92 lines
3.0 KiB
Python
92 lines
3.0 KiB
Python
# Copyright 2015 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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"""Provides a method for reading events from an event file via an iterator."""
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from tensorflow.core.util import event_pb2
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from tensorflow.python.lib.io import tf_record
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from tensorflow.python.util.tf_export import tf_export
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class _SummaryIterator(object):
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"""Yields `Event` protocol buffers from a given path."""
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def __init__(self, path):
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self._tf_record_iterator = tf_record.tf_record_iterator(path)
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def __iter__(self):
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return self
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def __next__(self):
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r = next(self._tf_record_iterator)
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return event_pb2.Event.FromString(r)
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next = __next__
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@tf_export(v1=['train.summary_iterator'])
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def summary_iterator(path):
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# pylint: disable=line-too-long
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"""Returns a iterator for reading `Event` protocol buffers from an event file.
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You can use this function to read events written to an event file. It returns
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a Python iterator that yields `Event` protocol buffers.
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Example: Print the contents of an events file.
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```python
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for e in tf.compat.v1.train.summary_iterator(path to events file):
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print(e)
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```
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Example: Print selected summary values.
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```python
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# This example supposes that the events file contains summaries with a
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# summary value tag 'loss'. These could have been added by calling
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# `add_summary()`, passing the output of a scalar summary op created with
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# with: `tf.compat.v1.summary.scalar('loss', loss_tensor)`.
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for e in tf.compat.v1.train.summary_iterator(path to events file):
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for v in e.summary.value:
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if v.tag == 'loss':
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print(tf.make_ndarray(v.tensor))
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```
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Example: Continuously check for new summary values.
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```python
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summaries = tf.compat.v1.train.summary_iterator(path to events file)
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while True:
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for e in summaries:
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for v in e.summary.value:
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if v.tag == 'loss':
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print(tf.make_ndarray(v.tensor))
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# Wait for a bit before checking the file for any new events
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time.sleep(wait time)
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```
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See the protocol buffer definitions of
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[Event](https://www.tensorflow.org/code/tensorflow/core/util/event.proto)
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and
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[Summary](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)
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for more information about their attributes.
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Args:
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path: The path to an event file created by a `SummaryWriter`.
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Returns:
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A iterator that yields `Event` protocol buffers
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"""
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return _SummaryIterator(path)
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