實現代碼：
from keras.callbacks import Callback
from sklearn.metrics import confusion_matrix, f1_score, precision_score, recall_score
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.asarray(self.model.predict(self.validation_data[0]))).round()##.model
val_targ = self.validation_data[1]###.model
_val_f1 = f1_score(val_targ, val_predict,average='micro')
_val_recall = recall_score(val_targ, val_predict,average=None)###
_val_precision = precision_score(val_targ, val_predict,average=None)###
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" %(_val_f1, _val_precision, _val_recall))
print("— val_f1: %f "%_val_f1)
return
f1=Metrics()
hist=cnn_net.fit(x_train,y_train,batch_size=batch_size,epochs=35,verbose=1,validation_data=(x_train,y_train),callbacks=[f1])#50validation_split=0.3
問題一：用print輸出_val_f1, _val_precision, _val_recall時，會報錯：TypeError: only length-1 arrays can be converted to Python scalars
目前還不知道原因，但是隻輸出 _val_f1卻不會報錯。
問題二：直接調用別人代碼
_val_f1 = f1_score(val_targ, val_predict)
_val_recall = recall_score(val_targ, val_predict)
_val_precision = precision_score(val_targ, val_predict)
會報錯：ValueError: Target is multilabel-indicator but average='binary'. Please choose another average setting.
添加參數 average後，未報錯。參考sklearn metrics for multiclass classification
問題三：計算F1要添加validation_data，我的代碼中爲了方便直接用的訓練集。直接調用
self.model.validation_data[0]
self.model.validation_data[1]
會報錯：AttributeError: 'Sequential' object has no attribute 'validation_data'
應該將其改爲self.validation_data[0] self.validation_data[1]，參考Sequential has no attribution “validation_data”