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# 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
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