100 lines
3.6 KiB
Python
100 lines
3.6 KiB
Python
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# Copyright 2017 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.
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A scalar summary stores a single floating-point value, as a rank-0 tensor.
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NOTE: This module is in beta, and its API is subject to change, but the
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data that it stores to disk will be supported forever.
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import tensorflow as tf
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import numpy as np
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from tensorboard.plugins.scalar import metadata
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def op(name,
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data,
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display_name=None,
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description=None,
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collections=None):
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"""Create a scalar summary op.
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Arguments:
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name: A unique name for the generated summary node.
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data: A real numeric rank-0 `Tensor`. Must have `dtype` castable
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to `float32`.
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display_name: Optional name for this summary in TensorBoard, as a
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constant `str`. Defaults to `name`.
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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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collections: Optional list of graph collections keys. The new
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summary op is added to these collections. Defaults to
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`[Graph Keys.SUMMARIES]`.
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Returns:
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A TensorFlow summary op.
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"""
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if display_name is None:
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display_name = name
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summary_metadata = metadata.create_summary_metadata(
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display_name=display_name, description=description)
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with tf.name_scope(name):
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with tf.control_dependencies([tf.assert_scalar(data)]):
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return tf.summary.tensor_summary(name='scalar_summary',
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tensor=tf.cast(data, tf.float32),
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collections=collections,
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summary_metadata=summary_metadata)
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def pb(name, data, display_name=None, description=None):
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"""Create a scalar summary protobuf.
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Arguments:
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name: A unique name for the generated summary, including any desired
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name scopes.
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data: A rank-0 `np.array` or array-like form (so raw `int`s and
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`float`s are fine, too).
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display_name: Optional name for this summary in TensorBoard, as a
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`str`. Defaults to `name`.
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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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Returns:
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A `tf.Summary` protobuf object.
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"""
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data = np.array(data)
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if data.shape != ():
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raise ValueError('Expected scalar shape for data, saw shape: %s.'
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% data.shape)
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if data.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' % data.dtype.name)
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tensor = tf.make_tensor_proto(data.astype(np.float32))
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if display_name is None:
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display_name = name
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summary_metadata = metadata.create_summary_metadata(
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display_name=display_name, description=description)
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summary = tf.Summary()
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summary.value.add(tag='%s/scalar_summary' % name,
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metadata=summary_metadata,
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tensor=tensor)
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return summary
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