深度学习网络的图形表示? [英] Graphical representation of deep-learning network?

问题描述

有人可以为我提供深度学习网络的图形表示吗?

Can someone provide me with a graphical representation of a deep-learning network?

就像第1层，第2层，第3层等，以及所有层的神经元和层中的神经元之间的权重，它们如何连接等等.

Like this is layer 1, layer 2, layer 3, etc. and the weights between the neurones at all the layers and the neurones in the layers, how they are all connected and so on.

我不想要任何大的东西，我只是希望它们在矩阵中显示，因为我实际上不能将手指放在如何将整个网络表示为互连的矩阵上.

I don't want anything big I just want them to be shown in matrices because I can't actually put my finger on how to represent the whole network as interconnected matrices.

即使矩阵是2x2，也很好，我只想举个例子.

Even if the matrices are 2x2 it's fine I just want to have an example to build on.

推荐答案

矩阵表示形式

您不会将神经元建模为矩阵.相反，您只需要将权重图层表示为单独的矩阵即可.

Matrix representation

You will not be modelling the neurons as matrices. Instead you only need to represent the weight layers as individual matrices.

0个隐藏层
在这种情况下，您只需要一个矩阵.这将是大小:

0 hidden layers
In this instance you would only need a single matrix. This will be of size:

n x m //    n: inputs,   m: outputs

矩阵的元素将相应地代表给定层中的各个权重:

The elements of the matrix will represent the individual weights in the given layer accordingly:

n个隐藏层
每个权重层都有自己的矩阵.矩阵的大小为:

n hidden layers
Each weight layer has its own matrix. The matrix will be of size:

n x m //    n: inputs to this layer,   m: outputs from this layer

具有单个隐藏层的网络的图形可视化:

A graphic visualization of a network with a single hidden layer:

您将不得不在输入信号和权重矩阵之间逐步执行点积运算:

You will have to incrementally perform a dot product between the input signals and the weight matrices:

input_vector: 1 x n matrix,    n: number of inputs
weight_layer: n x m matrix,    n: number of inputs to this layer     m: number of outputs from this layer

input_vector.dot( weight_layer ) # forward calculation

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