Neural networks in JavaScript ai artificial-intelligence brain brainjs brain.js feed forward neural network classifier neural network neural-networks machine-learning synapse recurrent View more voidvoxel published1.0.0•4 months agopublished 1.0.0 4 months ago ...
While this was expected, I was still a bit disappointed to have to wait so long to test out a new network structure or new learning parameters. A simple application like this shouldn't take so long, but that's the price you pay for an all-JavaScript implementation. To test the network...
brainis a JavaScriptneural networklibrary. Here's an example of using it to approximate the XOR function: varnet=newbrain.NeuralNetwork();net.train([{input:[0,0],output:[0]},{input:[0,1],output:[1]},{input:[1,0],output:[1]},{input:[1,1],output:[0]}]);varoutput=net.run(...
@cazalaforSynapticwhich pioneered architecture free neural networks in javascript and was the starting point for Neataptic @robertleeplummerjrforGPU.jswhich makes using GPU in JS easy andBrain.jswhich has inspired Carrot's development
Simple Neural Network library (JavaScript) The point of this library is to allow very simple neural networks to be easily created, with no extra fluff. Installation This library can be easily installed with yarn or npm. The installation commands is as follows....
什么是GNNs?是对图上所有属性进行可以优化的变换,变换能保持图的对称信息(节点重新排序后,结果不变)。message passing neural network是一种GNNs的框架,当然GNN是可以用别的方式构建。 GNNs是“graph-in, graph-out”(即进出模型都是graph的数据结构),他会对节点、边的信息进行变换,但是图连接性是不变的。
代码语言:javascript 复制 # forward passW=np.random.randn(5,10)X=np.random.randn(10,3)D=W.dot(X)# now suppose we had the gradient onDfrom aboveinthe circuit dD=np.random.randn(*D.shape)# same shapeasDdW=dD.dot(X.T)#.Tgives the transposeofthe matrix ...
代码语言:javascript 复制 来源:PaperWeekly本文约3600字,建议阅读7分钟本文以可逆残差网络(The Reversible Residual Network:Backpropagation Without Storing Activations)作为基础进行分析。 为什么要用可逆网络呢? 因为编码和解码使用相同的参数,所以 model 是轻量级的。可逆的降噪网络 InvDN 只有 DANet 网络参数量的 4.2...
The circuit takes two real-valued inputs x and y and computes x * y with the * gate. Javascript version of this would very simply look something like this: var forwardMultiplyGate = function(x, y) { ...
代码语言:javascript 复制 nn=Network(Dense(...),# making some primary embeddingODESolve(...),#"infinite-layer neural network"Dense(...)# output layer) 我们忘了一件事…神经网络是一个可微函数,所以我们可以用基于梯度的优化手段来训练它。我们应该如何通过ODESolve()函数进行反向传播呢?在我们的例子中...