image_data_generator: Generate minibatches of image data with real-time data augmentation.

Description

Generate minibatches of image data with real-time data augmentation.

Usage

image_data_generator(featurewise_center = FALSE, samplewise_center = FALSE,
  featurewise_std_normalization = FALSE,
  samplewise_std_normalization = FALSE, zca_whitening = FALSE,
  rotation_range = 0, width_shift_range = 0, height_shift_range = 0,
  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)

Arguments

featurewise_center

set input mean to 0 over the dataset.

samplewise_center

set each sample mean to 0.

featurewise_std_normalization

divide inputs by std of the dataset.

samplewise_std_normalization

divide each input by its std.

zca_whitening

apply ZCA whitening.

rotation_range

degrees (0 to 180).

width_shift_range

fraction of total width.

height_shift_range

fraction of total height.

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

points outside the boundaries are filled according to the given mode ('constant', 'nearest', 'reflect' or 'wrap'). Default is 'nearest'.

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".