我正在尝试使用TensorFlow数据集API使用from_generator
方法读取HDF5文件。除非批量大小不均匀地分成事件数量,否则一切正常。我不太清楚如何使用API制作灵活的批处理。
如果事情没有均匀分配,则会出现以下错误:
2018-08-31 13:47:34.274303: W tensorflow/core/framework/op_kernel.cc:1263] Invalid argument: ValueError: `generator` yielded an element of shape (1, 28, 28, 1) where an element of shape (11, 28, 28, 1) was expected.
Traceback (most recent call last):
File "/Users/perdue/miniconda3/envs/py3a/lib/python3.6/site-packages/tensorflow/python/ops/script_ops.py", line 206, in __call__
ret = func(*args)
File "/Users/perdue/miniconda3/envs/py3a/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 452, in generator_py_func
"of shape %s was expected." % (ret_array.shape, expected_shape))
ValueError: `generator` yielded an element of shape (1, 28, 28, 1) where an element of shape (11, 28, 28, 1) was expected.
我有一个脚本可以重现错误(以及获取几个MB所需数据文件的说明 - Fashion MNIST):
https://gist.github.com/gnperdue/b905a9c2dd4c08b53e0539d6aa3d3dc6
最重要的代码可能是:
def make_fashion_dset(file_name, batch_size, shuffle=False):
dgen = _make_fashion_generator_fn(file_name, batch_size)
features_shape = [batch_size, 28, 28, 1]
labels_shape = [batch_size, 10]
ds = tf.data.Dataset.from_generator(
dgen, (tf.float32, tf.uint8),
(tf.TensorShape(features_shape), tf.TensorShape(labels_shape))
)
...
其中dgen
是从hdf5读取的生成器函数:
def _make_fashion_generator_fn(file_name, batch_size):
reader = FashionHDF5Reader(file_name)
nevents = reader.openf()
def example_generator_fn():
start_idx, stop_idx = 0, batch_size
while True:
if start_idx >= nevents:
reader.closef()
return
yield reader.get_examples(start_idx, stop_idx)
start_idx, stop_idx = start_idx + batch_size, stop_idx + batch_size
return example_generator_fn
问题的核心是我们必须在from_generator
中声明张量形状,但我们需要灵活地在迭代时改变该形状。
有一些解决方法 - 删除最后几个样本以获得均匀分区,或者只使用批量大小为1 ...但如果您不能丢失任何样本并且批量大小为1非常慢,则第一个是坏的。
有什么想法或意见吗?谢谢!
在from_generator
中指定Tensor形状时,可以使用None
作为元素来指定可变大小的尺寸。这样您就可以容纳不同大小的批次,特别是“剩余”批次,这些批次比您要求的批量大小一点。所以你会用
def make_fashion_dset(file_name, batch_size, shuffle=False):
dgen = _make_fashion_generator_fn(file_name, batch_size)
features_shape = [None, 28, 28, 1]
labels_shape = [None, 10]
ds = tf.data.Dataset.from_generator(
dgen, (tf.float32, tf.uint8),
(tf.TensorShape(features_shape), tf.TensorShape(labels_shape))
)
...