python – Numpy到TFrecords:有没有更简单的方法来处理来自tfrecords的批量输入?
作者:互联网
我的问题是如何从多个(或分片)tfrecords获得批量输入.我已经阅读了示例https://github.com/tensorflow/models/blob/master/inception/inception/image_processing.py#L410.基本管道是,以训练集为例,(1)首先生成一系列tfrecords(例如,000-train-of-005,train-001-of-005 ,. ..),(2)从这些文件名中,生成一个列表并将它们输入到tf.train.string_input_producer中以获取队列,(3)同时生成一个tf.RandomShuffleQueue来做其他的事情,(4)使用tf.train .batch_join生成批输入.
我认为这很复杂,我不确定这个程序的逻辑.在我的情况下,我有一个.npy文件列表,我想生成分片的tfrecords(多个分离的tfrecords,而不只是一个单个大文件).这些.npy文件中的每一个都包含不同数量的正样本和负样本(2个类).一种基本方法是生成一个单个大型tfrecord文件.但文件太大(~20Gb).所以我采用分片的tfrecords.有没有更简单的方法来做到这一点?谢谢.
解决方法:
使用Dataset API简化整个过程.以下是两个部分:(1):将numpy数组转换为tfrecords和(2,3,4):读取tfrecords以生成批次.
1.从numpy数组创建tfrecords:
def npy_to_tfrecords(...):
# write records to a tfrecords file
writer = tf.python_io.TFRecordWriter(output_file)
# Loop through all the features you want to write
for ... :
let say X is of np.array([[...][...]])
let say y is of np.array[[0/1]]
# Feature contains a map of string to feature proto objects
feature = {}
feature['X'] = tf.train.Feature(float_list=tf.train.FloatList(value=X.flatten()))
feature['y'] = tf.train.Feature(int64_list=tf.train.Int64List(value=y))
# Construct the Example proto object
example = tf.train.Example(features=tf.train.Features(feature=feature))
# Serialize the example to a string
serialized = example.SerializeToString()
# write the serialized objec to the disk
writer.write(serialized)
writer.close()
2.使用Dataset API读取tfrecords(tensorflow> = 1.2):
# Creates a dataset that reads all of the examples from filenames.
filenames = ["file1.tfrecord", "file2.tfrecord", ..."fileN.tfrecord"]
dataset = tf.contrib.data.TFRecordDataset(filenames)
# for version 1.5 and above use tf.data.TFRecordDataset
# example proto decode
def _parse_function(example_proto):
keys_to_features = {'X':tf.FixedLenFeature((shape_of_npy_array), tf.float32),
'y': tf.FixedLenFeature((), tf.int64, default_value=0)}
parsed_features = tf.parse_single_example(example_proto, keys_to_features)
return parsed_features['X'], parsed_features['y']
# Parse the record into tensors.
dataset = dataset.map(_parse_function)
# Shuffle the dataset
dataset = dataset.shuffle(buffer_size=10000)
# Repeat the input indefinitly
dataset = dataset.repeat()
# Generate batches
dataset = dataset.batch(batch_size)
# Create a one-shot iterator
iterator = dataset.make_one_shot_iterator()
# Get batch X and y
X, y = iterator.get_next()
标签:python,tensorflow,tensorflow-datasets,tfrecord 来源: https://codeday.me/bug/20190917/1809981.html