.Output("writer: resource")
.Attr("shared_name: string = ''")
.Attr("container: string = ''")
- .SetShapeFn(shape_inference::ScalarShape)
- .Doc(R"doc(
-Returns a handle to be used to access a summary writer.
-
-The summary writer is an in-graph resource which can be used by ops to write
-summaries to event files.
-
-writer: the summary writer resource. Scalar handle.
-)doc");
+ .SetShapeFn(shape_inference::ScalarShape);
REGISTER_OP("CreateSummaryFileWriter")
.Input("writer: resource")
.Input("max_queue: int32")
.Input("flush_millis: int32")
.Input("filename_suffix: string")
- .SetShapeFn(shape_inference::NoOutputs)
- .Doc(R"doc(
-Creates a summary file writer accessible by the given resource handle.
-
-writer: A handle to the summary writer resource
-logdir: Directory where the event file will be written.
-max_queue: Size of the queue of pending events and summaries.
-flush_millis: How often, in milliseconds, to flush the pending events and
- summaries to disk.
-filename_suffix: Every event file's name is suffixed with this suffix.
-)doc");
+ .SetShapeFn(shape_inference::NoOutputs);
REGISTER_OP("CreateSummaryDbWriter")
.Input("writer: resource")
.Input("experiment_name: string")
.Input("run_name: string")
.Input("user_name: string")
- .SetShapeFn(shape_inference::NoOutputs)
- .Doc(R"doc(
-Creates summary database writer accessible by given resource handle.
-
-This can be used to write tensors from the execution graph directly
-to a database. Only SQLite is supported right now. This function
-will create the schema if it doesn't exist. Entries in the Users,
-Experiments, and Runs tables will be created automatically if they
-don't already exist.
-
-writer: Handle to SummaryWriter resource to overwrite.
-db_uri: For example "file:/tmp/foo.sqlite".
-experiment_name: Can't contain ASCII control characters or <>. Case
- sensitive. If empty, then the Run will not be associated with any
- Experiment.
-run_name: Can't contain ASCII control characters or <>. Case sensitive.
- If empty, then each Tag will not be associated with any Run.
-user_name: Must be valid as both a DNS label and Linux username. If
- empty, then the Experiment will not be associated with any User.
-)doc");
+ .SetShapeFn(shape_inference::NoOutputs);
REGISTER_OP("FlushSummaryWriter")
.Input("writer: resource")
- .SetShapeFn(shape_inference::NoOutputs)
- .Doc(R"(
-Flushes the writer's unwritten events.
-
-writer: A handle to the summary writer resource.
-)");
+ .SetShapeFn(shape_inference::NoOutputs);
REGISTER_OP("CloseSummaryWriter")
.Input("writer: resource")
- .SetShapeFn(shape_inference::NoOutputs)
- .Doc(R"(
-Flushes and closes the summary writer.
-
-Also removes it from the resource manager. To reopen, use another
-CreateSummaryFileWriter op.
-
-writer: A handle to the summary writer resource.
-)");
+ .SetShapeFn(shape_inference::NoOutputs);
REGISTER_OP("WriteSummary")
.Input("writer: resource")
.Input("tag: string")
.Input("summary_metadata: string")
.Attr("T: type")
- .SetShapeFn(shape_inference::NoOutputs)
- .Doc(R"doc(
-Outputs a `Summary` protocol buffer with a tensor.
-
-writer: A handle to a summary writer.
-step: The step to write the summary for.
-tensor: A tensor to serialize.
-tag: The summary's tag.
-summary_metadata: Serialized SummaryMetadata protocol buffer containing
- plugin-related metadata for this summary.
-)doc");
+ .SetShapeFn(shape_inference::NoOutputs);
REGISTER_OP("ImportEvent")
.Input("writer: resource")
.Input("event: string")
- .SetShapeFn(shape_inference::NoOutputs)
- .Doc(R"doc(
-Outputs a `tf.Event` protocol buffer.
-
-When CreateSummaryDbWriter is being used, this op can be useful for
-importing data from event logs.
-
-writer: A handle to a summary writer.
-event: A string containing a binary-encoded tf.Event proto.
-)doc");
+ .SetShapeFn(shape_inference::NoOutputs);
REGISTER_OP("WriteScalarSummary")
.Input("writer: resource")
.Input("tag: string")
.Input("value: T")
.Attr("T: realnumbertype")
- .SetShapeFn(shape_inference::NoOutputs)
- .Doc(R"doc(
-Writes a `Summary` protocol buffer with scalar values.
-
-The input `tag` and `value` must have the scalars.
-
-writer: A handle to a summary writer.
-step: The step to write the summary for.
-tag: Tag for the summary.
-value: Value for the summary.
-)doc");
+ .SetShapeFn(shape_inference::NoOutputs);
REGISTER_OP("WriteHistogramSummary")
.Input("writer: resource")
.Input("tag: string")
.Input("values: T")
.Attr("T: realnumbertype = DT_FLOAT")
- .SetShapeFn(shape_inference::NoOutputs)
- .Doc(R"doc(
-Writes a `Summary` protocol buffer with a histogram.
-
-The generated
-[`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)
-has one summary value containing a histogram for `values`.
-
-This op reports an `InvalidArgument` error if any value is not finite.
-
-writer: A handle to a summary writer.
-step: The step to write the summary for.
-tag: Scalar. Tag to use for the `Summary.Value`.
-values: Any shape. Values to use to build the histogram.
-)doc");
+ .SetShapeFn(shape_inference::NoOutputs);
REGISTER_OP("WriteImageSummary")
.Input("writer: resource")
.Input("bad_color: uint8")
.Attr("max_images: int >= 1 = 3")
.Attr("T: {uint8, float, half} = DT_FLOAT")
- .SetShapeFn(shape_inference::NoOutputs)
- .Doc(R"doc(
-Writes a `Summary` protocol buffer with images.
-
-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 `unit8` 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.
-
-writer: A handle to a summary writer.
-step: The step to write the summary for.
-tag: Scalar. Used to build the `tag` attribute of the summary values.
-tensor: 4-D of shape `[batch_size, height, width, channels]` where
- `channels` is 1, 3, or 4.
-max_images: Max number of batch elements to generate images for.
-bad_color: Color to use for pixels with non-finite values.
-)doc");
+ .SetShapeFn(shape_inference::NoOutputs);
REGISTER_OP("WriteAudioSummary")
.Input("writer: resource")
.Input("tensor: float")
.Input("sample_rate: float")
.Attr("max_outputs: int >= 1 = 3")
- .SetShapeFn(shape_inference::NoOutputs)
- .Doc(R"doc(
-Writes 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` argument is a scalar `Tensor` of type `string`. It is used to
-build the `tag` of the summary values:
-
-* If `max_outputs` is 1, the summary value tag is '*tag*/audio'.
-* If `max_outputs` is greater than 1, the summary value tags are
- generated sequentially as '*tag*/audio/0', '*tag*/audio/1', etc.
-
-writer: A handle to a summary writer.
-step: The step to write the summary for.
-tag: Scalar. Used to build the `tag` attribute of the summary values.
-tensor: 2-D of shape `[batch_size, frames]`.
-sample_rate: The sample rate of the signal in hertz.
-max_outputs: Max number of batch elements to generate audio for.
-)doc");
+ .SetShapeFn(shape_inference::NoOutputs);
REGISTER_OP("WriteGraphSummary")
.Input("writer: resource")
.Input("step: int64")
.Input("tensor: string")
- .SetShapeFn(shape_inference::NoOutputs)
- .Doc(R"doc(
-Writes a `GraphDef` protocol buffer to a `SummaryWriter`.
-
-writer: Handle of `SummaryWriter`.
-step: The step to write the summary for.
-tensor: A scalar string of the serialized tf.GraphDef proto.
-)doc");
+ .SetShapeFn(shape_inference::NoOutputs);
} // namespace tensorflow
projects / platform / upstream / tensorflow.git / commitdiff