tf.contrib.learn.read_batch_record_features
Reads TFRecord, queues, batches and parses Example
proto. (deprecated)
tf.contrib.learn.read_batch_record_features( file_pattern, batch_size, features, randomize_input=True, num_epochs=None, queue_capacity=10000, reader_num_threads=1, name='dequeue_record_examples' )
See more detailed description in read_examples
.
Args | |
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file_pattern |
List of files or patterns of file paths containing Example records. See tf.io.gfile.glob for pattern rules. |
batch_size |
An int or scalar Tensor specifying the batch size to use. |
features |
A dict mapping feature keys to FixedLenFeature or VarLenFeature values. |
randomize_input |
Whether the input should be randomized. |
num_epochs |
Integer specifying the number of times to read through the dataset. If None, cycles through the dataset forever. NOTE - If specified, creates a variable that must be initialized, so call tf.compat.v1.local_variables_initializer() and run the op in a session. |
queue_capacity |
Capacity for input queue. |
reader_num_threads |
The number of threads to read examples. In order to have predictable and repeatable order of reading and enqueueing, such as in prediction and evaluation mode, reader_num_threads should be 1. |
name |
Name of resulting op. |
Returns | |
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A dict of Tensor or SparseTensor objects for each in features . |
Raises | |
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ValueError |
for invalid inputs. |
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/contrib/learn/read_batch_record_features