在keras里面实现计算f1-score的代码

我就废话不多说了，大家还是直接看代码吧！

### 以下链接里面的code
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 “ — val_f1: ％f — val_precision: ％f — val_recall ％f” ％(_val_f1, _val_precision, _val_recall)
return

metrics = Metrics()
model.fit(
train_instances.x,
train_instances.y,
batch_size,
epochs,
verbose=2,
callbacks=[metrics],
validation_data=(valid_instances.x, valid_instances.y),
)

补充知识：Keras可使用的评价函数

1：binary_accuracy（对二分类问题，计算在所有预测值上的平均正确率）

binary_accuracy(y_true, y_pred)

2：categorical_accuracy（对多分类问题，计算在所有预测值上的平均正确率）

categorical_accuracy(y_true, y_pred)

3：sparse_categorical_accuracy（与categorical_accuracy相同，在对稀疏的目标值预测时有用 ）

sparse_categorical_accuracy(y_true, y_pred)

4：top_k_categorical_accuracy（计算top-k正确率，当预测值的前k个值中存在目标类别即认为预测正确 ）

top_k_categorical_accuracy(y_true, y_pred, k=5)

5：sparse_top_k_categorical_accuracy（与top_k_categorical_accracy作用相同，但适用于稀疏情况）

sparse_top_k_categorical_accuracy(y_true, y_pred, k=5)

来源：https://blog.csdn.net/qq_23069955/article/details/80709037