Python 如何使用tensorflow hub.module访问嵌入件？

我使用以下代码访问使用TF Hub通用句子编码器的嵌入

将tensorflow导入为tf
将tensorflow_hub导入为hub
型号=轮毂。负载（“https://tfhub.dev/google/universal-sentence-encoder/4")
def嵌入（输入）：
返回模型（输入）
messages=[“段落的长度没有硬性限制。大致上，段落越长，嵌入的内容就越“稀释”。]
消息嵌入=嵌入（消息）

---

现在如何访问实际向量

可以从变量

message\u embeddings

访问实际嵌入向量

message\u embeddings

是

shape=（1512）

的向量，这意味着

USE-4

返回的向量的

维度是512

换句话说，每个句子都被编码到
512
列向量中

代码的输出

print(message_embeddings)

是

希望这有帮助。学习愉快

tf.Tensor(
[[-0.00366504 -0.00703163 -0.0061244   0.02026021 -0.09436475  0.00027828
   0.05004153 -0.01591516  0.088241    0.07551358 -0.01868021  0.04386544
   0.00105771  0.03730893 -0.05554571  0.02852311  0.01709696  0.08152976
  -0.03092775  0.00683713 -0.08059237  0.042355   -0.07580714 -0.00443942
  -0.03430099  0.03240041 -0.05212452 -0.04247908 -0.05534476 -0.02328587
  -0.0438301  -0.03972115  0.01639873  0.00163302  0.07708091 -0.02310511
   0.01288455  0.04831124  0.0089498  -0.02632253 -0.01840279  0.02118563
   0.03758964  0.08740229  0.02880297 -0.00486817  0.0115555  -0.00451289
  -0.00162866  0.01446948  0.00189139 -0.07941346 -0.0216493  -0.02580371
  -0.00930381 -0.00526039 -0.01272183  0.02215818  0.04742621  0.02226813
   0.0110765  -0.01790449  0.01739751 -0.08388933  0.05826297 -0.05230762
  -0.07484917  0.06905693  0.01646299  0.00850342 -0.0022191  -0.07555264
   0.01601691  0.06028103  0.00524664  0.03776945 -0.05246941  0.03556651
   0.06253887 -0.04647287 -0.03415112 -0.03473583  0.04833042 -0.01264609
   0.01788526 -0.07143527 -0.02432756  0.04081429 -0.0524265  -0.05402376
  -0.02753968  0.06558003  0.01936845 -0.08112626  0.0157347   0.05620547
  -0.06219236 -0.03654391  0.03936478 -0.01247254 -0.03957544  0.07394353
  -0.06131149 -0.0550663   0.08301188 -0.01699291  0.03726438  0.00248359
  -0.00569713  0.04109528 -0.05154289  0.05428214 -0.06594346  0.06009263
   0.02753788  0.01492724 -0.01422153  0.02779302  0.02881143 -0.01985389
   0.05809831 -0.02661227 -0.06907296  0.01192496 -0.03630216  0.03146286
  -0.02979902  0.05192203 -0.0479207   0.03564131  0.05351846  0.02681697
   0.02597373 -0.03392426 -0.05286925 -0.05110073  0.01331552 -0.00612995
  -0.04932296 -0.0185418  -0.0841584   0.02415963 -0.01051812  0.05603031
  -0.0083728  -0.05966095  0.0321536  -0.03968453  0.03799454 -0.05958865
  -0.07585841  0.04390398 -0.03674331  0.01918785  0.03446485 -0.04106916
  -0.05183128  0.02947152 -0.03531763  0.03698466  0.06261521 -0.00646621
   0.01130813 -0.02275244 -0.04280937  0.01955702 -0.03919312  0.00476116
   0.01887495 -0.00195181 -0.02401051 -0.06942239 -0.06978329  0.06458326
   0.00362934  0.03588834  0.04921037 -0.03195003  0.02806171 -0.0193333
   0.00994556 -0.02342404  0.10165592 -0.02853323  0.04147425  0.00914851
   0.00497671  0.00073764 -0.00318258  0.03595887 -0.01817959  0.01496308
  -0.03551586  0.02536247 -0.07170779 -0.03153825 -0.04042004 -0.01769615
   0.00958568  0.00038516  0.00799816  0.04089458  0.02171035 -0.08852603
  -0.06747856  0.05664572 -0.06597329  0.02299296  0.03397151 -0.03845559
   0.00395073  0.00314357  0.01119022  0.05957965 -0.05583638  0.02908287
   0.0112076   0.07695369 -0.03935304 -0.02383705 -0.04208985 -0.00359387
   0.06851663 -0.05395376 -0.00246254 -0.01888378 -0.01391678 -0.07573339
   0.05811501  0.02059502 -0.00418438 -0.01210096 -0.06286791 -0.07645103
  -0.02463043 -0.03153505  0.05593796 -0.02202086 -0.00274707  0.04458077
  -0.06263509  0.06126784 -0.04235342  0.00322403  0.02189728 -0.06388599
  -0.03919036 -0.00010863  0.02531325  0.02581233 -0.01304512 -0.03001025
  -0.02754986  0.0531372  -0.02369525 -0.04376267  0.0641819   0.09532097
  -0.06730784  0.04478338  0.02004733  0.05244097 -0.01885018 -0.06137342
  -0.08407518 -0.00084469 -0.02145135 -0.0091182  -0.06907462  0.06986497
   0.0600312  -0.04390564 -0.00131028  0.06390417  0.03533437  0.03813365
   0.04030495 -0.01402102 -0.06857175 -0.06571147  0.01421791 -0.0381003
  -0.04138157  0.05040992 -0.05724671  0.01490439 -0.07905842 -0.03806996
  -0.01071311 -0.01229521 -0.00771822 -0.03641455 -0.04578875  0.00925799
   0.0403841   0.00132017  0.031641    0.01162737  0.0101506  -0.01761867
   0.0579349   0.03595775 -0.01147426 -0.01525036  0.05006553  0.03747585
  -0.05307707 -0.08915938  0.02942844 -0.05546442 -0.0128964   0.04225868
  -0.01534053 -0.04580414  0.01088955 -0.03184818  0.02326705 -0.08861458
  -0.07253686 -0.02572111 -0.03711193  0.0474383  -0.05628109 -0.01391787
   0.00941848 -0.06177152 -0.06071901 -0.0092127  -0.10220838 -0.01376523
   0.03162379  0.03983926  0.00640659 -0.00418033 -0.01612685  0.01891562
  -0.04313575  0.01139805 -0.00378637  0.08349139  0.08300766 -0.0494319
  -0.03658734  0.00325003 -0.05251636 -0.04457545 -0.079386   -0.05799922
  -0.01254137  0.02311826 -0.00766293 -0.06729192 -0.03971054 -0.0663051
   0.08720677  0.04582898 -0.08557201 -0.01054355 -0.02762848  0.06243869
  -0.08848279  0.02289506  0.05723204 -0.01221769 -0.0393519  -0.00582338
   0.02841124 -0.03293297 -0.03143778 -0.00352248  0.0073043   0.01209227
  -0.00148794  0.03695554  0.03136331 -0.03311655 -0.0221175  -0.07959055
  -0.04138357 -0.00950083 -0.01173625  0.01499144 -0.0121095   0.00823302
   0.07642982  0.05198056  0.05955188  0.03240911  0.09211077 -0.05317325
  -0.06024589  0.00489183  0.04719653  0.02498623  0.03750401 -0.02352423
   0.05042319 -0.01633615 -0.02236294  0.04443104  0.02694818  0.00881322
   0.02469178 -0.06206469 -0.00215397 -0.02641553  0.00405129 -0.07184313
  -0.02841844  0.0309756   0.02459977 -0.03155032  0.01407542  0.00524732
  -0.01893367  0.0102607  -0.00333736  0.02885202 -0.03275619 -0.08507563
   0.02076722 -0.02471628 -0.00449985  0.0004644  -0.0923043   0.02101186
   0.0352884   0.03790538 -0.00372656  0.06751391  0.02638355  0.01678842
   0.03843728  0.10451197 -0.06375936 -0.05324562  0.03276567 -0.01112294
  -0.0082361  -0.01735083 -0.03767544 -0.04266915 -0.04767371  0.07573947
  -0.01247379 -0.01048137 -0.02308911 -0.01484709 -0.00733855  0.06788232
  -0.08163249 -0.01530467 -0.01805264 -0.07910046 -0.06530869  0.07402557
   0.06713054 -0.01659747 -0.00980262  0.05586078  0.03396358 -0.06102567
  -0.06640005  0.02269907  0.03265672 -0.01353668 -0.08313932 -0.02356159
  -0.03383274  0.05942128 -0.08610516 -0.08445066 -0.01306568 -0.05279852
   0.00986506  0.00461306  0.08119206  0.00604     0.10107437  0.00191085
  -0.05926891  0.01157635  0.0284292  -0.08671403  0.01851062  0.05745851
  -0.06798992  0.02700593  0.00208116 -0.00829788  0.08901995 -0.00418414
  -0.06217562 -0.07832154  0.02027107  0.06713033  0.04617893  0.05885412
  -0.04505047  0.09581003  0.033753   -0.00888314 -0.07608356 -0.03729891
   0.02724086  0.02371461 -0.01081131 -0.00809431 -0.04376922 -0.04656423
   0.00886904  0.01995739]], shape=(1, 512), dtype=float32)