def get_probs(self, x, **kwargs):
    """
    :param x: A symbolic representation (Tensor) of the network input
    :return: A symbolic representation (Tensor) of the output
    probabilities (i.e., the output values produced by the softmax layer).
    """
    d = self.fprop(x, **kwargs)
    if self.O_PROBS in d:
      output = d[self.O_PROBS]
      min_prob = tf.reduce_min(output)
      max_prob = tf.reduce_max(output)
      asserts = [utils_tf.assert_greater_equal(min_prob,
                                               tf.cast(0., min_prob.dtype)),
                 utils_tf.assert_less_equal(max_prob,
                                            tf.cast(1., min_prob.dtype))]
      with tf.control_dependencies(asserts):
        output = tf.identity(output)
      return output
    elif self.O_LOGITS in d:
      return tf.nn.softmax(logits=d[self.O_LOGITS])
    else:
      raise ValueError('Cannot find probs or logits.')
def get_probs ( self , x , ** kwargs ) : d = self . fprop ( x , ** kwargs ) if self . O_PROBS in d : output = d [ self . O_PROBS ] min_prob = tf . reduce_min ( output ) max_prob = tf . reduce_max ( output ) asserts = [ utils_tf . assert_greater_equal ( min_prob , tf . cast ( 0. , min_prob . dtype ) ) , utils_tf . assert_less_equal ( max_prob , tf . cast ( 1. , min_prob . dtype ) ) ] with tf . control_dependencies ( asserts ) : output = tf . identity ( output ) return output elif self . O_LOGITS in d : return tf . nn . softmax ( logits = d [ self . O_LOGITS ] ) else : raise ValueError ( 'Cannot find probs or logits.' )
:param x: A symbolic representation (Tensor) of the network input
    :return: A symbolic representation (Tensor) of the output
    probabilities (i.e., the output values produced by the softmax layer).