original_docstring: Matching loss for an exponential activation function.
docstring: Matching loss for an exponential activation function.
docstring_tokens: Matching loss for an exponential activation function .
code: def unnormalized_negative_entropy(y, y_hat):
      '''Matching loss for an exponential activation function.'''
      cross_entropy_terms = tf.where(
          tf.greater(y, 0.0), y * tf.log(y_hat), tf.zeros_like(y))
      return tf.reduce_sum(y_hat - cross_entropy_terms, axis=1)
code_tokens: def unnormalized_negative_entropy ( y , y_hat ) : cross_entropy_terms = tf . where ( tf . greater ( y , 0.0 ) , y * tf . log ( y_hat ) , tf . zeros_like ( y ) ) return tf . reduce_sum ( y_hat - cross_entropy_terms , axis = 1 )
short_docstring: Matching loss for an exponential activation function.
short_docstring_tokens: Matching loss for an exponential activation function .
comment: '''Matching loss for an exponential activation function.'''