```
test(model): 修复矩阵形状不匹配的测试用例 - 将bias从vector改为1x2矩阵以匹配输出形状 - 更新梯度初始化为矩阵格式而非向量 - 修改输入数据创建方式，使用NewMatrix替代NewVector - 调整期望输出计算逻辑以正确处理矩阵运算 fix(optimizer): 修复Adam优化器在矩阵参数下的索引访问 - 添加对1维和2维形状的分别处理逻辑 - 修正一阶矩和二阶矩估计的索引访问方式 - 确保矩阵参数的每个元素都能正确更新 test(optimizer): 增强优化器测试覆盖矩阵参数场景 - 添加矩阵参数的Adam优化器测试用例 - 验证内部状态矩阵形状与参数保持一致 - 确保参数沿梯度反方向正确更新 ```
This commit is contained in:
kingecg
parent
16c2277474
commit
28cf1f533a
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@ -57,7 +57,7 @@ func TestSequential(t *testing.T) {
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if err != nil {
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t.Fatalf("Failed to create weight matrix: %v", err)
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}
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bias, err := gomatrix.NewMatrix([]float64{0.5, 0.5}, []int{2})
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bias, err := gomatrix.NewMatrix([]float64{0.5, 0.5}, []int{1, 2}) // 改为 1x2 矩阵以匹配输出形状
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if err != nil {
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t.Fatalf("Failed to create bias vector: %v", err)
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}
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@ -69,7 +69,7 @@ func TestSequential(t *testing.T) {
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},
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Bias: &Tensor{
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Data: bias,
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Grad: Must(gomatrix.NewVector([]float64{0, 0})),
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Grad: Must(gomatrix.NewMatrix([]float64{0, 0}, []int{1, 2})),
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},
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}
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@ -79,7 +79,13 @@ func TestSequential(t *testing.T) {
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}
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// 测试前向传播
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input := Must(NewVector([]float64{1, 1}))
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inputData, err := gomatrix.NewMatrix([]float64{1, 1}, []int{1, 2})
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if err != nil {
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t.Fatalf("Failed to create input vector: %v", err)
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}
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input := &Tensor{
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Data: inputData,
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}
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output, err := seq.Forward(input)
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if err != nil {
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t.Errorf("Sequential forward failed: %v", err)
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@ -121,9 +127,9 @@ func TestSaveLoadModel(t *testing.T) {
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if err != nil {
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t.Fatalf("Failed to create weight matrix: %v", err)
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}
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bias, err := gomatrix.NewVector([]float64{0.5, 0.5})
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bias, err := gomatrix.NewMatrix([]float64{0.5, 0.5}, []int{1, 2}) // 改为 1x2 矩阵以匹配输出形状
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if err != nil {
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t.Fatalf("Failed to create bias vector: %v", err)
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t.Fatalf("Failed to create bias matrix: %v", err)
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}
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linearLayer := &Linear{
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@ -133,7 +139,7 @@ func TestSaveLoadModel(t *testing.T) {
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},
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Bias: &Tensor{
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Data: bias,
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Grad: Must(gomatrix.NewVector([]float64{0, 0})),
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Grad: Must(gomatrix.NewMatrix([]float64{0, 0}, []int{1, 2})),
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},
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}
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@ -169,21 +175,42 @@ func TestSaveLoadModel(t *testing.T) {
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// TestLinearLayer 测试线性层功能
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func TestLinearLayer(t *testing.T) {
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weight := Must(NewTensor([]float64{2, 0, 0, 3}, []int{2, 2}))
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bias := Must(NewVector([]float64{0.5, 0.5}))
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weightData, err := gomatrix.NewMatrix([]float64{2, 0, 0, 3}, []int{2, 2})
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if err != nil {
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t.Fatalf("Failed to create weight matrix: %v", err)
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}
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weight := &Tensor{
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Data: weightData,
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Grad: Must(gomatrix.NewMatrix([]float64{0, 0, 0, 0}, []int{2, 2})),
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}
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biasData, err := gomatrix.NewMatrix([]float64{0.5, 0.5}, []int{1, 2}) // 改为 1x2 矩阵
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if err != nil {
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t.Fatalf("Failed to create bias vector: %v", err)
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}
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bias := &Tensor{
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Data: biasData,
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Grad: Must(gomatrix.NewVector([]float64{0, 0})),
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}
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layer := NewLinear(weight, bias)
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// 测试前向传播
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input := Must(NewVector([]float64{1, 1}))
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inputData, err := gomatrix.NewMatrix([]float64{1, 1}, []int{1, 2})
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if err != nil {
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t.Fatalf("Failed to create input vector: %v", err)
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}
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input := &Tensor{
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Data: inputData,
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}
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output, err := layer.Forward(input)
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if err != nil {
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t.Fatalf("Linear layer forward failed: %v", err)
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}
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// 计算期望输出: weight * input + bias = [[2,0],[0,3]] * [1,1] + [0.5,0.5] = [2.5,3.5]
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expected0, _ := output.Data.Get(0)
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expected1, _ := output.Data.Get(1)
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// 计算期望输出: input * weight^T + bias = [1,1] * [[2,0],[0,3]]^T + [0.5,0.5] = [1,1] * [[2,0],[0,3]] + [0.5,0.5] = [2,3] + [0.5,0.5] = [2.5,3.5]
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expected0, _ := output.Data.Get(0, 0)
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expected1, _ := output.Data.Get(0, 1)
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if math.Abs(expected0-2.5) > 1e-9 {
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t.Errorf("Expected output[0] to be 2.5, got %v", expected0)
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32
optimizer.go
32
optimizer.go
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@ -164,18 +164,38 @@ func (a *Adam) Step() {
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// 计算偏差修正的一阶矩估计
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mHatData := make([]float64, param.Size())
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mHatShape := shape
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for idx := 0; idx < param.Size(); idx++ {
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mVal, _ := newM.Data.Get(idx)
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mHatData[idx] = mVal / (1 - math.Pow(a.Beta1, float64(a.T)))
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if len(shape) == 1 {
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for idx := 0; idx < shape[0]; idx++ {
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mVal, _ := newM.Data.Get(idx)
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mHatData[idx] = mVal / (1 - math.Pow(a.Beta1, float64(a.T)))
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}
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} else if len(shape) == 2 {
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rows, cols := shape[0], shape[1]
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for r := 0; r < rows; r++ {
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for c := 0; c < cols; c++ {
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mVal, _ := newM.Data.Get(r, c)
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mHatData[r*cols+c] = mVal / (1 - math.Pow(a.Beta1, float64(a.T)))
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}
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}
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}
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mHat, _ := NewTensor(mHatData, mHatShape)
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// 计算偏差修正的二阶矩估计
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vHatData := make([]float64, param.Size())
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vHatShape := shape
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for idx := 0; idx < param.Size(); idx++ {
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vVal, _ := newV.Data.Get(idx)
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vHatData[idx] = vVal / (1 - math.Pow(a.Beta2, float64(a.T)))
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if len(shape) == 1 {
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for idx := 0; idx < shape[0]; idx++ {
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vVal, _ := newV.Data.Get(idx)
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vHatData[idx] = vVal / (1 - math.Pow(a.Beta2, float64(a.T)))
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}
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} else if len(shape) == 2 {
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rows, cols := shape[0], shape[1]
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for r := 0; r < rows; r++ {
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for c := 0; c < cols; c++ {
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vVal, _ := newV.Data.Get(r, c)
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vHatData[r*cols+c] = vVal / (1 - math.Pow(a.Beta2, float64(a.T)))
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}
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}
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}
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vHat, _ := NewTensor(vHatData, vHatShape)
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@ -1,53 +1,63 @@
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package gotensor
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import (
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"testing"
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"math"
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"testing"
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"git.kingecg.top/kingecg/gomatrix"
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)
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func NewMatrix(data [][]float64) (*gomatrix.Matrix, error) {
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c := make([]float64, len(data)*len(data[0]))
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for i := 0; i < len(c); i++ {
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c[i] = data[i/len(data[0])][i%len(data[0])]
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}
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return gomatrix.NewMatrix(c, []int{len(data), len(data[0])})
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}
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// TestSGD 测试SGD优化器
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func TestSGD(t *testing.T) {
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// 创建一些参数用于测试
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weightData, _ := NewMatrix([][]float64{{1.0, 2.0}, {3.0, 4.0}})
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weightGrad, _ := NewMatrix([][]float64{{0.1, 0.2}, {0.3, 0.4}})
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params := []*Tensor{
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{
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Data: Must(NewVector([]float64{1.0, 2.0, 3.0})),
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Grad: Must(NewVector([]float64{0.1, 0.2, 0.3})),
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Data: Must(gomatrix.NewMatrix([]float64{1.0, 2.0, 3.0}, []int{3, 1})),
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Grad: Must(gomatrix.NewMatrix([]float64{0.1, 0.2, 0.3}, []int{3, 1})),
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},
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{
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Data: weightData,
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Grad: weightGrad,
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},
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}
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// 创建SGD优化器
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lr := 0.1
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sgd := NewSGD(params, lr)
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// 保存原始参数值
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origVec0, _ := params[0].Data.Get(0)
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origVec0, _ := params[0].Data.Get(0, 0)
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origMat00, _ := params[1].Data.Get(0, 0)
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// 执行一步优化
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sgd.Step()
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// 检查参数是否已更新
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newVec0, _ := params[0].Data.Get(0)
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newVec0, _ := params[0].Data.Get(0, 0)
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newMat00, _ := params[1].Data.Get(0, 0)
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expectedVec0 := origVec0 - lr*0.1
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expectedMat00 := origMat00 - lr*0.1
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if math.Abs(newVec0-expectedVec0) > 1e-9 {
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t.Errorf("Expected updated param[0][0] to be %v, got %v", expectedVec0, newVec0)
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}
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if math.Abs(newMat00-expectedMat00) > 1e-9 {
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t.Errorf("Expected updated param[1][0,0] to be %v, got %v", expectedMat00, newMat00)
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}
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// 测试ZeroGrad
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sgd.ZeroGrad()
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for _, param := range params {
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@ -72,38 +82,38 @@ func TestAdam(t *testing.T) {
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// 创建一些参数用于测试
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params := []*Tensor{
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{
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Data: Must(NewVector([]float64{1.0, 2.0})),
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Grad: Must(NewVector([]float64{0.1, 0.2})),
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Data: Must(gomatrix.NewMatrix([]float64{1.0, 2.0}, []int{2, 1})),
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Grad: Must(gomatrix.NewMatrix([]float64{0.1, 0.2}, []int{2, 1})),
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},
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}
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// 创建Adam优化器
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lr := 0.001
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beta1 := 0.9
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beta2 := 0.999
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epsilon := 1e-8
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adam := NewAdam(params, lr, beta1, beta2, epsilon)
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// 保存原始参数值
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origVec0, _ := params[0].Data.Get(0)
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origVec0, _ := params[0].Data.Get(0, 0)
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// 执行几步优化
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for i := 0; i < 3; i++ {
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adam.Step()
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}
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// 检查参数是否已更新
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newVec0, _ := params[0].Data.Get(0)
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newVec0, _ := params[0].Data.Get(0, 0)
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if math.Abs(newVec0-origVec0) < 1e-9 {
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t.Errorf("Expected parameter to be updated, but it wasn't. Original: %v, New: %v", origVec0, newVec0)
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}
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// 验证内部状态是否已创建
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if len(adam.M) != len(params) || len(adam.V) != len(params) {
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t.Error("Adam internal states M and V not properly initialized")
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}
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// 测试ZeroGrad
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adam.ZeroGrad()
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for _, param := range params {
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@ -127,7 +137,7 @@ func TestAdam(t *testing.T) {
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func TestAdamWithMatrix(t *testing.T) {
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matrixData, _ := NewMatrix([][]float64{{1.0, 2.0}, {3.0, 4.0}})
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matrixGrad, _ := NewMatrix([][]float64{{0.1, 0.2}, {0.3, 0.4}})
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// 创建矩阵参数用于测试
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params := []*Tensor{
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{
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@ -135,23 +145,65 @@ func TestAdamWithMatrix(t *testing.T) {
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Grad: matrixGrad,
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},
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}
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// 创建Adam优化器
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lr := 0.001
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adam := NewAdam(params, lr, 0.9, 0.999, 1e-8)
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// 保存原始参数值
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origMat00, _ := params[0].Data.Get(0, 0)
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// 验证内部状态是否已正确创建
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if len(adam.M) != len(params) || len(adam.V) != len(params) {
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t.Fatalf("Adam internal states M and V not properly initialized. Expected %d states, got M:%d, V:%d",
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len(params), len(adam.M), len(adam.V))
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}
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// 验证内部状态矩阵的形状与参数一致
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mShape := adam.M[0]["tensor"].Shape()
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vShape := adam.V[0]["tensor"].Shape()
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paramShape := params[0].Shape()
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if mShape[0] != paramShape[0] || mShape[1] != paramShape[1] ||
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vShape[0] != paramShape[0] || vShape[1] != paramShape[1] {
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t.Errorf("Adam internal state shapes don't match parameter shape. "+
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"Param: %v, M: %v, V: %v", paramShape, mShape, vShape)
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}
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// 保存原始参数值的副本
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originalData := make([][]float64, paramShape[0])
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for i := 0; i < paramShape[0]; i++ {
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originalData[i] = make([]float64, paramShape[1])
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for j := 0; j < paramShape[1]; j++ {
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originalData[i][j], _ = params[0].Data.Get(i, j)
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}
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}
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// 执行几步优化
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for i := 0; i < 5; i++ {
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adam.Step()
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}
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// 检查参数是否已更新
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newMat00, _ := params[0].Data.Get(0, 0)
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if math.Abs(newMat00-origMat00) < 1e-9 {
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t.Errorf("Expected parameter to be updated, but it wasn't. Original: %v, New: %v", origMat00, newMat00)
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// 检查所有参数是否已更新
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updated := false
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for i := 0; i < paramShape[0]; i++ {
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for j := 0; j < paramShape[1]; j++ {
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newVal, _ := params[0].Data.Get(i, j)
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if math.Abs(newVal-originalData[i][j]) > 1e-9 {
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updated = true
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return
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}
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}
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}
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}
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if !updated {
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t.Errorf("Expected parameters to be updated, but none were changed")
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}
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// 额外验证更新值是否合理（应该向梯度相反方向移动）
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firstOrig := originalData[0][0]
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firstNew, _ := params[0].Data.Get(0, 0)
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firstGrad, _ := params[0].Grad.Get(0, 0)
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// 参数应该沿着梯度的反方向更新
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if (firstNew-firstOrig)*firstGrad > 0 {
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t.Errorf("Parameter updated in wrong direction. delta=%v, grad=%v",
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firstNew-firstOrig, firstGrad)
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}
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}
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Loading…
Reference in New Issue