single_output.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow_datasets as tfds\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import random\n",
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sys.path.append(\"C:/Users/User/Desktop/hopfield-izhikevich/hopfield/\")\n",
    "from hopfield import Hopfield\n",
    "sys.path.pop();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_iter = tfds.as_numpy(tfds.load('mnist', split='train', shuffle_files=True, as_supervised=True))\n",
    "dataset = np.fromiter(dataset_iter, dtype=tuple)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = [[] for i in range(10)]\n",
    "index = 0\n",
    "for d in dataset:\n",
    "    digits[d[1]].append(index)\n",
    "    index += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "memory_images = []\n",
    "scale = 0.6 # scaling into image \n",
    "threshold = 100 # binary image threshold \n",
    "shape = ()\n",
    "for digit in range(10):\n",
    "    index = random.choice(digits[ digit ])\n",
    "    image = tf.image.central_crop(dataset[index][0], scale)\n",
    "    image = tf.where(image>threshold, 1, 0)\n",
    "    memory_images.append(image.numpy().flatten())\n",
    "\n",
    "    \n",
    "shape = tuple(image.shape)[:2]\n",
    "memory_images = np.array(memory_images, dtype=np.float32)*2-1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = random.choice(digits[ 6 ]) # choosing random image of '6'\n",
    "image = tf.image.central_crop(dataset[index][0], scale)\n",
    "image = tf.where(image>threshold, 1, 0)\n",
    "image = image.numpy().flatten()\n",
    "image = np.array(image, np.float32)*2-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Hopfield()\n",
    "model.train(memory_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = model.run(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x206dd6a13c0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(np.resize(result['output'], shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "afb734500600fd355917ca529030176ea0ca205570884b88f2f6f7d791fd3fbe"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}