Help protect the Great Barrier Reef with TensorFlow on Kaggle Join Challenge


Write an image summary.

Used in the notebooks

Used in the tutorials

See also tf.summary.scalar, tf.summary.SummaryWriter.

Writes a collection of images to the current default summary writer. Data appears in TensorBoard's 'Images' dashboard. Like tf.summary.scalar points, each collection of images is associated with a step and a name. All the image collections with the same name constitute a time series of image collections.

This example writes 2 random grayscale images:

w = tf.summary.create_file_writer('test/logs')
with w.as_default():
  image1 = tf.random.uniform(shape=[8, 8, 1])
  image2 = tf.random.uniform(shape=[8, 8, 1])
  tf.summary.image("grayscale_noise", [image1, image2], step=0)

To avoid clipping, data should be converted to one of the following:

  • floating point values in the range [0,1], or
  • uint8 values in the range [0,255]
# Convert the original dtype=int32 `Tensor` into `dtype=float64`.
rgb_image_float = tf.constant([
  [[1000, 0, 0], [0, 500, 1000]],
]) / 1000
tf.summary.image("picture", [rgb_image_float], step=0)

# Convert original dtype=uint8 `Tensor` into proper range.
rgb_image_uint8 = tf.constant([
  [[1, 1, 0], [0, 0, 1]],
], dtype=tf.uint8) * 255
tf.summary.image("picture", [rgb_image_uint8], step=1)

name A name for this summary. The summary tag used for TensorBoard will be this name prefixed by any active name scopes.
data A Tensor representing pixel data with shape [k, h, w, c], where k is the number of images, h and w are the height and width of the images, and c is the number of channels, which should be 1, 2, 3, or 4 (grayscale, grayscale with alpha, RGB, RGBA). Any of the dimensions may be statically unknown (i.e., None). Floating point data will be clipped to the range [0,1]. Other data types will be clipped into an allowed range for safe casting to uint8, using tf.image.convert_image_dtype.
step Explicit int64-castable monotonic step value for this summary. If omitted, this defaults to tf.summary.experimental.get_step(), which must not be None.
max_outputs Optional int or rank-0 integer Tensor. At most this many images will be emitted at each step. When more than max_outputs many images are provided, the first max_outputs many images will be used and the rest silently discarded.
description Optional long-form description for this summary, as a constant str. Markdown is supported. Defaults to empty.

True on success, or false if no summary was emitted because no default summary writer was available.

ValueError if a default writer exists, but no step was provided and tf.summary.experimental.get_step() is None.