先使用 CNN 提取图像的特征，再使用 Transformer 对提取的图像特征和数值数据进行融合处理和分析，最后将融合的特征作为输入基于CNN全链接层进行回归预测。实现代码

以下是使用 PyTorch 实现的代码：

import torch
import torch.nn as nn
import torch.nn.functional as F

class ImageFeatureExtractor(nn.Module):
    def __init__(self):
        super(ImageFeatureExtractor, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
        self.conv3 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc = nn.Linear(256 * 7 * 7, 512)  # 7x7 is the size of the output of the conv layers
        
    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = self.pool(F.relu(self.conv2(x)))
        x = self.pool(F.relu(self.conv3(x)))
        x = x.view(-1, 256 * 7 * 7)
        x = F.relu(self.fc(x))
        return x

class Transformer(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, num_heads, dropout):
        super(Transformer, self).__init__()
        self.encoder_layer = nn.TransformerEncoderLayer(
            d_model=input_size, nhead=num_heads, dim_feedforward=hidden_size, dropout=dropout)
        self.transformer_encoder = nn.TransformerEncoder(self.encoder_layer, num_layers=num_layers)
        
    def forward(self, x):
        x = x.permute(1, 0, 2)  # change to shape (seq_len, batch_size, input_size)
        x = self.transformer_encoder(x)
        x = x.permute(1, 0, 2)  # change back to shape (batch_size, seq_len, input_size)
        return x

class ImageAndNumericalDataRegressor(nn.Module):
    def __init__(self, num_numerical_features, image_feature_size, hidden_size, num_layers, num_heads, dropout):
        super(ImageAndNumericalDataRegressor, self).__init__()
        self.image_feature_extractor = ImageFeatureExtractor()
        self.transformer = Transformer(
            input_size=num_numerical_features + image_feature_size, hidden_size=hidden_size, num_layers=num_layers,
            num_heads=num_heads, dropout=dropout)
        self.fc = nn.Linear(hidden_size, 1)
        
    def forward(self, x_image, x_numerical):
        x_image = self.image_feature_extractor(x_image)
        x = torch.cat([x_image, x_numerical], dim=1)
        x = self.transformer(x)
        x = x.mean(dim=1)
        x = self.fc(x)
        return x

在上面的代码中，我们定义了三个模块：

- `ImageFeatureExtractor`：使用卷积神经网络从图像中提取特征。
- `Transformer`：对提取的图像特征和数值数据进行融合处理和分析。
- `ImageAndNumericalDataRegressor`：将融合的特征作为输入基于 CNN 全链接层进行回归预测。

我们可以通过以下代码创建一个模型实例：

model = ImageAndNumericalDataRegressor(
    num_numerical_features=10, image_feature_size=512, hidden_size=2048, num_layers=6, num_heads=8, dropout=0.1)

其中，`num_numerical_features` 表示数值数据的特征数量，`image_feature_size` 表示从图像中提取的特征维度，`hidden_size` 表示 Transformer 中隐藏层的大小，`num_layers` 表示 Transformer 中堆叠的编码器层数，`num_heads` 表示 Transformer 中多头自注意力机制的头数，`dropout` 表示 Dropout 层的丢弃概率。