tf.keras.preprocessing.image.DirectoryIterator     View source on GitHub    Iterator capable of reading images from a directory on disk. Inherits From: Iterator, Sequence  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.keras.preprocessing.image.DirectoryIterator  
tf.keras.preprocessing.image.DirectoryIterator(
    directory, image_data_generator, target_size=(256, 256),
    color_mode='rgb', classes=None, class_mode='categorical',
    batch_size=32, shuffle=True, seed=None, data_format=None, save_to_dir=None,
    save_prefix='', save_format='png', follow_links=False,
    subset=None, interpolation='nearest', dtype=None
)

 


 Arguments
  directory   Path to the directory to read images from. Each subdirectory in this directory will be considered to contain images from one class, or alternatively you could specify class subdirectories via the classes argument.  
  image_data_generator   Instance of ImageDataGenerator to use for random transformations and normalization.  
  target_size   tuple of integers, dimensions to resize input images to.  
  color_mode   One of "rgb", "rgba", "grayscale". Color mode to read images.  
  classes   Optional list of strings, names of subdirectories containing images from each class (e.g. ["dogs", "cats"]). It will be computed automatically if not set.  
  class_mode   Mode for yielding the targets: "binary": binary targets (if there are only two classes), "categorical": categorical targets, "sparse": integer targets, "input": targets are images identical to input images (mainly used to work with autoencoders), None: no targets get yielded (only input images are yielded).  
  batch_size   Integer, size of a batch.  
  shuffle   Boolean, whether to shuffle the data between epochs.  
  seed   Random seed for data shuffling.  
  data_format   String, one of channels_first, channels_last.  
  save_to_dir   Optional directory where to save the pictures being yielded, in a viewable format. This is useful for visualizing the random transformations being applied, for debugging purposes.  
  save_prefix   String prefix to use for saving sample images (if save_to_dir is set).  
  save_format   Format to use for saving sample images (if save_to_dir is set).  
  subset   Subset of data ("training" or "validation") if validation_split is set in ImageDataGenerator.  
  interpolation   Interpolation method used to resample the image if the target size is different from that of the loaded image. Supported methods are "nearest", "bilinear", and "bicubic". If PIL version 1.1.3 or newer is installed, "lanczos" is also supported. If PIL version 3.4.0 or newer is installed, "box" and "hamming" are also supported. By default, "nearest" is used.  
  dtype   Dtype to use for generated arrays.   
 


 Attributes
  filepaths   List of absolute paths to image files  
  labels   Class labels of every observation  
  sample_weight  
   Methods next View source 
next()
 For python 2.x.
 


 Returns   The next batch.  
 on_epoch_end View source 
on_epoch_end()
 reset View source 
reset()
 set_processing_attrs View source 
set_processing_attrs(
    image_data_generator, target_size, color_mode, data_format, save_to_dir,
    save_prefix, save_format, subset, interpolation
)
 Sets attributes to use later for processing files into a batch.
 


 Arguments
  image_data_generator   Instance of ImageDataGenerator to use for random transformations and normalization.  
  target_size   tuple of integers, dimensions to resize input images to.  
  color_mode   One of "rgb", "rgba", "grayscale". Color mode to read images.  
  data_format   String, one of channels_first, channels_last.  
  save_to_dir   Optional directory where to save the pictures being yielded, in a viewable format. This is useful for visualizing the random transformations being applied, for debugging purposes.  
  save_prefix   String prefix to use for saving sample images (if save_to_dir is set).  
  save_format   Format to use for saving sample images (if save_to_dir is set).  
  subset   Subset of data ("training" or "validation") if validation_split is set in ImageDataGenerator.  
  interpolation   Interpolation method used to resample the image if the target size is different from that of the loaded image. Supported methods are "nearest", "bilinear", and "bicubic". If PIL version 1.1.3 or newer is installed, "lanczos" is also supported. If PIL version 3.4.0 or newer is installed, "box" and "hamming" are also supported. By default, "nearest" is used.    __getitem__ View source 
__getitem__(
    idx
)
 __iter__ View source 
__iter__()
 __len__ View source 
__len__()

 


 Class Variables
  allowed_class_modes  
 
  white_list_formats