tensorflow入门:TFRecordDataset变长数据的batch读取详解
在上一篇文章tensorflow入门:tfrecord 和tf.data.TFRecordDataset的使用里,讲到了使用如何使用tf.data.TFRecordDatase来对tfrecord文件进行batch读取,即使用dataset的batch方法进行;但如果每条数据的长度不一样(常见于语音、视频、NLP等领域),则不能直接用batch方法获取数据,这时则有两个解决办法:
1.在把数据写入tfrecord时,先把数据pad到统一的长度再写入tfrecord;这个方法的问题在于:若是有大量数据的长度都远远小于最大长度,则会造成存储空间的大量浪费。
2.使用dataset中的padded_batch方法来进行,参数padded_shapes #指明每条记录中各成员要pad成的形状,成员若是scalar,则用[],若是list,则用[mx_length],若是array,则用[d1,...,dn],假如各成员的顺序是scalar数据、list数据、array数据,则padded_shapes=([], [mx_length], [d1,...,dn]);该方法的函数说明如下:
padded_batch( batch_size, padded_shapes, padding_values=None #默认使用各类型数据的默认值,一般使用时可忽略该项 )
使用mnist数据来举例说明,首先在把mnist写入tfrecord之前,把mnist数据进行更改,以使得每个mnist图像的大小不等,如下:
import tensorflow as tf from tensorflow.contrib.learn.python.learn.datasets.mnist import read_data_sets mnist = read_data_sets("MNIST_data/", one_hot=True) def get_tfrecords_example(feature, label): tfrecords_features = {} feat_shape = feature.shape tfrecords_features['feature'] = tf.train.Feature(float_list=tf.train.FloatList(value=feature)) tfrecords_features['shape'] = tf.train.Feature(int64_list=tf.train.Int64List(value=list(feat_shape))) tfrecords_features['label'] = tf.train.Feature(float_list=tf.train.FloatList(value=label)) return tf.train.Example(features=tf.train.Features(feature=tfrecords_features)) def make_tfrecord(data, outf_nm='mnist-train'): feats, labels = data outf_nm += '.tfrecord' tfrecord_wrt = tf.python_io.TFRecordWriter(outf_nm) ndatas = len(labels) print(feats[0].dtype, feats[0].shape, ndatas) assert len(labels[0]) > 1 for inx in range(ndatas): ed = random.randint(0,3) #随机丢掉几个数据点,以使长度不等 exmp = get_tfrecords_example(feats[inx][:-ed], labels[inx]) exmp_serial = exmp.SerializeToString() tfrecord_wrt.write(exmp_serial) tfrecord_wrt.close() import random nDatas = len(mnist.train.labels) inx_lst = range(nDatas) random.shuffle(inx_lst) random.shuffle(inx_lst) ntrains = int(0.85*nDatas) # make training set data = ([mnist.train.images[i] for i in inx_lst[:ntrains]], \ [mnist.train.labels[i] for i in inx_lst[:ntrains]]) make_tfrecord(data, outf_nm='mnist-train') # make validation set data = ([mnist.train.images[i] for i in inx_lst[ntrains:]], \ [mnist.train.labels[i] for i in inx_lst[ntrains:]]) make_tfrecord(data, outf_nm='mnist-val') # make test set data = (mnist.test.images, mnist.test.labels) make_tfrecord(data, outf_nm='mnist-test')
用dataset加载批量数据,在解析数据时用到tf.VarLenFeature(tf.datatype),而非tf.FixedLenFeature([], tf.datatype)},且要配合tf.sparse_tensor_to_dense函数使用,如下:
import tensorflow as tf train_f, val_f, test_f = ['mnist-%s.tfrecord'%i for i in ['train', 'val', 'test']] def parse_exmp(serial_exmp): feats = tf.parse_single_example(serial_exmp, features={'feature':tf.VarLenFeature(tf.float32),\ 'label':tf.FixedLenFeature([10],tf.float32), 'shape':tf.FixedLenFeature([], tf.int64)}) image = tf.sparse_tensor_to_dense(feats['feature']) #使用VarLenFeature读入的是一个sparse_tensor,用该函数进行转换 label = tf.reshape(feats['label'],[2,5]) #把label变成[2,5],以说明array数据如何padding shape = tf.cast(feats['shape'], tf.int32) return image, label, shape def get_dataset(fname): dataset = tf.data.TFRecordDataset(fname) return dataset.map(parse_exmp) # use padded_batch method if padding needed epochs = 16 batch_size = 50 padded_shapes = ([784],[3,5],[]) #把image pad至784,把label pad至[3,5],shape是一个scalar,不输入数字 # training dataset dataset_train = get_dataset(train_f) dataset_train = dataset_train.repeat(epochs).shuffle(1000).padded_batch(batch_size, padded_shapes=padded_shapes)
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