无法为Keras模型开发自定义度量

这是我的模型:

nb_classes = 4

model = Sequential()

model.add(LSTM(
                units=50,
                return_sequences=True, 
                input_shape=(20,18),
                dropout=0.2, 
                recurrent_dropout=0.2
              )
         )

model.add(Dropout(0.2))

model.add(Flatten())

model.add(Dense(units=nb_classes,
               activation='softmax'))

model.compile(loss="categorical_crossentropy",optimizer='adadelta')


history = model.fit(np.array(X_train), y_train, 
                    validation_data=(np.array(X_test), y_test),
                    epochs=50,
                    batch_size=2,
                    callbacks=[model_metrics],
                    shuffle=False,
                    verbose=1)

这是怎么回事 model_metrics 定义如下:

class Metrics(Callback):

    def on_train_begin(self, logs={}):
        self.val_f1s = []
        self.val_recalls = []
        self.val_precisions = []

    def on_epoch_end(self, epoch, logs={}):
        val_predict = np.argmax((np.asarray(self.model.predict(self.validation_data[0]))).round(), axis=1)
        val_targ = np.argmax(self.validation_data[1], axis=1)
        _val_f1 = metrics.f1_score(val_targ, val_predict, average='weighted')
        _val_recall = metrics.recall_score(val_targ, val_predict, average='weighted')
        _val_precision = metrics.precision_score(val_targ, val_predict, average='weighted')
        self.val_f1s.append(_val_f1)
        self.val_recalls.append(_val_recall)
        self.val_precisions.append(_val_precision)
        print(" — val_f1: %f — val_precision: %f — val_recall %f".format(_val_f1, _val_precision, _val_recall))
        return

model_metrics = Metrics() 

fit ,我得到这个结果:

Train on 400 samples, validate on 80 samples
Epoch 1/50
400/400 [==============================] - 7s 17ms/step - loss: 0.6892 - val_loss: 4.8016
 — val_f1: %f — val_precision: %f — val_recall %f
Epoch 2/50
 20/400 [>.............................] - ETA: 3s - loss: 2.8010
/Users/tau/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
/Users/tau/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
400/400 [==============================] - 3s 9ms/step - loss: 0.7593 - val_loss: 4.5832
 — val_f1: %f — val_precision: %f — val_recall %f
Epoch 3/50
400/400 [==============================] - 4s 9ms/step - loss: 0.6809 - val_loss: 4.9039
 — val_f1: %f — val_precision: %f — val_recall %f

你可以看到 val_f1: %f — val_precision: %f — val_recall %f . 没有度量值。为什么?我做错了什么?