Beispiel #1
0
func ClassifyCmd(netPath, imgPath string) {
	networkData, err := ioutil.ReadFile(netPath)
	if err != nil {
		fmt.Fprintln(os.Stderr, "Error reading network:", err)
		os.Exit(1)
	}
	network, err := neuralnet.DeserializeNetwork(networkData)
	if err != nil {
		fmt.Fprintln(os.Stderr, "Error deserializing network:", err)
		os.Exit(1)
	}

	img, width, height, err := ReadImageFile(imgPath)
	if err != nil {
		fmt.Fprintln(os.Stderr, "Error reading image:", err)
		os.Exit(1)
	}

	firstLayer := network[1].(*neuralnet.ConvLayer)
	if width != firstLayer.InputWidth || height != firstLayer.InputHeight {
		fmt.Fprintf(os.Stderr, "Expected dimensions %dx%d but got %dx%d\n",
			firstLayer.InputWidth, firstLayer.InputHeight, width, height)
	}

	output := network.Apply(&autofunc.Variable{Vector: img}).Output()

	for i, x := range output {
		fmt.Printf("Class %d: probability %f\n", i, math.Exp(x))
	}
}
Beispiel #2
0
// DeserializeNetworkSeqFunc deserializes a NetworkSeqFunc
// that was previously serialized.
func DeserializeNetworkSeqFunc(d []byte) (*NetworkSeqFunc, error) {
	net, err := neuralnet.DeserializeNetwork(d)
	if err != nil {
		return nil, err
	}
	return &NetworkSeqFunc{Network: net}, nil
}
Beispiel #3
0
func DreamCmd(netPath, imgPath string) {
	networkData, err := ioutil.ReadFile(netPath)
	if err != nil {
		fmt.Fprintln(os.Stderr, "Error reading network:", err)
		os.Exit(1)
	}
	network, err := neuralnet.DeserializeNetwork(networkData)
	if err != nil {
		fmt.Fprintln(os.Stderr, "Error deserializing network:", err)
		os.Exit(1)
	}

	convIn := network[1].(*neuralnet.ConvLayer)
	inputImage := &autofunc.Variable{
		Vector: make(linalg.Vector, convIn.InputWidth*convIn.InputHeight*
			convIn.InputDepth),
	}
	for i := range inputImage.Vector {
		inputImage.Vector[i] = rand.Float64()*0.01 + 0.5
	}

	desiredOut := linalg.Vector{0, 1}
	cost := neuralnet.DotCost{}
	grad := autofunc.NewGradient([]*autofunc.Variable{inputImage})
	for i := 0; i < 1000; i++ {
		output := network.Apply(inputImage)
		costOut := cost.Cost(desiredOut, output)
		grad.Zero()
		log.Println("cost is", costOut.Output()[0])
		costOut.PropagateGradient(linalg.Vector{1}, grad)
		grad.AddToVars(-0.01)
	}

	newImage := image.NewRGBA(image.Rect(0, 0, convIn.InputWidth, convIn.InputHeight))
	var idx int
	for y := 0; y < convIn.InputHeight; y++ {
		for x := 0; x < convIn.InputWidth; x++ {
			r := uint8(0xff * inputImage.Vector[idx])
			g := uint8(0xff * inputImage.Vector[idx+1])
			b := uint8(0xff * inputImage.Vector[idx+2])
			newImage.SetRGBA(x, y, color.RGBA{
				R: r,
				G: g,
				B: b,
				A: 0xff,
			})
			idx += 3
		}
	}

	output, err := os.Create(imgPath)
	if err != nil {
		fmt.Fprintln(os.Stderr, "Failed to create output file:", err)
		os.Exit(1)
	}
	defer output.Close()
	png.Encode(output, newImage)
}
Beispiel #4
0
func Run() {
	encoderPath := os.Args[2]

	encoderData, err := ioutil.ReadFile(encoderPath)
	if err != nil {
		fmt.Fprintln(os.Stderr, err)
		os.Exit(1)
	}

	network, err := neuralnet.DeserializeNetwork(encoderData)
	if err != nil {
		fmt.Fprintln(os.Stderr, err)
		os.Exit(1)
	}

	inputPath := os.Args[3]
	outputPath := os.Args[4]

	f, err := os.Open(inputPath)
	if err != nil {
		fmt.Fprintln(os.Stderr, err)
		os.Exit(1)
	}
	defer f.Close()
	inputImage, _, err := image.Decode(f)
	if err != nil {
		fmt.Fprintln(os.Stderr, err)
		os.Exit(1)
	}

	res := network.Apply(&autofunc.Variable{Vector: ImageTensor(inputImage).Data})

	tensor := &neuralnet.Tensor3{
		Width:  inputImage.Bounds().Dx(),
		Height: inputImage.Bounds().Dy(),
		Depth:  3,
		Data:   res.Output(),
	}

	image := ImageFromTensor(tensor)
	outFile, err := os.Create(outputPath)
	if err != nil {
		fmt.Fprintln(os.Stderr, err)
		os.Exit(1)
	}
	defer outFile.Close()
	if err := png.Encode(outFile, image); err != nil {
		fmt.Fprintln(os.Stderr, err)
		os.Exit(1)
	}
}
Beispiel #5
0
func TrainCmd(netPath, dirPath string) {
	log.Println("Loading samples...")
	images, width, height, err := LoadTrainingImages(dirPath)
	if err != nil {
		fmt.Fprintln(os.Stderr, err)
		os.Exit(1)
	}

	log.Println("Creating network...")

	var network neuralnet.Network
	networkData, err := ioutil.ReadFile(netPath)
	if err == nil {
		network, err = neuralnet.DeserializeNetwork(networkData)
		if err != nil {
			fmt.Fprintln(os.Stderr, "Failed to load network:", err)
			os.Exit(1)
		}
		log.Println("Loaded network from file.")
	} else {
		mean, stddev := sampleStatistics(images)
		convLayer := &neuralnet.ConvLayer{
			FilterCount:  FilterCount,
			FilterWidth:  4,
			FilterHeight: 4,
			Stride:       2,

			InputWidth:  width,
			InputHeight: height,
			InputDepth:  ImageDepth,
		}
		maxLayer := &neuralnet.MaxPoolingLayer{
			XSpan:       3,
			YSpan:       3,
			InputWidth:  convLayer.OutputWidth(),
			InputHeight: convLayer.OutputHeight(),
			InputDepth:  convLayer.OutputDepth(),
		}
		convLayer1 := &neuralnet.ConvLayer{
			FilterCount:  FilterCount1,
			FilterWidth:  3,
			FilterHeight: 3,
			Stride:       2,

			InputWidth:  maxLayer.OutputWidth(),
			InputHeight: maxLayer.OutputHeight(),
			InputDepth:  maxLayer.InputDepth,
		}
		network = neuralnet.Network{
			&neuralnet.RescaleLayer{
				Bias:  -mean,
				Scale: 1 / stddev,
			},
			convLayer,
			neuralnet.HyperbolicTangent{},
			maxLayer,
			neuralnet.HyperbolicTangent{},
			convLayer1,
			neuralnet.HyperbolicTangent{},
			&neuralnet.DenseLayer{
				InputCount: convLayer1.OutputWidth() * convLayer1.OutputHeight() *
					convLayer1.OutputDepth(),
				OutputCount: HiddenSize,
			},
			neuralnet.HyperbolicTangent{},
			&neuralnet.DenseLayer{
				InputCount:  HiddenSize,
				OutputCount: len(images),
			},
			&neuralnet.LogSoftmaxLayer{},
		}
		network.Randomize()
		log.Println("Created new network.")
	}

	samples := neuralSamples(images)
	sgd.ShuffleSampleSet(samples)

	validationCount := int(ValidationFraction * float64(samples.Len()))
	validationSamples := samples.Subset(0, validationCount)
	trainingSamples := samples.Subset(validationCount, samples.Len())

	costFunc := neuralnet.DotCost{}
	gradienter := &sgd.Adam{
		Gradienter: &neuralnet.BatchRGradienter{
			Learner: network.BatchLearner(),
			CostFunc: &neuralnet.RegularizingCost{
				Variables: network.Parameters(),
				Penalty:   Regularization,
				CostFunc:  costFunc,
			},
		},
	}
	sgd.SGDInteractive(gradienter, trainingSamples, StepSize, BatchSize, func() bool {
		log.Printf("Costs: validation=%d/%d cost=%f",
			countCorrect(network, validationSamples), validationSamples.Len(),
			neuralnet.TotalCost(costFunc, network, trainingSamples))
		return true
	})

	data, _ := network.Serialize()
	if err := ioutil.WriteFile(netPath, data, 0755); err != nil {
		fmt.Fprintln(os.Stderr, "Failed to save:", err)
		os.Exit(1)
	}
}