定义卷积神经网络实现宝石识别 # --------------------------------------------------------补充完成网络结构定义部分，实现宝石分类------------------------------------------------------------ class MyCNN(nn.Layer): def init(self): super(MyCNN,self).init() self.conv0=nn.Conv2D(in_channels=3, out_channels=64, kernel_size=3, stride=1) self.pool0=nn.MaxPool2D(kernel_size=2, stride=2) self.conv1=nn.Conv2D(in_channels=64, out_channels=128, kernel_size=4, stride=1) self.pool1=nn.MaxPool2D(kernel_size=2, stride=2) self.conv2=nn.Conv2D(in_channels=128, out_channels=50, kernel_size=5) self.pool2=nn.MaxPool2D(kernel_size=2, stride=2) self.conv3=nn.Conv2D(in_channels=50, out_channels=50, kernel_size=5) self.pool3=nn.MaxPool2D(kernel_size=2, stride=2) self.conv4=nn.Conv2D(in_channels=50, out_channels=50, kernel_size=5) self.pool4=nn.MaxPool2D(kernel_size=2, stride=2) self.fc1=nn.Linear(in_features=5033, out_features=25) def forward(self,input): print("input.shape:",input.shape) # 进行第一次卷积和池化操作 x=self.conv0(input) print("x.shape:",x.shape) x=self.pool0(x) print('x0.shape:',x.shape) # 进行第二次卷积和池化操作 x=self.conv1(x) print(x.shape) x=self.pool1(x) print('x1.shape:',x.shape) # 进行第三次卷积和池化操作 x=self.conv2(x) print(x.shape) x=self.pool2(x) print('x2.shape:',x.shape) # 进行第四次卷积和池化操作 x=self.conv3(x) print(x.shape) x=self.pool3(x) print('x3.shape:',x.shape) # 进行第五次卷积和池化操作 x=self.conv4(x) print(x.shape) x=self.pool4(x) print('x4.shape:',x.shape) # 将卷积层的输出展开成一维向量 x=paddle.reshape(x, shape=[-1, 5033]) print('x3.shape:',x.shape) # 进行全连接层操作 y=self.fc1(x) print('y.shape:', y.shape) return y改进代码

好的，这里是一个改进后的卷积神经网络实现宝石分类的代码：

import paddle
import paddle.nn as nn

class MyCNN(nn.Layer):
    def __init__(self):
        super(MyCNN, self).__init__()
        self.conv1 = nn.Conv2D(in_channels=3, out_channels=32, kernel_size=3, padding=1)
        self.bn1 = nn.BatchNorm2D(num_features=32)
        self.relu1 = nn.ReLU()
        self.pool1 = nn.MaxPool2D(kernel_size=2, stride=2)
        
        self.conv2 = nn.Conv2D(in_channels=32, out_channels=64, kernel_size=3, padding=1)
        self.bn2 = nn.BatchNorm2D(num_features=64)
        self.relu2 = nn.ReLU()
        self.pool2 = nn.MaxPool2D(kernel_size=2, stride=2)
        
        self.conv3 = nn.Conv2D(in_channels=64, out_channels=128, kernel_size=3, padding=1)
        self.bn3 = nn.BatchNorm2D(num_features=128)
        self.relu3 = nn.ReLU()
        self.pool3 = nn.MaxPool2D(kernel_size=2, stride=2)
        
        self.conv4 = nn.Conv2D(in_channels=128, out_channels=256, kernel_size=3, padding=1)
        self.bn4 = nn.BatchNorm2D(num_features=256)
        self.relu4 = nn.ReLU()
        self.pool4 = nn.MaxPool2D(kernel_size=2, stride=2)
        
        self.fc1 = nn.Linear(in_features=256*6*6, out_features=512)
        self.relu5 = nn.ReLU()
        self.dropout1 = nn.Dropout(p=0.5)
        
        self.fc2 = nn.Linear(in_features=512, out_features=256)
        self.relu6 = nn.ReLU()
        self.dropout2 = nn.Dropout(p=0.5)
        
        self.fc3 = nn.Linear(in_features=256, out_features=25)
        
    def forward(self, input):
        x = self.conv1(input)
        x = self.bn1(x)
        x = self.relu1(x)
        x = self.pool1(x)
        
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu2(x)
        x = self.pool2(x)
        
        x = self.conv3(x)
        x = self.bn3(x)
        x = self.relu3(x)
        x = self.pool3(x)
        
        x = self.conv4(x)
        x = self.bn4(x)
        x = self.relu4(x)
        x = self.pool4(x)
        
        x = paddle.flatten(x, start_axis=1, stop_axis=-1)
        
        x = self.fc1(x)
        x = self.relu5(x)
        x = self.dropout1(x)
        
        x = self.fc2(x)
        x = self.relu6(x)
        x = self.dropout2(x)
        
        x = self.fc3(x)
        
        return x

这个卷积神经网络包括了四个卷积层和三个全连接层。卷积层用于提取图像特征，全连接层用于将特征映射到分类标签。在卷积层中，我们使用了卷积、批量归一化、ReLU激活和最大池化等操作。在全连接层中，我们使用了ReLU激活和Dropout正则化。最后一层全连接层输出25个类别的概率得分，对应于25种不同的宝石类型。