data_colored_mnist.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.datasets import mnist\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28)\n",
      "(60000,)\n",
      "(10000, 28, 28)\n",
      "(10000,)\n"
     ]
    }
   ],
   "source": [
    "(X_train, Y_train), (X_test, Y_test) = mnist.load_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": 3,
   "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": [
    "plt.imshow(X_train[0], cmap=\"gray\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28, 3)\n",
      "(10000, 28, 28, 3)\n"
     ]
    }
   ],
   "source": [
    "# X_train_new = np.repeat(X_train[:, :, :,np.newaxis], 3, axis=3)\n",
    "x_train = np.zeros((X_train.shape[0], X_train.shape[1], X_train.shape[2], 3)).astype(np.uint8)\n",
    "x_test =  np.zeros((X_test.shape[0], X_test.shape[1], X_test.shape[2], 3)).astype(np.uint8)\n",
    "s_train = np.zeros(X_train.shape[0]).astype(np.uint8)\n",
    "s_test = np.zeros(X_test.shape[0]).astype(np.uint8)\n",
    "print(x_train.shape)\n",
    "print(x_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30000 10000 10000\n"
     ]
    }
   ],
   "source": [
    "train_idx = np.random.permutation(60000)\n",
    "n_chan_0 = int(3/6 * 60000)\n",
    "n_chan_1 = int(2/6 * 60000)\n",
    "n_chan_2 = int(1/6 * 60000)\n",
    "print(n_chan_0, n_chan_1, n_chan_2)\n",
    "\n",
    "x_train[train_idx[:n_chan_0], :, :, 0] = X_train[train_idx[:n_chan_0]]\n",
    "x_train[train_idx[n_chan_0:(n_chan_0+n_chan_1)], :, :, 1] = X_train[train_idx[n_chan_0:(n_chan_0+n_chan_1)]]\n",
    "x_train[train_idx[(n_chan_0+n_chan_1):], :, :, 2] = X_train[train_idx[(n_chan_0+n_chan_1):]]\n",
    "\n",
    "s_train[train_idx[0:n_chan_0]] = 0\n",
    "s_train[train_idx[n_chan_0:(n_chan_0+n_chan_1)]] = 1\n",
    "s_train[train_idx[(n_chan_0+n_chan_1):]] = 2\n",
    "\n",
    "\n",
    "test_idx = np.random.permutation(10000)\n",
    "\n",
    "x_test[test_idx[0:3333], :, :, 0] = X_test[test_idx[0:3333], :, :]\n",
    "x_test[test_idx[3333:6666], :, :, 1] = X_test[test_idx[3333:6666], :, :]\n",
    "x_test[test_idx[6666:], :, :, 2] = X_test[test_idx[6666:], :, :]\n",
    "\n",
    "s_test[test_idx[0:3333]] = 0\n",
    "s_test[test_idx[3333:6666]] = 1\n",
    "s_test[test_idx[6666:]] = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": []
   },
   "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": [
    "plt.imshow(x_train[3])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_path = \"./Colored_MNIST\"\n",
    "#unbalanced\n",
    "np.savez(f\"{save_path}/colored_mnist_unbalanced.npz\", x_train=x_train, y_train=Y_train, s_train=s_train, x_test=x_test, y_test=Y_test, s_test=s_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28, 3)\n"
     ]
    }
   ],
   "source": [
    "save_path = \"./Colored_MNIST\"\n",
    "data = np.load(f\"{save_path}/colored_mnist_unbalanced.npz\")\n",
    "print(data['x_train'].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 2, 0, 1, 1, 0, 0, 1, 0], dtype=uint8)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_train[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tensorflow-23",
   "language": "python",
   "name": "tensorflow-23"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}