数中,我们首先定义了输入层,然后通过循环添加多个TemporalBlock层,每个TemporalBlock层都具有不同的扩张率。在所有TemporalBlock层完成后,我们将注意力层添加到模型中,最后通过一个1x1的卷积层将输出通道数降至目标类别数,并使用softmax激活函数输出最终结果。
希望这段代码对你有所帮助!