Exercise:Learning color features with Sparse Autoencoders 习题链接:Exercise:Learning color features with Sparse Autoencoders sparseAutoencoderLinearCost.m function [cost,grad,features] = sparseAutoencoderLinearCost(theta, visibleSize, hiddenSize, ... lam…

Exercise:Self-Taught Learning 习题链接:Exercise:Self-Taught Learning feedForwardAutoencoder.m function [activation] = feedForwardAutoencoder(theta, hiddenSize, visibleSize, data) % theta: trained weights from the autoencoder % visibleSize: the number of…

Exercise:Convolution and Pooling 习题链接:Exercise:Convolution and Pooling cnnExercise.m %% CS294A/CS294W Convolutional Neural Networks Exercise % Instructions % ------------ % % This file contains code that helps you get started on the % convolutional n…

Exercise: Implement deep networks for digit classification 习题链接:Exercise: Implement deep networks for digit classification stackedAEPredict.m function [pred] = stackedAEPredict(theta, inputSize, hiddenSize, numClasses, netconfig, data) % stackedAEPre…

Exercise:PCA and Whitening 习题链接:Exercise:PCA and Whitening pca_gen.m %%================================================================ %% Step 0a: Load data % Here we provide the code to load natural image data into x. % x will be a * matrix, where…

Exercise:Softmax Regression 习题的链接:Exercise:Softmax Regression softmaxCost.m function [cost, grad] = softmaxCost(theta, numClasses, inputSize, lambda, data, labels) % numClasses - the number of classes % inputSize - the size N of the input vector % la…

Exercise:PCA in 2D 习题的链接:Exercise:PCA in 2D pca_2d.m close all %%================================================================ %% Step : Load data % We have provided the code to load data from pcaData.txt into x. % x * matrix, where the kth column…

Exercise:Vectorization 习题的链接:Exercise:Vectorization 注意点: MNIST图片的像素点已经经过归一化. 如果再使用Exercise:Sparse Autoencoder中的sampleIMAGES.m进行归一化, 将使得训练得到的可视化权值如下图: 更改train.m的参数设置 visibleSize = *; % number of input units hiddenSize = ; % number of hidden units spar…

Exercise:Sparse Autoencoder 习题的链接:Exercise:Sparse Autoencoder 注意点: 1.训练样本像素值需要归一化. 因为输出层的激活函数是logistic函数,值域(0,1), 如果训练样本每个像素点没有进行归一化,那将无法进行自编码. 2.训练阶段,向量化实现比for循环实现快十倍. 3.最后产生的图片阵列是将W1权值矩阵的转置,每一列作为一张图片. 第i列其实就是最大可能激活第i个隐藏节点的图片xi,再乘以常数因子C(其中C就是W1第i行元素…

这个exercise需要完成cnn中的forward pass,cost,error和gradient的计算.需要弄清楚每一层的以上四个步骤的原理,并且要充分利用matlab的矩阵运算.大概把过程总结了一下如下图所示: STEP 1:Implement CNN Objective STEP 1a: Forward Propagation Forward Propagation主要是为了计算输入图片经过神经网络后的输出,这个网络有三层:convolution->pooling->softmax(…