def mean_squared_logarithmic_error(y_true, y_pred):
 first_log = tf.log((tf.clip_by_value(y_pred, 1e-08, np.inf) + 1.0))
 second_log = tf.log((tf.clip_by_value(y_true, 1e-08, np.inf) + 1.0))
 return tf.reduce_mean(tf.square((first_log - second_log)))

def mean_squared_logarithmic_error ( y_true , y_pred ) : first_log = tf . log ( ( tf . clip_by_value ( y_pred , 1e-08 , np . inf ) + 1.0 ) ) second_log = tf . log ( ( tf . clip_by_value ( y_true , 1e-08 , np . inf ) + 1.0 ) ) return tf . reduce_mean ( tf . square ( ( first_log - second_log ) ) )
mean squared logarithmic error loss .