我正在编写一个函数来比较两个字符串的相似性，使用Google的通用句子编码器。按照此处提供的笔记本中的说明，我的类中有以下方法，该方法将两个句子作为输入并打印它们之间的相似性。
def tf_sim(self, text1, text2):
# Reduce logging output.
tf.logging.set_verbosity(tf.logging.ERROR)
sim_input1 = tf.placeholder(tf.string, shape=(None), name="sim_input1")
sim_input2 = tf.placeholder(tf.string, shape=(None), name="sim_input2")
embedding1 = self.embed([sim_input1])
embedding2 = self.embed([sim_input2])
encode1 = tf.nn.l2_normalize(embedding1, axis=1)
encode2 = tf.nn.l2_normalize(embedding2, axis=1)
sim_scores = -tf.acos(tf.reduce_sum(tf.multiply(encode1, encode2), axis=1))
init_vars = tf.global_variables_initializer()
init_tables = tf.tables_initializer()
with tf.Session() as sess:
sess.run([init_vars, init_tables])
sess.run(sim_scores, feed_dict={sim_input1: text1, sim_input2: text2})
print (sim_scores.eval())
作为参考，self.embed 属性如下所示：
self.embed = hub.Module("https://tfhub.dev/google/universal-sentence-encoder/2")
当我调用函数时：
str1 = "Is Python a better first language to learn than Java"
str2 = "Which of Python or Java is a better programming language to
start out with"
tf_sim(str1, str2)
我得到以下堆栈跟踪：
Traceback (most recent call last):
File "SimilarityCalculator.py", line 143, in <module>
sc.tf_sim(str1, str2)
File "SimilarityCalculator.py", line 67, in tf_sim
print (sim_scores.eval())
File "/Users/kaushik.visvanathan/anaconda3/envs/Community/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 710, in eval
return _eval_using_default_session(self, feed_dict, self.graph, session)
File "/Users/kaushik.visvanathan/anaconda3/envs/Community/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 5180, in _eval_using_default_session
return session.run(tensors, feed_dict)
File "/Users/kaushik.visvanathan/anaconda3/envs/Community/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 900, in run
run_metadata_ptr)
File "/Users/kaushik.visvanathan/anaconda3/envs/Community/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1135, in _run
feed_dict_tensor, options, run_metadata)
File "/Users/kaushik.visvanathan/anaconda3/envs/Community/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1316, in _do_run
run_metadata)
File "/Users/kaushik.visvanathan/anaconda3/envs/Community/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1335, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'sim_input1' with dtype string
[[Node: sim_input1 = Placeholder[dtype=DT_STRING, shape=<unknown>, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
Caused by op 'sim_input1', defined at:
File "SimilarityCalculator.py", line 143, in <module>
sc.tf_sim(str1, str2)
File "SimilarityCalculator.py", line 40, in tf_sim
sim_input1 = tf.placeholder(tf.string, shape=(None), name="sim_input1")
File "/Users/kaushik.visvanathan/anaconda3/envs/Community/lib/python3.6/site-packages/tensorflow/python/ops/array_ops.py", line 1808, in placeholder
return gen_array_ops.placeholder(dtype=dtype, shape=shape, name=name)
File "/Users/kaushik.visvanathan/anaconda3/envs/Community/lib/python3.6/site-packages/tensorflow/python/ops/gen_array_ops.py", line 4848, in placeholder
"Placeholder", dtype=dtype, shape=shape, name=name)
File "/Users/kaushik.visvanathan/anaconda3/envs/Community/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "/Users/kaushik.visvanathan/anaconda3/envs/Community/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 3392, in create_op
op_def=op_def)
File "/Users/kaushik.visvanathan/anaconda3/envs/Community/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1718, in __init__
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'sim_input1' with dtype string
[[Node: sim_input1 = Placeholder[dtype=DT_STRING, shape=<unknown>, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
基本上告诉我我需要向占位符输入一个字符串，即使看起来我正在这样做。我错过了什么吗？任何帮助将不胜感激！
sim_scores.eval()
相当于做：
sess.run(sim_scores, feed_dict={})
所以TensorFlow(理所当然地(抱怨你没有喂sim_input1(或sim_input2(
而是存储sess.run()调用的结果并打印该结果，或者为eval()调用提供feed_dict参数。
正如f4所说，您已经评估了sim_scores的值。您只需要存储和打印它：
with tf.Session() as sess:
sess.run([init_vars, init_tables])
evaluated_sim_scores = sess.run(sim_scores, feed_dict={sim_input1: text1, sim_input2: text2})
print (evaluated_sim_scores)
注意：不要使用相同的名称来存储sim_scores。