100 lines
2.5 KiB
Go
100 lines
2.5 KiB
Go
package main
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import (
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"fmt"
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"git.kingecg.top/kingecg/gotensor"
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)
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func main() {
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fmt.Println("=== 卷积神经网络示例 ===")
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// 创建一个简单的4x4灰度图像（批大小为1）
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imageData := []float64{
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1.0, 2.0, 3.0, 4.0,
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5.0, 6.0, 7.0, 8.0,
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9.0, 10.0, 11.0, 12.0,
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13.0, 14.0, 15.0, 16.0,
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}
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// 形状为 [batch, channel, height, width]
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imageShape := []int{1, 1, 4, 4}
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imageTensor, err := gotensor.NewTensor(imageData, imageShape)
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if err != nil {
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panic(err)
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}
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fmt.Printf("输入图像:\n%s\n", imageTensor.String())
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// 创建一个简单的卷积核 (3x3)
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kernelData := []float64{
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1.0, 0.0, -1.0,
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1.0, 0.0, -1.0,
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1.0, 0.0, -1.0,
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}
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// 形状为 [input_channels, output_channels, kernel_height, kernel_width]
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kernelShape := []int{1, 1, 3, 3}
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kernelTensor, err := gotensor.NewTensor(kernelData, kernelShape)
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if err != nil {
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panic(err)
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}
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fmt.Printf("卷积核:\n%s\n", kernelTensor.String())
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// 执行卷积操作 (stride=1, padding=0)
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convResult, err := imageTensor.Conv2D(kernelTensor, 1, 0)
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if err != nil {
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panic(err)
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}
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fmt.Printf("卷积结果:\n%s\n", convResult.String())
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// 应用ReLU激活函数
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reluResult := convResult.ReLU()
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fmt.Printf("ReLU结果:\n%s\n", reluResult.String())
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// 执行最大池化 (kernel_size=2, stride=2)
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poolResult, err := reluResult.MaxPool2D(2, 2)
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if err != nil {
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panic(err)
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}
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fmt.Printf("池化结果:\n%s\n", poolResult.String())
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// 展平操作
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flattened := poolResult.Flatten()
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fmt.Printf("展平后大小: %d\n", flattened.Size())
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// 创建一些随机权重进行全连接层操作
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weightsData := []float64{0.1, 0.2, 0.3, 0.4, 0.5, 0.6}
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weightsShape := []int{flattened.Size(), 2} // 输出2类（猫/狗）
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weights, err := gotensor.NewTensor(weightsData, weightsShape)
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if err != nil {
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panic(err)
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}
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// 全连接层计算 (矩阵乘法)
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fcResult, err := flattened.MatMul(weights)
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if err != nil {
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panic(err)
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}
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fmt.Printf("全连接输出:\n%s\n", fcResult.String())
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// 应用Softmax
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softmaxResult := fcResult.Softmax()
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fmt.Printf("Softmax输出 (概率分布):\n%s\n", softmaxResult.String())
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// 创建目标标签 (猫=1, 狗=0)
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targetData := []float64{1.0, 0.0}
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target, err := gotensor.NewTensor(targetData, []int{1, 2})
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if err != nil {
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panic(err)
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}
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// 计算交叉熵损失
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loss := softmaxResult.CrossEntropy(target)
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lossVal, _ := loss.Data.Get(0)
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fmt.Printf("交叉熵损失: %f\n", lossVal)
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fmt.Println("=== 模型前向传播完成 ===")
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} |