Documentation
¶
Overview ¶
Package cifar provides a library of tools to download and manipulate Cifar-10 dataset. Information about it in https://www.cs.toronto.edu/~kriz/cifar.html
Index ¶
- Constants
- Variables
- func ConvertToGoImage(images *tensor.Local, exampleNum int) *image.NRGBA
- func DownloadCifar10(baseDir string) error
- func DownloadCifar100(baseDir string) error
- func NewDataset(manager *Manager, name, baseDir string, source DataSource, dtype shapes.DType, ...) *data.InMemoryDataset
- func ResetCache()
- type DataSource
- type ImagesAndLabels
- type Partition
- type PartitionedImagesAndLabels
Constants ¶
const ( C10Url = "https://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz" C10TarName = "cifar-10-binary.tar.gz" C10SubDir = "cifar-10-batches-bin" C100Url = "https://www.cs.toronto.edu/~kriz/cifar-100-binary.tar.gz" C100TarName = "cifar-100-binary.tar.gz" C100SubDir = "cifar-100-binary" // NumExamples is the total number of examples, including training and testing. // The value is the same for both, Cifar-10 and Cifar-100. NumExamples = 60000 // NumTrainExamples is the number of examples reserved for training, the starting ones. // The value is the same for both, Cifar-10 and Cifar-100. NumTrainExamples = 50000 // NumTestExamples is the number of examples reserved for testing, the last ones. // The value is the same for both, Cifar-10 and Cifar-100. NumTestExamples = 10000 )
const ( Width int = 32 Height int = 32 Depth int = 3 )
Width, Height and Depth are the dimensions of the images, the same for Cifar-10 and Cifar-100.
const C10ExamplesPerFile = 10000
Variables ¶
var ( C10Labels = []string{"airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck"} C100CoarseLabels = []string{"aquatic_mammals", "fish", "flowers", "food_containers", "fruit_and_vegetables", "household_electrical_devices", "household_furniture", "insects", "large_carnivores", "large_man-made_outdoor_things", "large_natural_outdoor_scenes", "large_omnivores_and_herbivores", "medium_mammals", "non-insect_invertebrates", "people", "reptiles", "small_mammals", "trees", "vehicles_1", "vehicles_2"} C100FineLabels = []string{"apple", "aquarium_fish", "baby", "bear", "beaver", "bed", "bee", "beetle", "bicycle", "bottle", "bowl", "boy", "bridge", "bus", "butterfly", "camel", "can", "castle", "caterpillar", "cattle", "chair", "chimpanzee", "clock", "cloud", "cockroach", "couch", "crab", "crocodile", "cup", "dinosaur", "dolphin", "elephant", "flatfish", "forest", "fox", "girl", "hamster", "house", "kangaroo", "keyboard", "lamp", "lawn_mower", "leopard", "lion", "lizard", "lobster", "man", "maple_tree", "motorcycle", "mountain", "mouse", "mushroom", "oak_tree", "orange", "orchid", "otter", "palm_tree", "pear", "pickup_truck", "pine_tree", "plain", "plate", "poppy", "porcupine", "possum", "rabbit", "raccoon", "ray", "road", "rocket", "rose", "sea", "seal", "shark", "shrew", "skunk", "skyscraper", "snail", "snake", "spider", "squirrel", "streetcar", "sunflower", "sweet_pepper", "table", "tank", "telephone", "television", "tiger", "tractor", "train", "trout", "tulip", "turtle", "wardrobe", "whale", "willow_tree", "wolf", "woman", "worm"} )
Functions ¶
func DownloadCifar10 ¶
func DownloadCifar100 ¶
func NewDataset ¶
func NewDataset(manager *Manager, name, baseDir string, source DataSource, dtype shapes.DType, partition Partition) *data.InMemoryDataset
NewDataset returns a Dataset for the training data, which implements train.Dataset and hence can be used by train.Trainer methods.
It automatically downloads the data from the web, and then loads the data into memory if it hasn't been loaded yet. It caches the result, so multiple Datasets can be created without any extra costs in time/memory.
func ResetCache ¶
func ResetCache()
Types ¶
type DataSource ¶
type DataSource int
DataSource refers to Cifar-10 (C10) or Cifar-100 (C100).
const ( C10 DataSource = iota C100 )
type ImagesAndLabels ¶
type ImagesAndLabels struct {
// contains filtered or unexported fields
}
type Partition ¶
type Partition int
Partition refers to the train or test partitions of the datasets.
type PartitionedImagesAndLabels ¶ added in v0.5.0
type PartitionedImagesAndLabels [2]ImagesAndLabels
PartitionedImagesAndLabels holds for each partition (Train, Test), one set of Images and Labels.
func LoadCifar10 ¶
func LoadCifar10(manager *Manager, baseDir string, dtype shapes.DType) (partitioned PartitionedImagesAndLabels)
LoadCifar10 into 2 tensors of the given DType: images with given dtype and shaped [NumExamples=60000, Height=32, Width=32, Depth=3], and labels shaped [NumExamples=60000, 1] of Int64. The first 50k examples are for training, and the last 10k for testing. Only Float32 and Float64 dtypes are supported for now.
func LoadCifar100 ¶
func LoadCifar100(manager *Manager, baseDir string, dtype shapes.DType) (partitioned PartitionedImagesAndLabels)
LoadCifar100 into 2 tensors of the given DType: images with given dtype and shaped [NumExamples=60000, Height=32, Width=32, Depth=3], and labels shaped [NumExamples=60000, 1] of Int64. The first 50k examples are for training, and the last 10k for testing. Only Float32 and Float64 dtypes are supported for now.
Source Files
Directories