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tensorflow中dataset.shuffle和dataset.batch dataset.repeat應該注意什么

發(fā)布時間：2020-07-18 11:17:49 來源：億速云 閱讀：368 作者：小豬 欄目：開發(fā)技術

這篇文章主要為大家展示了tensorflow中dataset.shuffle和dataset.batch dataset.repeat應該注意什么，內容簡而易懂，希望大家可以學習一下，學習完之后肯定會有收獲的，下面讓小編帶大家一起來看看吧。

batch很好理解，就是batch size。注意在一個epoch中最后一個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的使用順序可能會導致個epoch的混合

dataset.shuffle就是說維持一個buffer size 大小的 shuffle buffer，圖中所需的每個樣本從shuffle buffer中獲取，取得一個樣本后，就從源數據集中加入一個樣本到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))
#源數據集
[[ 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batch樣本個數為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batch樣本個數為3

1、按照shuffle中設置的buffer size，首先從源數據集取得三個樣本：
shuffle buffer：
[ 0.5488135 0.71518937]
[ 0.60276338 0.54488318]
[ 0.4236548 0.64589411]
2、從buffer中取一個樣本到batch中得：
shuffle buffer：
[ 0.5488135 0.71518937]
[ 0.60276338 0.54488318]
batch：
[ 0.4236548 0.64589411]
3、shuffle buffer不足三個樣本，從源數據集提取一個樣本：
shuffle buffer：
[ 0.5488135 0.71518937]
[ 0.60276338 0.54488318]
[ 0.43758721 0.891773 ]
4、從buffer中取一個樣本到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。這就意味中如果shuffle 的buffer size=1，數據集不打亂。如果shuffle 的buffer size=數據集樣本數量，隨機打亂整個數據集

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，但模糊了數據樣本的epoch關系

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]  #出現相同樣本出現在同一個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

以上就是關于tensorflow中dataset.shuffle和dataset.batch dataset.repeat應該注意什么的內容，如果你們有學習到知識或者技能，可以把它分享出去讓更多的人看到。

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