Module: Chainer::Datasets::Mnist

Defined in:
lib/chainer/datasets/mnist.rb

Class Method Summary collapse

Class Method Details

.get_mnist(withlabel: true, ndim: 1, scale: 1.0, dtype: Numo::SFloat, label_dtype: Numo::Int32) ⇒ Object 



6
7
8
9
10
11
12
13
 
# File 'lib/chainer/datasets/mnist.rb', line 6

def self.get_mnist(withlabel: true, ndim: 1, scale: 1.0, dtype: Numo::SFloat, label_dtype: Numo::Int32)
  train_raw = retrieve_mnist_training
  train = preprocess_mnist(train_raw, withlabel, ndim, scale, dtype, label_dtype)

  test_raw = retrieve_mnist_test
  test = preprocess_mnist(test_raw, withlabel, ndim, scale, dtype, label_dtype)
  [train, test]
end

.make_npz(path, urls) ⇒ Object 



56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
 
# File 'lib/chainer/datasets/mnist.rb', line 56

def self.make_npz(path, urls)
  x_url, y_url = urls
  x_path = Chainer::Dataset::Download.cached_download(x_url)
  y_path = Chainer::Dataset::Download.cached_download(y_url)

  x = nil
  y = nil

  Zlib::GzipReader.open(x_path) do |fx|
    Zlib::GzipReader.open(y_path) do |fy|
      fx.read(4)
      fy.read(4)

      n = fx.read(4).unpack('i>')[0]
      fy.read(4)
      fx.read(8)

      x = Numo::UInt8.new(n, 784).rand(n)
      y = Numo::UInt8.new(n).rand(n)

      n.times do |i|
        y[i] = fy.read(1).ord
        784.times do |j|
          x[i, j] = fx.read(1).ord
        end
      end
    end
  end

  { x: x, y: y}
end

.preprocess_mnist(raw, withlabel, ndim, scale, image_dtype, label_dtype) ⇒ Object 



15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
 
# File 'lib/chainer/datasets/mnist.rb', line 15

def self.preprocess_mnist(raw, withlabel, ndim, scale, image_dtype, label_dtype)
  images = raw[:x]
  if ndim == 2
    images = images.reshape(true, 28, 28)
  elsif ndim == 3
    images = images.reshape(true, 1, 28, 28)
  elsif ndim != 1
    raise "invalid ndim for MNIST dataset"
  end

  images = images.cast_to(image_dtype)
  images *= scale / 255.0
  
  if withlabel
    labels = raw[:y].cast_to(label_dtype)
    TupleDataset.new(images, labels)
  else
    images
  end
end

.retrieve_mnist(name, urls) ⇒ Object 



48
49
50
51
52
53
54
 
# File 'lib/chainer/datasets/mnist.rb', line 48

def self.retrieve_mnist(name, urls)
  root = Chainer::Dataset::Download.get_dataset_directory('pfnet/chainer/mnist')
  path = File.expand_path(name, root)
  Chainer::Dataset::Download.cache_or_load_file(path) do
    make_npz(path, urls)
  end
end

.retrieve_mnist_test ⇒ Object 



42
43
44
45
46
 
# File 'lib/chainer/datasets/mnist.rb', line 42

def self.retrieve_mnist_test
  urls = ['http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz',
          'http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz']
  retrieve_mnist('test.npz', urls)
end

.retrieve_mnist_training ⇒ Object 



36
37
38
39
40
 
# File 'lib/chainer/datasets/mnist.rb', line 36

def self.retrieve_mnist_training
  urls = ['http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz',
          'http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz']
  retrieve_mnist('train.npz', urls)
end