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package nn
import (
"fmt"
"gonum.org/v1/gonum/mat"
)
func (nn *NN) Backprop() {
var (
activation = *nn.ActivationFunc
// lossHist []float64
)
for i := 0; i < nn.Epochs; i++ {
// compute output with current w + b
// then compute loss & backprop
hiddenOutput, err := computeOutput(
nn.XTrain,
nn.Wh,
nn.Bh,
activation,
)
if err != nil {
fmt.Printf("error computing hidden output: %v", err)
}
yHat, err := computeOutput(
hiddenOutput,
nn.Wo,
nn.Bo,
activation,
)
if err != nil {
fmt.Printf("error computing yHat: %v", err)
}
mse := meanSquaredError(nn.YTrain, yHat)
fmt.Println(mse)
}
}
func computeOutput(arr, w, b *mat.Dense, activationFunc func(float64) float64) (*mat.Dense, error) {
// Check if any of the input matrices is nil
if arr == nil || w == nil || b == nil {
return nil, fmt.Errorf("Input matrices cannot be nil")
}
// Check input dimensions
arrRows, arrCols := arr.Dims()
wRows, wCols := w.Dims()
bRows, bCols := b.Dims()
if arrCols != wRows || bCols != wCols {
return nil, fmt.Errorf("Matrix dimension mismatch: arr[%d, %d], w[%d, %d], b[%d, %d]", arrRows, arrCols, wRows, wCols, bRows, bCols)
}
// Compute the dot product between the input matrix 'arr' and the weight matrix 'w'
var product mat.Dense
product.Mul(arr, w)
// Check dimensions of product and bias
productRows, productCols := product.Dims()
if productCols != bCols {
return nil, fmt.Errorf("Matrix dimension mismatch: product[%d, %d], b[%d, %d]", productRows, productCols, bRows, bCols)
}
// Add the bias matrix 'b' to the product
var result mat.Dense
result.Add(&product, b)
// Apply the activation function to the result
applyActivation(&result, activationFunc)
return &result, nil
}
func applyActivation(m *mat.Dense, f func(float64) float64) {
r, c := m.Dims()
data := m.RawMatrix().Data
for i := 0; i < r*c; i++ {
data[i] = f(data[i])
}
}
func meanSquaredError(y, yHat *mat.Dense) float64 {
var sum float64
r, c := y.Dims()
for row := 0; row < r; row++ {
for col := 0; col < c; col++ {
diff := y.At(row, col) - yHat.At(row, col)
sum += (diff * diff)
}
}
return sum / float64((r * c))
}
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