Explicit interface

log_value(logger, name, value; step=step(logger))

Logs a floating-point variable with name name at step step. If value is a complex number, the real and imaginary part are logged separately.

log_histogram(logger, name, (bins,weights); step=step(logger))

Logs a histogram under the tag name on given step. The histogram must be passed as a tuple holding the N+1 bin edges and the height of the N bins.

You can also pass the raw data, and a binning algorithm from StatsBase.jl will be used to bin the data.

log_histogram(logger, name, data::Vector; step=step(logger))

Bins the values found in data and logs them as an histogram under the tag name.

log_vector(logger, name, data::Vector; step=step(logger))

Logs the vector found in data as an histogram under the name name.

log_text(logger::TBLogger, name::String, text::Any; step=step(logger))

Logs text with name name at step step

• text: If text is a 2-D or 3-D Array, it will be rendered as a table or a list. Any other data will be represented as string

log_image(logger::TBLogger, name::AbstractString, imgArray::AbstractArray, format::ImageFormat; step=step(logger))

Log an image using image data and format

• imgArray: image data. A 1-D, 2-D or 3-D Array of pixel values. pixel values can be Real [0, 1] or Integer[0, 255]
• format: format of the image. It can be one of {L, LN, NL, CL, LC, NCL, NLC, CLN, LCN, HW, WH, HWC, WHC, CHW, CWH, HWN, WHN, NHW, NWH, HWCN, WHCN, CHWN, CWHN, NHWC, NWHC, NCHW, NCWH}
  • L: Length
  • C: Channel/Color
  • H: Height
  • W: Width
  • N: Observation

logimage(logger, name, obj; [step=currentstep])

Renders the object to PNG and sends it to TensorBoard as an image with tag name. showable("image/png", obj) must be true.

log_images(logger::TBLogger, name::AbstractString, imgArrays::AbstractArray, format::ImageFormat; step=step(logger))

Log multiple images using Array of images and format

• imgArrays: Array of images, e.g. Array{Array{Float64, 3}, 1}. Array of images can be multidimensional.
• format: format which applies to each image in the Array of images. It can be one of {L, LN, NL, CL, LC, NCL, NLC, CLN, LCN, HW, WH, HWC, WHC, CHW, CWH, HWN, WHN, NHW, NWH, HWCN, WHCN, CHWN, CWHN, NHWC, NWHC, NCHW, NCWH}

log_audio(logger::TBLogger, name::AbstractString, samples::AbstractArray, samplerate::Real; step=step(logger))

Logs an audio clip with name name at step step

• samples: Array of samples N*C where N = number of samples and C = number of channels
• samplerate: Sampling rate or Sampling frequency: a Real value

log_audios(logger::TBLogger, name::AbstractString, samples::AbstractArray, samplerate::Real; step=step(logger))

Logs multiple audio clips at step step

• samples: Array of audio clips which are Arrays of samples N*C where N = number of samples and C = number of channel
• samplerate: Sampling rate or Sampling frequency: a Real value same for all clips

log_embeddings(logger::TBLogger, name::AbstractString, mat::AbstractMatrix; metadata, metadata_header, img_labels, step=step(logger))

Log embedding data to tensorboard and visualize in 3-D or 2-D with PCA, t-SNE or UMAP.

• mat: 2-D Matrix of data, with rows representing the samples and columns representing the features
• metadata: Array of labels for each sample. Each element across 1st dimenstion will be converted to string
• metadata_header: 1-D Array. Useful when samples have multiple labels. size should be same as size of each row in metadata
• img_labels: TBImages object representing image labels for each sample.
  • each number of images (N) must be equal to number of samples in mat
  • each image must be a square (H == W)
  • the value √N * W must be less than or equal to 8192 because of tensorboard restrictions.

log_custom_scalar(logger, layout::AbstractDict; step = step(logger))

Groups multiple scalars in the same plot to be visualized by the CUSTOMSCALARS plugin. Note that this function sets the metadata: the actual values must be logged separately with `logvalue` and referenced with the correct tag.

The layout argument is structured as follows:

layout = Dict(category => Dict(name => (chart_type, [tag1, tag2, ...])))

where category is the main tag for the plot, name is the plot's name, chart_type is one between tb_multiline and tb_margin and the array of tags contains the actual references to the logged scalars.