{ "cells": [ { "cell_type": "markdown", "id": "4d3f178449808176", "metadata": { "collapsed": false }, "source": [ "# Discrete Hopfield Network\n", "\n", "[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/brainpy/examples/blob/main/attractors/discrete_hopfield_network.ipynb)\n", "[![Open in Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/brainpy/examples/blob/main/attractors/discrete_hopfield_network.ipynb)" ] }, { "cell_type": "markdown", "id": "8f5fb709-2f65-4e05-8af0-16f6d444af96", "metadata": {}, "source": [ "In this article, we describe core ideas behind discrete hopfield\n", "networks and try to understand how it works. In addition, we explore\n", "main problems related to this algorithm. And finally, we take a look\n", "into simple example that aims to memorize digit patterns and reconstruct\n", "them from corrupted samples.\n", "\n", "In this article we are going to learn about Discrete Hopfield Network\n", "algorithm.\n", "\n", "Discrete Hopfield Network is a type of algorithms which is called -\n", "[Autoassociative\n", "memories](https://en.wikipedia.org/wiki/Autoassociative_memory) Don't be\n", "scared of the word Autoassociative. The\n", "idea behind this type of algorithms is very simple. It can store useful\n", "information in memory and later it is\n", "able to reproduce this information from partially broken patterns. You\n", "can perceive it as human memory. For instance, imagine that you look at\n", "an old picture of a place where you were long time ago, but this picture\n", "is of very bad quality and very blurry. By looking at the picture you\n", "manage to recognize a few objects or places that make sense to you and\n", "form some objects even though they are blurry. It can be a house, a lake\n", "or anything that can add up to the whole picture and bring out some\n", "associations about this place. With these details that you got from your\n", "memory so far other parts of picture start to make even more sense.\n", "Though you don't clearly see all objects in the picture, you start to\n", "remember things and withdraw from your memory some images, that cannot\n", "be seen in the picture, just because of those very familiarly-shaped\n", "details that you've got so far. That's what it is all about.\n", "Autoassociative memory networks is a possibly to interpret functions of\n", "memory into neural network model.\n", "\n", "Don't worry if you have only basic knowledge in Linear Algebra; in this\n", "article I'll try to explain the idea as simple as possible.\n", "\n", "## Architecture\n", "\n", "Discrete Hopfield Network is an easy algorithm. It's simple because you\n", "don't need a lot of background knowledge in Maths for using it.\n", "Everything you need to know is how to make a basic Linear Algebra\n", "operations, like outer product or sum of two matrices.\n", "\n", "Let's begin with a basic thing. What do we know about this neural\n", "network so far? Just the name and the type. From the name we can\n", "identify one useful thing about the network. It's\n", "Discrete. It means that network only\n", "works with binary vectors. But for this network we wouldn't use binary\n", "numbers in a typical form. Instead, we will use bipolar numbers. They\n", "are almost the same, but instead of 0 we are going to use -1 to decode a\n", "negative state. We can't use zeros. And there are two main reasons for\n", "it. The first one is that zeros reduce information from the network\n", "weight, later in this article you are going to see it. The second one is\n", "more complex, it depends on the nature of bipolar vectors. Basically\n", "they are more likely to be orthogonal to each other which is a critical\n", "moment for the Discrete Hopfield Network. But as I mentioned before we\n", "won't talk about proofs or anything not related to basic understanding\n", "of Linear Algebra operations.\n", "\n", "So, let's look at how we can train and use the Discrete Hopfield\n", "Network.\n", "\n", "## Training procedure\n", "\n", "We can't use memory without any patterns stored in it. So first of all\n", "we are going to learn how to train the network. For the Discrete\n", "Hopfield Network train procedure doesn't require any iterations. It\n", "includes just an outer product between input vector and transposed input\n", "vector.\n", "\n", "$$\\begin{aligned}\n", "\\begin{align*}\n", " W = x \\cdot x^T =\n", " \\left[\n", " \\begin{array}{c}\n", " x_1\\\\\n", " x_2\\\\\n", " \\vdots\\\\\n", " x_n\n", " \\end{array}\n", " \\right]\n", " \\cdot\n", " \\left[\n", " \\begin{array}{c}\n", " x_1 & x_2 & \\cdots & x_n\n", " \\end{array}\n", " \\right]\n", "\\end{align*}\n", "\\end{aligned}$$\n", "\n", "$$\\begin{aligned}\n", "\\begin{align*}\n", " =\n", " \\left[\n", " \\begin{array}{c}\n", " x_1^2 & x_1 x_2 & \\cdots & x_1 x_n \\\\\n", " x_2 x_1 & x_2^2 & \\cdots & x_2 x_n \\\\\n", " \\vdots\\\\\n", " x_n x_1 & x_n x_2 & \\cdots & x_n^2 \\\\\n", " \\end{array}\n", " \\right]\n", "\\end{align*}\n", "\\end{aligned}$$\n", "\n", "$W$ is a weight matrix and $x$ is an input vector. Each value $x_i$ in\n", "the input vector can only be -1 or 1. So on the matrix diagonal we only\n", "have squared values and it means we will always see 1s at those places.\n", "Think about it, every time, in any case, values on the diagonal can take\n", "just one possible state. We can't use this information, because it\n", "doesn't say anything useful about patterns that are stored in the memory\n", "and even can make incorrect contribution into the output result. For\n", "this reason we need to set up all the diagonal values equal to zero. The\n", "final weight formula should look like this one below.\n", "\n", "$$\\begin{aligned}\n", "\\begin{align*}\n", " W =\n", " x x^T - I =\n", " \\left[\n", " \\begin{array}{c}\n", " 0 & x_1 x_2 & \\cdots & x_1 x_n \\\\\n", " x_2 x_1 & 0 & \\cdots & x_2 x_n \\\\\n", " \\vdots\\\\\n", " x_n x_1 & x_n x_2 & \\cdots & 0 \\\\\n", " \\end{array}\n", " \\right]\n", "\\end{align*}\n", "\\end{aligned}$$\n", "\n", "Where $I$ is an identity matrix.\n", "\n", "But usually we need to store more values in memory. For another pattern\n", "we have to do exactly the same procedure as before and then just add the\n", "generated weight matrix to the old one.\n", "\n", "$$W = W_{old} + W_{new}$$\n", "\n", "And this procedure generates us a new weight that would be valid for\n", "both previously stored patterns. Later you can add other patterns using\n", "the same algorithm.\n", "\n", "But if you need to store multiple vectors inside the network at the same\n", "time you don't need to compute the weight for each vector and then sum\n", "them up. If you have a matrix $X \\in \\Bbb R^{m\\times n}$ where each row\n", "is the input vector, then you can just make product matrix between\n", "transposed input matrix and input matrix.\n", "\n", "$$W = X^T X - m I$$\n", "\n", "Where $I$ is an identity matrix ($I \\in \\Bbb R^{n\\times n}$), $n$ is a\n", "number of features in the input vector and $m$ is a number of input\n", "patterns inside the matrix $X$. Term $m I$ removes all values from the\n", "diagonal. Basically we remove 1s for each stored pattern and since we\n", "have $m$ of them, we should do it $m$ times. Practically, it's not very\n", "good to create an identity matrix just to set up zeros on the diagonal,\n", "especially when dimension on the matrix is very big. Usually linear\n", "algebra libraries give you a possibility to set up diagonal values\n", "without creating an additional matrix and this solution would be more\n", "efficient. For example in NumPy library it's a\n", "[numpy.fill_diagonal](http://docs.scipy.org/doc/numpy/reference/generated/numpy.fill_diagonal.html)\n", "function.\n", "\n", "Let's check an example just to make sure that everything is clear. Let's\n", "pretend we have a vector $u$.\n", "\n", "$$\\begin{aligned}\n", "u = \\left[\\begin{align*}1 \\\\ -1 \\\\ 1 \\\\ -1\\end{align*}\\right]\n", "\\end{aligned}$$\n", "\n", "Assume that network doesn't have patterns inside of it, so the vector\n", "$u$ would be the first one. Let's compute weights for the network.\n", "\n", "$$\\begin{aligned}\n", "\\begin{align*}\n", " U = u u^T =\n", " \\left[\n", " \\begin{array}{c}\n", " 1 \\\\\n", " -1 \\\\\n", " 1 \\\\\n", " -1\n", " \\end{array}\n", " \\right]\n", " \\left[\n", " \\begin{array}{c}\n", " 1 & -1 & 1 & -1\n", " \\end{array}\n", " \\right]\n", " =\n", " \\left[\n", " \\begin{array}{cccc}\n", " 1 & -1 & 1 & -1\\\\\n", " -1 & 1 & -1 & 1\\\\\n", " 1 & -1 & 1 & -1\\\\\n", " -1 & 1 & -1 & 1\n", " \\end{array}\n", " \\right]\n", "\\end{align*}\n", "\\end{aligned}$$\n", "\n", "Look closer to the matrix $U$ that we got. Outer product just repeats\n", "vector 4 times with the same or inversed values. First and third columns\n", "(or rows, it doesn't matter, because matrix is symmetrical) are exactly\n", "the same as the input vector. The second and fourth are also the same,\n", "but with an opposite sign. That's because in the vector $u$ we have 1 on\n", "the first and third places and -1 on the other.\n", "\n", "To make weight from the $U$ matrix, we need to remove ones from the\n", "diagonal.\n", "\n", "$$\\begin{aligned}\n", "W = U - I = \\left[\n", " \\begin{array}{cccc}\n", " 1 & -1 & 1 & -1\\\\\n", " -1 & 1 & -1 & 1\\\\\n", " 1 & -1 & 1 & -1\\\\\n", " -1 & 1 & -1 & 1\n", " \\end{array}\n", "\\right] -\n", "\\left[\n", " \\begin{array}{cccc}\n", " 1 & 0 & 0 & 0\\\\\n", " 0 & 1 & 0 & 0\\\\\n", " 0 & 0 & 1 & 0\\\\\n", " 0 & 0 & 0 & 1\n", " \\end{array}\n", "\\right]\n", "\\end{aligned}$$\n", "\n", "$$\\begin{aligned}\n", "= \\left[\n", " \\begin{array}{cccc}\n", " 0 & -1 & 1 & -1\\\\\n", " -1 & 0 & -1 & 1\\\\\n", " 1 & -1 & 0 & -1\\\\\n", " -1 & 1 & -1 & 0\n", " \\end{array}\n", "\\right]\n", "\\end{aligned}$$\n", "\n", "$I$ is the identity matrix and $I \\in \\Bbb R^{n \\times n}$, where $n$ is\n", "a number of features in the input vector.\n", "\n", "When we have one stored vector inside the weights we don't really need\n", "to remove 1s from the diagonal. The main problem would appear when we\n", "have more than one vector stored in the weights. Each value on the\n", "diagonal would be equal to the number of stored vectors in it.\n", "\n", "## Recovery from memory\n", "\n", "The main advantage of Autoassociative network is that it is able to\n", "recover pattern from the memory using just a partial information about\n", "the pattern. There are already two main approaches to this situation,\n", "synchronous and asynchronous. We are going to master both of them.\n", "\n", "### Synchronous\n", "\n", "Synchronous approach is much more easier for understanding, so we are\n", "going to look at it firstly. To recover your pattern from memory you\n", "just need to multiply the weight matrix by the input vector.\n", "\n", "$$\\begin{aligned}\n", "\\begin{align*}\n", " s = {W}\\cdot{x}=\n", " \\left[\n", " \\begin{array}{cccc}\n", " w_{11} & w_{12} & \\ldots & w_{1n}\\\\\n", " w_{21} & w_{22} & \\ldots & w_{2n}\\\\\n", " \\vdots & \\vdots & \\ddots & \\vdots\\\\\n", " w_{n1} & w_{n2} & \\ldots & w_{nn}\n", " \\end{array}\n", " \\right]\n", " \\left[\n", " \\begin{array}{c}\n", " x_1\\\\\n", " x_2\\\\\n", " \\vdots\\\\\n", " x_n\n", " \\end{array}\n", " \\right]\n", "\\end{align*}\n", "\\end{aligned}$$\n", "\n", "$$\\begin{aligned}\n", "\\begin{align*}\n", " =\n", " \\left[\n", " \\begin{array}{c}\n", " w_{11}x_1+w_{12}x_2 + \\cdots + w_{1n} x_n\\\\\n", " w_{21}x_1+w_{22}x_2 + \\cdots + w_{2n} x_n\\\\\n", " \\vdots\\\\\n", " w_{n1}x_1+w_{n2}x_2 + \\cdots + w_{nn} x_n\\\\\n", " \\end{array}\n", " \\right]\n", "\\end{align*}\n", "\\end{aligned}$$\n", "\n", "Let's analyze the result. We summed up all information from the weights\n", "where each value can be any integer with an absolute value equal to or\n", "smaller than the number of patterns inside the network. It's clear that\n", "total sum value for $s_i$ is not necessary equal to -1 or 1, so we have\n", "to make additional operations that will make bipolar vector from the\n", "vector $s$.\n", "\n", "Let's think about this product operation. What does it actualy do?\n", "Basically after training procedure we saved our pattern dublicated $n$\n", "times (where $n$ is a number of input vector features) inside the\n", "weight. When we store more patterns we get interception between them\n", "(it's called a **crosstalk**) and each pattern add some noise to other\n", "patterns. So, after perfoming product matrix between $W$ and $x$ for\n", "each value from the vector $x$ we'll get a recovered vector with a\n", "little bit of noise. For $x_1$ we get a first column from the matrix\n", "$W$, for the $x_2$ a second column, and so on. Then we sum up all\n", "vectors together. This operation can remind you of voting. For example\n", "we have 3 vectors. If the first two vectors have 1 in the first position\n", "and the third one has -1 at the same position, the winner should be 1.\n", "We can perform the same procedure with $sign$ function. So the output\n", "value should be 1 if total value is greater then zero and -1 otherwise.\n", "\n", "$$\\begin{aligned}\n", "sign(x) = \\left\\{\n", " \\begin{array}{lr}\n", " &1 && : x \\ge 0\\\\\n", " &-1 && : x < 0\n", " \\end{array}\n", "\\right.\\\\\n", "\\end{aligned}$$$$y = sign(s)$$\n", "\n", "That's it. Now $y$ store the recovered pattern from the input vector\n", "$x$.\n", "\n", "Maybe now you can see why we can't use zeros in the input vectors. In\n", "voting procedure we use each row that was\n", "multiplied by bipolar number, but if values had been zeros they would\n", "have ignored columns from the weight matrix and we would have used only\n", "values related to ones in the input pattern.\n", "\n", "Of course you can use 0 and 1 values and sometime you will get the\n", "correct result, but this approach give you much worse results than\n", "explained above.\n", "\n", "### Asynchronous\n", "\n", "Previous approach is good, but it has some limitations. If you change\n", "one value in the input vector it can change your output result and value\n", "won't converge to the known pattern. Another popular approach is an\n", "**asynchronous**. This approach is more likely to remind you of real\n", "memory. At the same time in network activates just one random neuron\n", "instead of all of them. In terms of neural networks we say that **neuron\n", "fires**. We iteratively repeat this operation multiple times and after\n", "some point network will converge to some pattern.\n", "\n", "Let's look at this example: Consider that we already have a weight\n", "matrix $W$ with one pattern $x$ inside of it.\n", "\n", "$$\\begin{aligned}\n", "\\begin{align*}\n", " W =\n", " \\left[\n", " \\begin{array}{cccc}\n", " 0 & 1 & -1 \\\\\n", " 1 & 0 & -1 \\\\\n", " -1 & -1 & 0\n", " \\end{array}\n", " \\right]\n", "\\end{align*}\n", "\\end{aligned}$$\n", "\n", "$$\\begin{aligned}\n", "\\begin{align*}\n", " x =\n", " \\left[\n", " \\begin{array}{c}\n", " 1\\\\\n", " 1\\\\\n", " -1\n", " \\end{array}\n", " \\right]\n", "\\end{align*}\n", "\\end{aligned}$$\n", "\n", "Let's assume that we have a vector $x^{'}$ from which we want to recover\n", "the pattern.\n", "\n", "$$\\begin{aligned}\n", "\\begin{align*}\n", " x^{'} =\n", " \\left[\n", " \\begin{array}{c}\n", " 1\\\\\n", " -1\\\\\n", " -1\n", " \\end{array}\n", " \\right]\n", "\\end{align*}\n", "\\end{aligned}$$\n", "\n", "In first iteration one neuron fires. Let it be the second one. So we\n", "multiply the first column by this selected value.\n", "\n", "$$\\begin{aligned}\n", "\\begin{align*}\n", " x^{'}_2 =\n", " sign(\\left[\n", " \\begin{array}{c}\n", " 1 & -1 & -1\n", " \\end{array}\n", " \\right] \\cdot \\left[\n", " \\begin{array}{c}\n", " 1\\\\\n", " 0\\\\\n", " -1\n", " \\end{array}\n", " \\right]) = sign(2) = 1\n", "\\end{align*}\n", "\\end{aligned}$$\n", "\n", "And after this operation we set up a new value into the input vector\n", "$x$.\n", "\n", "$$\\begin{aligned}\n", "\\begin{align*}\n", " x^{'} =\n", " \\left[\n", " \\begin{array}{c}\n", " 1\\\\\n", " 1\\\\\n", " -1\n", " \\end{array}\n", " \\right]\n", "\\end{align*}\n", "\\end{aligned}$$\n", "\n", "As you can see, after first iteration value is exactly the same as $x$\n", "but we can keep going. In second iteration random neuron fires again.\n", "Let's pretend that this time it was the third neuron.\n", "\n", "$$\\begin{aligned}\n", "\\begin{align*}\n", " x^{'}_3 =\n", " sign(\\left[\n", " \\begin{array}{c}\n", " 1 & 1 & -1\n", " \\end{array}\n", " \\right] \\cdot \\left[\n", " \\begin{array}{c}\n", " -1\\\\\n", " -1\\\\\n", " 0\n", " \\end{array}\n", " \\right]) = sign(-2) = -1\n", "\\end{align*}\n", "\\end{aligned}$$\n", "\n", "$x^{'}_3$ is exactly the same as in the $x^{'}$ vector so we don't need\n", "to update it. We can repeat it as many times as we want, but we will be\n", "getting the same value.\n", "\n", "## Memory limit\n", "\n", "Obviously, you can't store infinite number of vectors inside the\n", "network. There are two good rules of thumb.\n", "\n", "Consider that $n$ is the dimension (number of features) of your input\n", "vector and $m$ is the number of patterns that you want to store in the\n", "network. The first rule gives us a simple ration between $m$ and $n$.\n", "\n", "$$m \\approx 0.18 n$$\n", "\n", "The main problem with this rule is that proof assumes that stored\n", "vectors inside the weight are completely random with an equal\n", "probability. Unfortunately, that is not always true. Let's suppose we\n", "save some images of numbers from 0 to 9. Pictures are black and white,\n", "so we can encode them in bipolar vectors. Will the probabilities be the\n", "same for seeing as many white pixels as black ones? Usually no. More\n", "likely that number of white pixels would be greater than number of black\n", "ones. Before use this rule you have to think about type of your input\n", "patterns.\n", "\n", "The second rule uses a logarithmic proportion.\n", "\n", "$$m = \\left \\lfloor \\frac{n}{2 \\cdot log(n)} \\right \\rfloor$$\n", "\n", "Both of these rules are good assumptions about the nature of data and\n", "its possible limits in memory. Of course, you can find situations when\n", "these rules will fail.\n", "\n", "## Hallucinations\n", "\n", "Hallucinations is one of the main problems in the Discrete Hopfield\n", "Network. Sometimes network output can be something that we hasn't taught\n", "it.\n", "\n", "To understand this phenomena we should firstly define the Hopfield\n", "energy function.\n", "\n", "$$E = -\\frac{1}{2} \\sum_{i=1}^{n} \\sum_{j=1}^{n} w_{ij} x_i x_j + \\sum_{i=1}^{n} \\theta_i x_i$$\n", "\n", "Where $w_{ij}$ is a weight value on the $i$-th row and $j$-th column.\n", "$x_i$ is a $i$-th values from the input vector $x$. $\\theta$ is a\n", "threshold. Threshold defines the bound to the sign function. For this\n", "reason $\\theta$ is equal to 0 for the Discrete Hopfield Network. In\n", "terms of a linear algebra we can write formula for the Discrete Hopfield\n", "Network energy Function more simpler.\n", "\n", "$$E = -\\frac{1}{2} x^T W x$$\n", "\n", "But linear algebra notation works only with the $x$ vector, we can't use\n", "matrix $X$ with multiple input patterns instead of the $x$ in this\n", "formula. For the energy function we're always interested in finding a\n", "minimum value, for this reason it has minus sign at the beginning.\n", "\n", "Let's try to visualize it. Assume that values for vector $x$ can be\n", "continous in order and we can visualize them using two parameters. Let's\n", "pretend that we have two vectors \\[1,\n", "-1\\] and \\[-1, 1\\] stored inside\n", "the network. Below you can see the plot that visualizes energy function\n", "for this situation." ] }, { "cell_type": "code", "execution_count": 3, "id": "9fe9969a39d66486", "metadata": { "ExecuteTime": { "end_time": "2023-10-06T07:06:46.312492900Z", "start_time": "2023-10-06T07:06:45.476287800Z" }, "collapsed": false }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from mpl_toolkits.mplot3d import Axes3D\n", "\n", "\n", "def energy(input_vector):\n", " input_vector = np.array(input_vector)\n", " X = np.array([[1, -1], [-1, 1]])\n", " weight = X.T.dot(X) - 2 * np.eye(2)\n", " return -0.5 * input_vector.dot(weight).dot(input_vector)\n", "\n", "\n", "fig = plt.figure(figsize=(9, 9))\n", "ax = fig.add_subplot(111, projection='3d')\n", "\n", "x = y = np.arange(-1.0, 1.0, 0.01)\n", "X, Y = np.meshgrid(x, y)\n", "energies = map(energy, zip(np.ravel(X), np.ravel(Y)))\n", "zs = np.array(list(energies))\n", "Z = zs.reshape(X.shape)\n", "\n", "ax.view_init(elev=6, azim=-72)\n", "ax.plot_surface(X, Y, Z, cmap='Reds')\n", "ax.set_xlabel('x1')\n", "ax.set_ylabel('x2')\n", "ax.set_zlabel('Energy')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "56cca395e590025c", "metadata": { "collapsed": false }, "source": [ "\n", "As you can see we have two minimum values at the same points as those\n", "patterns that are already stored inside the network. But between these\n", "two patterns function creates a saddle point somewhere at the point with\n", "coordinates $(0, 0)$. In this case we can't stick to the points\n", "$(0, 0)$. But in situation with more dimensions this saddle points can\n", "be at the level of available values and they could be hallucination.\n", "Unfortunately, we are very limited in terms of numbers of dimensions we\n", "could plot, but the problem is still the same." ] }, { "cell_type": "markdown", "id": "34c21334f8f2a9bf", "metadata": { "collapsed": false }, "source": [ "## Implementation" ] }, { "cell_type": "code", "execution_count": 4, "id": "d7ddc0760052ceef", "metadata": { "ExecuteTime": { "end_time": "2023-10-06T07:06:46.327532500Z", "start_time": "2023-10-06T07:06:46.323961700Z" }, "collapsed": false }, "outputs": [], "source": [ "import brainpy as bp\n", "import brainpy.math as bm\n", "\n", "class HopfieldNet(bp.DynamicalSystem):\n", " def __init__(self, num):\n", " super().__init__()\n", " self.num = num\n", " self.weight = bm.Variable(bm.zeros([num, num]))\n", "\n", " # Train function for the Hopfield net. (By updating the weights)\n", " @bm.cls_jit\n", " def store_patterns(self, samples):\n", " # data: An array with [d, N]. 'd' is the number of data samples used to train. (In this case 2)\n", " assert samples.ndim == 2\n", " assert samples.shape[1] == self.num\n", "\n", " # Loop through all data samples.\n", " bm.for_loop(self.store, samples)\n", "\n", " # Hopfield nets are a form of RNNs, albeit without self-connections, and so,\n", " # we need to make sure that the diagonal elements of the final weight matrix are zero.\n", " bm.fill_diagonal(self.weight, 0)\n", "\n", " # Storing one sample pattern\n", " @bm.cls_jit\n", " def store(self, sample):\n", " # sample is an array with the shape of (N,)\n", " assert self.num == sample.shape[0]\n", "\n", " # Data cross-product gives neural hopfield update rule.\n", " w_update = bm.outer(sample, sample)\n", "\n", " # Sum all pattern cross-products.\n", " self.weight += w_update\n", "\n", " def async_recover(self, sample, n, energy=False):\n", " # n: the number of iterations to recover\n", " # energy: calculate the energy function\n", " idxs = bm.random.randint(0, self.num, n) # the sampled positions\n", " # JIT compilation requires to label the value to be changed as Variable\n", " sample = bm.Variable(sample)\n", "\n", " def recover(i):\n", " # i: the position to update\n", " # update\n", " sample[i] = bm.sign(bm.inner(self.weight[i], sample))\n", " # return energy\n", " if energy:\n", " return self.energy(sample)\n", "\n", " r = bm.for_loop(recover, idxs) # for loop JIT\n", " return (sample, r) if energy else sample\n", "\n", " def sync_recover(self, sample, n, energy=False):\n", " # n: the number of iterations to recover\n", " # energy: calculate the energy function\n", " idxs = bm.arange(n) # the update times\n", " # JIT compilation requires to label the value to be changed as Variable\n", " sample = bm.Variable(sample)\n", "\n", " def recover(i):\n", " # i: the position to update\n", " # update\n", " sample.value = bm.sign(self.weight @ sample)\n", " # return energy\n", " if energy:\n", " return self.energy(sample)\n", "\n", " r = bm.for_loop(recover, idxs) # for loop JIT\n", " return (sample, r) if energy else sample\n", "\n", " # This function computes the Hopfield nets' energy.\n", " def energy(self, x):\n", " # x: [N] data vector\n", " return bm.inner(- x @ self.weight, x)" ] }, { "cell_type": "markdown", "id": "f7d65f8d7ad5ea8", "metadata": { "collapsed": false }, "source": [ "## Reading more\n", "\n", "- [Image reconstruction of Discrete Hopfield network](./discrete_hopfield_demo_for_image_reconstruction.ipynb)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 5 }