require('dataset/mnist')
m = Mnist.dataset()
d:size()                      -- => 60000
d:sample(100)                 -- => {data = tensor, class = label}
-- scale values between [0,1] (by default they are in the range [0,255])
m = dataset.Mnist({scale = {0, 1}})
-- or normalize (subtract mean and divide by std)
m = dataset.Mnist({normalize = true})
-- only import a subset of the data (imports full 60,000 samples otherwise),
-- sorted by class label
m = dataset.Mnist({size = 1000, sort = true})

for sample in m:sampler() do
  net:forward(sample.data)
end

local batch = m:mini_batch(1)
-- or use directly
net:forward(m:mini_batch(1).data)
-- set the batch size using an options table
local batch = m:mini_batch(1, {size = 100})

for batch in m:mini_batches() do
   net:forward(batch.data)
end

local anim_options = {
    frames      = 10,
    rotation    = {-20, 20},
    translation = {-5, 5, -5, 5},
    zoom        = {0.6, 1.4}
 }
 s = dataset:sampler({animate = anim_options})

 s = dataset:sampler({pad = 5, binarize = true, flatten = true})

 s = dataset:sampler({pipeline = my_pipeline})

 require 'datset/imageset'
 d = ImageSet.dataset({dir='your-data-directory'})
 while true do w=image.display({image=d().data,win=w}) util.sleep(1/10) end

 require 'datset/videoset'
 d = VideoSet.dataset({dir='KTH'})
 while true do w=image.display({image=d().data,win=w}) util.sleep(1/10) end