roc_auc.py 文件源码

def on_epoch_end(self, epoch, logs={}):
    if epoch % self.interval == 0:
      y_pred = self.model.predict(self.X_val, verbose=0)
      #print(y_pred.shape)
      if self.mymil:
        y_true = self.y_val.max(axis=1)
        y_score = y_pred.max(axis=1)>0.5
      else:
        y_true = np.argmax(self.y_val, axis=1)
        y_score = y_pred[np.arange(len(y_true)), y_true] #y_pred[:, y_true] #np.argmax(y_pred, axis=1)
      loss = -np.mean(np.log(y_score+1e-6)) #-np.mean(y_true*np.log(y_score+1e-6) + (1-y_true)*np.log(1-y_score+1e-6))
      print('')
      print("interval evaluation - epoch: {:d} - loss: {:.2f}".format(epoch, loss))
      if loss < self.loss:
        self.loss = loss
        for f in os.listdir('./'):
          if f.startswith(self.filepath+'loss'):
            os.remove(f)
        self.model.save(self.filepath+'loss'+str(loss)+'ep'+str(epoch)+'.hdf5')