class_docstr: Class for evaluating the performance of mapping W
class_docstr_tok: ["Class","for","evaluating","the","performance","of","mapping","W"]
docstr: Builds a similarity measurement graph
  
  Arguments:
      name(str): Name of the graph
  Returns:
      Returns a handle to the graph
docstr_tok: ["Builds","a","similarity","measurement","graph","Arguments",":","name","(","str",")",":","Name","of","the","graph","Returns",":","Returns","a","handle","to","the","graph"]
code: def build_sim_graph(self, name):
      """
      Builds a similarity measurement graph
  
      Arguments:
          name(str): Name of the graph
      Returns:
          Returns a handle to the graph
      """
      with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
          # Look up the words
          emb_lut = tf.nn.embedding_lookup(
              self.src_ten, self.src_ph, name="Src_Lut"
          )
          query_lut = tf.nn.embedding_lookup(
              self.tgt_ten, self.tgt_ph, name="Tgt_Lut"
          )
          # Cast
          emb = tf.cast(emb_lut, tf.float32)
          query = tf.cast(query_lut, tf.float32)
          # Score it
          score = emb * query
          cos_sum = tf.reduce_sum(score, axis=1)
          cos_mean = tf.reduce_mean(cos_sum)
          return cos_mean
  
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 5 for calling the Evaluate obj's underlying member method code with example usage: obj.build_sim_graph(name) and returns: cos_mean