我有一个非常大的模型，包含许多损失和度量。当我执行print(np.array(self.model.metrics_names))时，得到的结果是：

['loss' 'autoencoder_loss' 'autoencoder_loss' 'autoencoder_loss' 'autoencoder_loss' 's_regularisation_phase_loss' 'gen_regularisation_phase_loss' 's_regularisation_phase_loss' 'z_regularisation_phase_loss' 'gen_regularisation_phase_loss' 'z_regularisation_phase_loss' 'gen_regularisation_phase_loss' 'gen_regularisation_phase_loss' 'autoencoder_categorical_accuracy' 'autoencoder_output' 'autoencoder_categorical_accuracy_1' 'autoencoder_output_1' 'autoencoder_categorical_accuracy_2' 'autoencoder_output_2' 'autoencoder_categorical_accuracy_3' 'autoencoder_output_3' 's_regularisation_phase_categorical_accuracy' 's_regularisation_phase_output' 'gen_regularisation_phase_categorical_accuracy' 'gen_regularisation_phase_output' 's_regularisation_phase_categorical_accuracy_1' 's_regularisation_phase_output_1' 'z_regularisation_phase_categorical_accuracy' 'z_regularisation_phase_output' 'gen_regularisation_phase_categorical_accuracy_1' 'gen_regularisation_phase_output_1' 'z_regularisation_phase_categorical_accuracy_1' 'z_regularisation_phase_output_1' 'gen_regularisation_phase_categorical_accuracy_2' 'gen_regularisation_phase_output_2' 'gen_regularisation_phase_categorical_accuracy_3' 'gen_regularisation_phase_output_3']

有没有办法给它们赋予更有意义的名称？

回答：

每个_loss和_accuracy前面的名称来自输出层的名称。如果您想修改这些名称，您应该重命名输出层。

考虑以下模型。

input_ =  keras.layers.Input(shape=(8,))x =  keras.layers.Dense(16)(input_)output1 = keras.layers.Dense(32, name="output1")(x)output2 = keras.layers.Dense(32, name="output2")(x)model = keras.models.Model(inputs=input_, outputs=[output1, output2])model.compile(loss=["mse", "mae"], optimizer="adam", metrics={"output1":"accuracy","output2":"accuracy"})

现在model.metrics_names将会给出以下列表

['loss', 'output1_loss', 'output2_loss', 'output1_acc', 'output2_acc']