title: TypeError: Fetch argument array has invalid type numpy.ndarray, must be a string or Tensor. (Can not convert a ndarray into a Tensor or Operation.)
body: <p>
  I am trying to reproduce the results in siaseme LSTM to compare the semantic similarity of two sentences from here :- <a href="https://github.com/dhwajraj/deep-siamese-text-similarity" rel="nofollow noreferrer">https://github.com/dhwajraj/deep-siamese-text-similarity</a></p>
  
  <p>I am using tensorflow 1.4 &amp; python 2.7</p>
  
  <p>The train.py is working properly. For evaluating the model, I created a match_valid.tsv file which is a subset of "train_snli.txt" available there. I have modified the getTsvTestData function present in the input_helpers.py file. </p>
  
  <pre><code>def getTsvTestData(self, filepath):
          print("Loading testing/labelled data from "+filepath+"\n")
          x1=[]
          x2=[]
          y=[]
          # positive samples from file
          for line in open(filepath):
              l=line.strip().split("\t")
              if len(l)&lt;3:
                  continue
              x1.append(l[1].lower()) # text
              x2.append(l[0].lower()) # text
              y.append(int(l[2])) # similarity score 0 or 1
          return np.asarray(x1),np.asarray(x2),np.asarray(y)
  </code></pre>
  
  <p>I am getting error from this part of code in eval.py</p>
  
  <pre><code>for db in batches:
              x1_dev_b,x2_dev_b,y_dev_b = zip(*db)
              #x1_dev_b = tf.convert_to_tensor(x1_dev_b,)
              print("type x1_dev_b {}".format(type(x1_dev_b))) # tuple
              print("type x2_dev_b {}".format(type(x2_dev_b))) # tuple
              print("type y_dev_b {}\n".format(type(y_dev_b))) # tuple
  
              feed = {input_x1: x1_dev_b, 
                      input_x2: x2_dev_b, 
                      input_y:y_dev_b, 
                      dropout_keep_prob: 1.0}
  
              batch_predictions, batch_acc, sim = sess.run([predictions,accuracy,sim], feed_dict=feed)
  
              print("type batch_predictions {}".format(type(batch_predictions))) # numpy.ndarray
              print("type batch_acc {}".format(type(batch_acc))) # numpy.float32
              print("type sim {}".format(type(sim))) # numpy.ndarray
  
              all_predictions = np.concatenate([all_predictions, batch_predictions])
  
              print("\n printing batch predictions {} \n".format(batch_predictions))
  
              all_d = np.concatenate([all_d, sim])
  
              print("DEV acc {} \n".format(batch_acc))
  </code></pre>
  
  <p>
  I am getting this error. I tried to use print statement in sess.run() to find the type but it didn`t work.</p>
  
  <pre><code>Traceback (most recent call last):
    File "eval.py", line 92, in &lt;module&gt;
      batch_predictions, batch_acc, sim = sess.run([predictions,accuracy,sim], feed_dict=feed)
    File "/home/joe/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 889, in run
      run_metadata_ptr)
    File "/home/joe/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1105, in _run
      self._graph, fetches, feed_dict_tensor, feed_handles=feed_handles)
    File "/home/joe/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 414, in __init__
      self._fetch_mapper = _FetchMapper.for_fetch(fetches)
    File "/home/joe/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 234, in for_fetch
      return _ListFetchMapper(fetch)
    File "/home/joe/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 341, in __init__
      self._mappers = [_FetchMapper.for_fetch(fetch) for fetch in fetches]
    File "/home/joe/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 242, in for_fetch
      return _ElementFetchMapper(fetches, contraction_fn)
    File "/home/joe/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 275, in __init__
      % (fetch, type(fetch), str(e)))
  TypeError: Fetch argument array([ 1.,  1.,  0.,  0.,  0.,  1.,  1.,  0.,  1.,  0.,  0.,  1.,  0.,
          0.,  0.,  1.,  1.,  0.,  0.,  1.,  0.,  0.,  0.,  1.,  0.,  0.,
          0.,  1.,  0.,  1.,  1.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  1.,
          0.,  0.,  1.,  1.,  1.,  0.,  1.,  1.,  0.,  1.,  1.,  1.,  1.,
          1.,  0.,  0.,  0.,  0.,  1.,  0.,  1.,  1.,  0.,  0.,  1.,  0.,
          0.,  0.,  0.,  0.,  0.,  0.,  1.,  1.,  1.,  1.,  1.,  1.,  0.,
          0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,
          0.,  0.,  1.,  1.,  0.,  0.,  0.,  1.,  1.,  1.,  0.,  0.,  0.,
          0.,  0.,  0.,  1.,  1.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,
          0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
          0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
          1.,  0.,  0.,  1.,  0.,  0.,  1.,  0.,  1.,  1.,  0.,  1.,  0.,
          0.,  0.,  0.,  0.,  0.,  1.,  1.,  0.,  0.,  1.,  0.,  0.,  0.,
          1.,  1.,  1.,  1.,  0.,  1.,  1.,  0.,  0.,  1.,  0.,  0.,  1.,
          1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  0.,  0.,  0.,  1.,  0.,
          0.,  1.,  0.,  0.,  1.,  0.,  0.,  1.,  1.,  0.,  0.,  1.,  0.,
          0.,  0.,  1.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
          0.,  1.,  0.,  0.,  1.,  0.,  1.,  1.,  0.,  1.,  0.,  1.,  0.,
          0.,  0.,  0.,  1.,  0.,  0.,  0.,  1.,  0.,  1.,  0.,  0.,  1.,
          1.,  0.,  0.,  1.,  0.,  1.,  0.,  0.,  0.], dtype=float32) has invalid type &lt;type 'numpy.ndarray'&gt;, must be a string or Tensor. (Can not convert a ndarray into a Tensor or Operation.)
  </code></pre>
  
  
  
  <p>
  Actually, I am trying to do a query similarity, comparing the query vector to all document vectors in my corpus and rank the sentences based on the similarity score. I know that currently the LSTM is just comparing two sentences with each other and output the similarity as 0 or 1. How can I do that? </p>