Deep feedforward networks, also often calledfeedforward neural networks, ormultilayer perceptrons(MLPs), are the quintessential(精髓) deep learning models.The goal of a feedforward network is to approximate some function f ∗ f^{*} f∗.For example, for a classifier, y = f ∗ ( x ) ...
Xavier Uniform:wi,j∼U(−6nin+nout,6nin+nout) He初始化: 条件:正向传播时激活值方差保持不变,反向传播时梯度的方差保持不变.适用ReLU.(个人理解因为ReLU在小于零部分截断,所以方差近似减小1/2,所以要把这个部分乘回来) He Normal:w_{i,j} \sim \mathcal{N}(0,\sqrt{\dfrac{2}{n_i}}) He...
Neural networks: historically inspired by the way computation works in the brain 神经网络:历史上受到大脑中计算方式的启发 Consists of computation units called neurons 由称为神经元的计算单元组成 1.2 Feed-forward NN Aka multilayer perceptrons 又名多层感知器 Each arrow carries a weight, reflecting its i...
Deep feedforward network (DFN) is a conceptual stepping stone of many well-known deep neural networks (DNN) in image classification and natural language application. The development on the standard DFN can rarely be found in the literature recently due to the popularity in convolutional networks. ...
Deep Feedforward Networks(3) Back-Propagation and Other Differentiation Algorithms When we use a feedforward neural network to accept an inputx xxand produce an outputy ^ \hat{\boldsymbol{y}}y^, information flows forward through the network. The inputsx \boldsymbol{x}xprovide the ...
【Deep Learning】笔记:Understanding the difficulty of training deep feedforward neural networks 这几天读了这篇论文,在这里将大致内容写在这里。 Abstract 介绍这篇论文的主要内容就是尝试更好的理解为什么使用“标准随机初始化”来计算使用标准梯度下降的网络效果通常来讲都不是很好。 首先研究了不同的非线性**...
M. Telgarsky. Representation Benefits of Deep Feedforward Networks. In ArXiv, pp. 1-5, 2015.M. Telgarsky. Representation benefits of deep feedforward networks. ArXiv preprint: 1509.08101, 2015.arXiv:1705.05502v1 (2017) [15] M. Telgrasky. Representation benefits of deep feedforward ...
1.前向传播:用文中的话说:From a forward-propagation point of view, to keep information flowing we would like that: , 就是说,为了在前向传播过程中,可以让信息向前传播,做法就是让:激活单元的输出值的方差持不变。为什么要这样呢??有点小不理解。。
Deep residual networks (ResNets) have demonstrated better generalization performance than deep feedforward networks (FFNets). However, the theory behind such a phenomenon is still largely unknown. This paper studies this fundamental problem in deep learning from a so-called "neural tangent kernel" pe...
Xavier——Understanding the difficulty of training deep feedforward neural networks 1. 摘要 本文尝试解释为什么在深度的神经网络中随机初始化会让梯度下降表现很差,并且在此基础上来帮助设计更好的算法。 作者发现 sigmoid 函数不适合深度网络,在这种情况下,随机初始化参数会让较深的隐藏层陷入到饱和区域。 作者...