class Residual(nn.Module): def __init__(self,in_c,out_c): super(Residual,self).__init__() self.conv = nn.Sequential( nn.Conv2d(in_channels = in_c,out_channels = out_c,kernel_size = 3,padding = 1), nn.BatchNorm2d(out_c), nn.ReLU(), nn.Conv2d(in_channels = out_c,out_channels = out_c,kernel_size = 3,padding = 1), nn.BatchNorm2d(out_c), nn.ReLU(), ) self.botneck = nn.Conv2d(in_channels = in_c,out_channels = out_c,kernel_size = 1) self.pool = nn.MaxPool2d(kernel_size = 2,stride = 2) def forward(self,x): x_prim = x x = self.conv(x) x = self.botneck(x_prim) + x x = self.pool(x) return x 用tensorflow定义
时间: 2023-11-25 12:07:54 浏览: 98
以下是使用tensorflow定义的Residual类:
```
import tensorflow as tf
from tensorflow.keras import layers
class Residual(layers.Layer):
def __init__(self, in_c, out_c):
super(Residual, self).__init__()
self.conv = tf.keras.Sequential([
layers.Conv2D(out_c, kernel_size=3, padding='same'),
layers.BatchNormalization(),
layers.ReLU(),
layers.Conv2D(out_c, kernel_size=3, padding='same'),
layers.BatchNormalization(),
layers.ReLU()
])
self.botneck = layers.Conv2D(out_c, kernel_size=1)
self.pool = layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2))
def call(self, x):
x_prim = x
x = self.conv(x)
x = self.botneck(x_prim) + x
x = self.pool(x)
return x
```
在这个tensorflow版本的Residual类中,我使用了tensorflow的layers.Layer模块,并且对应用到了tensorflow中的层。使用layers.Conv2D()代替了nn.Conv2d(),使用layers.BatchNormalization()代替了nn.BatchNorm2d(),使用layers.ReLU()代替了nn.ReLU()。同时,我使用了layers.MaxPooling2D()代替了nn.MaxPool2d()。最后,在call()方法中,我使用了tensorflow的加法操作tf.add()代替了+。
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