Python 如何计算神经网络模型中的精确度、召回率和F1分数?
我使用Keras来预测我的输出是1还是0。数据如下所示:Python 如何计算神经网络模型中的精确度、召回率和F1分数?,python,tensorflow,keras,neural-network,Python,Tensorflow,Keras,Neural Network,我使用Keras来预测我的输出是1还是0。数据如下所示: funded_amnt emp_length avg_cur_bal num_actv_rev_tl loan_status 10000 5.60088 19266 2 1 13750 5.60088 2802 6 0
funded_amnt emp_length avg_cur_bal num_actv_rev_tl loan_status
10000 5.60088 19266 2 1
13750 5.60088 2802 6 0
26100 10.0000 19241 17 1
目标为loan\u status
,其余为功能。在开始建立神经网络模型之前,我已经对数据进行了标准化
以下是我的培训和测试数据:
print(X_train.shape,Y_train.shape)
# Output: (693, 4) (693,)
print(X_test.shape,Y_test.shape)
# Output: (149, 4) (149,)
我构建神经网络的过程如下:
# define the keras model
model = Sequential()
model.add(Dense(4, input_dim=4,activation='relu'))
model.add(Dense(4 ,activation='relu'))
model.add(Dense(1,activation='sigmoid'))
# compile the keras model
model.compile(loss='binary_crossentropy',optimizer='adam', metrics=['accuracy'])
# fit the keras model on the dataset
hist = model.fit(X_train, Y_train, validation_data=(X_test, Y_test) ,epochs=10, batch_size=2)
运行hist
后的输出:
Epoch 1/20
693/693 [==============================] - 1s 2ms/step - loss: 0.5974 - acc: 0.7605 - val_loss: 0.5499 - val_acc: 0.7785
Epoch 2/20
693/693 [==============================] - 0s 659us/step - loss: 0.5369 - acc: 0.7778 - val_loss: 0.5380 - val_acc: 0.7785
Epoch 3/20
693/693 [==============================] - 0s 700us/step - loss: 0.5330 - acc: 0.7778 - val_loss: 0.5369 - val_acc: 0.7785
Epoch 4/20
693/693 [==============================] - 0s 670us/step - loss: 0.5316 - acc: 0.7778 - val_loss: 0.5355 - val_acc: 0.7785
Epoch 5/20
693/693 [==============================] - 0s 720us/step - loss: 0.5307 - acc: 0.7778 - val_loss: 0.5345 - val_acc: 0.7785
Epoch 6/20
693/693 [==============================] - 0s 668us/step - loss: 0.5300 - acc: 0.7778 - val_loss: 0.5339 - val_acc: 0.7785
Epoch 7/20
现在,我想计算准确度、回忆度和F1分数,而不仅仅是准确度。我试过跟随。但我一直得到以下错误:
ValueError:分类指标无法处理二进制和连续目标的混合
还有别的办法吗?你能试试以下方法吗:
import numpy as np
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.model.validation_data[0]))).round()
val_targ = self.model.validation_data[1]
_val_f1 = f1_score(val_targ, val_predict)
_val_recall = recall_score(val_targ, val_predict)
_val_precision = precision_score(val_targ, val_predict)
self.val_f1s.append(_val_f1)
self.val_recalls.append(_val_recall)
self.val_precisions.append(_val_precision)
print(f" — val_f1: {_val_f1} — val_precision: {_val_precision} — val_recall _val_recall")
return
metrics = Metrics()
hist = model.fit(X_train, Y_train,
validation_data=(X_test, Y_test), epochs=10,
batch_size=2, callbacks=[metrics])
在代码中,您需要按如下方式使用它:
import numpy as np
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.model.validation_data[0]))).round()
val_targ = self.model.validation_data[1]
_val_f1 = f1_score(val_targ, val_predict)
_val_recall = recall_score(val_targ, val_predict)
_val_precision = precision_score(val_targ, val_predict)
self.val_f1s.append(_val_f1)
self.val_recalls.append(_val_recall)
self.val_precisions.append(_val_precision)
print(f" — val_f1: {_val_f1} — val_precision: {_val_precision} — val_recall _val_recall")
return
metrics = Metrics()
hist = model.fit(X_train, Y_train,
validation_data=(X_test, Y_test), epochs=10,
batch_size=2, callbacks=[metrics])
请看这里: