1.MNIST-Preparation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading train-images-idx3-ubyte.gz...\n",
      "Downloading t10k-images-idx3-ubyte.gz...\n",
      "Downloading train-labels-idx1-ubyte.gz...\n",
      "Downloading t10k-labels-idx1-ubyte.gz...\n",
      "Download complete.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from urllib import request\n",
    "import gzip\n",
    "import pickle\n",
    "\n",
    "filenames = [\"train-images-idx3-ubyte.gz\", \"t10k-images-idx3-ubyte.gz\", \n",
    "            \"train-labels-idx1-ubyte.gz\", \"t10k-labels-idx1-ubyte.gz\"]\n",
    "\n",
    "\n",
    "folder = 'data_mnist/'\n",
    "def download_mnist():\n",
    "    base_url = \"http://yann.lecun.com/exdb/mnist/\"\n",
    "    for name in filenames:\n",
    "        print(\"Downloading \" + name + \"...\")\n",
    "        request.urlretrieve(base_url + name, folder + name)\n",
    "    print(\"Download complete.\")\n",
    "\n",
    "download_mnist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 784)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import gzip\n",
    "\n",
    "# load training images\n",
    "with gzip.open('data_mnist/train-images-idx3-ubyte.gz', 'rb') as f:\n",
    "    X_train = np.frombuffer(f.read(), np.uint8, offset=16).reshape(-1,784)\n",
    "    \n",
    "print(X_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 784)\n",
      "(60000,)\n",
      "(10000, 784)\n",
      "(10000,)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import gzip\n",
    "\n",
    "# load training images\n",
    "with gzip.open('data_mnist/train-images-idx3-ubyte.gz', 'rb') as f:\n",
    "    X_train = np.frombuffer(f.read(), np.uint8, offset=16).reshape(60000,28*28)\n",
    "\n",
    "# load testing images\n",
    "with gzip.open('data_mnist/t10k-images-idx3-ubyte.gz', 'rb') as f:\n",
    "    X_test = np.frombuffer(f.read(), np.uint8, offset=16).reshape(10000,28*28)\n",
    "\n",
    "# load training labels\n",
    "with gzip.open('data_mnist/train-labels-idx1-ubyte.gz', 'rb') as f:\n",
    "    y_train = np.frombuffer(f.read(), np.uint8, offset=8)\n",
    "    \n",
    "# load testing labels\n",
    "with gzip.open('data_mnist/t10k-labels-idx1-ubyte.gz', 'rb') as f:\n",
    "    y_test = np.frombuffer(f.read(), np.uint8, offset=8)\n",
    "    \n",
    "# check data\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 784)\n",
      "(60000,)\n",
      "(10000, 784)\n",
      "(10000,)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import gzip\n",
    "\n",
    "# load training images\n",
    "with gzip.open('data_mnist/train-images-idx3-ubyte.gz', 'rb') as f:\n",
    "    X_train = np.frombuffer(f.read(), np.uint8, offset=16).reshape(-1,28*28)\n",
    "\n",
    "# load testing images\n",
    "with gzip.open('data_mnist/t10k-images-idx3-ubyte.gz', 'rb') as f:\n",
    "    X_test = np.frombuffer(f.read(), np.uint8, offset=16).reshape(-1,28*28)\n",
    "\n",
    "# load training labels\n",
    "with gzip.open('data_mnist/train-labels-idx1-ubyte.gz', 'rb') as f:\n",
    "    y_train = np.frombuffer(f.read(), np.uint8, offset=8)\n",
    "    \n",
    "# load testing labels\n",
    "with gzip.open('data_mnist/t10k-labels-idx1-ubyte.gz', 'rb') as f:\n",
    "    y_test = np.frombuffer(f.read(), np.uint8, offset=8)\n",
    "    \n",
    "\n",
    "# check data\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)\n",
    "\n",
    "np.save('data_mnist/X_train.npy', X_train)\n",
    "np.save('data_mnist/y_train.npy', y_train)\n",
    "np.save('data_mnist/X_test.npy', X_test)\n",
    "np.save('data_mnist/y_test.npy', y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 784)\n"
     ]
    }
   ],
   "source": [
    "from PIL import Image\n",
    "import numpy as np  \n",
    "\n",
    "X_train = np.load('data_mnist/X_train.npy', allow_pickle=True)\n",
    "print(X_train.shape)\n",
    "\n",
    "for i in range(10):\n",
    "    im = Image.fromarray(X_train[i].reshape(28,28))\n",
    "    im.save(\"data_mnist/images/image_\" + str(i) +\".jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 64 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dùng vẽ đồ thị, hình ảnh\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "# Tạo dang sách 9 phần tử ngẫu nhiên từ m_train có 60.000 phần tử\n",
    "indices = list(np.random.randint(60000, size=64))\n",
    "\n",
    "fig = plt.figure(figsize=(9,9))\n",
    "columns = 8\n",
    "rows = 8\n",
    "for i in range(1, columns*rows +1):\n",
    "    img = X_train[indices[i-1]].reshape(28,28)\n",
    "    fig.add_subplot(rows, columns, i)\n",
    "    \n",
    "    plt.axis('off')\n",
    "    plt.imshow(img, cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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