如何使用tf-1.4在具有字符串值的tf张量上进行字符串样式拆分

Question

我有一个张量(例如标签(，它从外部数据源读取数据。张量的值是具有格式"0"的字符串；标签1、标签2"；(例如"0，1"(。现在，我想使用分隔符"将字符串值拆分为一个列表，因此结果将类似于["0"，"1"]
我尝试过流动：

# option-1 with error: tensor type has no attribute split.
label_list = input_label_tensor.split(',') 
# option-2 with error: module has no method split.
label_list = tf.strings.split(input_label_tensor, ',')
# option-3 with error: 
label_list = tf.string_split(input_label_tensor, ',')

选项3中的错误为：

File "/usr/lib/python2.7/site-packages/tensorflow/python/ops/string_ops.py", line 113, in string_split
source = ops.convert_to_tensor(source, dtype=dtypes.string)
File "/usr/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 836, in convert_to_tensor
as_ref=False)
File "/usr/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 926, in internal_convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/usr/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 774, in _TensorTensorConversionFunction
(dtype.name, t.dtype.name, str(t)))
ValueError: Tensor conversion requested dtype string for Tensor with dtype float32: 'Tensor("StagingArea_get:467", shape=(?,), dtype=float32, device=/job:worker/task:0/device:CPU:0)'

进行拆分操作的正确方法是什么？我正在使用TF-1.4

Answer 1

我的最佳选择是input_label_tensor实际上不是string张量。例如：

%tensorflow_version 1.x
import tensorflow as tf
input_label_tensor = tf.constant(['0', '1'], dtype=tf.string)
label_list = tf.string_split(input_label_tensor, ',')

但如果我使用float张量，我可以重现你的错误：

input_label_tensor = tf.constant([0, 1], dtype=tf.float32)
label_list = tf.string_split(input_label_tensor, ',')

要访问SparseTensor的值，请尝试以下操作：

%tensorflow_version 1.x
import tensorflow as tf
input_label_tensor = tf.constant(['0', '1'], dtype=tf.string)
label_list = tf.string_split(input_label_tensor, ',').values
x, y = label_list[0], label_list[1]

更新1：

%tensorflow_version 1.x
import tensorflow as tf
input_label_tensor = tf.constant(['0,1', '1,0', '1,1', '0,0'], dtype=tf.string)
label_list = tf.string_split(input_label_tensor, ',')
c = tf.sparse.to_dense(label_list)
c = tf.string_to_number(c, tf.float32)
with tf.Session() as sess:
result = c.eval()

print(result)

[[0. 1.]
[1. 0.]
[1. 1.]
[0. 0.]]