Python 无法为Keras模型开发自定义度量
我正在开发一个带有自定义度量的多类分类问题(4类)的Keras模型。 问题是,我无法为该模型开发自定义度量。当我运行模型时,度量值是空的 这是我的模型:Python 无法为Keras模型开发自定义度量,python,tensorflow,keras,Python,Tensorflow,Keras,我正在开发一个带有自定义度量的多类分类问题(4类)的Keras模型。 问题是,我无法为该模型开发自定义度量。当我运行模型时,度量值是空的 这是我的模型: nb_classes = 4 model = Sequential() model.add(LSTM( units=50, return_sequences=True, input_shape=(20,18),
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)
这就是model_metrics
的定义方式:
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()
当我运行fit
时,我得到以下结果:
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
您可以看到
val\u f1:%f-val\u精度:%f-val\u调用%f
。没有度量值。为什么?我做错了什么?你的问题不在Keras。您使用的Python是错误的。以下是正确的用法:
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))