飞浆中草药识别vgg代码

以下是使用PaddlePaddle框架实现的草药识别VGG模型代码：

import paddle
from paddle.nn import Conv2D, MaxPool2D, Linear, Flatten, Dropout
import paddle.nn.functional as F

class VGG(paddle.nn.Layer):
    def __init__(self, num_classes=1000):
        super(VGG, self).__init__()

        self.conv1_1 = Conv2D(in_channels=3, out_channels=64, kernel_size=3, padding=1)
        self.conv1_2 = Conv2D(in_channels=64, out_channels=64, kernel_size=3, padding=1)
        self.pool1 = MaxPool2D(kernel_size=2, stride=2)

        self.conv2_1 = Conv2D(in_channels=64, out_channels=128, kernel_size=3, padding=1)
        self.conv2_2 = Conv2D(in_channels=128, out_channels=128, kernel_size=3, padding=1)
        self.pool2 = MaxPool2D(kernel_size=2, stride=2)

        self.conv3_1 = Conv2D(in_channels=128, out_channels=256, kernel_size=3, padding=1)
        self.conv3_2 = Conv2D(in_channels=256, out_channels=256, kernel_size=3, padding=1)
        self.conv3_3 = Conv2D(in_channels=256, out_channels=256, kernel_size=3, padding=1)
        self.pool3 = MaxPool2D(kernel_size=2, stride=2)

        self.conv4_1 = Conv2D(in_channels=256, out_channels=512, kernel_size=3, padding=1)
        self.conv4_2 = Conv2D(in_channels=512, out_channels=512, kernel_size=3, padding=1)
        self.conv4_3 = Conv2D(in_channels=512, out_channels=512, kernel_size=3, padding=1)
        self.pool4 = MaxPool2D(kernel_size=2, stride=2)

        self.conv5_1 = Conv2D(in_channels=512, out_channels=512, kernel_size=3, padding=1)
        self.conv5_2 = Conv2D(in_channels=512, out_channels=512, kernel_size=3, padding=1)
        self.conv5_3 = Conv2D(in_channels=512, out_channels=512, kernel_size=3, padding=1)
        self.pool5 = MaxPool2D(kernel_size=2, stride=2)

        self.flatten = Flatten()

        self.fc1 = Linear(in_features=512*7*7, out_features=4096)
        self.drop1 = Dropout(p=0.5)

        self.fc2 = Linear(in_features=4096, out_features=4096)
        self.drop2 = Dropout(p=0.5)

        self.fc3 = Linear(in_features=4096, out_features=num_classes)

    def forward(self, x):
        x = self.conv1_1(x)
        x = F.relu(x)
        x = self.conv1_2(x)
        x = F.relu(x)
        x = self.pool1(x)

        x = self.conv2_1(x)
        x = F.relu(x)
        x = self.conv2_2(x)
        x = F.relu(x)
        x = self.pool2(x)

        x = self.conv3_1(x)
        x = F.relu(x)
        x = self.conv3_2(x)
        x = F.relu(x)
        x = self.conv3_3(x)
        x = F.relu(x)
        x = self.pool3(x)

        x = self.conv4_1(x)
        x = F.relu(x)
        x = self.conv4_2(x)
        x = F.relu(x)
        x = self.conv4_3(x)
        x = F.relu(x)
        x = self.pool4(x)

        x = self.conv5_1(x)
        x = F.relu(x)
        x = self.conv5_2(x)
        x = F.relu(x)
        x = self.conv5_3(x)
        x = F.relu(x)
        x = self.pool5(x)

        x = self.flatten(x)

        x = self.fc1(x)
        x = F.relu(x)
        x = self.drop1(x)

        x = self.fc2(x)
        x = F.relu(x)
        x = self.drop2(x)

        x = self.fc3(x)

        return x

这里使用的是VGG16模型，包含了16个卷积层和3个全连接层。你可以根据自己的需求修改或者使用其他的模型。