tensorflow dataset.shuffle dataset.batch dataset.repeat 理解注意点

浅浅的花香味﹌ 2021-06-24 16:00 451阅读 0赞

batch很好理解，就是batch size。注意在一个epoch中最后一个batch大小可能小于等于batch size  
dataset.repeat就是俗称epoch，但在tf中与dataset.shuffle的使用顺序可能会导致个epoch的混合  
dataset.shuffle就是说维持一个buffer size 大小的 shuffle buffer，图中所需的每个样本从shuffle buffer中获取，取得一个样本后，就从源数据集中加入一个样本到shuffle buffer中。

#源数据集
    [[ 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]]
    
    # 通过shuffle batch后取得的样本
    [[ 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]]  #最后一个batch样本个数为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]] #最后一个batch样本个数为3

1、按照shuffle中设置的buffer size，首先从源数据集取得三个样本：  
shuffle buffer：  
\[ 0.5488135 0.71518937\]  
\[ 0.60276338 0.54488318\]  
\[ 0.4236548 0.64589411\]  
2、从buffer中取一个样本到batch中得：  
shuffle buffer：  
\[ 0.5488135 0.71518937\]  
\[ 0.60276338 0.54488318\]  
batch：  
\[ 0.4236548 0.64589411\]  
3、shuffle buffer不足三个样本，从源数据集提取一个样本：  
shuffle buffer：  
\[ 0.5488135 0.71518937\]  
\[ 0.60276338 0.54488318\]  
\[ 0.43758721 0.891773 \]  
4、从buffer中取一个样本到batch中得：  
shuffle buffer：  
\[ 0.5488135 0.71518937\]  
\[ 0.43758721 0.891773 \]  
batch：  
\[ 0.4236548 0.64589411\]  
\[ 0.60276338 0.54488318\]  
5、如此反复。这就意味中如果shuffle 的buffer size=1，数据集不打乱。如果shuffle 的buffer size=数据集样本数量，随机打乱整个数据集

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：**  
官方说repeat在shuffle之前使用能提高性能，但模糊了数据样本的epoch关系

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] #出现相同样本出现在同一个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]]          #可以看到最后个batch为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