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我们。
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