我正在使用自定义指标为多类分类问题（4个类）开发Keras模型。 问题是我无法为此模型开发自定义指标。运行模型时，指标的值为空。
这是我的模特
convert(DateTime.new(2018, 07))
=> Sun, 01 Jul 2018 02:00:00 +0200
convert(DateTime.new(2018, 12))
=> Sat, 01 Dec 2018 01:00:00 +0100
这是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)
的定义方式：
model_metrics运行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()
时，我得到以下结果：
fit您可以看到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
。没有指标值。为什么？我在做什么错了？
答案 0 :(得分：0)
您的问题不在Keras。您使用的Python string formatting错误。这是正确的用法：
print(" — val_f1: {:f} — val_precision: {:f} — val_recall {:f}".format(_val_f1, _val_precision, _val_recall))
或者：
print(" — val_f1: %f — val_precision: %f — val_recall %f" % (_val_f1, _val_precision, _val_recall))