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 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 in...
M. Telgarsky, "Representation benefits of deep feedforward networks," arXiv preprint arXiv:1509.08101v2 [cs.LG] 29 Sep 2015, 2015.Matus Telgarsky. Representation benefits of deep feedforward networks. In COLT, 2016.M. Telgarsky, Representation benefits of deep feedforward networks, ArXiv...
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。。
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...
RNNs, once unfolded in time (Fig. 5), can be seen as very deep feedforward networks in which all the layers share the same weights. Although their main purpose is to learn long-term dependencies, theoretical and empirical evidence shows that it is difficult to learn to store information fo...
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。
Feed-forward Networks Gradient-based Learning Hidden Units Architecture Design Backward Propagation and Differentiation Forward/Backward Propagation Computational Graphs Chain Rule in Backprop Symbol-symbol Derivatives General Backprop Other Differentiation Algorithms ...