tf.raw_ops.ImageSummary

Outputs a Summary protocol buffer with images.

tf.raw_ops.ImageSummary(
    tag, tensor, max_images=3, bad_color=_execute.make_tensor(\n    'dtype: DT_UINT8
    tensor_shape { dim { size: 4 } } int_val: 255 int_val: 0 int_val: 0 int_val:
    255'\n    , 'bad_color'), name=None
)

The summary has up to max_images 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 argument is a scalar Tensor of type string. It is used to build the tag of the summary values:

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

The bad_color argument is the color to use in the generated images for non-finite input values. It is a uint8 1-D tensor of length channels. Each element must be in the range [0, 255] (It represents the value of a pixel in the output image). Non-finite values in the input tensor are replaced by this tensor in the output image. The default value is the color red.