Python Keras度量意外行为

当您在model.compile中使用calculate_snr作为度量时，它将在训练期间分批应用，然后对这些分批的值求平均值以计算最终度量.这可能会导致计算的SNR与在代码末尾进行预测后在整个数据集上手动计算时的SNR有所不同.

您可以通过将snr_metric定义为一个类来克服这一限制.

class SNRMetric(keras.metrics.Metric):
    def __init__(self, **kwargs):
        super(SNRMetric, self).__init__(**kwargs)
        self.clean_power = self.add_weight(name="clean_power", initializer="zeros")
        self.noise_power = self.add_weight(name="noise_power", initializer="zeros")
        self.count = self.add_weight(name="count", initializer="zeros")

    def update_state(self, y_true, y_pred, sample_weight=None):
        clean_power = tf.reduce_sum(tf.square(y_true))
        noise_power = tf.reduce_sum(tf.square(y_true - y_pred))

        self.clean_power.assign_add(clean_power)
        self.noise_power.assign_add(noise_power)
        self.count.assign_add(1)

    def result(self):
        snr_db = 10 * tf.math.log(self.clean_power / self.noise_power) / tf.math.log(10.0)
        return snr_db

然后，您可以修改用于培训和测试的代码，如下所示:

# TODO define your model

model.compile(
   loss='mean_squared_error', 
   optimizer=keras.optimizers.Adam(learning_rate=learning_rate), 
   metrics=[SNRMetric()] # here the crucial point
)

# Train 
sound_denoising_history = model.fit(x=X_abs.T, y=S_abs.T, epochs=200, batch_size=100, validation_data=(X_test_01_abs.T, S_test_01_abs.T))

# Calculate SNR using the custom metric after training
snr_metric = SNRMetric()
snr_metric.update_state(S_test_01_abs.T, model.predict(X_test_01_abs.T))
snr_value = snr_metric.result()
print(f"SNR after training: {snr_value.numpy()}")