qrbm_reconstruct_img.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "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.qrbm import QRBM\n",
    "\n",
    "image_height = 6\n",
    "image_width = 6\n",
    "\n",
    "len_x = image_height * image_width\n",
    "len_y = 0\n",
    "\n",
    "bm = QRBM(n_visible=len_x, n_hidden=56)\n",
    "f = open(\"6x6.txt\")\n",
    "data = [[int(data) for data in f.read() if data in \"01\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x10b261a10>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = np.array(data).reshape(image_height,-1)\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "bm.image_height = image_height\n",
    "bm.tqdm = tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8baf13ce1af144d5945c34d5634c6b63",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=50), HTML(value=u'')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stopped training early because the model can reconstruct the inputs\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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yklIZGUmpjIykVEZGUiojIynV/wMdswiJE69aUgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bm.train(data, len_x, len_y, epochs = 50, num_samps = 1, lr = 1000, decay = 0.01, epochs_to_test = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### See random sampling results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for test in range(3):\n",
    "    samples = bm.sample_qubo(num_samps=1)\n",
    "    sample = samples[0]\n",
    "    plt.figure()\n",
    "    plt.axis('off')\n",
    "    plt.title(\"Image reconstructed after training\", y=1.03)\n",
    "    plt.imshow(sample[:len_x].reshape(image_height, -1))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}