我有一个张量对象,我想对其进行切片。
tf_a1 = tf.Variable([ [9.968594, 8.655439, 0., 0. ],
[0., 8.3356, 0., 8.8974 ],
[0., 0., 6.103182, 7.330564 ],
[6.609862, 0., 3.0614321, 0. ],
[9.497023, 0., 3.8914037, 0. ],
[0., 8.457685, 8.602337, 0. ],
[0., 0., 5.826657, 8.283971 ],
[0., 0., 0., 0. ]])
另外,我有这个数组:
tf_a2 = tf.constant([[1, 2, 5],
[1, 4, 6],
[0, 7, 7],
[2, 3, 6],
[2, 4, 7]])
我想像切片一样做这个numpy:
tf_a1[tf_a2]
numpy代码的预期输出如下:
[[[0. 8.3356 0. 8.8974 ]
[0. 0. 6.103182 7.330564 ]
[0. 8.457685 8.602337 0. ]]
[[0. 8.3356 0. 8.8974 ]
[9.497023 0. 3.8914037 0. ]
[0. 0. 5.826657 8.283971 ]]
[[9.968594 8.655439 0. 0. ]
[0. 0. 0. 0. ]
[0. 0. 0. 0. ]]
[[0. 0. 6.103182 7.330564 ]
[6.609862 0. 3.0614321 0. ]
[0. 0. 5.826657 8.283971 ]]
[[0. 0. 6.103182 7.330564 ]
[9.497023 0. 3.8914037 0. ]
[0. 0. 0. 0. ]]]
我认为我可以使用以下方法在张量流中执行类似的操作
tf.gather_nd(tf_a1, tf_a2)
但是会引发此错误:
tensorflow.python.framework.errors_impl.InvalidArgumentError: index innermost dimension length must be <= params rank; saw: 3 vs. 2 [Op:GatherNd]
感谢您的帮助:)
答案 0 :(得分:1)
我认为您可以使用tf.gather
:
tf.gather(tf_a1, tf_a2, axis=0)
# <tf.Tensor 'GatherV2_10:0' shape=(5, 3, 4) dtype=float32>
TensorFlow 2.0上的可复制示例
tf.__version__
# '2.0.0-beta0'
tf.gather(tf_a1, tf_a2, axis=0)
<tf.Tensor: id=9, shape=(5, 3, 4), dtype=float32, numpy=
array([[[0. , 8.3356 , 0. , 8.8974 ],
[0. , 0. , 6.103182 , 7.330564 ],
[0. , 8.457685 , 8.602337 , 0. ]],
[[0. , 8.3356 , 0. , 8.8974 ],
[9.497023 , 0. , 3.8914037, 0. ],
[0. , 0. , 5.826657 , 8.283971 ]],
[[9.968594 , 8.655439 , 0. , 0. ],
[0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. ]],
[[0. , 0. , 6.103182 , 7.330564 ],
[6.609862 , 0. , 3.0614321, 0. ],
[0. , 0. , 5.826657 , 8.283971 ]],
[[0. , 0. , 6.103182 , 7.330564 ],
[9.497023 , 0. , 3.8914037, 0. ],
[0. , 0. , 0. , 0. ]]], dtype=float32)>