loss:训练集损失值 accuracy:训练集准确率 val_loss:测试集损失值 val_accruacy:测试集准确率 以下5种情况可供参考: train loss 不断下降,test loss不断下降,说明网络仍在学习;(最好的) train loss 不断下降,test loss趋于不变
train loss 不断上升,test loss不断上升,说明网络结构设计不当,训练超参数设置不当,数据集经过清洗等问题。(最不好的情况) 这种情况,loss在下降,val_loss趋于不变,说明网络过拟合状态
一般设置为 总样本数/BATCH_SIZEglobal_step=tf.Variable(0,trainable=False)# 只需要这一行代码即可learning_rate=tf.train.exponential_decay(LEARNING_RATE_BASE,global_step,LEARNING_RATE_STEP,LEARNING_RATE_DECAY,staircase
不一定，要看数据规模，数据分布等。如果你的训练集确实包含了验证集的特性，分布。那就有可能是过拟合...
如果在training set上loss一直下降，accuracy一直上升，同时在validation set上loss缓慢上升，accuracy缓慢下降...
# 损失绘图 import matplotlib.pyplot as plt history_dict = history.history...accuracy = history_dict["accuracy"] val_accuracy = history_dict["val_accuracy"] epochs = range(1,len..., # val_loss "b", label="val_accuracy" ) plt.title("Accuracy and Val_Accuracy...image/255 pred_ = mo...
前一篇文章分享了循环神经网络RNN的原理知识，并采用Keras实现手写数字识别的RNN分类案例及可视化呈现。这篇...
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(2)示例:如上history字典中包含'accuracy',则若报KeyError: 'acc',就将报错语句中'acc'替换为'accuracy'即可。 (3)history字典输出为: ---history_dict.keys()---: dict_keys(['val_loss','loss']) 此时需要直接注释掉报错语句,因为会出现:报KeyError: ...
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