tfplot.summary

Summary Op utilities.

tfplot.summary.wrap(plot_func, _sentinel=None, batch=False, name=None, **kwargs)[source]

Wrap a plot function as a TensorFlow summary builder. It will return a python function that creates a TensorFlow op which evaluates to Summary protocol buffer with image.

The resulting function (say summary_wrapped) will have the following signature:

summary_wrapped(name, tensor, # [more input tensors ...],
                max_outputs=3, collections=None)

Examples

Given a plot function which returns a matplotlib Figure,

>>> def figure_heatmap(data, cmap='jet'):
>>>     fig, ax = tfplot.subplots()
>>>     ax.imshow(data, cmap=cmap)
>>>     return fig

we can wrap it as a summary builder function:

>>> summary_heatmap = tfplot.summary.wrap(figure_heatmap, batch=True)

Now, when building your computation graph, call it to build summary ops like tf.summary.image:

>>> heatmap_tensor
<tf.Tensor 'heatmap_tensor:0' shape=(16, 128, 128) dtype=float32>
>>>
>>> summary_heatmap("heatmap/original", heatmap_tensor)
>>> summary_heatmap("heatmap/cmap_gray", heatmap_tensor, cmap=gray)
>>> summary_heatmap("heatmap/no_default_collections", heatmap_tensor, collections=[])
Parameters:
  • plot_func – A python function or callable to wrap. See the documentation of tfplot.plot() for details.
  • batch – If True, all the tensors passed as argument will be assumed to be batched. Default value is False.
  • name – A default name for the plot op (optional). If not given, the name of plot_func will be used.
  • kwargs – Optional keyword arguments that will be passed by default to plot().
Returns:

A python function that will create a TensorFlow summary operation, passing the provided arguments into plot op.
tfplot.summary.plot(name, plot_func, in_tensors, collections=None, **kwargs)[source]

Create a TensorFlow op that outpus a Summary protocol buffer, to which a single plot operation is executed (i.e. image summary).

Basically, it is a one-liner wrapper of tfplot.ops.plot() and tf.summary.image() calls.

The generated Summary object contains single image summary value of the image of the plot drawn.

Parameters:
  • name – The name of scope for the generated ops and the summary op. Will also serve as a series name prefix in TensorBoard.
  • plot_func – A python function or callable, specifying the plot operation as in tfplot.plot(). See the documentation at tfplot.plot().
  • in_tensors – A list of Tensor objects, as in plot().
  • collections – Optional list of ops.GraphKeys. The collections to add the summary to. Defaults to [_ops.GraphKeys.SUMMARIES].
  • kwargs – Optional keyword arguments passed to plot().
Returns:

A scalar Tensor of type string. The serialized Summary protocol buffer (tensorflow operation).
tfplot.summary.plot_many(name, plot_func, in_tensors, max_outputs=3, collections=None, **kwargs)[source]

Create a TensorFlow op that outputs a Summary protocol buffer, where plots could be drawn in a batch manner. This is a batch version of tfplot.summary.plot().

Specifically, all the input tensors in_tensors to plot_func is assumed to have the same batch size. Tensors corresponding to a single batch element will be passed to plot_func as input.

The resulting Summary contains multiple (up to max_outputs) image summary values, each of which contains a plot rendered by plot_func.

Parameters:
  • name – The name of scope for the generated ops and the summary op. Will also serve as a series name prefix in TensorBoard.
  • plot_func – A python function or callable, specifying the plot operation as in tfplot.plot(). See the documentation at tfplot.plot().
  • in_tensors – A list of Tensor objects, the input to plot_func but each in a batch.
  • max_outputs – Max number of batch elements to generate plots for.
  • collections – Optional list of ops.GraphKeys. The collections to add the sumamry to. Defaults to [_ops.GraphKeys.SUMMARIES].
  • kwargs – Optional keyword arguments passed to plot().
Returns:

A scalar Tensor of type string. The serialized Summary protocol buffer (tensorflow operation).