在 Tensorflow 数据集 api 中：如何使用 padded_batch 以便在不指定 pads 数量的情况下填充具有特定值的 pads

Question

如果您不指定 apadding_values那么padded_batch将自动填充 0。但是，如果您想要不同的值，例如 -1，则不能只设置padded_batch = -1。您需要为需要填充的每个插槽输入一个序列。

但是，我正在使用一个具有随机数组长度值的数据集，所以我不能真正做到这一点，因为我不知道需要填充多少个数字。

由于padding_values会自动用 0 填充其余的值，我希望有某种方法可以使用不同的值（例如“-1”）来做到这一点。

这是一个最小的例子

import math
import numpy as np
import tensorflow as tf

cells = np.array([[0,1,2,3], [2,3,4], [3,6,5,4,3], [3,9]])
mells = np.array([[0], [2], [3], [9]])
print(cells)

writer = tf.python_io.TFRecordWriter('test.tfrecords')
for index in range(mells.shape[0]):
    example = tf.train.Example(features=tf.train.Features(feature={
        'num_value':tf.train.Feature(int64_list=tf.train.Int64List(value=mells[index])),
        'list_value':tf.train.Feature(int64_list=tf.train.Int64List(value=cells[index]))
    }))
    writer.write(example.SerializeToString())
writer.close()

#Generate Samples with batch size of 2

filenames = ["test.tfrecords"]
dataset = tf.data.TFRecordDataset(filenames)
def _parse_function(example_proto):
    keys_to_features = {'num_value':tf.VarLenFeature(tf.int64),
                        'list_value':tf.VarLenFeature(tf.int64)}
    parsed_features = tf.parse_single_example(example_proto, keys_to_features)
    return tf.sparse.to_dense(parsed_features['num_value']), \
           tf.sparse.to_dense(parsed_features['list_value'])
# Parse the record into tensors.
dataset = dataset.map(_parse_function)
# Shuffle the dataset
dataset = dataset.shuffle(buffer_size=1)
# Repeat the input indefinitly
dataset = dataset.repeat()  
# Generate batches
dataset = dataset.padded_batch(2, padded_shapes=([None],[None]), padding_values=-1)
# Create a one-shot iterator
iterator = dataset.make_one_shot_iterator()
i, data = iterator.get_next()

这是错误信息

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-8-65494605bf11> in <module>()
     14 dataset = dataset.repeat()
     15 # Generate batches
---> 16 dataset = dataset.padded_batch(2, padded_shapes=([None],[None]), padding_values=-1)
     17 # Create a one-shot iterator
     18 iterator = dataset.make_one_shot_iterator()

/usr/local/lib/python3.6/dist-packages/tensorflow/python/data/ops/dataset_ops.py in padded_batch(self, batch_size, padded_shapes, padding_values, drop_remainder)
    943     """
    944     return PaddedBatchDataset(self, batch_size, padded_shapes, padding_values,
--> 945                               drop_remainder)
    946 
    947   def map(self, map_func, num_parallel_calls=None):

/usr/local/lib/python3.6/dist-packages/tensorflow/python/data/ops/dataset_ops.py in __init__(self, input_dataset, batch_size, padded_shapes, padding_values, drop_remainder)
   2526     self._padding_values = nest.map_structure_up_to(
   2527         input_dataset.output_shapes, _padding_value_to_tensor, padding_values,
-> 2528         input_dataset.output_types)
   2529     self._drop_remainder = ops.convert_to_tensor(
   2530         drop_remainder, dtype=dtypes.bool, name="drop_remainder")

/usr/local/lib/python3.6/dist-packages/tensorflow/python/data/util/nest.py in map_structure_up_to(shallow_tree, func, *inputs)
    465     raise ValueError("Cannot map over no sequences")
    466   for input_tree in inputs:
--> 467     assert_shallow_structure(shallow_tree, input_tree)
    468 
    469   # Flatten each input separately, apply the function to corresponding elements,

/usr/local/lib/python3.6/dist-packages/tensorflow/python/data/util/nest.py in assert_shallow_structure(shallow_tree, input_tree, check_types)
    299       raise TypeError(
    300           "If shallow structure is a sequence, input must also be a sequence. "
--> 301           "Input has type: %s." % type(input_tree))
    302 
    303     if check_types and not isinstance(input_tree, type(shallow_tree)):

TypeError: If shallow structure is a sequence, input must also be a sequence. Input has type: <class 'int'>.

问题线是

# Generate batches
dataset = dataset.padded_batch(2, padded_shapes=([None],[None]), padding_values=-1)

如果删除 padding_values，它会生成带有填充零的批次没问题

with tf.Session() as sess:
    print(sess.run([i, data]))
    print(sess.run([i, data]))

[array([[0],
       [2]]), array([[0, 1, 2, 3],
       [2, 3, 4, 0]])]
[array([[3],
       [9]]), array([[3, 6, 5, 4, 3],
       [3, 9, 0, 0, 0]])]

Answer 1

你应该改变padding_values。

dataset = dataset.padded_batch(2, padded_shapes=([None],[None])
                               , padding_values=(tf.constant(-1, dtype=tf.int64)
                                                 ,tf.constant(-1, dtype=tf.int64)))
with tf.Session() as sess:
    print(sess.run([i, data]))
    print(sess.run([i, data]))

[array([[0],
       [2]]), array([[ 0,  1,  2,  3],
       [ 2,  3,  4, -1]])]
[array([[3],
       [9]]), array([[ 3,  6,  5,  4,  3],
       [ 3,  9, -1, -1, -1]])]

解释

中给出的每个条目padding_values表示用于相应组件的填充值。这意味着 的长度padded_shapes应该等于 的长度padding_values。后者用于填充这里每个数组的整个长度，前者长度相同，不需要填充-1。例如：

dataset = dataset.padded_batch(2, padded_shapes=([None],[None])
                               , padding_values=(tf.constant(-1, dtype=tf.int64)
                                                 ,tf.constant(-2, dtype=tf.int64)))
with tf.Session() as sess:
    print(sess.run([i, data]))
    print(sess.run([i, data]))

[array([[0],
       [2]]), array([[ 0,  1,  2,  3],
       [ 2,  3,  4, -2]])]
[array([[3],
       [9]]), array([[ 3,  6,  5,  4,  3],
       [ 3,  9, -2, -2, -2]])]