一般来说分类就是Softmax, 回归就是L2的loss. 但是要注意loss的错误范围(主要是回归), 你预测一个label是10000的值, 模型输出0, 你算算这loss多大, 这还是单变量的情况下. 一般结果都是nan. 所以不仅仅输入要做normalization, 输出也要。 准确率虽然是评测指标, 但是训练过程中还是要注意loss的. 你会发现有...
generator=Sequential()generator.add(Dense(256,input_dim=100))generator.add(LeakyReLU(0.2))generator.add(BatchNormalization())generator.add(Dense(512))generator.add(LeakyReLU(0.2))generator.add(BatchNormalization())generator.add(Dense(784,activation='tanh'))generator.add(Reshape((28,28,1)))# 定义...
为了加速和稳定深度CNN的训练,Sergey et al.提出batch normalization (BN)以减少网络内部协变量移位。具体地,它们为每个小batch执行归一化,并为每个通道训练两个额外的变换参数,以保持表示能力。由于BN校准了中间特征分布并减轻了消失梯度,因此它允许使用更高的学习率,并且对初始化不太在意。因此,SR模型广泛使用了这种...
An obstacle to answering this question was the notorious problem ofvanishing/exploding gradients[1, 9], which hamper convergence from the beginning. This problem, however, has been largely addressed by normalized initialization [23, 9, 37, 13] and intermediate normalization layers [16], which enab...
Xception: Deep Learning with Depthwise Separable Convolutions[13]. François Chollet. CVPR 2016. 3.3.5 Group Normalization Group Normalization(组归一化)是一种用于深度学习中的归一化技术,类似于批量归一化和层归一化,它可以应用于卷积神经网络和全连接神经网络中。不同于批量归一化和层归一化将整个批次或整...
我们把批量正则化(batch-normalization,BN)用在了每个卷积层和激活层之间,我们初始化了权重按照12说的方法,分别从0开始训练普通/残差网络。我们使用SGD算法,mini-batch的大小为256.学习速率初始化为0.1,当到达错误率平台时就把学习速率除以10,对各模型进行了长达60万次迭代,我们用的了权重衰减,参数设了0.0001,动量...
An obstacle to answering this question was the notorious problem ofvanishing/exploding gradients[1, 9], which hamper convergence from the beginning. This problem, however, has been largely addressed by normalized initialization [23, 9, 37, 13] and intermediate normalization layers [16], which enab...
An obstacle to answering this question was the notorious problem of vanishing/exploding gradients [14, 1, 8], which hamper convergence from the beginning. This problem, however, has been largely addressed by normalized initialization [23, 8, 36, 12] and intermediate normalization layers [16], ...
Deep Residual Learning for Image Recognition Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun 摘要 更深层次的神经网络更难训练。我们提出了一个残差学习框架,以简化对比以前使用的网络更深入的网络的训练。我们根据层输入显式地将层重新表示为学习残差函数( learning residual functions),而不是学习未定义函数。我们...
当然这个收敛问题可以通过初始化参数归一化和batch normalization来解决。 收敛问题解决之后,又一个问题暴露出来了,性能下降。随着网络深度的增加,网络的精度首先饱和,然后快速下降。出乎意料,这个下降不是过拟合造成的,对一个合适的模型加入更多的网络层,导致更高的训练误差,这一点我们的实验也验证了。如下图所示:...