tensorflow dataset.shuffle dataset.batch dataset.repeat 理解 注意点
batch很好理解，就是batch size。注意在一個(gè)epoch中最后一個(gè)batch大小可能小于等于batch size?
dataset.repeat就是俗稱epoch，但在tf中與dataset.shuffle的使用順序可能會(huì)導(dǎo)致個(gè)epoch的混合?
dataset.shuffle就是說維持一個(gè)buffer size 大小的 shuffle buffer，圖中所需的每個(gè)樣本從shuffle buffer中獲取，取得一個(gè)樣本后，就從源數(shù)據(jù)集中加入一個(gè)樣本到shuffle buffer中。
import os
os.environ['CUDA_VISIBLE_DEVICES'] = ""
import numpy as np
import tensorflow as tf
np.random.seed(0)
x = np.random.sample((11,2))
# make a dataset from a numpy array
print(x)
print()
dataset = tf.data.Dataset.from_tensor_slices(x)
dataset = dataset.shuffle(3)
dataset = dataset.batch(4)
dataset = dataset.repeat(2)
# create the iterator
iter = dataset.make_one_shot_iterator()
el = iter.get_next()
with tf.Session() as sess:
? ? print(sess.run(el))
? ? print(sess.run(el))
? ? print(sess.run(el))
? ? print(sess.run(el))
? ? print(sess.run(el))
? ? print(sess.run(el))
? ? print(sess.run(el))
? ? print(sess.run(el))
? ? print(sess.run(el))
? ? print(sess.run(el))
? ? print(sess.run(el))
? ? print(sess.run(el))
? ? print(sess.run(el))
#源數(shù)據(jù)集
[[ 0.5488135 ? 0.71518937]
?[ 0.60276338 ?0.54488318]
?[ 0.4236548 ? 0.64589411]
?[ 0.43758721 ?0.891773 ?]
?[ 0.96366276 ?0.38344152]
?[ 0.79172504 ?0.52889492]
?[ 0.56804456 ?0.92559664]
?[ 0.07103606 ?0.0871293 ]
?[ 0.0202184 ? 0.83261985]
?[ 0.77815675 ?0.87001215]
?[ 0.97861834 ?0.79915856]]
# 通過shuffle batch后取得的樣本
[[ 0.4236548 ? 0.64589411]
?[ 0.60276338 ?0.54488318]
?[ 0.43758721 ?0.891773 ?]
?[ 0.5488135 ? 0.71518937]]
[[ 0.96366276 ?0.38344152]
?[ 0.56804456 ?0.92559664]
?[ 0.0202184 ? 0.83261985]
?[ 0.79172504 ?0.52889492]]
[[ 0.07103606 ?0.0871293 ]
?[ 0.97861834 ?0.79915856]
?[ 0.77815675 ?0.87001215]] ?#最后一個(gè)batch樣本個(gè)數(shù)為3
[[ 0.60276338 ?0.54488318]
?[ 0.5488135 ? 0.71518937]
?[ 0.43758721 ?0.891773 ?]
?[ 0.79172504 ?0.52889492]]
[[ 0.4236548 ? 0.64589411]
?[ 0.56804456 ?0.92559664]
?[ 0.0202184 ? 0.83261985]
?[ 0.07103606 ?0.0871293 ]]
[[ 0.77815675 ?0.87001215]
?[ 0.96366276 ?0.38344152]
?[ 0.97861834 ?0.79915856]] #最后一個(gè)batch樣本個(gè)數(shù)為3
1、按照shuffle中設(shè)置的buffer size，首先從源數(shù)據(jù)集取得三個(gè)樣本：?
shuffle buffer：?
[ 0.5488135 0.71518937]?
[ 0.60276338 0.54488318]?
[ 0.4236548 0.64589411]?
2、從buffer中取一個(gè)樣本到batch中得：?
shuffle buffer：?
[ 0.5488135 0.71518937]?
[ 0.60276338 0.54488318]?
batch：?
[ 0.4236548 0.64589411]?
3、shuffle buffer不足三個(gè)樣本，從源數(shù)據(jù)集提取一個(gè)樣本：?
shuffle buffer：?
[ 0.5488135 0.71518937]?
[ 0.60276338 0.54488318]?
[ 0.43758721 0.891773 ]?
4、從buffer中取一個(gè)樣本到batch中得：?
shuffle buffer：?
[ 0.5488135 0.71518937]?
[ 0.43758721 0.891773 ]?
batch：?
[ 0.4236548 0.64589411]?
[ 0.60276338 0.54488318]?
5、如此反復(fù)。這就意味中如果shuffle 的buffer size=1，數(shù)據(jù)集不打亂。如果shuffle 的buffer size=數(shù)據(jù)集樣本數(shù)量，隨機(jī)打亂整個(gè)數(shù)據(jù)集
import os
os.environ['CUDA_VISIBLE_DEVICES'] = ""
import numpy as np
import tensorflow as tf
np.random.seed(0)
x = np.random.sample((11,2))
# make a dataset from a numpy array
print(x)
print()
dataset = tf.data.Dataset.from_tensor_slices(x)
dataset = dataset.shuffle(1)
dataset = dataset.batch(4)
dataset = dataset.repeat(2)
# create the iterator
iter = dataset.make_one_shot_iterator()
el = iter.get_next()
with tf.Session() as sess:
? ? print(sess.run(el))
? ? print(sess.run(el))
? ? print(sess.run(el))
? ? print(sess.run(el))
? ? print(sess.run(el))
? ? print(sess.run(el))
? ? print(sess.run(el))
? ? print(sess.run(el))
? ? print(sess.run(el))
? ? print(sess.run(el))
? ? print(sess.run(el))
? ? print(sess.run(el))
? ? print(sess.run(el))
[[ 0.5488135 ? 0.71518937]
?[ 0.60276338 ?0.54488318]
?[ 0.4236548 ? 0.64589411]
?[ 0.43758721 ?0.891773 ?]
?[ 0.96366276 ?0.38344152]
?[ 0.79172504 ?0.52889492]
?[ 0.56804456 ?0.92559664]
?[ 0.07103606 ?0.0871293 ]
?[ 0.0202184 ? 0.83261985]
?[ 0.77815675 ?0.87001215]
?[ 0.97861834 ?0.79915856]]
[[ 0.5488135 ? 0.71518937]
?[ 0.60276338 ?0.54488318]
?[ 0.4236548 ? 0.64589411]
?[ 0.43758721 ?0.891773 ?]]
[[ 0.96366276 ?0.38344152]
?[ 0.79172504 ?0.52889492]
?[ 0.56804456 ?0.92559664]
?[ 0.07103606 ?0.0871293 ]]
[[ 0.0202184 ? 0.83261985]
?[ 0.77815675 ?0.87001215]
?[ 0.97861834 ?0.79915856]]
[[ 0.5488135 ? 0.71518937]
?[ 0.60276338 ?0.54488318]
?[ 0.4236548 ? 0.64589411]
?[ 0.43758721 ?0.891773 ?]]
[[ 0.96366276 ?0.38344152]
?[ 0.79172504 ?0.52889492]
?[ 0.56804456 ?0.92559664]
?[ 0.07103606 ?0.0871293 ]]
[[ 0.0202184 ? 0.83261985]
?[ 0.77815675 ?0.87001215]
?[ 0.97861834 ?0.79915856]]
注意如果repeat在shuffle之前使用：?
官方說repeat在shuffle之前使用能提高性能，但模糊了數(shù)據(jù)樣本的epoch關(guān)系
import os
os.environ['CUDA_VISIBLE_DEVICES'] = ""
import numpy as np
import tensorflow as tf
np.random.seed(0)
x = np.random.sample((11,2))
# make a dataset from a numpy array
print(x)
print()
dataset = tf.data.Dataset.from_tensor_slices(x)
dataset = dataset.repeat(2)
dataset = dataset.shuffle(11)
dataset = dataset.batch(4)
# create the iterator
iter = dataset.make_one_shot_iterator()
el = iter.get_next()
with tf.Session() as sess:
? ? print(sess.run(el))
? ? print(sess.run(el))
? ? print(sess.run(el))
? ? print(sess.run(el))
? ? print(sess.run(el))
? ? print(sess.run(el))
? ? print(sess.run(el))
? ? print(sess.run(el))
? ? print(sess.run(el))
? ? print(sess.run(el))
? ? print(sess.run(el))
? ? print(sess.run(el))
? ? print(sess.run(el))
[[ 0.5488135 ? 0.71518937]
?[ 0.60276338 ?0.54488318]
?[ 0.4236548 ? 0.64589411]
?[ 0.43758721 ?0.891773 ?]
?[ 0.96366276 ?0.38344152]
?[ 0.79172504 ?0.52889492]
?[ 0.56804456 ?0.92559664]
?[ 0.07103606 ?0.0871293 ]
?[ 0.0202184 ? 0.83261985]
?[ 0.77815675 ?0.87001215]
?[ 0.97861834 ?0.79915856]]
[[ 0.56804456 ?0.92559664]
?[ 0.5488135 ? 0.71518937]
?[ 0.60276338 ?0.54488318]
?[ 0.07103606 ?0.0871293 ]]
[[ 0.96366276 ?0.38344152]
?[ 0.43758721 ?0.891773 ?]
?[ 0.43758721 ?0.891773 ?]
?[ 0.77815675 ?0.87001215]]
[[ 0.79172504 ?0.52889492] ? #出現(xiàn)相同樣本出現(xiàn)在同一個(gè)batch中
?[ 0.79172504 ?0.52889492]
?[ 0.60276338 ?0.54488318]
?[ 0.4236548 ? 0.64589411]]
[[ 0.07103606 ?0.0871293 ]
?[ 0.4236548 ? 0.64589411]
?[ 0.96366276 ?0.38344152]
?[ 0.5488135 ? 0.71518937]]
[[ 0.97861834 ?0.79915856]
?[ 0.0202184 ? 0.83261985]
?[ 0.77815675 ?0.87001215]
?[ 0.56804456 ?0.92559664]]
[[ 0.0202184 ? 0.83261985]
?[ 0.97861834 ?0.79915856]] ? ? ? ? ?#可以看到最后個(gè)batch為2，而前面都是4 ? ?
使用案例：
def input_fn(filenames, batch_size=32, num_epochs=1, perform_shuffle=False):
? ? print('Parsing', filenames)
? ? def decode_libsvm(line):
? ? ? ? #columns = tf.decode_csv(value, record_defaults=CSV_COLUMN_DEFAULTS)
? ? ? ? #features = dict(zip(CSV_COLUMNS, columns))
? ? ? ? #labels = features.pop(LABEL_COLUMN)
? ? ? ? columns = tf.string_split([line], ' ')
? ? ? ? labels = tf.string_to_number(columns.values[0], out_type=tf.float32)
? ? ? ? splits = tf.string_split(columns.values[1:], ':')
? ? ? ? id_vals = tf.reshape(splits.values,splits.dense_shape)
? ? ? ? feat_ids, feat_vals = tf.split(id_vals,num_or_size_splits=2,axis=1)
? ? ? ? feat_ids = tf.string_to_number(feat_ids, out_type=tf.int32)
? ? ? ? feat_vals = tf.string_to_number(feat_vals, out_type=tf.float32)
? ? ? ? #feat_ids = tf.reshape(feat_ids,shape=[-1,FLAGS.field_size])
? ? ? ? #for i in range(splits.dense_shape.eval()[0]):
? ? ? ? # ? ?feat_ids.append(tf.string_to_number(splits.values[2*i], out_type=tf.int32))
? ? ? ? # ? ?feat_vals.append(tf.string_to_number(splits.values[2*i+1]))
? ? ? ? #return tf.reshape(feat_ids,shape=[-1,field_size]), tf.reshape(feat_vals,shape=[-1,field_size]), labels
? ? ? ? return {"feat_ids": feat_ids, "feat_vals": feat_vals}, labels
? ? # Extract lines from input files using the Dataset API, can pass one filename or filename list
? ? dataset = tf.data.TextLineDataset(filenames).map(decode_libsvm, num_parallel_calls=10).prefetch(500000) ? ?# multi-thread pre-process then prefetch
? ? # Randomizes input using a window of 256 elements (read into memory)
? ? if perform_shuffle:
? ? ? ? dataset = dataset.shuffle(buffer_size=256)
? ? # epochs from blending together.
? ? dataset = dataset.repeat(num_epochs)
? ? dataset = dataset.batch(batch_size) # Batch size to use
? ? #return dataset.make_one_shot_iterator()
? ? iterator = dataset.make_one_shot_iterator()
? ? batch_features, batch_labels = iterator.get_next()
? ? #return tf.reshape(batch_ids,shape=[-1,field_size]), tf.reshape(batch_vals,shape=[-1,field_size]), batch_labels
? ? return batch_features, batch_labels
?
?
個(gè)人初步實(shí)驗(yàn)證明：如果? repeat()中采用默認(rèn)參數(shù)，其值epoch為無窮大
---------------------?
作者：青盞?
來源：CSDN?
原文：https://blog.csdn.net/qq_16234613/article/details/81703228?
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