code: def binary_crossentropy(expected, predicted):
      epsilon = 1e-7  # smallest positive 32-bit float number
      predicted = tf.clip_by_value(predicted, epsilon, 1.0 - epsilon)
  
      return tf.reduce_sum(
          expected * tf.log(predicted)
          + (1 - expected) * tf.log(1 - predicted),
          axis=1,
      )
  
file_path: neupy/examples/autoencoder/variational_autoencoder.py
imports: import numpy
  import tensorflow
  import matplotlib.pyplot
  import matplotlib.gridspec
  from sklearn import model_selection, datasets
  from neupy import algorithms
  from neupy.layers import *
  from neupy.exceptions import LayerConnectionError
label_desc: Use image node_id 3 for calling a global function with example usage: binary_crossentropy(expected, predicted) and returns: tf