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Summary Operations

[TOC]

Generation of summaries.

Class for writing Summaries

class tf.summary.FileWriter {#FileWriter}

Writes Summary protocol buffers to event files.

The FileWriter class provides a mechanism to create an event file in a given directory and add summaries and events to it. The class updates the file contents asynchronously. This allows a training program to call methods to add data to the file directly from the training loop, without slowing down training.

tf.summary.FileWriter.__init__(logdir, graph=None, max_queue=10, flush_secs=120, graph_def=None) {#FileWriter.init}

Creates a FileWriter and an event file.

On construction the summary writer creates a new event file in logdir. This event file will contain Event protocol buffers constructed when you call one of the following functions: add_summary(), add_session_log(), add_event(), or add_graph().

If you pass a Graph to the constructor it is added to the event file. (This is equivalent to calling add_graph() later).

TensorBoard will pick the graph from the file and display it graphically so you can interactively explore the graph you built. You will usually pass the graph from the session in which you launched it:

...create a graph...
# Launch the graph in a session.
sess = tf.Session()
# Create a summary writer, add the 'graph' to the event file.
writer = tf.summary.FileWriter(<some-directory>, sess.graph)

The other arguments to the constructor control the asynchronous writes to the event file:

  • flush_secs: How often, in seconds, to flush the added summaries and events to disk.
  • max_queue: Maximum number of summaries or events pending to be written to disk before one of the 'add' calls block.
Args:
  • logdir: A string. Directory where event file will be written.
  • graph: A Graph object, such as sess.graph.
  • max_queue: Integer. Size of the queue for pending events and summaries.
  • flush_secs: Number. How often, in seconds, to flush the pending events and summaries to disk.
  • graph_def: DEPRECATED: Use the graph argument instead.

tf.summary.FileWriter.add_summary(summary, global_step=None) {#FileWriter.add_summary}

Adds a Summary protocol buffer to the event file.

This method wraps the provided summary in an Event protocol buffer and adds it to the event file.

You can pass the result of evaluating any summary op, using Session.run() or Tensor.eval(), to this function. Alternatively, you can pass a tf.Summary protocol buffer that you populate with your own data. The latter is commonly done to report evaluation results in event files.

Args:
  • summary: A Summary protocol buffer, optionally serialized as a string.
  • global_step: Number. Optional global step value to record with the summary.

tf.summary.FileWriter.add_session_log(session_log, global_step=None) {#FileWriter.add_session_log}

Adds a SessionLog protocol buffer to the event file.

This method wraps the provided session in an Event protocol buffer and adds it to the event file.

Args:
  • session_log: A SessionLog protocol buffer.
  • global_step: Number. Optional global step value to record with the summary.

tf.summary.FileWriter.add_event(event) {#FileWriter.add_event}

Adds an event to the event file.

Args:
  • event: An Event protocol buffer.

tf.summary.FileWriter.add_graph(graph, global_step=None, graph_def=None) {#FileWriter.add_graph}

Adds a Graph to the event file.

The graph described by the protocol buffer will be displayed by TensorBoard. Most users pass a graph in the constructor instead.

Args:
  • graph: A Graph object, such as sess.graph.
  • global_step: Number. Optional global step counter to record with the graph.
  • graph_def: DEPRECATED. Use the graph parameter instead.
Raises:
  • ValueError: If both graph and graph_def are passed to the method.

tf.summary.FileWriter.add_run_metadata(run_metadata, tag, global_step=None) {#FileWriter.add_run_metadata}

Adds a metadata information for a single session.run() call.

Args:
  • run_metadata: A RunMetadata protobuf object.
  • tag: The tag name for this metadata.
  • global_step: Number. Optional global step counter to record with the StepStats.
Raises:
  • ValueError: If the provided tag was already used for this type of event.

tf.summary.FileWriter.get_logdir() {#FileWriter.get_logdir}

Returns the directory where event file will be written.

tf.summary.FileWriter.flush() {#FileWriter.flush}

Flushes the event file to disk.

Call this method to make sure that all pending events have been written to disk.

tf.summary.FileWriter.close() {#FileWriter.close}

Flushes the event file to disk and close the file.

Call this method when you do not need the summary writer anymore.

Other Methods

tf.summary.FileWriter.reopen() {#FileWriter.reopen}

Reopens the EventFileWriter.

Can be called after close() to add more events in the same directory. The events will go into a new events file.

Does nothing if the EventFileWriter was not closed.

class tf.summary.FileWriterCache {#FileWriterCache}

Cache for file writers.

This class caches file writers, one per directory.

tf.summary.FileWriterCache.clear() {#FileWriterCache.clear}

Clear cached summary writers. Currently only used for unit tests.

tf.summary.FileWriterCache.get(logdir) {#FileWriterCache.get}

Returns the FileWriter for the specified directory.

Args:
  • logdir: str, name of the directory.
Returns:

A FileWriter.

Summary Ops

tf.summary.tensor_summary(name, tensor, summary_description=None, collections=None) {#tensor_summary}

Outputs a Summary protocol buffer with a serialized tensor.proto.

The generated Summary has one summary value containing the input tensor.

Args:
  • name: A name for the generated node. Will also serve as the series name in TensorBoard.
  • tensor: A tensor of any type and shape to serialize.
  • summary_description: Optional summary_pb2.SummaryDescription()
  • collections: Optional list of graph collections keys. The new summary op is added to these collections. Defaults to [GraphKeys.SUMMARIES].
Returns:

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

tf.summary.scalar(name, tensor, collections=None) {#scalar}

Outputs a Summary protocol buffer containing a single scalar value.

The generated Summary has a Tensor.proto containing the input Tensor.

Args:
  • name: A name for the generated node. Will also serve as the series name in TensorBoard.
  • tensor: A real numeric Tensor containing a single value.
  • collections: Optional list of graph collections keys. The new summary op is added to these collections. Defaults to [GraphKeys.SUMMARIES].
Returns:

A scalar Tensor of type string. Which contains a Summary protobuf.

Raises:
  • ValueError: If tensor has the wrong shape or type.

tf.summary.histogram(name, values, collections=None) {#histogram}

Outputs a Summary protocol buffer with a histogram.

The generated Summary has one summary value containing a histogram for values.

This op reports an InvalidArgument error if any value is not finite.

Args:
  • name: A name for the generated node. Will also serve as a series name in TensorBoard.
  • values: A real numeric Tensor. Any shape. Values to use to build the histogram.
  • collections: Optional list of graph collections keys. The new summary op is added to these collections. Defaults to [GraphKeys.SUMMARIES].
Returns:

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

tf.summary.audio(name, tensor, sample_rate, max_outputs=3, collections=None) {#audio}

Outputs a Summary protocol buffer with audio.

The summary has up to max_outputs summary values containing audio. The audio is built from tensor which must be 3-D with shape [batch_size, frames, channels] or 2-D with shape [batch_size, frames]. The values are assumed to be in the range of [-1.0, 1.0] with a sample rate of sample_rate.

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/audio'.
  • If max_outputs is greater than 1, the summary value tags are generated sequentially as 'name/audio/0', 'name/audio/1', etc
Args:
  • name: A name for the generated node. Will also serve as a series name in TensorBoard.
  • tensor: A 3-D float32 Tensor of shape [batch_size, frames, channels] or a 2-D float32 Tensor of shape [batch_size, frames].
  • sample_rate: A Scalar float32 Tensor indicating the sample rate of the signal in hertz.
  • max_outputs: Max number of batch elements to generate audio for.
  • collections: Optional list of ops.GraphKeys. The collections to add the summary to. Defaults to [_ops.GraphKeys.SUMMARIES]
Returns:

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

tf.summary.image(name, tensor, max_outputs=3, collections=None) {#image}

Outputs a Summary protocol buffer with images.

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.
Args:
  • 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]
Returns:

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

tf.summary.merge(inputs, collections=None, name=None) {#merge}

Merges summaries.

This op creates a Summary protocol buffer that contains the union of all the values in the input summaries.

When the Op is run, it reports an InvalidArgument error if multiple values in the summaries to merge use the same tag.

Args:
  • inputs: A list of string Tensor objects containing serialized Summary protocol buffers.
  • collections: Optional list of graph collections keys. The new summary op is added to these collections. Defaults to [].
  • name: A name for the operation (optional).
Returns:

A scalar Tensor of type string. The serialized Summary protocol buffer resulting from the merging.

tf.summary.merge_all(key='summaries') {#merge_all}

Merges all summaries collected in the default graph.

Args:
  • key: GraphKey used to collect the summaries. Defaults to GraphKeys.SUMMARIES.
Returns:

If no summaries were collected, returns None. Otherwise returns a scalar Tensor of type string containing the serialized Summary protocol buffer resulting from the merging.

Utilities

tf.summary.get_summary_description(node_def) {#get_summary_description}

Given a TensorSummary node_def, retrieve its SummaryDescription.

When a Summary op is instantiated, a SummaryDescription of associated metadata is stored in its NodeDef. This method retrieves the description.

Args:
  • node_def: the node_def_pb2.NodeDef of a TensorSummary op
Returns:

a summary_pb2.SummaryDescription

Raises:
  • ValueError: if the node is not a summary op.

Other Functions and Classes

class tf.summary.SummaryDescription {#SummaryDescription}

tf.summary.SummaryDescription.ByteSize() {#SummaryDescription.ByteSize}

tf.summary.SummaryDescription.Clear() {#SummaryDescription.Clear}

tf.summary.SummaryDescription.ClearExtension(extension_handle) {#SummaryDescription.ClearExtension}

tf.summary.SummaryDescription.ClearField(field_name) {#SummaryDescription.ClearField}

tf.summary.SummaryDescription.CopyFrom(other_msg) {#SummaryDescription.CopyFrom}

Copies the content of the specified message into the current message.

The method clears the current message and then merges the specified message using MergeFrom.

Args:
  • other_msg: Message to copy into the current one.

tf.summary.SummaryDescription.DiscardUnknownFields() {#SummaryDescription.DiscardUnknownFields}

tf.summary.SummaryDescription.FindInitializationErrors() {#SummaryDescription.FindInitializationErrors}

Finds required fields which are not initialized.

Returns:

A list of strings. Each string is a path to an uninitialized field from the top-level message, e.g. "foo.bar[5].baz".

tf.summary.SummaryDescription.FromString(s) {#SummaryDescription.FromString}

tf.summary.SummaryDescription.HasExtension(extension_handle) {#SummaryDescription.HasExtension}

tf.summary.SummaryDescription.HasField(field_name) {#SummaryDescription.HasField}

tf.summary.SummaryDescription.IsInitialized(errors=None) {#SummaryDescription.IsInitialized}

Checks if all required fields of a message are set.

Args:
  • errors: A list which, if provided, will be populated with the field paths of all missing required fields.
Returns:

True iff the specified message has all required fields set.

tf.summary.SummaryDescription.ListFields() {#SummaryDescription.ListFields}

tf.summary.SummaryDescription.MergeFrom(msg) {#SummaryDescription.MergeFrom}

tf.summary.SummaryDescription.MergeFromString(serialized) {#SummaryDescription.MergeFromString}

tf.summary.SummaryDescription.ParseFromString(serialized) {#SummaryDescription.ParseFromString}

Parse serialized protocol buffer data into this message.

Like MergeFromString(), except we clear the object first and do not return the value that MergeFromString returns.

tf.summary.SummaryDescription.RegisterExtension(extension_handle) {#SummaryDescription.RegisterExtension}

tf.summary.SummaryDescription.SerializePartialToString() {#SummaryDescription.SerializePartialToString}

tf.summary.SummaryDescription.SerializeToString() {#SummaryDescription.SerializeToString}

tf.summary.SummaryDescription.SetInParent() {#SummaryDescription.SetInParent}

Sets the _cached_byte_size_dirty bit to true, and propagates this to our listener iff this was a state change.

tf.summary.SummaryDescription.WhichOneof(oneof_name) {#SummaryDescription.WhichOneof}

Returns the name of the currently set field inside a oneof, or None.

tf.summary.SummaryDescription.__deepcopy__(memo=None) {#SummaryDescription.deepcopy}

tf.summary.SummaryDescription.__eq__(other) {#SummaryDescription.eq}

tf.summary.SummaryDescription.__getstate__() {#SummaryDescription.getstate}

Support the pickle protocol.

tf.summary.SummaryDescription.__hash__() {#SummaryDescription.hash}

tf.summary.SummaryDescription.__init__(**kwargs) {#SummaryDescription.init}

tf.summary.SummaryDescription.__ne__(other_msg) {#SummaryDescription.ne}

tf.summary.SummaryDescription.__repr__() {#SummaryDescription.repr}

tf.summary.SummaryDescription.__setstate__(state) {#SummaryDescription.setstate}

Support the pickle protocol.

tf.summary.SummaryDescription.__str__() {#SummaryDescription.str}

tf.summary.SummaryDescription.__unicode__() {#SummaryDescription.unicode}

tf.summary.SummaryDescription.type_hint {#SummaryDescription.type_hint}

Magic attribute generated for "type_hint" proto field.

class tf.summary.TaggedRunMetadata {#TaggedRunMetadata}

tf.summary.TaggedRunMetadata.ByteSize() {#TaggedRunMetadata.ByteSize}

tf.summary.TaggedRunMetadata.Clear() {#TaggedRunMetadata.Clear}

tf.summary.TaggedRunMetadata.ClearExtension(extension_handle) {#TaggedRunMetadata.ClearExtension}

tf.summary.TaggedRunMetadata.ClearField(field_name) {#TaggedRunMetadata.ClearField}

tf.summary.TaggedRunMetadata.CopyFrom(other_msg) {#TaggedRunMetadata.CopyFrom}

Copies the content of the specified message into the current message.

The method clears the current message and then merges the specified message using MergeFrom.

Args:
  • other_msg: Message to copy into the current one.

tf.summary.TaggedRunMetadata.DiscardUnknownFields() {#TaggedRunMetadata.DiscardUnknownFields}

tf.summary.TaggedRunMetadata.FindInitializationErrors() {#TaggedRunMetadata.FindInitializationErrors}

Finds required fields which are not initialized.

Returns:

A list of strings. Each string is a path to an uninitialized field from the top-level message, e.g. "foo.bar[5].baz".

tf.summary.TaggedRunMetadata.FromString(s) {#TaggedRunMetadata.FromString}

tf.summary.TaggedRunMetadata.HasExtension(extension_handle) {#TaggedRunMetadata.HasExtension}

tf.summary.TaggedRunMetadata.HasField(field_name) {#TaggedRunMetadata.HasField}

tf.summary.TaggedRunMetadata.IsInitialized(errors=None) {#TaggedRunMetadata.IsInitialized}

Checks if all required fields of a message are set.

Args:
  • errors: A list which, if provided, will be populated with the field paths of all missing required fields.
Returns:

True iff the specified message has all required fields set.

tf.summary.TaggedRunMetadata.ListFields() {#TaggedRunMetadata.ListFields}

tf.summary.TaggedRunMetadata.MergeFrom(msg) {#TaggedRunMetadata.MergeFrom}

tf.summary.TaggedRunMetadata.MergeFromString(serialized) {#TaggedRunMetadata.MergeFromString}

tf.summary.TaggedRunMetadata.ParseFromString(serialized) {#TaggedRunMetadata.ParseFromString}

Parse serialized protocol buffer data into this message.

Like MergeFromString(), except we clear the object first and do not return the value that MergeFromString returns.

tf.summary.TaggedRunMetadata.RegisterExtension(extension_handle) {#TaggedRunMetadata.RegisterExtension}

tf.summary.TaggedRunMetadata.SerializePartialToString() {#TaggedRunMetadata.SerializePartialToString}

tf.summary.TaggedRunMetadata.SerializeToString() {#TaggedRunMetadata.SerializeToString}

tf.summary.TaggedRunMetadata.SetInParent() {#TaggedRunMetadata.SetInParent}

Sets the _cached_byte_size_dirty bit to true, and propagates this to our listener iff this was a state change.

tf.summary.TaggedRunMetadata.WhichOneof(oneof_name) {#TaggedRunMetadata.WhichOneof}

Returns the name of the currently set field inside a oneof, or None.

tf.summary.TaggedRunMetadata.__deepcopy__(memo=None) {#TaggedRunMetadata.deepcopy}

tf.summary.TaggedRunMetadata.__eq__(other) {#TaggedRunMetadata.eq}

tf.summary.TaggedRunMetadata.__getstate__() {#TaggedRunMetadata.getstate}

Support the pickle protocol.

tf.summary.TaggedRunMetadata.__hash__() {#TaggedRunMetadata.hash}

tf.summary.TaggedRunMetadata.__init__(**kwargs) {#TaggedRunMetadata.init}

tf.summary.TaggedRunMetadata.__ne__(other_msg) {#TaggedRunMetadata.ne}

tf.summary.TaggedRunMetadata.__repr__() {#TaggedRunMetadata.repr}

tf.summary.TaggedRunMetadata.__setstate__(state) {#TaggedRunMetadata.setstate}

Support the pickle protocol.

tf.summary.TaggedRunMetadata.__str__() {#TaggedRunMetadata.str}

tf.summary.TaggedRunMetadata.__unicode__() {#TaggedRunMetadata.unicode}

tf.summary.TaggedRunMetadata.run_metadata {#TaggedRunMetadata.run_metadata}

Magic attribute generated for "run_metadata" proto field.

tf.summary.TaggedRunMetadata.tag {#TaggedRunMetadata.tag}

Magic attribute generated for "tag" proto field.