qrmb_classification.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",
    "from qrbm.ClassifierQRBM import ClassQRBM\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 = 30\n",
    "qpu = False\n",
    "epochs = 1000\n",
    "lr = 0.1"
   ]
  },
  {
   "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": {},
   "outputs": [
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       "   label  1x1  1x2  1x3  1x4  1x5  1x6  1x7  1x8  1x9  ...  28x19  28x20  \\\n",
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       "[10 rows x 785 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist_dataset[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 0, 4, 1, 9, 2, 1, 3, 1, 4])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist_dataset[:10].values[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "X_train = mnist_dataset.values[:,1:]\n",
    "X_train2 = mnist_dataset.values[:,:]\n",
    "labels_train = mnist_dataset['label'].values\n",
    "\n",
    "X_test = mnist_test.values[:,1:]\n",
    "X_test2 = mnist_test.values[:,:]\n",
    "labels_test = mnist_test['label'].values\n",
    "\n",
    "# print(mnist_dataset.values[:,0])\n",
    "X_train3 = []\n",
    "X_test3 = []\n",
    "labels_train3 = []\n",
    "labels_test3 = []\n",
    "\n",
    "for digit in digits:\n",
    "    X_train3.append(mnist_dataset.values[np.where(mnist_dataset.values[:,0] == digit), 1:][0])\n",
    "    labels_train3.extend(mnist_dataset.values[np.where(mnist_dataset.values[:,0] == digit), 0][0].tolist())\n",
    "    X_test3.append(mnist_test.values[np.where(mnist_test.values[:,0] == digit), 1:][0])\n",
    "    labels_test3.extend(mnist_test.values[np.where(mnist_test.values[:,0] == digit), 0][0].tolist())\n",
    "# X_train3 = mnist_dataset.values[np.where(mnist_dataset.values[:,0] == 0), 1:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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"
    }
   ],
   "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": 8,
   "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": 9,
   "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": 10,
   "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"
    }
   ],
   "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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "input_data = []\n",
    "input_data_test = []\n",
    "for digit_index in range(len(digits)):\n",
    "    input_data.append([x.ravel().tolist() for x in data[digit_index]])\n",
    "    input_data_test.append([x.ravel().tolist() for x in data_test[digit_index]])\n",
    "\n",
    "print(input_data[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6742"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(input_data[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## shuffling the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "for digit in digits:\n",
    "    flat_input_data = np.array([item for sublist in input_data for item in sublist])\n",
    "    flat_input_data_test = np.array([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": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import tracemalloc \n",
    "  \n",
    "# tracemalloc.start() \n",
    "\n",
    "bm = ClassQRBM(flat_input_data[0], digits, n_hidden=n_hidden, qpu=qpu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "7863\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "bm.train(flat_input_data, labels_train3, epochs = epochs, lr = lr, lr_decay = 0.1)\n",
    "\n",
    "# snapshot = tracemalloc.take_snapshot() \n",
    "# top_stats = snapshot.statistics('lineno') \n",
    "  \n",
    "# for stat in top_stats[:10]: \n",
    "#    print(stat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bm.save('test_model')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "bm.load('test_model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trying classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for _ in range(5):\n",
    "    random_id = np.random.randint(0, len(flat_input_data_test))\n",
    "    test_img = np.reshape(flat_input_data_test[random_id], (image_width, image_height))\n",
    "    plt.figure()\n",
    "    plt.axis('off')\n",
    "    plt.title(\"Image from test set\", y=1.03)\n",
    "    plt.imshow(test_img)\n",
    "    plt.show()\n",
    "    print('label:', labels_test3[random_id])\n",
    "    result = bm.classify(flat_input_data_test[random_id], 5)\n",
    "    print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trying generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "for d in digits:\n",
    "    print(d)\n",
    "    result = bm.generate(d)\n",
    "    result_img = np.reshape(result, (image_width, image_height))\n",
    "    plt.figure()\n",
    "    plt.axis('off')\n",
    "    plt.title(\"Generated image\", y=1.03)\n",
    "    plt.imshow(result_img)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(10):\n",
    "    print(bm.encode_label(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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