dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "metadata": {
    "id": "ye3lcGS8p6HA"
   },
   "source": [
    "# NIST Dataset\n",
    "\n",
    "To learn more, we explore these datasets:\n",
    "\n",
    "- NIST Special Database 19\n",
    "- The MNIST Dataset\n",
    "- The EMNIST Dataset\n",
    "- The EMNIST dataset from kaggle"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03b7f8d5-9e89-463b-b83b-54e3d69991a2",
   "metadata": {
    "id": "NoYHfYtiwtMU"
   },
   "source": [
    "## NIST Special Database 19\n",
    "\n",
    "dataset homepage: https://www.nist.gov/srd/nist-special-database-19\n",
    "\n",
    "This is the main dataset that people use to create their own datasets.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5296be51-4470-42c8-82ec-518aeb2fbf30",
   "metadata": {
    "id": "CjRN2FD3NI9l"
   },
   "source": [
    "## The MNIST Dataset\n",
    "\n",
    "dataset homepage: http://yann.lecun.com/exdb/mnist\n",
    "\n",
    "The MNIST database of handwritten digits has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image.\n",
    "\n",
    "\n",
    "I got help from this link to read the dataset files: https://stackoverflow.com/questions/39969045/parsing-yann-lecuns-mnist-idx-file-format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb816323-dbdf-48f9-af0f-8406bab18ae4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 411,
     "status": "ok",
     "timestamp": 1725428367769,
     "user": {
      "displayName": "Mohammad Mahdi Moradi",
      "userId": "14020835665324614564"
     },
     "user_tz": -210
    },
    "id": "mLIqyBRiNtrj",
    "outputId": "d74ce999-a4f7-4285-8fe0-6f2a28105f27"
   },
   "outputs": [],
   "source": [
    "# make directory\n",
    "!mkdir -p dataset/mnist\n",
    "%cd dataset/mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3f2622b-5414-4de9-a4d1-812c2950c00a",
   "metadata": {
    "id": "9lPoozgcNLU6"
   },
   "outputs": [],
   "source": [
    "# download MNIST dataset from web.archive.org because main site didn't work\n",
    "!wget \"https://web.archive.org/web/20240424102229/https://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\"\n",
    "!wget \"https://web.archive.org/web/20240424102229/https://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\"\n",
    "!wget \"https://web.archive.org/web/20240424102229/https://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\"\n",
    "!wget \"https://web.archive.org/web/20240424102229/https://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad90ac68-fa4c-40ac-80a9-6b28bc20dbdb",
   "metadata": {
    "id": "I8NTD0ZMOYGS"
   },
   "outputs": [],
   "source": [
    "# decompress .gz files\n",
    "!gzip -d *.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9934298-71f2-440a-ac63-e4dde2d7ad48",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 471,
     "status": "ok",
     "timestamp": 1725428396635,
     "user": {
      "displayName": "Mohammad Mahdi Moradi",
      "userId": "14020835665324614564"
     },
     "user_tz": -210
    },
    "id": "wOWf7EgdOU-S",
    "outputId": "d752ddac-b1e1-4681-849e-49c911f6a949"
   },
   "outputs": [],
   "source": [
    "# check\n",
    "!pwd\n",
    "!ls -l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7231f5b8-a79b-445a-b63e-119bcb07be6c",
   "metadata": {
    "id": "4HmWhRJSgKkY"
   },
   "outputs": [],
   "source": [
    "# TRAINING SET IMAGE\n",
    "with open(\"train-images-idx3-ubyte\",\"rb\") as f:\n",
    "    magic, size = struct.unpack(\">II\", f.read(8))\n",
    "    nrows, ncols = struct.unpack(\">II\", f.read(8))\n",
    "    data = np.fromfile(f, dtype=np.dtype(np.uint8).newbyteorder('>'))\n",
    "    x_train = data.reshape((size, nrows, ncols))\n",
    "\n",
    "\n",
    "# TRAINING SET LABEL\n",
    "with open(\"train-labels-idx1-ubyte\",\"rb\") as f:\n",
    "    magic, size = struct.unpack(\">II\", f.read(8))\n",
    "    data = np.fromfile(f, dtype=np.dtype(np.uint8).newbyteorder('>'))\n",
    "    y_train = data.reshape((size,)) # or reshape to (1, size)\n",
    "\n",
    "\n",
    "# TEST SET IMAGE\n",
    "with open(\"t10k-images-idx3-ubyte\",\"rb\") as f:\n",
    "    magic, size = struct.unpack(\">II\", f.read(8))\n",
    "    nrows, ncols = struct.unpack(\">II\", f.read(8))\n",
    "    data = np.fromfile(f, dtype=np.dtype(np.uint8).newbyteorder('>'))\n",
    "    x_test = data.reshape((size, nrows, ncols))\n",
    "\n",
    "# TEST SET LABEL\n",
    "with open(\"t10k-labels-idx1-ubyte\",\"rb\") as f:\n",
    "    magic, size = struct.unpack(\">II\", f.read(8))\n",
    "    data = np.fromfile(f, dtype=np.dtype(np.uint8).newbyteorder('>'))\n",
    "    y_test = data.reshape((size,)) # or reshape to (1, size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8421d2aa-a459-434a-8ca0-278064f1e02a",
   "metadata": {
    "id": "YrpyjbM5kzvY"
   },
   "outputs": [],
   "source": [
    "print(\"Shape of x_train: \", x_train.shape)\n",
    "print(\"Shape of y_train: \", y_train.shape)\n",
    "print(\"Shape of x_test: \", x_test.shape)\n",
    "print(\"Shape of y_test: \", y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "909cda97-6337-4912-839e-ab38c7ff53d4",
   "metadata": {
    "id": "53W2_4d3hgOO"
   },
   "source": [
    "**POINT** This assumes you uncompressed the `.gz` file. You can also work with the compressed file:\n",
    "\n",
    "```python\n",
    "import gzip\n",
    "import struct\n",
    "import numpy as np\n",
    "\n",
    "with gzip.open('t10k-images-idx3-ubyte.gz','rb') as f:\n",
    "    magic, size = struct.unpack(\">II\", f.read(8))\n",
    "    nrows, ncols = struct.unpack(\">II\", f.read(8))\n",
    "    data = np.frombuffer(f.read(), dtype=np.dtype(np.uint8).newbyteorder('>'))\n",
    "    data = data.reshape((size, nrows, ncols))\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aae6bbba-2163-4129-8f6e-57e4a900943f",
   "metadata": {
    "id": "Odnpxb7zbmii"
   },
   "outputs": [],
   "source": [
    "# plot train dataset\n",
    "fig,axes = plt.subplots(5,5,figsize=(10,10))\n",
    "for i,ax in enumerate(axes.flat):\n",
    "    ax.imshow(x_train[i], cmap='gray')\n",
    "    ax.set_title(y_train[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df3361d5-a8e9-45c5-aa2d-e54ac688327b",
   "metadata": {
    "id": "9hHFhwcvjsIz"
   },
   "outputs": [],
   "source": [
    "# plot test dataset\n",
    "fig,axes = plt.subplots(5,5,figsize=(10,10))\n",
    "for i,ax in enumerate(axes.flat):\n",
    "    ax.imshow(x_test[i], cmap='gray')\n",
    "    ax.set_title(y_test[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3da5828-68dc-44df-85e0-3458a58b9b95",
   "metadata": {
    "id": "wGqI0TcGjBVU"
   },
   "outputs": [],
   "source": [
    "# delete mnist dataset files and directory\n",
    "%cd ../..\n",
    "!rm -rf dataset/mnist"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3dc473c-aafd-4ef9-ae45-7d4e2564d099",
   "metadata": {
    "id": "QqArMn4WpGss"
   },
   "source": [
    "## The EMNIST Dataset\n",
    "\n",
    "dataset homepage: https://www.nist.gov/itl/products-and-services/emnist-dataset\n",
    "\n",
    "The EMNIST dataset is a set of handwritten character digits derived from the NIST Special Database 19  and converted to a 28x28 pixel image format and dataset structure that directly matches the MNIST dataset.\n",
    "\n",
    "This dataset is provided in the same binary format as the original MNIST dataset. So we can repeat the same thing we did above for this data set.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc5b3420-e44a-4909-8758-bd7f27b18a1d",
   "metadata": {
    "id": "N47POMB1pICc"
   },
   "outputs": [],
   "source": [
    "# make directory\n",
    "!mkdir -p dataset/emnist\n",
    "%cd dataset/emnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c08ae402-97fe-4726-8887-89c006fef76b",
   "metadata": {
    "id": "0Q1obkZdpbUL"
   },
   "outputs": [],
   "source": [
    "# download the dataset\n",
    "!wget \"https://biometrics.nist.gov/cs_links/EMNIST/gzip.zip\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35043103-5d99-4dd6-a5da-a00eaf5a208b",
   "metadata": {
    "id": "ZvrktXw4pjjB"
   },
   "outputs": [],
   "source": [
    "# decompress\n",
    "!unzip gzip.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54ffdfb4-2c13-46ff-b2ea-7aadc054e293",
   "metadata": {
    "id": "gUeR-9pOpy5R"
   },
   "outputs": [],
   "source": [
    "!ls -l . gzip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72edad9f-9e03-438e-bfbb-8d068f1d1ae1",
   "metadata": {
    "id": "dNYTkDkfqB0i"
   },
   "outputs": [],
   "source": [
    "with gzip.open('gzip/emnist-letters-train-images-idx3-ubyte.gz','rb') as f:\n",
    "    magic, size = struct.unpack(\">II\", f.read(8))\n",
    "    nrows, ncols = struct.unpack(\">II\", f.read(8))\n",
    "    data = np.frombuffer(f.read(), dtype=np.dtype(np.uint8).newbyteorder('>'))\n",
    "    x_train = data.reshape((size, nrows, ncols))\n",
    "\n",
    "with gzip.open('gzip/emnist-letters-train-labels-idx1-ubyte.gz','rb') as f:\n",
    "    magic, size = struct.unpack(\">II\", f.read(8))\n",
    "    data = np.frombuffer(f.read(), dtype=np.dtype(np.uint8).newbyteorder('>'))\n",
    "    y_train = data.reshape((size,)) # or reshape to (1, size)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41de08c4-84ec-44cf-ba16-5472385247fa",
   "metadata": {
    "id": "uTykmyCssnod"
   },
   "outputs": [],
   "source": [
    "print(\"Shape of x_train: \", x_train.shape)\n",
    "print(\"Shape of y_train: \", y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab634c58-bc5e-41ef-8aa8-4faee636144e",
   "metadata": {
    "id": "XS1EzbLmv4sS"
   },
   "outputs": [],
   "source": [
    "fig,axes = plt.subplots(5,5,figsize=(10,10))\n",
    "for i,ax in enumerate(axes.flat):\n",
    "    ax.imshow(x_train[i], cmap='gray')\n",
    "    ax.set_title(chr(y_train[i]+96))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3503449-a804-4e43-90f4-6067b76769d2",
   "metadata": {
    "id": "1sJaU2hupsiA"
   },
   "outputs": [],
   "source": [
    "# delete dataset directory\n",
    "%cd ../..\n",
    "!rm -rf dataset/emnist"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95361cb5-aa12-4e09-b4ff-87aa2d77b88e",
   "metadata": {
    "id": "uID8JFhRIoL2"
   },
   "source": [
    "<p style=\"text-align: center; font-size: 30px;\">NOTE</p>\n",
    "EMNIST dataset images are <b>transposed</b>."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae4d1b27-40b9-4997-be9e-ea98d148e35d",
   "metadata": {
    "id": "bwL-p-VPqR9X"
   },
   "source": [
    "## The EMNIST dataset from kaggle\n",
    "\n",
    "dataset homepage: https://www.kaggle.com/datasets/crawford/emnist\n",
    "\n",
    "This dataset is exactly the same as the EMNIST dataset, but it has csv format and is easier to read.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "624fe14b-25ee-4bfc-b242-a96bedb0e30d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 952,
     "status": "ok",
     "timestamp": 1725458919218,
     "user": {
      "displayName": "Mohammad Mahdi Moradi",
      "userId": "14020835665324614564"
     },
     "user_tz": -210
    },
    "id": "Ss3NNGLitG6Z",
    "outputId": "c0d34fa7-47d1-4f28-df67-0b0921429aef"
   },
   "outputs": [],
   "source": [
    "# make directory\n",
    "!mkdir -p dataset/kaggleemnist\n",
    "%cd dataset/kaggleemnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67d67378-12a0-44df-806e-6bb6d9ffb677",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 955,
     "status": "ok",
     "timestamp": 1726087270315,
     "user": {
      "displayName": "Mohammad Mahdi Moradi",
      "userId": "14020835665324614564"
     },
     "user_tz": -210
    },
    "id": "UYQcaKQUznKC",
    "outputId": "922a61a4-5fc5-44b9-a916-d8eb26607bbd"
   },
   "outputs": [],
   "source": [
    "#\n",
    "!wget \"https://storage.googleapis.com/kaggle-data-sets/7160/10705/compressed/emnist-balanced-train.csv.zip?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=gcp-kaggle-com%40kaggle-161607.iam.gserviceaccount.com%2F20240904%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20240904T141525Z&X-Goog-Expires=259200&X-Goog-SignedHeaders=host&X-Goog-Signature=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\"\n",
    "!wget \"https://storage.googleapis.com/kaggle-data-sets/7160/10705/compressed/emnist-balanced-test.csv.zip?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=gcp-kaggle-com%40kaggle-161607.iam.gserviceaccount.com%2F20240904%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20240904T141554Z&X-Goog-Expires=259200&X-Goog-SignedHeaders=host&X-Goog-Signature=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\"\n",
    "\n",
    "!wget \"https://storage.googleapis.com/kaggle-data-sets/7160/10705/compressed/emnist-bymerge-test.csv.zip?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=gcp-kaggle-com%40kaggle-161607.iam.gserviceaccount.com%2F20240904%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20240904T141309Z&X-Goog-Expires=259200&X-Goog-SignedHeaders=host&X-Goog-Signature=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\"\n",
    "!wget \"https://storage.googleapis.com/kaggle-data-sets/7160/10705/compressed/emnist-bymerge-train.csv.zip?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=gcp-kaggle-com%40kaggle-161607.iam.gserviceaccount.com%2F20240904%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20240904T141109Z&X-Goog-Expires=259200&X-Goog-SignedHeaders=host&X-Goog-Signature=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\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ff5e60b-9697-4703-9faf-47410dc46fe4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 27575,
     "status": "ok",
     "timestamp": 1725460306519,
     "user": {
      "displayName": "Mohammad Mahdi Moradi",
      "userId": "14020835665324614564"
     },
     "user_tz": -210
    },
    "id": "u9YPW8FAZx6m",
    "outputId": "9d582552-0990-4441-d8a1-d218b5fcb222"
   },
   "outputs": [],
   "source": [
    "# run this cell only once\n",
    "!for file in *; do unzip $file; rm $file; done"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee7e6222-9c24-44a7-8f35-fd4027b5f1b2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 486,
     "status": "ok",
     "timestamp": 1725460343339,
     "user": {
      "displayName": "Mohammad Mahdi Moradi",
      "userId": "14020835665324614564"
     },
     "user_tz": -210
    },
    "id": "9pUeu1FeZeNE",
    "outputId": "d9d7f2cd-b75a-498e-c3ba-57ca5ae63392"
   },
   "outputs": [],
   "source": [
    "!ls -ltrha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57ee7da4-bf22-4717-a05d-96efa5710e1d",
   "metadata": {
    "id": "6wQtrS70eKsZ"
   },
   "outputs": [],
   "source": [
    "trainset =  np.loadtxt('emnist-balanced-train.csv', delimiter=\",\", dtype=np.int64)\n",
    "y_train = trainset[:,0]\n",
    "x_train = trainset[:,1:]\n",
    "\n",
    "testset =  np.loadtxt('emnist-balanced-test.csv', delimiter=\",\", dtype=np.int64)\n",
    "y_test = testset[:,0]\n",
    "x_test = testset[:,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7011fc2b-1042-44d6-a973-0f8f3d9e2241",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1725462930745,
     "user": {
      "displayName": "Mohammad Mahdi Moradi",
      "userId": "14020835665324614564"
     },
     "user_tz": -210
    },
    "id": "-tmSpdCMg9Jz",
    "outputId": "57e7056e-b212-472d-f678-7bc329e72480"
   },
   "outputs": [],
   "source": [
    "print(\"Shape of x_train: \", x_train.shape)\n",
    "print(\"Shape of y_train: \", y_train.shape)\n",
    "print(\"Shape of x_test: \", x_test.shape)\n",
    "print(\"Shape of y_test: \", y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91a57bf2-b338-4e49-a712-86989cfd7cc4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 873
    },
    "executionInfo": {
     "elapsed": 4873,
     "status": "ok",
     "timestamp": 1725462947851,
     "user": {
      "displayName": "Mohammad Mahdi Moradi",
      "userId": "14020835665324614564"
     },
     "user_tz": -210
    },
    "id": "fvZWTxykhag9",
    "outputId": "09c6b2fc-f368-4d27-a668-1b6a49133edd"
   },
   "outputs": [],
   "source": [
    "# plot train dataset\n",
    "fig,axes = plt.subplots(5,5,figsize=(10,10))\n",
    "for i,ax in enumerate(axes.flat):\n",
    "    ax.imshow(x_train[i].reshape((28,28)), cmap='gray')\n",
    "    ax.set_title(y_train[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c175f2ca-805e-43b1-a7ce-a1b1f6a7fa04",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 873
    },
    "executionInfo": {
     "elapsed": 6496,
     "status": "ok",
     "timestamp": 1725463021101,
     "user": {
      "displayName": "Mohammad Mahdi Moradi",
      "userId": "14020835665324614564"
     },
     "user_tz": -210
    },
    "id": "YHJ18vgZhag_",
    "outputId": "5780445e-588a-419c-f18b-1f366208ffdf"
   },
   "outputs": [],
   "source": [
    "# plot test dataset\n",
    "fig,axes = plt.subplots(5,5,figsize=(10,10))\n",
    "for i,ax in enumerate(axes.flat):\n",
    "    ax.imshow(x_test[i].reshape((28,28)), cmap='gray')\n",
    "    ax.set_title(y_test[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0287326a-ecb4-4277-8a53-206463e04307",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 499,
     "status": "ok",
     "timestamp": 1725464187590,
     "user": {
      "displayName": "Mohammad Mahdi Moradi",
      "userId": "14020835665324614564"
     },
     "user_tz": -210
    },
    "id": "hBKNsKDioGbH",
    "outputId": "0c8cf6e9-b152-447e-d5ee-cc3de9bf7054"
   },
   "outputs": [],
   "source": [
    "%cd ../.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1413dd9f-6d1a-4ed2-8374-6d7f9c09aae2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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