自定义keras的评价函数为F1score，且每回合显示

keras2中将F1scre函数移除了，但是此函数在训练集平衡时比较好用，所幸我们可以通过Callback函数自定义评价函数，下面是一个每回合打印F1score、准确率(precision)、召回率(recall)的示例(python3)：

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(va_targ, val)
    self.val_f1s.append(_val_f1)
    self.val_recalls.append(_val_recall)
    self.val_precisions.append(_val_precision)
    print('-val_f1: %.4f --val_precision: %.4f --val_recall: %.4f'%(_val_f1, _val_precision, _val_recall))
    return

metrics = Metrics()

需要查看训练过程中的评价函数值时，可以直接输出

print(metrics.val_f1s)

定义好模型后，使用新的评价函数来训练模型：

model.fit(training_data, training_target, 
          validation_data=(validation_data, validation_target),
          np_epoch=10, batch_size=64, callbacks =[metrics])

训练时的输出：