hopfield_network.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a9ae3227",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Hopfield Network model of associative memory\n",
    "# Getting started\n",
    "%matplotlib inline\n",
    "from neurodynex3.hopfield_network import network, pattern_tools, plot_tools\n",
    "\n",
    "pattern_size = 5 \n",
    "\n",
    "# create an instance of the class HopfieldNetwork\n",
    "hopfield_net = network.HopfieldNetwork(nr_neurons= pattern_size**2)\n",
    "\n",
    "# instantiate a pattern factory\n",
    "factory = pattern_tools.PatternFactory(pattern_size, pattern_size)\n",
    "\n",
    "# create a checkerboard pattern and add it to the pattern list\n",
    "checkerboard = factory.create_checkerboard()\n",
    "pattern_list = [checkerboard]\n",
    "\n",
    "# add random patterns to the list\n",
    "pattern_list.extend(factory.create_random_pattern_list(nr_patterns=3, on_probability=0.5))\n",
    "plot_tools.plot_pattern_list(pattern_list)\n",
    "\n",
    "# how similar are the random patterns and the checkerboard? Check the overlaps\n",
    "overlap_matrix = pattern_tools.compute_overlap_matrix(pattern_list)\n",
    "plot_tools.plot_overlap_matrix(overlap_matrix)\n",
    "\n",
    "# let the hopfield network \"learn\" the patterns. Note: they are not stored\n",
    "# explicitly but only network weights are updated !\n",
    "hopfield_net.store_patterns(pattern_list)\n",
    "\n",
    "# create a noisy version of a pattern and use that to initialize the network\n",
    "noisy_init_state = pattern_tools.flip_n(checkerboard, nr_of_flips=4)\n",
    "hopfield_net.set_state_from_pattern(noisy_init_state)\n",
    "\n",
    "# from this initial state, let the network dynamics evolve.\n",
    "states = hopfield_net.run_with_monitoring(nr_steps=4)\n",
    "\n",
    "# each network state is a vector. reshape it to the same shape used to create the patterns.\n",
    "states_as_patterns = factory.reshape_patterns(states)\n",
    "\n",
    "# plot the states of the network\n",
    "plot_tools.plot_state_sequence_and_overlap(states_as_patterns, pattern_list, reference_idx=0, suptitle=\"Network dynamics\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "82ad3a40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Exercise: N=4x4 Hopfield-network\n",
    "## Question: Storing a single pattern\n",
    "# Create a network with N=16 neurons.\n",
    "hopfield_net = network.HopfieldNetwork(nr_neurons= 16)\n",
    "\n",
    "# Create a single 4 by 4 checkerboard pattern\n",
    "factory = pattern_tools.PatternFactory(4, 4) \n",
    "\n",
    "checkerboard = factory.create_checkerboard()\n",
    "pattern_list = [checkerboard]\n",
    "\n",
    "pattern_list.extend(factory.create_random_pattern_list(nr_patterns=3, on_probability=0.5))\n",
    "plot_tools.plot_pattern_list(pattern_list)\n",
    "\n",
    "overlap_matrix = pattern_tools.compute_overlap_matrix(pattern_list)\n",
    "plot_tools.plot_overlap_matrix(overlap_matrix)\n",
    "\n",
    "# Store the checkerboard in the network\n",
    "hopfield_net.store_patterns(pattern_list)\n",
    "\n",
    "# Set the initial state of the network to a noisy version of the checkerboard\n",
    "noisy_init_state = pattern_tools.flip_n(checkerboard, nr_of_flips=5)\n",
    "# test whether the network can still retrieve the pattern if we increase the number of flipped pixels.\n",
    "hopfield_net.set_state_from_pattern(noisy_init_state)\n",
    "\n",
    "# Let the network dynamics evolve for 4 iterations\n",
    "states = hopfield_net.run_with_monitoring(nr_steps=4)\n",
    "\n",
    "states_as_patterns = factory.reshape_patterns(states)\n",
    "\n",
    "# Plot the sequence of network states along with the overlap of network state with the checkerboard\n",
    "plot_tools.plot_state_sequence_and_overlap(states_as_patterns, pattern_list, reference_idx=0, suptitle=\"Network dynamics\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6e5e45a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Question: the weights matrix\n",
    "# Create a new 4x4 network. Do not yet store any pattern\n",
    "hopfield_net = network.HopfieldNetwork(nr_neurons= 16)\n",
    "\n",
    "# Visualize the weight matrix\n",
    "plot_tools.plot_network_weights(hopfield_net)\n",
    "\n",
    "# Create a checkerboard, store it in the network\n",
    "factory = pattern_tools.PatternFactory(4, 4) \n",
    "\n",
    "checkerboard = factory.create_checkerboard()\n",
    "pattern_list = [checkerboard]\n",
    "\n",
    "pattern_list.extend(factory.create_random_pattern_list(nr_patterns=3, on_probability=0.5))\n",
    "plot_tools.plot_pattern_list(pattern_list)\n",
    "\n",
    "overlap_matrix = pattern_tools.compute_overlap_matrix(pattern_list)\n",
    "plot_tools.plot_overlap_matrix(overlap_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "3fa77fae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a new 4x4 network\n",
    "hopfield_net = network.HopfieldNetwork(nr_neurons= 16)\n",
    "\n",
    "# Create an L-shaped pattern, store it in the network\n",
    "factory = pattern_tools.PatternFactory(4,4)\n",
    "\n",
    "pattern_list = [factory.create_L_pattern(l_width=1)]\n",
    "\n",
    "plot_tools.plot_pattern_list(pattern_list)\n",
    "\n",
    "# Plot the weights matrix\n",
    "hopfield_net.store_patterns(pattern_list)\n",
    "plot_tools.plot_network_weights(hopfield_net)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "f7ae3731",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a new 4x4 network.\n",
    "hopfield_net = network.HopfieldNetwork(nr_neurons= 16)\n",
    "\n",
    "# Create a checkerboard and an L-shaped pattern, store both patterns in the network\n",
    "factory = pattern_tools.PatternFactory(4, 4) \n",
    "\n",
    "checkerboard = factory.create_checkerboard()\n",
    "pattern_list = [factory.create_L_pattern(l_width=1), checkerboard]\n",
    "\n",
    "plot_tools.plot_pattern_list(pattern_list)\n",
    "\n",
    "# Plot the weights matrix\n",
    "hopfield_net.store_patterns(pattern_list)\n",
    "plot_tools.plot_network_weights(hopfield_net)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "c1030c8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 11 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Exercise: Capacity of an N=100 Hopfield-network\n",
    "# C_store = 0.105\n",
    "# N = 100\n",
    "# K = M_max = 10.5\n",
    "# Create an N=10x10 network and store a checkerboard pattern together with (K−1) random patterns.\n",
    "hopfield_net = network.HopfieldNetwork(nr_neurons= 100)\n",
    "\n",
    "factory = pattern_tools.PatternFactory(10, 10) \n",
    "\n",
    "checkerboard = factory.create_checkerboard()\n",
    "pattern_list = [checkerboard]\n",
    "\n",
    "pattern_list.extend(factory.create_random_pattern_list(nr_patterns=10, on_probability=0.5))\n",
    "plot_tools.plot_pattern_list(pattern_list)\n",
    "\n",
    "hopfield_net.store_patterns(pattern_list)\n",
    "\n",
    "# initialize the network with the unchanged checkerboard pattern\n",
    "init_state = pattern_tools.flip_n(checkerboard, nr_of_flips=4)\n",
    "hopfield_net.set_state_from_pattern(init_state)\n",
    "\n",
    "# the network evolve for five iterations\n",
    "states = hopfield_net.run_with_monitoring(nr_steps=5)\n",
    "\n",
    "states_as_patterns = factory.reshape_patterns(states)\n",
    "\n",
    "plot_tools.plot_state_sequence_and_overlap(states_as_patterns, pattern_list, reference_idx=0, suptitle=\"Network dynamics\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "d1de14b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the alphabet is stored in an object of type: <class 'dict'>\n",
      "letters are patterns of size: (10, 10). Create a network of corresponding size\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 13 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Exercise: Non-random patterns\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from neurodynex3.hopfield_network import network, pattern_tools, plot_tools\n",
    "import numpy\n",
    "\n",
    "# the letters we want to store in the hopfield network\n",
    "letter_list = ['A', 'B', 'C', 'S', 'X', 'Y', 'Z']\n",
    "\n",
    "# add 'R' to letter list\n",
    "letter_list = ['A', 'B', 'C', 'S', 'X', 'Y', 'Z','R']\n",
    "\n",
    "# increase number of letters and observe the change of overlap between the network state and the reference pattern ‘A’ \n",
    "letter_list = ['A', 'B', 'C', 'S', 'X', 'Y', 'Z','R','P','O','N','M','T']\n",
    "\n",
    "# set a seed to reproduce the same noise in the next run\n",
    "# numpy.random.seed(123)\n",
    "\n",
    "abc_dictionary =pattern_tools.load_alphabet()\n",
    "print(\"the alphabet is stored in an object of type: {}\".format(type(abc_dictionary)))\n",
    "# access the first element and get it's size (they are all of same size)\n",
    "pattern_shape = abc_dictionary['A'].shape\n",
    "print(\"letters are patterns of size: {}. Create a network of corresponding size\".format(pattern_shape))\n",
    "# create an instance of the class HopfieldNetwork\n",
    "hopfield_net = network.HopfieldNetwork(nr_neurons= pattern_shape[0]*pattern_shape[1])\n",
    "\n",
    "# create a list using Pythons List Comprehension syntax:\n",
    "pattern_list = [abc_dictionary[key] for key in letter_list ]\n",
    "plot_tools.plot_pattern_list(pattern_list)\n",
    "\n",
    "# store the patterns\n",
    "hopfield_net.store_patterns(pattern_list)\n",
    "\n",
    "# # create a noisy version of a pattern and use that to initialize the network\n",
    "noisy_init_state = pattern_tools.get_noisy_copy(abc_dictionary['A'], noise_level=0.2)\n",
    "hopfield_net.set_state_from_pattern(noisy_init_state)\n",
    "\n",
    "# from this initial state, let the network dynamics evolve.\n",
    "states = hopfield_net.run_with_monitoring(nr_steps=4)\n",
    "\n",
    "# each network state is a vector. reshape it to the same shape used to create the patterns.\n",
    "states_as_patterns = pattern_tools.reshape_patterns(states, pattern_list[0].shape)\n",
    "\n",
    "# plot the states of the network\n",
    "plot_tools.plot_state_sequence_and_overlap(\n",
    "    states_as_patterns, pattern_list, reference_idx=0, suptitle=\"Network dynamics\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2587d16c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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