我有一個從外部資料源讀取資料的張量（例如標簽）。張量的值是一個字串，格式為“label1,label2”（例如“0,1”）。現在我想使用分隔符 ',' 將字串值拆分為一個串列，因此結果將類似于 ['0', '1']。
我試過流動：
# 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, ',')
選項 3 的錯誤是：
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)'
進行拆分操作的正確方法是什么？我正在使用TF-1.4
uj5u.com熱心網友回復：
我最好的賭注是它input_label_tensor實際上不是string張量。例如這有效：
%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, ',')
但是，如果我使用float張量，則可以重現您的錯誤：
input_label_tensor = tf.constant([0, 1], dtype=tf.float32)
label_list = tf.string_split(input_label_tensor, ',')
要訪問SparseTensor嘗試的值，請執行以下操作：
%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.]]
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標籤：Python python-2.7 张量流