tf.compat.v1.summary.image

Outputs a Summary protocol buffer with images.

tf.compat.v1.summary.image(
    name, tensor, max_outputs=3, collections=None, family=None
)

Migrate to TF2

For compatibility purposes, when invoked in TF2 where the outermost context is eager mode, this API will check if there is a suitable TF2 summary writer context available, and if so will forward this call to that writer instead. A "suitable" writer context means that the writer is set as the default writer, and there is an associated non-empty value for step (see tf.summary.SummaryWriter.as_default, tf.summary.experimental.set_step or alternatively tf.compat.v1.train.create_global_step). For the forwarded call, the arguments here will be passed to the TF2 implementation of tf.summary.image, and the return value will be an empty bytestring tensor, to avoid duplicate summary writing. This forwarding is best-effort and not all arguments will be preserved. Additionally:

  • The TF2 op does not do any of the normalization steps described above. Rather than rescaling data that's outside the expected range, it simply clips it.
  • The TF2 op just outputs the data under a single tag that contains multiple samples, rather than multiple tags (i.e. no "/0" or "/1" suffixes).

To migrate to TF2, please use tf.summary.image instead. Please check Migrating tf.summary usage to TF 2.0 for concrete steps for migration.

How to Map Arguments

TF1 Arg Name TF2 Arg Name Note
name name -
tensor data -
- step Explicit int64-castable monotonic step value. If omitted, this defaults to tf.summary.experimental.get_step().
max_outputs max_outputs -
collections Not Supported -
family Removed Please use tf.name_scope instead to manage summary name prefix.
- description Optional long-form str description for the summary. Markdown is supported. Defaults to empty.

Description

The summary has up to max_outputs summary values containing images. The images are built from tensor which must be 4-D with shape [batch_size, height, width, channels] and where channels can be:

  • 1: tensor is interpreted as Grayscale.
  • 3: tensor is interpreted as RGB.
  • 4: tensor is interpreted as RGBA.

The images have the same number of channels as the input tensor. For float input, the values are normalized one image at a time to fit in the range [0, 255]. uint8 values are unchanged. The op uses two different normalization algorithms:

  • If the input values are all positive, they are rescaled so the largest one is 255.

  • If any input value is negative, the values are shifted so input value 0.0 is at 127. They are then rescaled so that either the smallest value is 0, or the largest one is 255.

The tag in the outputted Summary.Value protobufs is generated based on the name, with a suffix depending on the max_outputs setting:

  • If max_outputs is 1, the summary value tag is 'name/image'.
  • If max_outputs is greater than 1, the summary value tags are generated sequentially as 'name/image/0', 'name/image/1', etc.

name A name for the generated node. Will also serve as a series name in TensorBoard.
tensor A 4-D uint8 or float32 Tensor of shape [batch_size, height, width, channels] where channels is 1, 3, or 4.
max_outputs Max number of batch elements to generate images for.
collections Optional list of ops.GraphKeys. The collections to add the summary to. Defaults to [_ops.GraphKeys.SUMMARIES]
family Optional; if provided, used as the prefix of the summary tag name, which controls the tab name used for display on Tensorboard.

A scalar Tensor of type string. The serialized Summary protocol buffer.