tf.data.Dataset from image files in a directory.
image_dataset_from_directory( directory, labels = "inferred", label_mode = "int", class_names = NULL, color_mode = "rgb", batch_size = 32, image_size = c(256, 256), shuffle = TRUE, seed = NULL, validation_split = NULL, subset = NULL, interpolation = "bilinear", follow_links = FALSE, crop_to_aspect_ratio = FALSE, ... )
Directory where the data is located. If labels is "inferred", it should contain subdirectories, each containing images for a class. Otherwise, the directory structure is ignored.
Either "inferred" (labels are generated from the directory structure), or a list/tuple of integer labels of the same size as the number of image files found in the directory. Labels should be sorted according to the alphanumeric order of the image file paths (obtained via os.walk(directory) in Python).
'int': labels are encoded as integers (e.g. for sparse_categorical_crossentropy loss).
'categorical': labels are encoded as a categorical vector (e.g. for categorical_crossentropy loss).
'binary': labels (there can be only 2) are encoded as float32 scalars with values 0 or 1 (e.g. for binary_crossentropy).
NULL: (no labels).
Only valid if "labels" is "inferred". This is the explict list of class names (must match names of subdirectories). Used to control the order of the classes (otherwise alphanumerical order is used).
One of "grayscale", "rgb", "rgba". Default: "rgb". Whether the images will be converted to have 1, 3, or 4 channels.
Size of the batches of data. Default: 32.
Size to resize images to after they are read from disk. Defaults to (256, 256). Since the pipeline processes batches of images that must all have the same size, this must be provided.
Whether to shuffle the data. Default: TRUE. If set to FALSE, sorts the data in alphanumeric order.
Optional random seed for shuffling and transformations.
Optional float between 0 and 1, fraction of data to reserve for validation.
One of "training" or "validation". Only used if validation_split is set.
String, the interpolation method used when resizing images. Defaults to bilinear. Supports bilinear, nearest, bicubic, area, lanczos3, lanczos5, gaussian, mitchellcubic.
Whether to visits subdirectories pointed to by symlinks. Defaults to FALSE.
TRUE, resize the images without aspect ratio
distortion. When the original aspect ratio differs from the target aspect
ratio, the output image will be cropped so as to return the largest
possible window in the image (of size image_size) that matches the target
aspect ratio. By default (crop_to_aspect_ratio=False), aspect ratio may not
A tf.data.Dataset object. If label_mode is
NULL, it yields float32
tensors of shape
(batch_size, image_size, image_size, num_channels),
encoding images (see below for rules regarding
Otherwise, it yields pairs of
(images, labels), where images has shape
(batch_size, image_size, image_size, num_channels), and labels
follows the format described below.
Rules regarding labels format:
if label_mode is int, the labels are an int32 tensor of shape
if label_mode is binary, the labels are a float32 tensor of 1s and 0s of
if label_mode is categorial, the labels are a float32 tensor of shape
(batch_size, num_classes), representing a one-hot encoding of the class
Rules regarding number of channels in the yielded images:
if color_mode is grayscale, there's 1 channel in the image tensors.
if color_mode is rgb, there are 3 channel in the image tensors.
if color_mode is rgba, there are 4 channel in the image tensors.
If your directory structure is:
main_directory/ ...class_a/ ......a_image_1.jpg ......a_image_2.jpg ...class_b/ ......b_image_1.jpg ......b_image_2.jpg
will return a
tf.data.Dataset that yields batches of images from the
subdirectories class_a and class_b, together with labels 0 and 1 (0
corresponding to class_a and 1 corresponding to class_b).
Supported image formats: jpeg, png, bmp, gif. Animated gifs are truncated to the first frame.