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

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).