126 lines
4.8 KiB
Python
126 lines
4.8 KiB
Python
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# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Scalar summaries and TensorFlow operations to create them, V2 versions.
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A scalar summary stores a single floating-point value, as a rank-0
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tensor.
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"""
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import numpy as np
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from tensorboard.compat import tf2 as tf
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from tensorboard.compat.proto import summary_pb2
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from tensorboard.plugins.scalar import metadata
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from tensorboard.util import tensor_util
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def scalar(name, data, step=None, description=None):
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"""Write a scalar summary.
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See also `tf.summary.image`, `tf.summary.histogram`, `tf.summary.SummaryWriter`.
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Writes simple numeric values for later analysis in TensorBoard. Writes go to
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the current default summary writer. Each summary point is associated with an
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integral `step` value. This enables the incremental logging of time series
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data. A common usage of this API is to log loss during training to produce
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a loss curve.
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For example:
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```python
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test_summary_writer = tf.summary.create_file_writer('test/logdir')
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with test_summary_writer.as_default():
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tf.summary.scalar('loss', 0.345, step=1)
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tf.summary.scalar('loss', 0.234, step=2)
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tf.summary.scalar('loss', 0.123, step=3)
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```
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Multiple independent time series may be logged by giving each series a unique
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`name` value.
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See [Get started with TensorBoard](https://www.tensorflow.org/tensorboard/get_started)
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for more examples of effective usage of `tf.summary.scalar`.
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In general, this API expects that data points are logged iwth a monotonically
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increasing step value. Duplicate points for a single step or points logged out
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of order by step are not guaranteed to display as desired in TensorBoard.
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Arguments:
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name: A name for this summary. The summary tag used for TensorBoard will
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be this name prefixed by any active name scopes.
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data: A real numeric scalar value, convertible to a `float32` Tensor.
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step: Explicit `int64`-castable monotonic step value for this summary. If
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omitted, this defaults to `tf.summary.experimental.get_step()`, which must
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not be None.
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description: Optional long-form description for this summary, as a
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constant `str`. Markdown is supported. Defaults to empty.
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Returns:
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True on success, or false if no summary was written because no default
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summary writer was available.
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Raises:
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ValueError: if a default writer exists, but no step was provided and
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`tf.summary.experimental.get_step()` is None.
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"""
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summary_metadata = metadata.create_summary_metadata(
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display_name=None, description=description
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)
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# TODO(https://github.com/tensorflow/tensorboard/issues/2109): remove fallback
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summary_scope = (
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getattr(tf.summary.experimental, "summary_scope", None)
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or tf.summary.summary_scope
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)
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with summary_scope(name, "scalar_summary", values=[data, step]) as (tag, _):
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tf.debugging.assert_scalar(data)
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return tf.summary.write(
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tag=tag,
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tensor=tf.cast(data, tf.float32),
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step=step,
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metadata=summary_metadata,
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)
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def scalar_pb(tag, data, description=None):
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"""Create a scalar summary_pb2.Summary protobuf.
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Arguments:
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tag: String tag for the summary.
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data: A 0-dimensional `np.array` or a compatible python number type.
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description: Optional long-form description for this summary, as a
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`str`. Markdown is supported. Defaults to empty.
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Raises:
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ValueError: If the type or shape of the data is unsupported.
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Returns:
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A `summary_pb2.Summary` protobuf object.
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"""
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arr = np.array(data)
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if arr.shape != ():
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raise ValueError(
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"Expected scalar shape for tensor, got shape: %s." % arr.shape
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)
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if arr.dtype.kind not in ("b", "i", "u", "f"): # bool, int, uint, float
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raise ValueError("Cast %s to float is not supported" % arr.dtype.name)
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tensor_proto = tensor_util.make_tensor_proto(arr.astype(np.float32))
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summary_metadata = metadata.create_summary_metadata(
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display_name=None, description=description
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)
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summary = summary_pb2.Summary()
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summary.value.add(tag=tag, metadata=summary_metadata, tensor=tensor_proto)
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return summary
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