PaddlePaddle复现VGG16，如何应用于识别？
摘要：import paddle.nn as nn class VGG16(nn.Layer): def __init__(self, num_classes=1000): super(VGG16, self).__init__() self.l
import paddle.nn as nn
class VGG16(nn.Layer):
def __init__(self, num_classes=1000):
super(VGG16, self).__init__()
self.layer1 = nn.Sequential(
nn.Conv2D(3, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2D(64),
nn.ReLU(),
)
self.layer2 = nn.Sequential(
nn.Conv2D(64, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2D(64),
nn.ReLU(),
nn.MaxPool2D(kernel_size=2, stride=2)
)
self.layer3 = nn.Sequential(
nn.Conv2D(64, 128, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2D(128),
nn.ReLU(),
)
self.layer4 = nn.Sequential(
nn.Conv2D(128, 128, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2D(128),
nn.ReLU(),
nn.MaxPool2D(kernel_size=2, stride=2)
)
self.layer5 = nn.Sequential(
nn.Conv2D(128, 256, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2D(256),
nn.ReLU(),
)
self.layer6 = nn.Sequential(
nn.Conv2D(256, 256, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2D(256),
nn.ReLU(),
nn.MaxPool2D(kernel_size=2, stride=2)
)
self.layer7 = nn.Sequential(
nn.Conv2D(256, 512, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2D(512),
nn.ReLU(),
)
self.layer8 = nn.Sequential(
nn.Conv2D(512, 512, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2D(512),
nn.ReLU(),
nn.MaxPool2D(kernel_size=2, stride=2)
)
self.layer9 = nn.Sequential(
nn.Conv2D(512, 512, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2D(512),
nn.ReLU(),
)
self.layer10 = nn.Sequential(
nn.Conv2D(512, 512, kernel_size=3, stride=1, padding=1),
nn.BatchNorm