Ejemplo n.º 1
0
Archivo: ntm.go Proyecto: philipz/ntm
func (r *RMSProp) update(a, b, c, d float64) {
	grad := blas64.Vector{Inc: 1, Data: r.C.WeightsGrad()}
	grad2 := blas64.Vector{Inc: 1, Data: make([]float64, len(grad.Data))}
	for i, w := range grad.Data {
		grad2.Data[i] = w * w
	}

	n := blas64.Vector{Inc: 1, Data: r.N}
	blas64.Scal(len(n.Data), a, n)
	blas64.Axpy(len(n.Data), 1-a, grad2, n)

	g := blas64.Vector{Inc: 1, Data: r.G}
	blas64.Scal(len(g.Data), a, g)
	blas64.Axpy(len(g.Data), 1-a, grad, g)

	rms := blas64.Vector{Inc: 1, Data: make([]float64, len(r.D))}
	for i, g := range r.G {
		rms.Data[i] = grad.Data[i] / math.Sqrt(r.N[i]-g*g+d)
	}
	rD := blas64.Vector{Inc: 1, Data: r.D}
	blas64.Scal(len(rD.Data), b, rD)
	blas64.Axpy(len(rD.Data), -c, rms, rD)

	val := blas64.Vector{Inc: 1, Data: r.C.WeightsVal()}
	blas64.Axpy(len(rD.Data), 1, rD, val)
}
Ejemplo n.º 2
0
// AddScaledVec adds the vectors a and alpha*b, placing the result in the receiver.
func (v *Vector) AddScaledVec(a *Vector, alpha float64, b *Vector) {
	if alpha == 1 {
		v.AddVec(a, b)
		return
	}
	if alpha == -1 {
		v.SubVec(a, b)
		return
	}

	ar := a.Len()
	br := b.Len()

	if ar != br {
		panic(matrix.ErrShape)
	}

	v.reuseAs(ar)

	if alpha == 0 {
		v.CopyVec(a)
		return
	}

	switch {
	case v == a && v == b: // v <- v + alpha * v = (alpha + 1) * v
		blas64.Scal(ar, alpha+1, v.mat)
	case v == a && v != b: // v <- v + alpha * b
		blas64.Axpy(ar, alpha, b.mat, v.mat)
	case v != a && v == b: // v <- a + alpha * v
		if v.mat.Inc == 1 && a.mat.Inc == 1 {
			// Fast path for a common case.
			v := v.mat.Data
			for i, a := range a.mat.Data {
				v[i] *= alpha
				v[i] += a
			}
			return
		}
		blas64.Scal(ar, alpha, v.mat)
		blas64.Axpy(ar, 1, a.mat, v.mat)
	default: // v <- a + alpha * b
		if v.mat.Inc == 1 && a.mat.Inc == 1 && b.mat.Inc == 1 {
			// Fast path for a common case.
			asm.DaxpyUnitary(alpha, b.mat.Data, a.mat.Data, v.mat.Data)
			return
		}
		blas64.Copy(ar, a.mat, v.mat)
		blas64.Axpy(ar, alpha, b.mat, v.mat)
	}
}
Ejemplo n.º 3
0
func (s *similarityCircuit) Backward() {
	uvuu := s.UV / (s.Unorm * s.Unorm)
	uvvv := s.UV / (s.Vnorm * s.Vnorm)
	uvg := s.TopGrad / (s.Unorm * s.Vnorm)
	u := blas64.Vector{Inc: 1, Data: s.UVal}
	v := blas64.Vector{Inc: 1, Data: s.VVal}

	ugrad := blas64.Vector{Inc: 1, Data: s.UGrad}
	blas64.Axpy(len(s.UGrad), uvg, v, ugrad)
	blas64.Axpy(len(s.UGrad), -uvuu*uvg, u, ugrad)

	vgrad := blas64.Vector{Inc: 1, Data: s.VGrad}
	blas64.Axpy(len(s.VGrad), uvg, u, vgrad)
	blas64.Axpy(len(s.VGrad), -uvvv*uvg, v, vgrad)
}
Ejemplo n.º 4
0
func (c *ConvLayer) convolveR(v autofunc.RVector, in, inR linalg.Vector, out *Tensor3) {
	inMat := c.inputToMatrix(in)
	inMatR := c.inputToMatrix(inR)
	filterMat := blas64.General{
		Rows:   c.FilterCount,
		Cols:   inMat.Cols,
		Stride: inMat.Stride,
		Data:   c.FilterVar.Vector,
	}
	outMat := blas64.General{
		Rows:   out.Width * out.Height,
		Cols:   out.Depth,
		Stride: out.Depth,
		Data:   out.Data,
	}
	blas64.Gemm(blas.NoTrans, blas.Trans, 1, inMatR, filterMat, 0, outMat)
	if filterRV, ok := v[c.FilterVar]; ok {
		filterMatR := blas64.General{
			Rows:   c.FilterCount,
			Cols:   inMat.Cols,
			Stride: inMat.Stride,
			Data:   filterRV,
		}
		blas64.Gemm(blas.NoTrans, blas.Trans, 1, inMat, filterMatR, 1, outMat)
	}

	if biasRV, ok := v[c.Biases]; ok {
		biasVec := blas64.Vector{Inc: 1, Data: biasRV}
		for i := 0; i < len(out.Data); i += outMat.Cols {
			outRow := out.Data[i : i+outMat.Cols]
			outVec := blas64.Vector{Inc: 1, Data: outRow}
			blas64.Axpy(len(outRow), 1, biasVec, outVec)
		}
	}
}
Ejemplo n.º 5
0
// replaces x with Q.x
func (f LQFactor) applyQTo(x *Dense, trans bool) {
	nh, nc := f.LQ.Dims()
	m, n := x.Dims()
	if m != nc {
		panic(ErrShape)
	}
	proj := make([]float64, n)

	if trans {
		for k := nh - 1; k >= 0; k-- {
			hh := f.LQ.RawRowView(k)[k:]

			sub := x.View(k, 0, m-k, n).(*Dense)

			blas64.Gemv(blas.Trans,
				1, sub.Mat, blas64.Vector{Inc: 1, Data: hh},
				0, blas64.Vector{Inc: 1, Data: proj},
			)
			for i := k; i < m; i++ {
				row := x.RawRowView(i)
				blas64.Axpy(n, -hh[i-k],
					blas64.Vector{Inc: 1, Data: proj},
					blas64.Vector{Inc: 1, Data: row},
				)
			}
		}
	} else {
		for k := 0; k < nh; k++ {
			hh := f.LQ.RawRowView(k)[k:]

			sub := x.View(k, 0, m-k, n).(*Dense)

			blas64.Gemv(blas.Trans,
				1, sub.Mat, blas64.Vector{Inc: 1, Data: hh},
				0, blas64.Vector{Inc: 1, Data: proj},
			)
			for i := k; i < m; i++ {
				row := x.RawRowView(i)
				blas64.Axpy(n, -hh[i-k],
					blas64.Vector{Inc: 1, Data: proj},
					blas64.Vector{Inc: 1, Data: row},
				)
			}
		}
	}
}
Ejemplo n.º 6
0
func (c *convLayerResult) propagateBiases(upstream linalg.Vector, grad autofunc.Gradient) {
	if biasGrad, ok := grad[c.Layer.Biases]; ok {
		biasGradVec := blas64.Vector{Inc: 1, Data: biasGrad}
		for i := 0; i < len(upstream); i += c.Layer.OutputDepth() {
			row := blas64.Vector{
				Inc:  1,
				Data: upstream[i : i+c.Layer.OutputDepth()],
			}
			blas64.Axpy(len(biasGrad), 1, row, biasGradVec)
		}
	}
}
Ejemplo n.º 7
0
// MulAdd adds the tensor t1, shifted
// by x1 and y1, and scaled by s, to t.
// It modifies t but leaves t1 alone.
//
// For instance, if x1=1 and y1=0, then
// the first column of t is not affected,
// and the first column of t1 is added to
// the second column of t.
//
// Both tensors must have the same depth.
func (t *Tensor3) MulAdd(x, y int, t1 *Tensor3, s float64) {
	if t.Depth != t1.Depth {
		panic("depths must match")
	}

	var sourceStartX, targetStartX int
	if x > 0 {
		targetStartX = x
	} else {
		sourceStartX = -x
	}

	var sourceStartY, targetStartY int
	if y > 0 {
		targetStartY = y
	} else {
		sourceStartY = -y
	}

	yCount := t.Height - targetStartY
	xCount := t.Width - targetStartX

	if sourceLimit := t1.Height - sourceStartY; sourceLimit < yCount {
		yCount = sourceLimit
	}
	if sourceLimit := t1.Width - sourceStartX; sourceLimit < xCount {
		xCount = sourceLimit
	}

	if rowSize := xCount * t.Depth; rowSize < minOptimizeTensorRowSize {
		for y := 0; y < yCount; y++ {
			for x := 0; x < xCount; x++ {
				for z := 0; z < t.Depth; z++ {
					val1 := t.Get(x+targetStartX, y+targetStartY, z)
					val2 := t1.Get(x+sourceStartX, y+sourceStartY, z)
					t.Set(x+targetStartX, y+targetStartY, z, val1+(val2*s))
				}
			}
		}
	} else {
		for y := 0; y < yCount; y++ {
			target := t.Data[((y+targetStartY)*t.Width+targetStartX)*t.Depth:]
			source := t1.Data[((y+sourceStartY)*t1.Width+sourceStartX)*t1.Depth:]
			targetVec := blas64.Vector{Inc: 1, Data: target}
			sourceVec := blas64.Vector{Inc: 1, Data: source}
			blas64.Axpy(rowSize, s, sourceVec, targetVec)
		}
	}
}
Ejemplo n.º 8
0
// LQ computes an LQ Decomposition for an m-by-n matrix a with m <= n by Householder
// reflections. The LQ decomposition is an m-by-n orthogonal matrix q and an m-by-m
// lower triangular matrix l so that a = l.q. LQ will panic with ErrShape if m > n.
//
// The LQ decomposition always exists, even if the matrix does not have full rank,
// so LQ will never fail unless m > n. The primary use of the LQ decomposition is
// in the least squares solution of non-square systems of simultaneous linear equations.
// This will fail if LQIsFullRank() returns false. The matrix a is overwritten by the
// decomposition.
func LQ(a *Dense) LQFactor {
	// Initialize.
	m, n := a.Dims()
	if m > n {
		panic(ErrShape)
	}

	lq := *a

	lDiag := make([]float64, m)
	projs := NewVector(m, nil)

	// Main loop.
	for k := 0; k < m; k++ {
		hh := lq.RawRowView(k)[k:]
		norm := blas64.Nrm2(len(hh), blas64.Vector{Inc: 1, Data: hh})
		lDiag[k] = norm

		if norm != 0 {
			hhNorm := (norm * math.Sqrt(1-hh[0]/norm))
			if hhNorm == 0 {
				hh[0] = 0
			} else {
				// Form k-th Householder vector.
				s := 1 / hhNorm
				hh[0] -= norm
				blas64.Scal(len(hh), s, blas64.Vector{Inc: 1, Data: hh})

				// Apply transformation to remaining columns.
				if k < m-1 {
					a = lq.View(k+1, k, m-k-1, n-k).(*Dense)
					projs = projs.ViewVec(0, m-k-1)
					projs.MulVec(a, false, NewVector(len(hh), hh))

					for j := 0; j < m-k-1; j++ {
						dst := a.RawRowView(j)
						blas64.Axpy(len(dst), -projs.at(j),
							blas64.Vector{Inc: 1, Data: hh},
							blas64.Vector{Inc: 1, Data: dst},
						)
					}
				}
			}
		}
	}
	*a = lq

	return LQFactor{a, lDiag}
}
Ejemplo n.º 9
0
func (c *ConvLayer) convolve(in linalg.Vector, out *Tensor3) {
	inMat := c.inputToMatrix(in)
	filterMat := blas64.General{
		Rows:   c.FilterCount,
		Cols:   inMat.Cols,
		Stride: inMat.Stride,
		Data:   c.FilterVar.Vector,
	}
	outMat := blas64.General{
		Rows:   out.Width * out.Height,
		Cols:   out.Depth,
		Stride: out.Depth,
		Data:   out.Data,
	}
	blas64.Gemm(blas.NoTrans, blas.Trans, 1, inMat, filterMat, 0, outMat)

	biasVec := blas64.Vector{Inc: 1, Data: c.Biases.Vector}
	for i := 0; i < len(out.Data); i += outMat.Cols {
		outRow := out.Data[i : i+outMat.Cols]
		outVec := blas64.Vector{Inc: 1, Data: outRow}
		blas64.Axpy(len(outRow), 1, biasVec, outVec)
	}
}