class_docstr: Class for evaluating the performance of mapping W
class_docstr_tok: ["Class","for","evaluating","the","performance","of","mapping","W"]
docstr: Calculates similarity score between two embeddings
  Arguments:
      sess(tf.session): Tensorflow Session
      batch_size(int): Size of batch to process
  Returns:
      Returns similarity score numpy array
docstr_tok: ["Calculates","similarity","score","between","two","embeddings","Arguments",":","sess","(","tf.session",")",":","Tensorflow","Session","batch_size","(","int",")",":","Size","of","batch","to","process","Returns",":","Returns","similarity","score","numpy","array"]
code: def calc_csls_score(self, sess, batch_size=512):
      """
      Calculates similarity score between two embeddings
      Arguments:
          sess(tf.session): Tensorflow Session
          batch_size(int): Size of batch to process
      Returns:
          Returns similarity score numpy array
      """
      score_val = []
      eval_size = len(self.src_ind)
      # Calculate scores
      for i in range(0, eval_size, batch_size):
          score_src_ids = [
              self.src_ind[x]
              for x in range(i, min(i + batch_size, eval_size))
          ]
          eval_dict = {
              self.src_ph: score_src_ids,
              self.tgt_ph: self.tgt_ids,
          }
          score_val.append(
              sess.run(
                  self.csls_subgraphs["ScoreGraph"], feed_dict=eval_dict
              )
          )
      score_val = np.concatenate(score_val)
      return score_val
  
file_path: nlp-architect/examples/crosslingembs/evaluate.py
imports: from __future__ import print_function, division
  import numpy
  import tensorflow
  from nlp_architect.data.fasttext_emb import get_eval_data
label_desc: Use image node_id 9 for calling the Evaluate obj's underlying member method code with example usage: obj.calc_csls_score(sess, batch_size) and returns: score_val