Tensorflow深度学习之三十二： tf.scatter_nd_update
一、tf.scatter_nd_update
函数定义：
tf.scatter_nd_update(
ref,
indices,
updates,
use_locking=True,
name=None
)
Applies sparse updates to individual values or slices in a Variable.
将稀疏updates应用于变量中的单个值或切片。
ref is a Tensor with rank P and indices is a Tensor of rank Q.
ref 是一个rank为P的 Tensor，indices 是一个rank为Q的 Tensor.
indices must be integer tensor, containing indices into ref. It must be shape [d_0, ..., d_{Q-2}, K] where 0 < K <= P.
indices 必须是一个由整数组成的tensor，包含了参考于 ref 的索引信息。shape必须满足下列条件：[d_0, ..., d_{Q-2}, K] ，其中 0 < K <= P.
The innermost dimension of indices (with length K) corresponds to indices into elements (if K = P) or slices (if K < P) along the Kth dimension of ref.
indices 的最内层维度（长度为 K）对应于沿着 ref 的 K 维度的元素（如果是 K = P）或切片（如果是 K
updates is Tensor of rank Q-1+P-K with shape:
updates 是一个rank为 Q-1+P-K，shape如下：
[d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].
For example, say we want to update 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that update would look like this:
例如，假设我们想要将4个分散元素更新为1级张量到8个元素。 在Python中，该更新将如下所示：
import tensorflow as tf
ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
indices = tf.constant([[4], [3], [1], [7]])
updates = tf.constant([9, 10, 11, 12])
update = tf.scatter_nd_update(ref, indices, updates)
with tf.Session() as sess:
tf.global_variables_initializer().run()
print sess.run(update)
The resulting update to ref would look like this:
ref 更新结果如下：
[1, 11, 3, 10, 9, 6, 7, 12]
See tf.scatter_nd for more details about how to make updates to slices.
有关如何更新切片的更多详细信息，请参阅 tf.scatter_nd。
二、参数
| 参数 | ||
|---|---|---|
ref |
A Variable. | 一个变量 |
indices |
A Tensor. Must be one of the following types: int32, int64. A tensor of indices into ref. |
一个 Tensor，必须为以下的数据类型： int32, int64, 参考于ref的索引张量。 |
updates |
A Tensor. Must have the same type as ref. A tensor of updated values to add to ref. |
一个Tensor，必须和ref保持相同的数据类型，含义为需要在ref中更新的值。 |
use_locking |
An optional bool. Defaults to True. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. |
一个可选的布尔数据， 默认为True。 如果为True，则分配将受锁保护; 否则行为未定义，但可能表现出较少的争用。 |
name |
A name for the operation (optional). | 名称，可选。 |
返回值：
The value of the variable after the update.
变量更新后的值。（注：返回的是一个Tensor，而不是一个Variable!）
三、代码示例
该函数的含义还是很好理解的，给定一些特定位置的数据进行更新，函数的意义以及参数在前面都有都有说明。这种稀疏更新的方法在numpy中普遍存在，但是在TensorFlow中，使用起来却十分别扭，往往还需要借助py_func来改写。不过有了这个函数就会方便很多。
有关返回值：
import tensorflow as tf
import tensorflow.contrib.eager as tfe
ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
indices = tf.constant([[4], [3], [1], [7]])
updates = tf.constant([9, 10, 11, 12])
update = tf.scatter_nd_update(ref, indices, updates)
print(update)
print(type(update))
结果如下：
Tensor("ScatterNdUpdate:0", shape=(8,), dtype=int32_ref)
二维矩阵的更新：
import tensorflow as tf
import tensorflow.contrib.eager as tfe
import numpy as np
tf.enable_eager_execution()
ref = tfe.Variable(np.zeros(shape=[6, 6], dtype=np.float32))
indices = tf.constant([[4, 4], [3, 0], [1, 5], [5, 0]], dtype=tf.int32)
updates = tf.constant([9, 10, 11, 12], dtype=tf.float32)
update = tf.scatter_nd_update(ref, indices, updates)
print(update)
结果如下：
tf.Tensor(
[[ 0. 0. 0. 0. 0. 0.]
[ 0. 0. 0. 0. 0. 11.]
[ 0. 0. 0. 0. 0. 0.]
[10. 0. 0. 0. 0. 0.]
[ 0. 0. 0. 0. 9. 0.]
[12. 0. 0. 0. 0. 0.]], shape=(6, 6), dtype=float32)
三维矩阵的更新1：
import tensorflow as tf
import tensorflow.contrib.eager as tfe
import numpy as np
tf.enable_eager_execution()
ref = tfe.Variable(np.zeros(shape=[6, 6, 3], dtype=np.float32))
indices = tf.constant([[4, 4, 1], [3, 0, 2], [1, 5, 0], [5, 0, 1]], dtype=tf.int32)
updates = tf.constant([9, 10, 11, 12], dtype=tf.float32)
update = tf.scatter_nd_update(ref, indices, updates)
print(update)
结果如下：
tf.Tensor(
[[[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]]
[[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]
[11. 0. 0.]]
[[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]]
[[ 0. 0. 10.]
[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]]
[[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 9. 0.]
[ 0. 0. 0.]]
[[ 0. 12. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]]], shape=(6, 6, 3), dtype=float32)
三维矩阵的更新2：
import tensorflow as tf
import tensorflow.contrib.eager as tfe
import numpy as np
tf.enable_eager_execution()
ref = tfe.Variable(np.zeros(shape=[6, 6, 3], dtype=np.float32))
indices = tf.constant([[4, 4], [3, 0], [1, 5], [5, 0]], dtype=tf.int32)
updates = tf.constant([[9, 9, 92], [10, 10, 120], [11, 11, 151], [12, 12, 55]], dtype=tf.float32)
update = tf.scatter_nd_update(ref, indices, updates)
print(update)
结果如下：
tf.Tensor(
[[[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]]
[[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]
[ 11. 11. 151.]]
[[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]]
[[ 10. 10. 120.]
[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]]
[[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]
[ 9. 9. 92.]
[ 0. 0. 0.]]
[[ 12. 12. 55.]
[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]]], shape=(6, 6, 3), dtype=float32)