标签：form 接下来 nbsp space weight tensor col als mono
%tensorflow_version 2.x import tensorflow as tf p=tf.Variable(tf.random.uniform([10,1])) b=tf.nn.embedding_lookup(p,[1,3]) p b
输出的结果分别为p：
<tf.Variable ‘Variable:0‘ shape=(10, 1) dtype=float32, numpy=
array([[0.79612887],
[0.28201234],
[0.20101798],
[0.1620121 ],
[0.88669086],
[0.4243393 ],
[0.51021874],
[0.09500039],
[0.12813437],
[0.42305255]], dtype=float32)>
b:
<tf.Tensor: shape=(2, 1), dtype=float32, numpy=
array([[0.28201234],
[0.1620121 ]], dtype=float32)>
可以看出，b是由p输出的向量上位置1和3上元素组成的。
接下来更改一下数值，上面是生成一个向量，接下来生成一个矩阵：
p=tf.Variable(tf.random.uniform([10,10],-1,1)) b=tf.nn.embedding_lookup(p,[1,3]) p b
产生的结果为p：
<tf.Variable ‘Variable:0‘ shape=(10, 10) dtype=float32, numpy=
array([[-0.7621522 , 0.6107156 , -0.47999907, 0.5350437 , 0.7630944 ,
0.37270713, -0.8395808 , -0.879581 , -0.47662497, -0.05092502],
[-0.21088243, -0.0150187 , -0.28028893, 0.3332212 , 0.4568975 ,
0.05019474, -0.19229984, -0.4012766 , 0.38493705, 0.8479743 ],
[ 0.3077824 , -0.8770895 , 0.12883782, 0.6170182 , -0.6244514 ,
-0.2808833 , 0.5709777 , 0.6452646 , 0.24578142, 0.3655765 ],
[-0.5822737 , -0.710577 , -0.997102 , 0.8577807 , 0.82289314,
-0.510561 , 0.95922303, -0.09372258, -0.80911994, 0.9954574 ],
[-0.15612102, -0.00413752, 0.41538835, 0.50921464, 0.7637322 ,
0.5406666 , -0.8686323 , -0.80358744, -0.12960792, 0.47586107],
[ 0.33130383, -0.65484834, -0.6364062 , -0.12607336, 0.10087228,
-0.54285645, 0.45991468, 0.36029506, 0.41191912, 0.65596604],
[ 0.90655327, 0.86263967, 0.97394824, -0.9905188 , -0.03838801,
-0.5840478 , -0.7306757 , -0.62264824, -0.19541001, 0.01948309],
[ 0.27840662, -0.23048878, 0.2640462 , 0.27937698, -0.13661599,
0.72016 , -0.43872857, -0.40881586, 0.9849553 , -0.4254725 ],
[ 0.824687 , -0.3534038 , 0.78239155, 0.22957778, -0.00436497,
-0.5633409 , -0.41481328, -0.35603738, -0.22372437, -0.64321375],
[-0.7983091 , 0.51379323, 0.87890744, -0.47110224, -0.91740274,
-0.26170492, -0.8321235 , -0.46379066, -0.2834475 , -0.7457466 ]],
dtype=float32)>
b：
<tf.Tensor: shape=(2, 10), dtype=float32, numpy=
array([[-0.21088243, -0.0150187 , -0.28028893, 0.3332212 , 0.4568975 ,
0.05019474, -0.19229984, -0.4012766 , 0.38493705, 0.8479743 ],
[-0.5822737 , -0.710577 , -0.997102 , 0.8577807 , 0.82289314,
-0.510561 , 0.95922303, -0.09372258, -0.80911994, 0.9954574 ]],
dtype=float32)>
可以看出，b依旧是p中第1行和第3行的值，也就是默认是作用在行上的
标签：form 接下来 nbsp space weight tensor col als mono
原文地址：https://www.cnblogs.com/liuxiangyan/p/12527188.html
