MLPY/Lib/site-packages/tensorboard/plugins/scalar/summary_v2.py

# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Scalar summaries and TensorFlow operations to create them, V2 versions.

A scalar summary stores a single floating-point value, as a rank-0
tensor.
"""


import numpy as np

from tensorboard.compat import tf2 as tf
from tensorboard.compat.proto import summary_pb2
from tensorboard.plugins.scalar import metadata
from tensorboard.util import tensor_util


def scalar(name, data, step=None, description=None):
    """Write a scalar summary.

    See also `tf.summary.image`, `tf.summary.histogram`, `tf.summary.SummaryWriter`.

    Writes simple numeric values for later analysis in TensorBoard.  Writes go to
    the current default summary writer. Each summary point is associated with an
    integral `step` value. This enables the incremental logging of time series
    data.  A common usage of this API is to log loss during training to produce
    a loss curve.

    For example:

    ```python
    test_summary_writer = tf.summary.create_file_writer('test/logdir')
    with test_summary_writer.as_default():
        tf.summary.scalar('loss', 0.345, step=1)
        tf.summary.scalar('loss', 0.234, step=2)
        tf.summary.scalar('loss', 0.123, step=3)
    ```

    Multiple independent time series may be logged by giving each series a unique
    `name` value.

    See [Get started with TensorBoard](https://www.tensorflow.org/tensorboard/get_started)
    for more examples of effective usage of `tf.summary.scalar`.

    In general, this API expects that data points are logged with a monotonically
    increasing step value. Duplicate points for a single step or points logged out
    of order by step are not guaranteed to display as desired in TensorBoard.

    Arguments:
      name: A name for this summary. The summary tag used for TensorBoard will
        be this name prefixed by any active name scopes.
      data: A real numeric scalar value, convertible to a `float32` Tensor.
      step: Explicit `int64`-castable monotonic step value for this summary. If
        omitted, this defaults to `tf.summary.experimental.get_step()`, which must
        not be None.
      description: Optional long-form description for this summary, as a
        constant `str`. Markdown is supported. Defaults to empty.

    Returns:
      True on success, or false if no summary was written because no default
      summary writer was available.

    Raises:
      ValueError: if a default writer exists, but no step was provided and
        `tf.summary.experimental.get_step()` is None.
    """
    summary_metadata = metadata.create_summary_metadata(
        display_name=None, description=description
    )
    # TODO(https://github.com/tensorflow/tensorboard/issues/2109): remove fallback
    summary_scope = (
        getattr(tf.summary.experimental, "summary_scope", None)
        or tf.summary.summary_scope
    )
    with summary_scope(name, "scalar_summary", values=[data, step]) as (tag, _):
        tf.debugging.assert_scalar(data)
        return tf.summary.write(
            tag=tag,
            tensor=tf.cast(data, tf.float32),
            step=step,
            metadata=summary_metadata,
        )


def scalar_pb(tag, data, description=None):
    """Create a scalar summary_pb2.Summary protobuf.

    Arguments:
      tag: String tag for the summary.
      data: A 0-dimensional `np.array` or a compatible python number type.
      description: Optional long-form description for this summary, as a
        `str`. Markdown is supported. Defaults to empty.

    Raises:
      ValueError: If the type or shape of the data is unsupported.

    Returns:
      A `summary_pb2.Summary` protobuf object.
    """
    arr = np.array(data)
    if arr.shape != ():
        raise ValueError(
            "Expected scalar shape for tensor, got shape: %s." % arr.shape
        )
    if arr.dtype.kind not in ("b", "i", "u", "f"):  # bool, int, uint, float
        raise ValueError("Cast %s to float is not supported" % arr.dtype.name)
    tensor_proto = tensor_util.make_tensor_proto(arr.astype(np.float32))
    summary_metadata = metadata.create_summary_metadata(
        display_name=None, description=description
    )
    summary = summary_pb2.Summary()
    summary.value.add(tag=tag, metadata=summary_metadata, tensor=tensor_proto)
    return summary