博文地址:Interview with Data Science Weekly on Neural Nets and ConvNetJS ConvNetJS在线使用:Deep Learning in your browser Andrej Karpathy 在采访中讨论了 ConvNetJS,这是一个在浏览器中训练深度学习模型的 JavaScript 库。他强调了其教育优势、简单设置和 JavaScript 的高效性。ConvNetJS 允许用户直接在浏览...
ConvNetJS是神经网络的一个JavaScript实现,可以让你在浏览器中训练深度网络。目前看来,它最重要的用途是帮助Deep Learning 初学者更快、更直观的理解算法。它当前支持: 常见的神经网络模块(全连接层,非线性) 分类(SVM/ SOFTMAX)和回归(L2)的成本函数 A MagicNet class for fully automatic neural network learning...
var trainer = new convnetjs.SGDTrainer(net, {learning_rate:0.01, momentum:0.9, batch_size:16, l2_decay:0.001}); for(var i=0;i<my_dataset.length;i++) { var x = new convnetjs.Vol(1,1,2,0.0); // a 1x1x2 volume initialized to 0's. x.w[0] = my_data[i][0]; // ...
w[0]); // prints 0.50101 var trainer = new convnetjs.SGDTrainer(net, {learning_rate:0.01, l2_decay:0.001}); trainer.train(x, 0); // train the network, specifying that x is class zero var prob2 = net.forward(x); console.log('probability that x is class 0: ' + prob2.w[0]...
Note: I am not actively maintaining ConvNetJS anymore because I simply don't have time. I think the npm repo might not work at this point. Online Demos Convolutional Neural Network on MNIST digits Convolutional Neural Network on CIFAR-10 ...
Convnet.js是一个用于在浏览器中运行神经网络模型的JavaScript库,而Keras是一个基于Python的深度学习框架,它可以与TensorFlow等后端引擎配合使用。要将Convnet.js神经网络模型转换为Keras Tensorflow,可以按照以下步骤进行操作: 导出Convnet.js模型:首先,需要将Convnet.js模型导出为JSON格式。Convnet.js提供了导...
var trainer = new convnetjs.SGDTrainer(net, {learning_rate:0.01, momentum:0.9, batch_size:16, l2_decay:0.001}); for(var i=0;i<my_dataset.length;i++) { var x = new convnetjs.Vol(1,1,2,0.0); // a 1x1x2 volume initialized to 0's. x.w[0] = my_data[i][0]; // ...
ConvNetJS is a Javascript implementation of Neural networks, together with nice browser-based demos. It currently supports: CommonNeural Network modules(fully connected layers, non-linearities) Classification (SVM/Softmax) and Regression (L2)cost functions ...
here: 2-D data // ConvNetJS works on 3-Dimensional volumes (sx, sy, depth), but if you're not dealing with images // then the first two dimensions (sx, sy) will always be kept at size 1 layer_defs.push({type:'input', out_sx:1, out_sy:1, out_depth:2}); // declare 20...
ConvNetJS is a Javascript implementation of Neural networks, together with nice browser-based demos. It currently supports: CommonNeural Network modules(fully connected layers, non-linearities) Classification (SVM/Softmax) and Regression (L2)cost functions ...