如何将自定义指标的值从自定义回调附加到我需要在张量板中使用的“日志”？

Question

I need to implement a custom callback to calculate AUC after every epoch, which I need to use as a metric in LSTM based neural network. This is the custom callback:

from tensorflow.keras.callbacks import Callback
class RocCallback(Callback):
    def __init__(self,training_data,validation_data):
        self.x = training_data[0]
        self.y = training_data[1]
        self.x_val = validation_data[0]
        self.y_val = validation_data[1]

        

    def on_train_begin(self, logs={}):
        self.roc_train_list = []
        self.roc_val_list = []
        self.roc_train=0
        self.roc_val=0
        logs["roc_train"] = []
        logs["roc_val"] = []
        return 


    def on_epoch_end(self, epoch, logs):
        y_pred_train = self.model.predict(self.x)
        roc_train = roc_auc_score(self.y, y_pred_train)
        y_pred_val = self.model.predict(self.x_val)
        roc_val = roc_auc_score(self.y_val, y_pred_val)
        #print('\rroc-auc_train: %s - roc-auc_val: %s' % (str(round(roc_train,4)),str(round(roc_val,4))),end=100*' '+'\n')
        
        # self.history['roc_auc_train'].append(round(roc_train,4))

        # self.history['roc_auc_val'].append(round(roc_val,4))
        self.roc_train = round(roc_train,4)
        self.roc_val = round(roc_val,4) 
        self.roc_train_list.append(self.roc_train)
        self.roc_val_list.append(self.roc_val)
        print("\rroc_train: %f — roc_val: %f" %(self.roc_train, self.roc_val))
        
        logs["roc_train"]= self.roc_train
        logs["roc_val"] = self.roc_val

        return logs

There are two things which aren't working properly:

1. The print("\\rroc_train: %f — roc_val: %f" %(self.roc_train, self.roc_val)) prints just before the epoch progress bar but it needs to print just after eg:

Epoch 2/20
roc_train: 0.550000 — roc_val: 0.547800
2561/2561 [==============================] - 89s 35ms/step - loss: 0.5326 - val_loss: 0.4513
Epoch 3/20
roc_train: 0.559800 — roc_val: 0.558000
2561/2561 [==============================] - 88s 34ms/step - loss: 0.5049 - val_loss: 0.4406

2. The logs in tensorboard only has epoch_loss as metric but no "roc_train" or "roc_val" values. I have tried

logs["roc_train"].append(self.roc_train)
logs["roc_val"].append(self.roc_val)

but it raises a Key Error.

Answer 1

As a quick alternative, have you tried using the built-in https://www.tensorflow.org/api_docs/python/tf/keras/metrics/AUC , metric,

tf.keras.metrics.AUC(
    num_thresholds=200, curve='ROC', summation_method='interpolation', name=None,
    dtype=None, thresholds=None, multi_label=False, label_weights=None
)

it may solve for the moment your problem.

Your code is indeed not wrong; in the callbacks list in model.fit(), could you please place your callback in the first position in the list; in my case it happened that I wanted once to save to a .csv and the CustomMetric() callback was the last, therefore the .csv was saved only with loss and val_loss not with my custom metrics.