Question

我正在尝试对一维信号的数据集进行二进制分类。我正在使用keras 2.2.4支持的tensorflow 1.12.0来完成此任务。我需要在验证数据上应用自定义指标，因此我创建了Metrics类。这是我正在使用的主要代码：

train_gen = load.data_generator(batch_size, preproc, *train)
dev_gen = load.data_generator(batch_size, preproc, *dev)
valid_metrics = Metrics(dev_gen, len(dev[0]) // batch_size, batch_size)
model.fit_generator(train_gen,
      steps_per_epoch=len(train[0]) // batch_size,
      epochs=MAX_EPOCHS,
      validation_data=dev_gen,
      validation_steps=len(dev[0]) // batch_size,
      callbacks=[valid_metrics, checkpointer, reduce_lr, tensorboard])


class Metrics(keras.callbacks.Callback):
    def __init__(self, val_data, step, batch_size=20):
        self.validation_data = val_data
        self.batch_size = batch_size
        self.validation_step = step
        print('validation_step  ' + str(step))

    def on_train_begin(self, logs={}):
        self._rocdata_perclass = []
        self._prdata_perclass = []
        self._accdata = []

    def on_epoch_end(self, batch, logs={}):

        for batch_index in range(self.validation_step):
            xVal, yVal = next(self.validation_data)
            predictions = self.model.predict(xVal)
            print(predictions.shape)
            print(yVal.shape)

****** Training output*****
1/12 [=>............................] - ETA: 2:27 - loss: 1.1788 - acc: 0.5113
 x shape (16, 38400, 1)
 y shape (16, 150, 2)
 2/12 [====>.........................] - ETA: 1:11 - loss: 2.1305 - acc: 0.4668 
 x shape (16, 38144, 1)
 y shape (16, 149, 2)
 3/12 [======>.......................] - ETA: 44s - loss: 2.0981 - acc: 0.4302
 x shape (16, 38400, 1)
 y shape (16, 150, 2)
 4/12 [=========>....................] - ETA: 30s - loss: 1.8517 - acc: 0.3823 
 x shape (16, 38400, 1)
 y shape (16, 150, 2)
 5/12 [===========>..................] - ETA: 21s - loss: 1.6540 - acc: 0.4184
 x shape (16, 38144, 1)
 y shape (16, 149, 2)
 6/12 [==============>...............] - ETA: 16s - loss: 1.5060 - acc: 0.4350
 x shape (16, 38912, 1)
 y shape (16, 152, 2)
 7/12 [================>.............] - ETA: 12s - loss: 1.4447 - acc: 0.3977
 x shape (16, 38400, 1)
 y shape (16, 150, 2)
 8/12 [===================>..........] - ETA: 8s - loss: 1.3394 - acc: 0.4423
 x shape (16, 41472, 1)
 y shape (16, 162, 2)
 9/12 [=====================>........] - ETA: 6s - loss: 1.2576 - acc: 0.4718
 x shape (16, 37888, 1)
 y shape (16, 148, 2)
10/12 [========================>.....] - ETA: 3s - loss: 1.1878 - acc: 0.5138
 x shape (16, 39424, 1)
 y shape (16, 154, 2)
11/12 [==========================>...] - ETA: 1s - loss: 1.1355 - acc: 0.5395
 x shape (16, 39168, 1)
 y shape (16, 153, 2)

正如您在上面的培训的输出中看到的那样，每个批处理输入（x）和输出（y）的形状互不相同，因为我将数据填充为具有相同的长度。一个信号具有n classes of dimension 2。我的问题与我需要使用Metrics类对验证数据进行评估有关。由于每个批次的验证输入和输出形状也不同，因此如何使用ROC曲线下的Area一次评估整个验证数据？正如您在上面的代码中看到的那样，我只能使用批量验证，而我需要整个验证数据，因此我可以对其进行一次评估。

******* Validation Metric output ********
x shape (16, 41984, 1)
y shape (16, 164, 2)
x shape (16, 38400, 1)
y shape (16, 150, 2)

谢谢。

Keras验证生成器评估指标

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