qrbm_reconstruct_img.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Restricted Boltzmann Machine using quantum annealing on D-wave's QPU"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.25.2\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import timeit\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "\n",
    "from qrbm.MSQRBM import MSQRBM\n",
    "from qrbm.classicalRBM import classicalRBM\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline   \n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "from skimage import data, color\n",
    "from skimage.transform import rescale, resize, downscale_local_mean\n",
    "from skimage import img_as_bool\n",
    "\n",
    "import cv2 as cv\n",
    "\n",
    "import random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Presets\n",
    "\n",
    "Here you can set hyperparameters for learning:\n",
    "\n",
    " - digits: list of digits to train on\n",
    " - THRESHOLD: how bright parts of image become 1 (darker become 0)\n",
    " - n_hidden: number of neurons in hidden layer\n",
    " - qpu: whether to use real D-wave's qpu (requires [setup](https://docs.ocean.dwavesys.com/en/stable/overview/sapi.html)) or a local simulator\n",
    " - epochs: number of epochs\n",
    " - lr: learning rate\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_height = 28\n",
    "image_width = 28\n",
    "\n",
    "# images will be flattened\n",
    "len_x = image_height * image_width\n",
    "len_y = 0\n",
    "\n",
    "# digits = [0, 1]\n",
    "digits = list(range(10))\n",
    "THRESHOLD = 100\n",
    "n_hidden = 100\n",
    "qpu = False\n",
    "epochs = 1000\n",
    "lr = 0.01"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## reading dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "mnist_dataset = pd.read_csv('./data/mnist_train.csv')\n",
    "mnist_test = pd.read_csv('./data/mnist_test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## prepare train and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "X_train = mnist_dataset.values[:,1:]\n",
    "X_train2 = mnist_dataset.values[:,:]\n",
    "\n",
    "X_test = mnist_test.values[:,1:]\n",
    "X_test2 = mnist_test.values[:,:]\n",
    "\n",
    "# print(mnist_dataset.values[:,0])\n",
    "X_train3 = []\n",
    "X_test3 = []\n",
    "\n",
    "for digit in digits:\n",
    "    X_train3.append(mnist_dataset.values[np.where(mnist_dataset.values[:,0] == digit), 1:][0])\n",
    "    X_test3.append(mnist_test.values[np.where(mnist_test.values[:,0] == digit), 1:][0])\n",
    "# X_train3 = mnist_dataset.values[np.where(mnist_dataset.values[:,0] == 0), 1:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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C\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "imgs = []\n",
    "imgs_test = []\n",
    "for digit_index in range(len(digits)):\n",
    "    imgs.append(np.resize(X_train3[digit_index], (len(X_train3[digit_index]), 28, 28)) / 255)\n",
    "    imgs_test.append(np.resize(X_test3[digit_index], (len(X_test3[digit_index]), 28, 28)) / 255)\n",
    "    \n",
    "plt.imshow(imgs[0][0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## resizing images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "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"
    }
   ],
   "source": [
    "images_resized = []\n",
    "images_resized_test = []\n",
    "for digit_index in range(len(digits)):\n",
    "    images_resized.append([resize(img, (image_width, image_height), anti_aliasing=True) for img in imgs[digit_index]])\n",
    "    images_resized_test.append([resize(img, (image_width, image_height), anti_aliasing=True) for img in imgs_test[digit_index]])\n",
    "\n",
    "    # images_resized = [resize(img, (image_width, image_height), anti_aliasing=True) for img in imgs]\n",
    "\n",
    "plt.imshow(images_resized[0][0])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## normalizing images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "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"
    }
   ],
   "source": [
    "images_normalized = []\n",
    "images_normalized_test = []\n",
    "\n",
    "for digit_index in range(len(digits)):\n",
    "    images_normalized.append([cv.normalize(image_resized, image_resized, 0, 255, cv.NORM_MINMAX) for image_resized in images_resized[digit_index]])\n",
    "    images_normalized_test.append([cv.normalize(image_resized, image_resized, 0, 255, cv.NORM_MINMAX) for image_resized in images_resized_test[digit_index]])\n",
    "\n",
    "    # images_normalized = [cv.normalize(image_resized, image_resized, 0, 255, cv.NORM_MINMAX) for image_resized in images_resized]\n",
    "\n",
    "plt.imshow(images_normalized[0][0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## converting to binary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAALUklEQVR4nO3dQaxc5XnG8f9TkmwIUk0RlktISSt2WZAKsSmq6CIRZQNZpAorR6nkLEqV7oLSRZCiSFHVpstKjoLiVilRJKAgVDVBKApZRRhEwcRKoBFJHCxbyK1KVmng7eIeo2tz753rOTNzzvX7/0mjmTl37jmvj/34+873zcyXqkLS1e93pi5A0mYYdqkJwy41YdilJgy71MT7NnmwJA79S2tWVdlp+6iWPcndSX6S5LUkD47Zl6T1yrLz7EmuAX4KfBw4AzwH3F9VP97jd2zZpTVbR8t+B/BaVf2sqn4DfBu4d8T+JK3RmLDfBPxy2/Mzw7ZLJDmW5GSSkyOOJWmkMQN0O3UV3tNNr6rjwHGwGy9NaUzLfga4edvzDwFvjCtH0rqMCftzwK1JPpLkA8CngSdXU5akVVu6G19Vv03yAPBd4Brg4ap6ZWWVSVqppafeljqY1+zS2q3lTTWSDg7DLjVh2KUmDLvUhGGXmjDsUhOGXWrCsEtNGHapCcMuNWHYpSYMu9SEYZeaMOxSE4ZdasKwS00YdqkJwy41YdilJgy71IRhl5rY6JLN6med316c7PglqtqFLbvUhGGXmjDsUhOGXWrCsEtNGHapCcMuNeE8u0bZ5CrAV3ps5+EvNSrsSV4H3gLeBn5bVbevoihJq7eKlv3PqurNFexH0hp5zS41MTbsBXwvyfNJju30giTHkpxMcnLksSSNkDEDLEl+v6reSHIj8DTw11X17B6vn240R2sx5QDdIl0H6Kpqxz/4qJa9qt4Y7s8DjwN3jNmfpPVZOuxJrk1y3cXHwCeAU6sqTNJqjRmNPww8PnSV3gf8a1X9x0qq0mzMuZuuKzPqmv2KD+Y1+4FzkMPuNfulnHqTmjDsUhOGXWrCsEtNGHapCT/i2tycR9sXjaYvqn3ku0OX/t25smWXmjDsUhOGXWrCsEtNGHapCcMuNWHYpSacZ78KzHmuXPNhyy41YdilJgy71IRhl5ow7FIThl1qwrBLTTjPrgNr7Ofdu7Fll5ow7FIThl1qwrBLTRh2qQnDLjVh2KUmnGc/AKacLx77/el71X41fjf7nC1s2ZM8nOR8klPbtl2f5Okkrw73h9ZbpqSx9tON/yZw92XbHgSeqapbgWeG55JmbGHYq+pZ4MJlm+8FTgyPTwD3rbYsSau27DX74ao6C1BVZ5PcuNsLkxwDji15HEkrsvYBuqo6DhwHSOInE6SJLDv1di7JEYDh/vzqSpK0DsuG/Ung6PD4KPDEasqRtC5ZNIeb5BHgLuAG4BzwJeDfgO8AHwZ+AXyqqi4fxNtpX3bjd3CQ59HnbJ3ndc7nrap2LG5h2FfJsO/MsK+HYb+Ub5eVmjDsUhOGXWrCsEtNGHapCT/iugHrHm2f88iw5sOWXWrCsEtNGHapCcMuNWHYpSYMu9SEYZeacJ79AHAeXatgyy41YdilJgy71IRhl5ow7FIThl1qwrBLTTjPvgJTfjustF+27FIThl1qwrBLTRh2qQnDLjVh2KUmDLvUhGGXmlgY9iQPJzmf5NS2bQ8l+VWSF4fbPestU9JY+2nZvwncvcP2f6yq24bbv6+2LEmrtjDsVfUscGEDtUhaozHX7A8keWno5h/a7UVJjiU5meTkiGNJGin7+RBHkluAp6rqo8Pzw8CbQAFfBo5U1Wf3sZ+r8hMjLtw4T+v8e5nz30lV7VjcUi17VZ2rqrer6h3g68AdY4qTtH5LhT3JkW1PPwmc2u21kuZh4efZkzwC3AXckOQM8CXgriS3sdWNfx343PpKvPrNuUs4Z1276cva1zX7yg7mNfuOrsZ/WJtg2He20mt2SQePYZeaMOxSE4ZdasKwS034VdKaLUfbV8uWXWrCsEtNGHapCcMuNWHYpSYMu9SEYZeacJ5dk/EbfjbLll1qwrBLTRh2qQnDLjVh2KUmDLvUhGGXmnCeXaNs8tuJL+c8+pWxZZeaMOxSE4ZdasKwS00YdqkJwy41YdilJpxnn4FFc9XrnE+ecp58EefRV2thy57k5iTfT3I6yStJPj9svz7J00leHe4Prb9cSctauD57kiPAkap6Icl1wPPAfcBngAtV9dUkDwKHquoLC/Y132ZkhIP8jSu27Fefpddnr6qzVfXC8Pgt4DRwE3AvcGJ42Qm2/gOQNFNXdM2e5BbgY8CPgMNVdRa2/kNIcuMuv3MMODayTkkjLezGv/vC5IPAD4CvVNVjSf6nqn5328//u6r2vG63G78cu/G6Ekt34wGSvB94FPhWVT02bD43XM9fvK4/v4pCJa3HfkbjA3wDOF1VX9v2oyeBo8Pjo8ATqy9PsNX6rus2pSR73rRa+xmNvxP4IfAy8M6w+YtsXbd/B/gw8AvgU1V1YcG+5ttnHGHq0BxUBno9duvG7/uafRUMu7Yz7Osx6ppd0sFn2KUmDLvUhGGXmjDsUhN+xFWjOKJ+cNiyS00YdqkJwy41YdilJgy71IRhl5ow7FITzrOvwKK55jl/Ks558j5s2aUmDLvUhGGXmjDsUhOGXWrCsEtNGHapCefZN8C5bM2BLbvUhGGXmjDsUhOGXWrCsEtNGHapCcMuNbGf9dlvTvL9JKeTvJLk88P2h5L8KsmLw+2e9ZcraVn7WZ/9CHCkql5Ich3wPHAf8BfAr6vq7/d9sKt0yWZpTnZbsnnhO+iq6ixwdnj8VpLTwE2rLU/Sul3RNXuSW4CPAT8aNj2Q5KUkDyc5tMvvHEtyMsnJcaVKGmNhN/7dFyYfBH4AfKWqHktyGHgTKODLbHX1P7tgH3bjpTXbrRu/r7AneT/wFPDdqvraDj+/BXiqqj66YD+GXVqz3cK+n9H4AN8ATm8P+jBwd9EngVNji5S0PvsZjb8T+CHwMvDOsPmLwP3AbWx1418HPjcM5u21L1t2ac1GdeNXxbBL67d0N17S1cGwS00YdqkJwy41YdilJgy71IRhl5ow7FIThl1qwrBLTRh2qQnDLjVh2KUmDLvUxKaXbH4T+Pm25zcM2+ZorrXNtS6wtmWtsrY/2O0HG/08+3sOnpysqtsnK2APc61trnWBtS1rU7XZjZeaMOxSE1OH/fjEx9/LXGuba11gbcvaSG2TXrNL2pypW3ZJG2LYpSYmCXuSu5P8JMlrSR6coobdJHk9ycvDMtSTrk83rKF3PsmpbduuT/J0kleH+x3X2Juotlks473HMuOTnruplz/f+DV7kmuAnwIfB84AzwH3V9WPN1rILpK8DtxeVZO/ASPJnwK/Bv754tJaSf4OuFBVXx3+ozxUVV+YSW0PcYXLeK+ptt2WGf8ME567VS5/vowpWvY7gNeq6mdV9Rvg28C9E9Qxe1X1LHDhss33AieGxyfY+seycbvUNgtVdbaqXhgevwVcXGZ80nO3R10bMUXYbwJ+ue35Gea13nsB30vyfJJjUxezg8MXl9ka7m+cuJ7LLVzGe5MuW2Z8NudumeXPx5oi7DstTTOn+b8/qao/Bv4c+Kuhu6r9+Sfgj9haA/As8A9TFjMsM/4o8DdV9b9T1rLdDnVt5LxNEfYzwM3bnn8IeGOCOnZUVW8M9+eBx9m67JiTcxdX0B3uz09cz7uq6lxVvV1V7wBfZ8JzNywz/ijwrap6bNg8+bnbqa5Nnbcpwv4ccGuSjyT5APBp4MkJ6niPJNcOAyckuRb4BPNbivpJ4Ojw+CjwxIS1XGIuy3jvtsw4E5+7yZc/r6qN34B72BqR/y/gb6eoYZe6/hD4z+H2ytS1AY+w1a37P7Z6RH8J/B7wDPDqcH/9jGr7F7aW9n6JrWAdmai2O9m6NHwJeHG43TP1udujro2cN98uKzXhO+ikJgy71IRhl5ow7FIThl1qwrBLTRh2qYn/B/lvIMPkjI1EAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = []\n",
    "data_test = []\n",
    "for digit_index in range(len(digits)):\n",
    "    data.append([np.where(image_resized > THRESHOLD, 1, 0) for image_resized in images_resized[digit_index]])\n",
    "    data_test.append([np.where(image_resized > THRESHOLD, 1, 0) for image_resized in images_resized_test[digit_index]])\n",
    "    \n",
    "# data = [np.where(image_resized > THRESHOLD, 1, 0) for image_resized in images_resized]\n",
    "\n",
    "plt.imshow(data[0][0])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## flattening the image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n"
     ]
    }
   ],
   "source": [
    "input_data = []\n",
    "input_data_test = []\n",
    "for digit_index in range(len(digits)):\n",
    "    input_data.append([x.flatten().tolist() for x in data[digit_index]])\n",
    "    input_data_test.append([x.flatten().tolist() for x in data_test[digit_index]])\n",
    "    \n",
    "print(input_data[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_picture_tab = []\n",
    "for digit_index in range(len(digits)):\n",
    "    result_picture_tab.append(np.mean(input_data_test[digit_index], axis = 0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## shuffling the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "for digit in digits:\n",
    "    flat_input_data = [item for sublist in input_data for item in sublist]\n",
    "    flat_input_data_test = [item for sublist in input_data_test for item in sublist]\n",
    "\n",
    "random.shuffle(flat_input_data)\n",
    "random.shuffle(flat_input_data_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "bm = MSQRBM(n_visible=len_x, n_hidden=n_hidden, qpu=qpu)\n",
    "bm.image_height = image_height\n",
    "bm.tqdm = tqdm\n",
    "bm.result_picture_tab = result_picture_tab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bm.train(flat_input_data, len_x, len_y, epochs = epochs, lr = lr, lr_decay = 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bm.train(flat_input_data, len_x, len_y, epochs = 5*epochs, lr = lr, lr_decay = 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "200d6143b81340a5a45b27301900f60d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bm.train(flat_input_data, len_x, len_y, epochs = 10*epochs, lr = lr, lr_decay = 0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### See random sampling results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "evaluation:  105.81316983451083\n"
     ]
    },
    {
     "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 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "evaluation:  74.24906673911778\n"
     ]
    },
    {
     "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 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "evaluation:  71.46136510252182\n"
     ]
    },
    {
     "data": {
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      "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 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "evaluation:  90.91111092332193\n"
     ]
    },
    {
     "data": {
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      "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 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "evaluation:  48.34135355058599\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for test in range(5):\n",
    "    flat_input_data_test_img = np.reshape(flat_input_data_test[test], (image_width, image_height))\n",
    "    plt.figure()\n",
    "    plt.axis('off')\n",
    "    plt.title(\"Image from test set\", y=1.03)\n",
    "    plt.imshow(flat_input_data_test_img)\n",
    "    plt.show()\n",
    "    \n",
    "    generated_pic = bm.generate(test_img = flat_input_data_test[test])\n",
    "    evalutation = bm.evaluate(generated_pic, flat_input_data_test[test])\n",
    "    print(\"evaluation: \", evalutation)\n",
    "#     print(generated_pic)\n",
    "    plt.figure()\n",
    "    plt.axis('off')\n",
    "    plt.title(\"Image reconstructed after training\", y=1.03)\n",
    "    plt.imshow(np.array(generated_pic).reshape(image_height, -1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}