code: def evaluate(
      model,
      vocab,
      data_path,
      seed=1234,
      evaltest=False,
      vocab_size_layer=20002,
  ):
      """
      Run experiment
      """
      neon_logger.display("Preparing SICK evaluation data...")
      # Check if SICK data exists in specified directory, otherwise download
      sick_data = SICK(path=data_path)
      train, dev, test, scores = sick_data.load_eval_data()
  
      np.random.seed(seed)
      shuf_idxs = np.random.permutation(range(len(train[0])))
      train_A_shuf = train[0][shuf_idxs]
      train_B_shuf = train[1][shuf_idxs]
      scores_shuf = scores[0][shuf_idxs]
  
      train_A_tok = tokenize_input(train_A_shuf, vocab=vocab)
      train_B_tok = tokenize_input(train_B_shuf, vocab=vocab)
      dev_A_tok = tokenize_input(dev[0], vocab=vocab)
      dev_B_tok = tokenize_input(dev[1], vocab=vocab)
  
      # Get iterator from tokenized data
      train_set_A = SentenceEncode(
          train_A_tok,
          [],
          len(train_A_tok),
          vocab_size_layer,
          max_len=30,
          index_from=2,
      )
      train_set_B = SentenceEncode(
          train_B_tok,
          [],
          len(train_B_tok),
          vocab_size_layer,
          max_len=30,
          index_from=2,
      )
  
      # Compute embeddings using iterator
      trainA = model.get_outputs(train_set_A)
      trainB = model.get_outputs(train_set_B)
  
      dev_set_A = SentenceEncode(
          dev_A_tok,
          [],
          len(dev_A_tok),
          vocab_size_layer,
          max_len=30,
          index_from=2,
      )
      dev_set_B = SentenceEncode(
          dev_B_tok,
          [],
          len(dev_B_tok),
          vocab_size_layer,
          max_len=30,
          index_from=2,
      )
  
      devA = model.get_outputs(dev_set_A)
      devB = model.get_outputs(dev_set_B)
  
      trainF = np.c_[np.abs(trainA - trainB), trainA * trainB]
      devF = np.c_[np.abs(devA - devB), devA * devB]
  
      trainY = encode_labels(scores_shuf, ndata=len(trainF))
      devY = encode_labels(scores[1], ndata=len(devF))
  
      lrmodel, opt, cost = prepare_model(ninputs=trainF.shape[1])
  
      neon_logger.display("Training the regression model...")
      bestlrmodel = train_model(
          lrmodel,
          opt,
          cost,
          trainF,
          trainY,
          devF,
          devY,
          scores[1][: len(devF)],
      )
  
      if evaltest:
          test_A_tok = tokenize_input(test[0], vocab=vocab)
          test_B_tok = tokenize_input(test[1], vocab=vocab)
  
          test_set_A = SentenceEncode(
              test_A_tok,
              [],
              len(test_A_tok),
              vocab_size_layer,
              max_len=30,
              index_from=2,
          )
          test_set_B = SentenceEncode(
              test_B_tok,
              [],
              len(test_B_tok),
              vocab_size_layer,
              max_len=30,
              index_from=2,
          )
  
          testA = model.get_outputs(test_set_A)
          testB = model.get_outputs(test_set_B)
  
          testF = np.c_[np.abs(testA - testB), testA * testB]
  
          neon_logger.display(
              "Evaluating using vectors and linear regression model"
          )
          r = np.arange(1, 6)
          yhat = np.dot(
              bestlrmodel.get_outputs(ArrayIterator(testF)), r
          )
          pr = pearsonr(yhat, scores[2][: len(yhat)])[0]
          sr = spearmanr(yhat, scores[2][: len(yhat)])[0]
          se = np.mean((yhat - scores[2][: len(yhat)]) ** 2)
          neon_logger.display("Test Pearson: " + str(pr))
          neon_logger.display("Test Spearman: " + str(sr))
          neon_logger.display("Test MSE: " + str(se))
  
          return yhat
  
imports: from __future__ import division
  from __future__ import print_function
  import numpy
  import copy
  from scipy.stats import pearsonr
  from scipy.stats import spearmanr
  from neon.util.argparser import NeonArgparser
  from neon.util.persist import load_obj
  from neon.initializers import Gaussian
  from neon.layers import GeneralizedCost, Affine
  from neon.data import ArrayIterator
  from neon.models import Model
  from neon.optimizers import Adam
  from neon.transforms import Softmax, CrossEntropyMulti
  from neon.callbacks.callbacks import Callbacks
  from neon.data import SICK
  from neon import logger
  from data_iterator import SentenceEncode
  from data_loader import load_data
  from util import load_sent_encoder, tokenize, get_w2v_vocab
label_desc: Use image node_id 2 for calling a global function with example usage: evaluate(model, vocab, data_path, seed, evaltest, vocab_size_layer) and returns: yhat