pool1 = tf.nn.max_pool(residual1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
时间: 2023-06-25 11:03:29 浏览: 38
这段代码是使用 TensorFlow 实现的最大池化操作。它的作用是对输入的 residual1 进行池化操作,将其划分为不重叠的子区域,并在每个子区域中选取最大值作为输出。这里的 ksize 参数指定了池化窗口的大小,strides 参数指定了在输入上滑动池化窗口的步长。padding 参数指定了是否要在输入周围进行填充操作(SAME 表示需要填充)。该操作可以减小特征图的空间大小,同时保留重要的特征信息。
相关问题
使用paddle将以下LeNet代码改为ResNet网络模型class LeNet(paddle.nn.Layer): def __init__(self): super(LeNet, self).__init__() # 创建卷积和池化层块,每个卷积层使用relu激活函数,后面跟着一个2x2的池化 self.conv1 = paddle.nn.Conv2D(3, 32, 3, 1, 1) self.relu1 = paddle.nn.ReLU() self.max_pool1 = paddle.nn.MaxPool2D(2, 2) self.conv2 = paddle.nn.Conv2D(32, 64, 3, 1, 1) self.relu2 = paddle.nn.ReLU() self.max_pool2 = paddle.nn.MaxPool2D(2, 2) self.avg_pool = AdaptiveAvgPool2D(1) self.linear= paddle.nn.Linear(64, 2) # 网络的前向计算过程 def forward(self, x): x = self.max_pool1(self.relu1(self.conv1(x))) x = self.max_pool2(self.relu2(self.conv2(x))) x = self.avg_pool(x) x = paddle.reshape(x, [x.shape[0],-1]) x = self.linear(x) return x paddle.Model(LeNet()).summary((-1,3,256,256))
以下是将LeNet代码改为ResNet网络模型的代码:
```
import paddle.nn.functional as F
class ResNet(paddle.nn.Layer):
def __init__(self, num_classes=2):
super(ResNet, self).__init__()
# 第一个卷积层,输出通道数为64,卷积核大小为7x7,步长为2,padding为3
self.conv1 = paddle.nn.Conv2D(3, 64, kernel_size=7, stride=2, padding=3, bias_attr=False)
self.bn1 = paddle.nn.BatchNorm2D(64)
self.relu = paddle.nn.ReLU()
self.max_pool = paddle.nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
# ResNet的主体部分,包括4个残差块
self.layer1 = self._make_layer(64, 3)
self.layer2 = self._make_layer(128, 4, stride=2)
self.layer3 = self._make_layer(256, 6, stride=2)
self.layer4 = self._make_layer(512, 3, stride=2)
# 全局平均池化层
self.avg_pool = paddle.nn.AdaptiveAvgPool2D((1, 1))
# 分类器
self.fc = paddle.nn.Linear(512, num_classes)
def _make_layer(self, channels, blocks, stride=1):
layers = []
# 下采样,对输入进行降维
downsample = None
if stride != 1 or self.in_channels != channels:
downsample = paddle.nn.Sequential(
paddle.nn.Conv2D(self.in_channels, channels, kernel_size=1, stride=stride, bias_attr=False),
paddle.nn.BatchNorm2D(channels)
)
layers.append(ResidualBlock(self.in_channels, channels, stride, downsample))
self.in_channels = channels
for _ in range(1, blocks):
layers.append(ResidualBlock(channels, channels))
return paddle.nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.max_pool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avg_pool(x)
x = paddle.flatten(x, 1)
x = self.fc(x)
return x
class ResidualBlock(paddle.nn.Layer):
def __init__(self, in_channels, out_channels, stride=1, downsample=None):
super(ResidualBlock, self).__init__()
self.conv1 = paddle.nn.Conv2D(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias_attr=False)
self.bn1 = paddle.nn.BatchNorm2D(out_channels)
self.relu = paddle.nn.ReLU()
self.conv2 = paddle.nn.Conv2D(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias_attr=False)
self.bn2 = paddle.nn.BatchNorm2D(out_channels)
self.downsample = downsample
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
paddle.Model(ResNet()).summary((-1,3,256,256))
```
这里我们定义了一个ResNet网络模型,包括一个卷积层,一个最大池化层,4个残差块,一个全局平均池化层和一个分类器。其中,每个残差块包含两个卷积层和一个跳跃连接,卷积层的输出通道数和步长根据网络深度不同而不同。在模型前向传播过程中,我们先使用一次卷积和池化操作,然后将特征图输入到各个残差块中,最后进行全局平均池化和分类。
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改为tensorflow模型
以下是使用TensorFlow实现的Residual Block类的代码:
```
import tensorflow as tf
class Residual(tf.keras.Model):
def __init__(self, in_c, out_c):
super(Residual, self).__init__()
self.conv1 = tf.keras.layers.Conv2D(out_c, 3, padding='same', activation='relu')
self.bn1 = tf.keras.layers.BatchNormalization()
self.conv2 = tf.keras.layers.Conv2D(out_c, 3, padding='same', activation='relu')
self.bn2 = tf.keras.layers.BatchNormalization()
self.botneck = tf.keras.layers.Conv2D(out_c, 1)
self.pool = tf.keras.layers.MaxPool2D(pool_size=(2,2), strides=(2,2))
def call(self, x):
x_prim = x
x = self.conv1(x)
x = self.bn1(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.botneck(x_prim) + x
x = self.pool(x)
return x
```
这个类与PyTorch实现的Residual Block类非常相似,唯一的区别是使用了TensorFlow的卷积层和批归一化层,并且在调用函数时需要使用call()函数而不是forward()函数。