用tensorflow2写Gcnet的带啊
时间: 2024-03-25 17:35:55 浏览: 11
好的,以下是一个使用tensorflow2实现Gcnet的示例代码:
```python
import tensorflow as tf
class GcNet(tf.keras.Model):
def __init__(self, channels=64, reduction=16):
super(GcNet, self).__init__()
self.channels = channels
self.reduction = reduction
self.conv1 = tf.keras.layers.Conv2D(channels, kernel_size=3, strides=1, padding='same')
self.bn1 = tf.keras.layers.BatchNormalization()
self.relu1 = tf.keras.layers.ReLU()
self.conv2 = tf.keras.layers.Conv2D(channels, kernel_size=3, strides=1, padding='same')
self.bn2 = tf.keras.layers.BatchNormalization()
self.relu2 = tf.keras.layers.ReLU()
self.conv3 = tf.keras.layers.Conv2D(channels, kernel_size=3, strides=1, padding='same')
self.bn3 = tf.keras.layers.BatchNormalization()
self.relu3 = tf.keras.layers.ReLU()
self.avg_pool = tf.keras.layers.GlobalAveragePooling2D()
self.fc1 = tf.keras.layers.Dense(channels // reduction, activation='relu')
self.fc2 = tf.keras.layers.Dense(channels, activation='sigmoid')
def call(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu1(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu2(out)
out = self.conv3(out)
out = self.bn3(out)
out = self.relu3(out)
w = self.avg_pool(out)
w = self.fc1(w)
w = self.fc2(w)
w = tf.reshape(w, [-1, self.channels, 1, 1])
out = tf.multiply(out, w)
out += residual
return out
```
这是一个简单的GcNet模型实现,包含了三个卷积层和两个全连接层。在前向传播中,输入通过三个卷积层得到特征表示,然后通过全局平均池化操作得到每个特征通道的权重,再通过两个全连接层得到一个权重向量,最后将权重向量与特征表示相乘得到加权特征表示。最后再将加权特征表示与输入进行相加得到输出。
希望这个示例代码能够对您有所帮助。