|---| | [ View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.21.0/tensorflow/python/summary/tb_summary.py#L161-L240) | Write an image summary. tf.summary.image( name, data, step=None, max_outputs=3, description=None ) ### Used in the notebooks | Used in the tutorials | |---| | - [Displaying image data in TensorBoard](https://www.tensorflow.org/tensorboard/image_summaries) | See also [`tf.summary.scalar`](https://www.tensorflow.org/api_docs/python/tf/summary/scalar), [`tf.summary.SummaryWriter`](https://www.tensorflow.org/api_docs/python/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`](https://www.tensorflow.org/api_docs/python/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)


| ## Arguments || |---|---| | `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`](https://www.tensorflow.org/api_docs/python/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()`](https://www.tensorflow.org/api_docs/python/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. |



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



| ## Raises || |---|---| | `ValueError` | if a default writer exists, but no step was provided and [`tf.summary.experimental.get_step()`](https://www.tensorflow.org/api_docs/python/tf/summary/experimental/get_step) is None. |