Python keras.objectives 模块，mean_squared_error() 实例源码

def print_regression_model_summary(prefix, y_test, y_pred, parmsFromNormalization):
    y_test = (y_test*parmsFromNormalization.std*parmsFromNormalization.sqrtx2) + parmsFromNormalization.mean
    y_pred = (y_pred*parmsFromNormalization.std*parmsFromNormalization.sqrtx2) + parmsFromNormalization.mean

    mse = mean_squared_error(y_test, y_pred)
    error_AC, rmsep, mape, rmse = almost_correct_based_accuracy(y_test, y_pred, 10)
    rmsle = calculate_rmsle(y_test, y_pred)
    print ">> %s AC_errorRate=%.1f RMSEP=%.6f MAPE=%6f RMSE=%6f mse=%f rmsle=%.5f" %(prefix, error_AC, rmsep, mape, rmse, mse, rmsle)
    log.write("%s AC_errorRate=%.1f RMSEP=%.6f MAPE=%6f RMSE=%6f mse=%f rmsle=%.5f" %(prefix, error_AC, rmsep, mape, rmse, mse, rmsle))


# Utility function to report best scores

def gmse_factory(gamma):
    def gamma_mse(y_true, y_pred):
        return gamma * mean_squared_error(y_true, y_pred)
    return gamma_mse

def vae_loss(x, x_decoded_mean):
    x_d = Flatten()(x)
    x_dec_d = Flatten()(x_decoded_mean)
    xent_loss = input_dim * objectives.mean_squared_error(x_d, x_dec_d) 
    kl_loss = - 0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1) 
    return  xent_loss + kl_loss