Generate batches of image data with real-time data augmentation. The data will be looped over (in batches).

image_data_generator(featurewise_center = FALSE, samplewise_center = FALSE,
  featurewise_std_normalization = FALSE,
  samplewise_std_normalization = FALSE, zca_whitening = FALSE,
  zca_epsilon = 1e-06, rotation_range = 0, width_shift_range = 0,
  height_shift_range = 0, brightness_range = NULL, shear_range = 0,
  zoom_range = 0, channel_shift_range = 0, fill_mode = "nearest",
  cval = 0, horizontal_flip = FALSE, vertical_flip = FALSE,
  rescale = NULL, preprocessing_function = NULL, data_format = NULL,
  validation_split = 0)

Arguments

featurewise_center	Set input mean to 0 over the dataset, feature-wise.
samplewise_center	Boolean. Set each sample mean to 0.
featurewise_std_normalization	Divide inputs by std of the dataset, feature-wise.
samplewise_std_normalization	Divide each input by its std.
zca_whitening	apply ZCA whitening.
zca_epsilon	Epsilon for ZCA whitening. Default is 1e-6.
rotation_range	degrees (0 to 180).
width_shift_range	fraction of total width.
height_shift_range	fraction of total height.
brightness_range	the range of brightness to apply
shear_range	shear intensity (shear angle in radians).
zoom_range	amount of zoom. if scalar z, zoom will be randomly picked in the range `[1-z, 1+z]`. A sequence of two can be passed instead to select this range.
channel_shift_range	shift range for each channels.
fill_mode	One of "constant", "nearest", "reflect" or "wrap". Points outside the boundaries of the input are filled according to the given mode: "constant": `kkkkkkkk\|abcd\|kkkkkkkk` (`cval=k`) "nearest": `aaaaaaaa\|abcd\|dddddddd` "reflect": `abcddcba\|abcd\|dcbaabcd` "wrap": `abcdabcd\|abcd\|abcdabcd`
cval	value used for points outside the boundaries when fill_mode is 'constant'. Default is 0.
horizontal_flip	whether to randomly flip images horizontally.
vertical_flip	whether to randomly flip images vertically.
rescale	rescaling factor. If NULL or 0, no rescaling is applied, otherwise we multiply the data by the value provided (before applying any other transformation).
preprocessing_function	function that will be implied on each input. The function will run before any other modification on it. The function should take one argument: one image (tensor with rank 3), and should output a tensor with the same shape.
data_format	'channels_first' or 'channels_last'. In 'channels_first' mode, the channels dimension (the depth) is at index 1, in 'channels_last' mode it is at index 3. It defaults to the `image_data_format` value found in your Keras config file at `~/.keras/keras.json`. If you never set it, then it will be "channels_last".
validation_split	fraction of images reserved for validation (strictly between 0 and 1).