f1 score 代码_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)

以上这篇在keras里面实现计算f1-score的代码就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持脚本之家。