shallow Neural network in TF.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "def32b6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow \n",
    "from tensorflow.keras.datasets import mnist\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.optimizers import SGD\n",
    "from matplotlib import pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a5ada472",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
      "11490434/11490434 [==============================] - 7s 1us/step\n"
     ]
    }
   ],
   "source": [
    "(X_train, y_train), (X_valid, y_valid) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "88a6e082",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fbb2a007",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeoAAAGuCAYAAABfpEVAAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAj1UlEQVR4nO3de5yN5frH8UfjOAYhEjkfNiqkQmFvIUQqbJTIoVRTCSG7XZucdlOSQ04RIeWQnb1fU1460HbKKadNJDnFVMwQNc7G/P57PNflN2vWmllrzTVrfd5/3d/XPWutO3O4eta17vvJk56enu4AAACTrsvpBQAAgIxRqAEAMIxCDQCAYRRqAAAMo1ADAGAYhRoAAMMo1AAAGEahBgDAMAo1AACGUagBADCMQg0AgGEUagAADKNQAwBgGIUaAADDKNQAABhGoQYAwDAKNQAAhlGoAQAwjEINAIBhFGoAAAyjUAMAYBiFGgAAwyjUAAAYljenFwCE05EjR0SeOHGiyOPHjxd54MCBIvfv398dly9fPsirA4BrcUUNAIBhFGoAAAyjUAMAYFie9PT09JxeRChduXJF5AsXLvj92Llz54p85swZkXfv3i3yhAkT3PHf//53MTd58mSRCxUqJPK4ceNEjo+P93udyFhSUpLIdevWFfnUqVMBPV/x4sXdcXJycpbXhdxjz549Irds2VLk7du3i1yqVKlQLwlZMHPmTJGfeeYZd6zrxN69e0WuUaNG6BbmB66oAQAwjEINAIBhFGoAAAzLFfuoT58+7Y7T0tLE3I4dO0T+4osvRNY9yBkzZgRtXZUqVRJ50KBB7njWrFlirlixYiI3bdpU5ObNmwdtXdHu8OHD7rhZs2Zi7rfffhM5T548IuvvU4ECBUQ+fvy4Oz5w4ICYq1ixosgxMTH+LTgX2bdvn8j637NBgwbhXE5YbNy4UeQWLVrk0EoQiBUrVoj84osvinzddRlfp+q/CzmNK2oAAAyjUAMAYBiFGgAAw0z2qI8ePSpyvXr13LHuiYWT7mnoPrR3b/QTTzwh5kqXLi1yXFycyOy99N+lS5dE9vakHcdx2rRp44712d6Z8f6sOY7jjBkzRuQmTZq44+rVq4s5/fkH/TMQCXTf7/vvvxc5UnrU3uMldF/+hx9+CPdykAX6+3T+/PkcWkn2cUUNAIBhFGoAAAyjUAMAYJjJHnXJkiVFvvHGG91xMHvUrVq18vm6n3zyich6T63eo4vwGDJkiMj6HPXsWLVqlcj6fPcOHTq4Y/3zsW3btqCtw6pJkyaJrH+HIkVqaqo7fv3118Wc957kjsPnS6zQ91547bXXfH59/fr13bE+f6Nw4cJBW1cwcEUNAIBhFGoAAAwz+da3vgXknDlz3PGSJUvE3N133y1yp06dfD63d3vNf/7zHzGXP39+kX/99VeRJ06c6PO5ERp6i9X8+fNF9nWnVu9b1Y5z7c9H9+7dRS5fvrzItWrVEnno0KHuWP8sRvgdYx3HufYI30jlvQWipn8mkDN+/PFHkdu2bSvyyZMnfT4+ISHBHeujg63hihoAAMMo1AAAGEahBgDAMJM9au2uu+5yx3Xq1BFzuq/80ksvifzmm2+KPGrUqAwfq5UpU0ZkvU0DoZGUlCTy7bffLrK+dam+Jd1jjz3mjmfOnCnm9BYOPf/II4+IHBsbK3LZsmXdsT5S9oMPPhD5b3/7m8i6/50b/PzzzyLr702k8tXfvO+++8K4EmTkvffeEzmz44I7duwo8r333hv0NYUKV9QAABhGoQYAwDAKNQAAhuWKHrWXPsZTK168uM957xGITZs2FXO614nwSUlJccdvvPGGmNPHxnqPlHUcx6lcubLI8fHx7lh/DkHfxlLn7Dh79qzIY8eOFVkfv5kb6KMV9X9jpNBHxe7cuTPDr9VHDSM8Mvv90p8Z0d8n7+eTchuuqAEAMIxCDQCAYRRqAAAMy3U96swMGDBA5E2bNom8dOlSd/zdd9+JuVtvvTVk64J0+fJlkQcPHuyO9Vne+hzezz//XORq1aqJfOnSpWAsMdsOHjyY00vItl27dvmcD2aPPye98sorInv3j2d2dgNCx3tmwkMPPRTQY/VtLmvWrBmEFeUMrqgBADCMQg0AgGEUagAADIu4HrXuH82YMUPkFStWuGPd83j44YdFbty4scj63sbsu866n376SWTdl/basGGDyDVq1PD53Pp+5gidhg0b5vQS/l8XLlwQecuWLSLrvwuLFi3K8Ln0/veCBQtmc3Xw15o1a9zxN9984/NrO3fuLHKvXr1CsaQcwRU1AACGUagBADAs4t761kqUKCGyd2tPmzZtxNyECRN85tmzZ4vcqVMnkePi4rK4yujz3HPPiZyenu6OdYshs7e6c8qVK1dE1kcYev+bIpW+5Wgg9C009b/nqlWrRNbb3S5evOiO33nnHTGXlpYmcuHChUVu1aqVyPrtbO8Wv1q1al2zdoTG5s2bRe7Zs2eGX9u+fXuR9S1rI6lFwRU1AACGUagBADCMQg0AgGER36PWGjRo4I71EaIDBw4U+eOPPxa5T58+Iu/fv1/kIUOGuOMiRYpka52RZtu2bSKvXr1aZO9WN73Nwirdk9bb9e68885wLickYmNjRdb/jQ8++KDIf/rTn/x+7vXr14use/p588o/T/ozIN6tYd4jaB3n2lvY6qNOdc+6fPnyIntve1mqVCm9dASJ/oxDo0aN/H6sPjpYf08jCVfUAAAYRqEGAMAwCjUAAIblSY+GzZ5+On/+vMj66MqWLVuKrP/p/vrXv7pjX0cSRiPdj9Q9xLJly7rj3bt3i7mc3J+ub8fpPU7S+5kEx7m2tz5v3jyRI+H2iHPnzhX5v//9b9Ceu1u3biLrHmTlypWD9lrLli0T+YEHHhDZe0tE/fOI4PnHP/4hckJCgt+P1fvwI/mzBFxRAwBgGIUaAADDKNQAABgWdfuofdFnwzZr1kzkmJgYkXX/8t///rc73rt3r5gLZH9pNPL+21vqSU+bNk3kl156yR1XqlRJzL3yyisiR0JPWtNnL/s6i9myTz/91Oe8PjMBwZGUlCTykiVL/H5s7969RY7knrTGFTUAAIZRqAEAMIxCDQCAYVHdo9b78D755BOR9d5f3b/U7rrrLnds9R7KVvXo0SNHXlf3zN544w2Rp06dKrK3T6bvf4vI0bFjx5xeQkTS59+npKT4/PrWrVu748mTJ4dkTbkBV9QAABhGoQYAwDAKNQAAhkV8jzo5OVnkKVOmuOP3339fzB09ejSg59b7qr37avV9e6OdPhdd5zlz5rhjff5vMC1YsEDkfv36ifzbb7+J/MILL4g8fvz40CwMiALHjx8XWd/TXRs6dKg7jsRzCfzFFTUAAIZRqAEAMCzXv/WdmpoqcmJiosgjR44U+YcffsjyazVv3lxkfUu2O+64I8vPHel0K0Bnb9tBf8+eeOIJkYsUKSLyd999J/K7777rjtesWSPmDh06JHLVqlVFfuSRR0TWb30jMulWzOHDh91xlSpVwr2ciDF48GCRr1y5EtDj69SpE8zl5FpcUQMAYBiFGgAAwyjUAAAYlit61GfOnHHHR44cEXPdu3cXedu2bVl+nVatWok8YsQIkb1HhDoOW7CCKS0tzR3rHvWsWbNELlGihMg7d+70+3Xuv/9+kdu0aSPy888/7/dzIXLo3+VAe6m4ynssr76Npd6OVaBAAZGHDx8ucuHChYO8utyJK2oAAAyjUAMAYBiFGgAAw0z0qM+dOyfygAEDRF67dq07/v7777P1Wm3btnXHw4YNE3P16tUTOV++fNl6LVx1yy23iNyyZUuRv/rqqwwfq4921bem1EqXLu2O4+PjxVwojydF5Fi5cqU7btGiRQ6uJPfxnm2R2e+q99hlx5FHhuIqrqgBADCMQg0AgGEUagAADAtLj1qfr/zPf/5TZN2f9J6zG6jY2FiRR40aJfKzzz7rjqP5tmnhVrRoUZH1/sp58+a540DP1x49erTIffv2dcclS5YM6LkQnfRZ34AlXFEDAGAYhRoAAMMo1AAAGBaWHvW//vUvkfXZzZmpX7++O3700UfFXN688j/hqaeeErlgwYIBvRbCIy4uTmTvZwe8YyAUOnXqJPL06dNzaCWRp1y5cu64Xbt2Yi4xMTHcy4kIXFEDAGAYhRoAAMMo1AAAGJYnnQ2EAACYxRU1AACGUagBADCMQg0AgGEUagAADKNQAwBgGIUaAADDKNQAABhGoQYAwDAKNQAAhlGoAQAwjEINAIBhFGoAAAyjUAMAYBiFGgAAwyjUAAAYRqEGAMAwCjUAAIZRqAEAMIxCDQCAYRRqAAAMo1ADAGAYhRoAAMMo1AAAGEahBgDAMAo1AACGUagBADCMQg0AgGEUagAADKNQAwBgGIUaAADDKNQAABhGoQYAwDAKNQAAhlGoAQAwjEINAIBhFGoAAAyjUAMAYBiFGgAAwyjUAAAYRqEGAMAwCjUAAIZRqAEAMIxCDQCAYRRqAAAMo1ADAGAYhRoAAMMo1AAAGEahBgDAMAo1AACGUagBADCMQg0AgGEUagAADKNQAwBgGIUaAADDKNQAABhGoQYAwDAKNQAAhlGoAQAwjEINAIBhFGoAAAyjUAMAYBiFGgAAwyjUAAAYRqEGAMAwCjUAAIZRqAEAMIxCDQCAYRRqAAAMo1ADAGAYhRoAAMPy5vQCAMAfo0aNEnnYsGHuuEGDBmLuiy++ELlYsWKhWxgQYlxRAwBgGIUaAADDKNQAABiWJz09PT2nFwGEy4ULF0S+dOmSyGvXrhU5KSlJ5J49e7rjvHn5iEconTp1SuTq1auLfPLkSXecJ08eMbdt2zaRb7vttuAuDkGRkpIi8uXLl0XetGmTO37ooYfE3HXXBe86s3fv3iK/++67IsfExATttbKCK2oAAAyjUAMAYBiFGgAAw2iyIeJ4e5vjxo0TcytXrhR548aNAT23t2ft3ceL4IuNjRX5wQcfFHnOnDlhXA2y4tdffxV53rx5Is+YMUPkK1euiPzTTz+5Y92T1p9LyA79s1S8eHGRR48eLXKBAgWC9tr+4IoaAADDKNQAABhGoQYAwLCI30d96NAhkb29iOXLl4u5zZs3+3yuDz/8UOTy5cuL/OWXX7rjXr16iblKlSr5Xij8lpycLPLEiRMzzOfOnRNz+se9cuXKIpcsWVLkLVu2iHzjjTe64+3bt4u5UqVK+Vg1skv3CYcPH+6O2Udtk/47OH/+/Cw/l/7dDWaPOjN79+4VuWrVqmF7bcfhihoAANMo1AAAGEahBgDAsIjbR71u3TqRu3TpIvKxY8fcse55dOzYUeQjR46I3L17d5+v7X0+3UedMmWKz8fiqvPnz4use5PTpk0T+fTp034/t+5Vrlq1SmR91rC3J+048udHvy496uDSPwe67wz72rdvL3JmPeqyZcuKPHjwYHes91hndtb3mjVrRF66dKnPr7eMK2oAAAyjUAMAYFiue+tbv/2ht1+1a9dO5NTUVJEffvhhd6zfUtW30UtLSxO5T58+Ii9cuDDDdd5zzz0ZzsE33b5ISEjI8nPVrl1b5NWrV4tctGhRkU+cOJHl10Jw6VuQ7t692+/HbtiwQeQKFSqIXKxYsawvDH7r0KGDyN5bk/5/9NvZcXFxWX7tp59+WuRatWqJ7D2eVNN/6ytWrJjldQQDV9QAABhGoQYAwDAKNQAAhuW6HvXXX38tcuvWrX1+fdeuXUWePXu2O87sVmVr164V2VdP2nHkMaG6NwP/BXr7who1aojcvHlzdzxmzBgxp3vS2uHDhwN6bYROkSJFRB44cKDI8fHxGT5Wz+mjYfVWTISG7jln9vsXTFu3bhU5JSXF78fqzzTkzZuzpZIragAADKNQAwBgGIUaAADDckWPetKkSe5Y96n0rc6GDRsm8tChQ0XOrC/tNWDAAL+/1nEcZ9GiRe44NjY2oMfiqqlTp4p89913i9ymTRuR9TGfhQsXzvJrHz9+PMuPRWg99dRTIvvqUSP66M8U6dvfnj171u/nGjJkSFDWFCxcUQMAYBiFGgAAwyjUAAAYZrJHPX36dJG9fWndY37kkUdEfvnll0XOly9fhq+jb2m4Y8cOkfft2yeyvi2mt3fuOI5z5513Zvha8J/eP/vss8+G7bVXrlwZttdC9njP/c/slofI/fQ5/YMGDRL5u+++E/nixYt+P3fTpk1FtvbzZGs1AABAoFADAGAYhRoAAMNM9KjPnz8v8qhRo0T27pXWPWnv2d3+8N4PVZ8Drs8R1/T9Tfv27RvQayM8lixZ4o5///13Mac/Z6D34W/ZssXnc3vvd16lSpWsLhFB4O0j6u8jbDh16pTIixcvFnnZsmV+P1diYqLIgX7Pr7/+epHnzZvnjps0aSLmfH22KSdwRQ0AgGEUagAADDPx1ndaWprIx44dy/Brx48fL/KZM2dE9r7t6TjyWE/HcZz169e7Y/22qH4rRecnn3xS5Pz582e4TgTPpUuXRP75559F1sfGzp8/P8Pn8m7pcZzMt2GUL19e5Pfff9/vxwLR6JdffnHHzZo1E3P79+8P82quat++vcht27bNoZUEjr80AAAYRqEGAMAwCjUAAIaZ6FHHxMSIXKZMGZF//fVXd1yiRAkxF+hH9CtUqOCO9cf1jxw5IrK+fWL9+vUDei34z/s5haNHj4o53efS3yd9S1FvX/n+++8XcwsWLBA5NTXV57r0MbOfffaZO+7WrZuY0z/HQLTT2yF1DkSgny/RvNuxHMdx+vfv747r1auX5XWFA1fUAAAYRqEGAMAwCjUAAIaZ6FEXLFhQ5LVr14rcqFEjd5ycnCzmateuLXKPHj1Efvzxx0UuXLhwhl+re5/x8fG+lo1s0Hvnt2/f7o4bNmzo87FTp04VuUWLFiJXrVrVHZ87d07M/e9//xN548aNPl/L+/kIx3Gc3r17u2N9hKhed968Jn69IlYgt7n88ssvRe7YsWNI1gTHuemmm9zx5s2bxdzHH38scqtWrUTOztkUs2bNEnn48OFZfi5ruKIGAMAwCjUAAIZRqAEAMCxPenY2tuVC+/btc8c1atQQc7rPpW/J1qlTp9AtLMLpnvTEiRNFfumllzJ8rN6vPGPGDJH1ZxzOnj3rjh944AExt2rVKpELFCgg8tixY0X29s4dR571rXXp0kVkfQZ5XFxcho91HMe5+eabfc5D8u5bD/Q8haSkJJH1mQnIffTtkjP7ffv222/dMfuoAQBAllGoAQAwjEINAIBhUbfR09vH0D1p3efS50TDf/pc3gkTJog8dOhQkYsUKeKO58yZI+Zat24tsu5JHz58WOS+ffu649WrV4u52267TeSFCxeKXLNmTZEvXLggcr9+/dzx7NmzxdzcuXNF1p9x0PQ+7B9++MHn10N69dVX3fGYMWMCeuzMmTMzfC7kTlu3bs3pJYQMV9QAABhGoQYAwDAKNQAAhkVdj1r3KBEan376qci6J633OCYmJrrjO+64Q8zt3btX5OnTp4s8f/58kb3ne0+ePFnM6T3ZRYsWvWbtXnqfdZ06ddyx7rvrffa6D6qNHz/e5zx8834vED76TISdO3eKfMstt7jjfPnyhWwd+vz2zp07h+y1chpX1AAAGEahBgDAsKg7QtT7No0+Nk5vz/r9999Fjo2NDdm6Io0+DlPfLlJvsfK+3X369Gkxt2vXroBee9q0ae74iSeeEHOZ3Q4RuZNuae3evdvn1+vtgydOnBC5RIkSwVlYBPAeu+w4jvPaa6+JvGjRIpFPnjzpjjNrLWXG28batGmTmNO3KtV/NzT999v7fHpbpjX81QIAwDAKNQAAhlGoAQAwLOq2Zx04cCCnlxAVKlWqJLLuUetb0q1bty7D5+revbvI9913n8j6qNfrr7/eHdOTjg4NGjQQec+ePT6/np8L//Xq1UvkjRs3+vx677bD7Paovds29S1qM7u1qe5hDxo0SGTrfWkvfloBADCMQg0AgGEUagAADIu6fdS//PKLOy5btqyY032rP/74Q2T2UftP3x5y/fr1Iuue9E033eSOu3btKub0nuuYmJhgLBERZMeOHSLrY2g1/WcvOTlZZPZRX9W4cWORM+tRh4r+npUrV07kHj16iDxixAiR8+bNvR/J4ooaAADDKNQAABhGoQYAwLCo61F76fOB9d5LfcZt5cqVQ74mAIHT5zy3atVK5C1btohMj9p/R48eFXnSpEkiv/3220F7rdq1a4vs3Yetv6d9+/YV2fs5l0jDFTUAAIZRqAEAMIxCDQCAYVHdo16xYoXIrVu3FrlDhw4iT548WeQbb7wxNAsDAKMuX74s8vLly0V+8skn3XFKSoqY69Onj8gPPvigyM2aNRM5Li4uq8uMKFxRAwBgGIUaAADDKNQAABgW1T1qfR517969RV68eLHIet/exIkTRc6fP38QVwcAAFfUAACYRqEGAMCwqH7rW9NvhSckJIg8atQokZOSkkRmuxYAINi4ogYAwDAKNQAAhlGoAQAwjB41AACGcUUNAIBhFGoAAAyjUAMAYBiFGgAAwyjUAAAYRqEGAMAwCjUAAIZRqAEAMIxCDQCAYRRqAAAMo1ADAGAYhRoAAMMo1AAAGEahBgDAMAo1AACGUagBADCMQg0AgGEUagAADKNQAwBgGIUaAADDKNQAABhGoQYAwDAKNQAAhlGoAQAwjEINAIBhFGoAAAyjUAMAYBiFGgAAwyjUAAAYRqEGAMAwCjUAAIZRqAEAMIxCDQCAYRRqAAAMo1ADAGAYhRoAAMMo1AAAGEahBgDAMAo1AACGUagBADCMQg0AgGEUagAADKNQAwBgGIUaAADDKNQAABhGoQYAwDAKNQAAhlGoAQAwjEINAIBhFGoAAAyjUAMAYBiFGgAAwyjUAAAYRqEGAMAwCjUAAIZRqAEAMIxCDQCAYRRqAAAMo1ADAGAYhRoAAMMo1AAAGEahBgDAMAo1AACG5c3pBQCRqnPnziKnp6eLvGTJknAuJ9c5duyYyJ9//rnICQkJ7rh58+ZirkGDBj6f+7HHHhM5JiYmK0sEwoIragAADKNQAwBgGIUaAADDIr5HnZaWJvL+/fvd8YABA8TcsmXLwrEkRKgxY8aI/Nlnn4k8cODAcC4n1/n0009F7tatm8h//PFHho/ds2ePyFOmTPH5WrqHXbNmTX+WCOQIrqgBADCMQg0AgGEUagAADIv4HvWFCxdE9vaibr75ZjGXmpoqclxcXOgWhlxv3LhxIusedf78+UVu165dyNeUm7Vo0UJk/fvnq0cdqMaNG4u8atUqkW+99dagvRaQXVxRAwBgGIUaAADDKNQAABgW8T1qX44ePSry6dOnRaZHDV/Wrl0r8sWLF0Vu3769yPfcc0/I15SbFSpUSOR3331X5EcffVTkM2fOuOMqVaqIuQMHDvh8rZMnT4qcmJgoMj3q6KL/9uvf5cWLF4s8evRon8/nPUv+rbfeyubquKIGAMA0CjUAAIZRqAEAMCyqe9T6/sDInfbt2yfysGHD3PHs2bPFnO6DBmrNmjXu+JtvvhFztWvXFnn8+PHZeq1op3v8devWFdn773/DDTeIucx61NozzzwT4OqQ2+zevVvkhQsXumN9Nvxvv/0mcp48eQJ6rRUrVgS4Ot+4ogYAwDAKNQAAhuVJj/D3f8+ePSuyry1XP/74o8h6ywdsqlevnsg7d+50x3v37hVz1apVy9Zr3XXXXe7422+/FXMbN24UWd9KEdmzYcMGkQcPHuyO161bl63nPnbsmMilS5fO1vMh/IYOHSry1q1bRQ7k7ehixYqJ3K9fP5GbNm0q8r333ity3rzB7SpzRQ0AgGEUagAADKNQAwBgWFRvz9K2b98uMj3q3KFo0aIie7dS6KMAA5WUlCSydyvYddfJ/8/Vt1RFcDVq1Ejk5cuXu+OWLVuKOf15gcy8+uqrIs+YMSPA1SHUzp07J/LIkSNFHjt2rMilSpUSuVmzZiK//vrr7lj/rde3qNU963DjihoAAMMo1AAAGEahBgDAsIjvUes+YvHixd2xPiZuz549YVkTsuedd94Ref369SLffvvt7rhSpUoBPbfuaXv7WI7jOKmpqe64devWYo7bWIbW6tWrRfb2oTdt2pSt527RokW2Ho/QGzdunMhvvvmmyCNGjBBZ76vWfefchCtqAAAMo1ADAGAYhRoAAMMivkddsGBBkb23zps3b164l4Ms+P3330VOSEgQOV++fCJ/+OGH7jg2Njag19J9runTp4tcoUIFd7xs2bKAnhu+JScni9yqVSuRd+3aJfLly5eD9tr6tRAely5dElnvX580aZI7/uijj8RcmzZtRNZn/gf7vO2cxBU1AACGUagBADCMQg0AgGGR8yY+IsYvv/wisj7HWd87WPeVa9So4fdrefvZjuM4b731ls+v9/bMEFwHDx4U+fvvvxc5mD1pTX9fhw8fHrLXwlWTJ08W2XuPccdxnPj4eHdct25dMRdJPejMcEUNAIBhFGoAAAyLnvcO/JCSkpLTS4gaV65cEfnrr792x3qrjP5afSzsqlWrRC5Tpow77tmzp5g7f/68yHPmzBE5PT1d5IEDB4r8wAMPOAiNBg0aiPzBBx+I/Pjjj4usb3uYHfp2pgiPF198UWTvLWodx3F69+7tjqPprW6NK2oAAAyjUAMAYBiFGgAAw/Kk66ZchOvVq5c71keIXn/99SKfPHkyDCuKTrqv7Os2g/pH9JZbbhF59+7dGT62efPmIu/bt0/kI0eOiOztbzuO4xw9ejTD50Z47dixQ2R9tKxXWlqayB06dBD51KlTIvft21dkfZQlQuO+++4TeeXKlSJXrFjRHScmJoo5/XcgknFFDQCAYRRqAAAMo1ADAGBY1PWoFy5c6I67desm5uhRh866detEbtasmcjeW1WWKFFCzH311VciFylSROQBAwaIvHTp0gzXoX/c9b5NnW+++WaRt2zZkuE6YYf+Pk+dOlXk559/XuRatWqJvH79endcrFixIK8ush06dMgdly9fXszFxMSIrPfCv//++yL369fPHRctWlTM7d27V+TSpUsHvNbcgitqAAAMo1ADAGAYhRoAAMOi7vDUypUrZzh38eJFkU+fPi0yvaqsGz9+vMjVqlUT2XubQb23MjP6Vnnevtfy5csDei7d23z44YdFpi+dO+h91LonrRUoUEBk/VkFXJWamipyu3btRPb2jhctWiTm/vKXv4hcqFAhkb3nXDiO7FHrffN6HfSoAQBAjqBQAwBgGIUaAADDoq5Hrffxeen+5KVLl0K9nKjRtWtXkVu3bi2y3iMZCN278u6B1dasWSNy1apVfT633luP3OHtt98O6OsHDx4scnZ+HiNdzZo1RdbnpnvvoaB70pl57733Mpzr0qWLyOXKlQvouXMzrqgBADCMQg0AgGEUagAADIu6s7696tevL/L27dtFfvXVV0UeOXJkqJcEP5w/f17khIQEkUeNGuWOa9euLeZ27twZuoXhmrOb4+PjRe7Tp487/vOf/xy019V7avUZ07qPqulz/YsXLx6UdUWi2bNni/zCCy+IfPbsWb+f69ZbbxV5165dInvPW1ixYoWY09/jSMYVNQAAhlGoAQAwLOq2Z3l17NhR5IMHD4o8bNiwcC4Hfvroo49EHj16tMg33XSTO9a310RoDR06VOS5c+eK7G0vLV68WMzdcMMNIuvjWo8cOSKy93aKL7/8spjL7K1u3S7Rt05FxrztC8e59vjVjRs3uuMlS5b4fK7k5GSRu3fvLvK4cePcccmSJQNaZyThihoAAMMo1AAAGEahBgDAsKjenqV7m/rYwRMnTojMre9yhr7daKNGjUT+8ccfRZ4wYYI7fu6550K2LlzrwIEDIut/f1+3Ha1evbrIDRs2FDkxMVFk/XPhpX9X69WrJ/KGDRtEzp8/f4bPBeQ0rqgBADCMQg0AgGEUagAADIvqfdSa3nu5adMmkXXPDOHRpEkTkfft2ydy//79RaYvnXOqVKkisr7NofdI0YceekjM6e+rzoHQe263bt2a5ecCchpX1AAAGEahBgDAMAo1AACGRfU+6goVKoickpIi8uHDh0UuVapUyNeEa82aNUvkp59+WmR9njefJbDr8uXL7njBggU+v1Z/RmTy5MkZfq2+LeWOHTtEjqZbIiLycEUNAIBhFGoAAAyjUAMAYFhU96j1flu911KfS1ysWLGQrwkAAC+uqAEAMIxCDQCAYRRqAAAMi+oeNQAA1nFFDQCAYRRqAAAMo1ADAGAYhRoAAMMo1AAAGEahBgDAMAo1AACGUagBADCMQg0AgGEUagAADKNQAwBgGIUaAADDKNQAABhGoQYAwDAKNQAAhlGoAQAwjEINAIBhFGoAAAyjUAMAYBiFGgAAw/4Pl0XiurxH+dsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 500x500 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "for k in range(12):\n",
    "    plt.subplot(3,4,k+1)\n",
    "    plt.imshow(X_train[k], cmap=\"Greys\")\n",
    "    plt.axis(\"off\")\n",
    "    \n",
    "plt.tight_layout()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "faa97625",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## plotting only single digit\n",
    "def plot_digit(image_data):\n",
    "    image = image_data.reshape(28,28)\n",
    "    plt.imshow(image, cmap = \"binary\")\n",
    "    plt.axis(\"off\")\n",
    "some_digit = X_train[0]\n",
    "plot_digit(some_digit)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9510471b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x24a335e6c50>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## plotting first digit of validation sets \n",
    "\n",
    "plt.imshow(X_valid[0], cmap=\"Greys\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c87bf6af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_valid[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f1b9e9b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "## prepeocessing of data\n",
    "\n",
    "X_train = X_train.reshape(60000, 784).astype(\"float32\")\n",
    "X_valid = X_valid.reshape(10000, 784).astype(\"float32\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "afb664f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "## to normalise the data we reshape the data \n",
    "\n",
    "X_train /= 255\n",
    "X_valid /=255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1ecf4162",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.01176471, 0.07058824, 0.07058824,\n",
       "       0.07058824, 0.49411765, 0.53333336, 0.6862745 , 0.10196079,\n",
       "       0.6509804 , 1.        , 0.96862745, 0.49803922, 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.11764706, 0.14117648, 0.36862746, 0.6039216 ,\n",
       "       0.6666667 , 0.99215686, 0.99215686, 0.99215686, 0.99215686,\n",
       "       0.99215686, 0.88235295, 0.6745098 , 0.99215686, 0.9490196 ,\n",
       "       0.7647059 , 0.2509804 , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.19215687, 0.93333334,\n",
       "       0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.99215686,\n",
       "       0.99215686, 0.99215686, 0.99215686, 0.9843137 , 0.3647059 ,\n",
       "       0.32156864, 0.32156864, 0.21960784, 0.15294118, 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.07058824, 0.85882354, 0.99215686, 0.99215686,\n",
       "       0.99215686, 0.99215686, 0.99215686, 0.7764706 , 0.7137255 ,\n",
       "       0.96862745, 0.94509804, 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.3137255 , 0.6117647 , 0.41960785, 0.99215686, 0.99215686,\n",
       "       0.8039216 , 0.04313726, 0.        , 0.16862746, 0.6039216 ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.05490196,\n",
       "       0.00392157, 0.6039216 , 0.99215686, 0.3529412 , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.54509807,\n",
       "       0.99215686, 0.74509805, 0.00784314, 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.04313726, 0.74509805, 0.99215686,\n",
       "       0.27450982, 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.13725491, 0.94509804, 0.88235295, 0.627451  ,\n",
       "       0.42352942, 0.00392157, 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.31764707, 0.9411765 , 0.99215686, 0.99215686, 0.46666667,\n",
       "       0.09803922, 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.1764706 ,\n",
       "       0.7294118 , 0.99215686, 0.99215686, 0.5882353 , 0.10588235,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.0627451 , 0.3647059 ,\n",
       "       0.9882353 , 0.99215686, 0.73333335, 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.9764706 , 0.99215686,\n",
       "       0.9764706 , 0.2509804 , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.18039216, 0.50980395,\n",
       "       0.7176471 , 0.99215686, 0.99215686, 0.8117647 , 0.00784314,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.15294118,\n",
       "       0.5803922 , 0.8980392 , 0.99215686, 0.99215686, 0.99215686,\n",
       "       0.98039216, 0.7137255 , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.09411765, 0.44705883, 0.8666667 , 0.99215686, 0.99215686,\n",
       "       0.99215686, 0.99215686, 0.7882353 , 0.30588236, 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.09019608, 0.25882354, 0.8352941 , 0.99215686,\n",
       "       0.99215686, 0.99215686, 0.99215686, 0.7764706 , 0.31764707,\n",
       "       0.00784314, 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.07058824, 0.67058825, 0.85882354,\n",
       "       0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.7647059 ,\n",
       "       0.3137255 , 0.03529412, 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.21568628, 0.6745098 ,\n",
       "       0.8862745 , 0.99215686, 0.99215686, 0.99215686, 0.99215686,\n",
       "       0.95686275, 0.52156866, 0.04313726, 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.53333336, 0.99215686, 0.99215686, 0.99215686,\n",
       "       0.83137256, 0.5294118 , 0.5176471 , 0.0627451 , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        ], dtype=float32)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9b112ccf",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_classes = 10 \n",
    "y_train = to_categorical(y_train, n_classes)\n",
    "y_valid = to_categorical(y_valid, n_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "786c2bd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 1., 0., 0., 0., 0.], dtype=float32)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d165561e",
   "metadata": {},
   "source": [
    "### design NN arch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a826308b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\ASUS\\.anaconda\\anaconda\\Lib\\site-packages\\keras\\src\\backend.py:873: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "## hidden layer\n",
    "model.add(Dense(64, activation=\"sigmoid\", input_shape = (784,)))\n",
    "\n",
    "## output layer\n",
    "\n",
    "model.add(Dense(10, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d39dca58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 64)                50240     \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 10)                650       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 50890 (198.79 KB)\n",
      "Trainable params: 50890 (198.79 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e2f3de0",
   "metadata": {},
   "source": [
    "# compile model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "61fbd2a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss= 'mean_squared_error', optimizer = SGD(learning_rate = 0.01), metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd0d1f91",
   "metadata": {},
   "source": [
    "# train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4f3ac842",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "WARNING:tensorflow:From C:\\Users\\ASUS\\.anaconda\\anaconda\\Lib\\site-packages\\keras\\src\\utils\\tf_utils.py:492: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\ASUS\\.anaconda\\anaconda\\Lib\\site-packages\\keras\\src\\utils\\tf_utils.py:492: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\ASUS\\.anaconda\\anaconda\\Lib\\site-packages\\keras\\src\\engine\\base_layer_utils.py:384: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\ASUS\\.anaconda\\anaconda\\Lib\\site-packages\\keras\\src\\engine\\base_layer_utils.py:384: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "469/469 [==============================] - 3s 4ms/step - loss: 0.0916 - accuracy: 0.1144 - val_loss: 0.0909 - val_accuracy: 0.1332\n",
      "Epoch 2/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0907 - accuracy: 0.1362 - val_loss: 0.0902 - val_accuracy: 0.1559\n",
      "Epoch 3/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0901 - accuracy: 0.1662 - val_loss: 0.0897 - val_accuracy: 0.1940\n",
      "Epoch 4/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0896 - accuracy: 0.2097 - val_loss: 0.0892 - val_accuracy: 0.2383\n",
      "Epoch 5/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0892 - accuracy: 0.2533 - val_loss: 0.0888 - val_accuracy: 0.2810\n",
      "Epoch 6/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0887 - accuracy: 0.2832 - val_loss: 0.0884 - val_accuracy: 0.3068\n",
      "Epoch 7/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0883 - accuracy: 0.3045 - val_loss: 0.0880 - val_accuracy: 0.3237\n",
      "Epoch 8/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0879 - accuracy: 0.3183 - val_loss: 0.0876 - val_accuracy: 0.3357\n",
      "Epoch 9/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0875 - accuracy: 0.3293 - val_loss: 0.0872 - val_accuracy: 0.3473\n",
      "Epoch 10/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0871 - accuracy: 0.3368 - val_loss: 0.0868 - val_accuracy: 0.3540\n",
      "Epoch 11/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0867 - accuracy: 0.3427 - val_loss: 0.0864 - val_accuracy: 0.3591\n",
      "Epoch 12/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0864 - accuracy: 0.3460 - val_loss: 0.0860 - val_accuracy: 0.3629\n",
      "Epoch 13/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0860 - accuracy: 0.3492 - val_loss: 0.0856 - val_accuracy: 0.3640\n",
      "Epoch 14/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0856 - accuracy: 0.3532 - val_loss: 0.0852 - val_accuracy: 0.3664\n",
      "Epoch 15/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0852 - accuracy: 0.3543 - val_loss: 0.0848 - val_accuracy: 0.3685\n",
      "Epoch 16/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0848 - accuracy: 0.3563 - val_loss: 0.0844 - val_accuracy: 0.3705\n",
      "Epoch 17/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0844 - accuracy: 0.3583 - val_loss: 0.0840 - val_accuracy: 0.3727\n",
      "Epoch 18/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0840 - accuracy: 0.3614 - val_loss: 0.0836 - val_accuracy: 0.3752\n",
      "Epoch 19/200\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0836 - accuracy: 0.3646 - val_loss: 0.0832 - val_accuracy: 0.3780\n",
      "Epoch 20/200\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0831 - accuracy: 0.3677 - val_loss: 0.0827 - val_accuracy: 0.3819\n",
      "Epoch 21/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0827 - accuracy: 0.3717 - val_loss: 0.0823 - val_accuracy: 0.3857\n",
      "Epoch 22/200\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0823 - accuracy: 0.3753 - val_loss: 0.0819 - val_accuracy: 0.3904\n",
      "Epoch 23/200\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0818 - accuracy: 0.3813 - val_loss: 0.0814 - val_accuracy: 0.3945\n",
      "Epoch 24/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0814 - accuracy: 0.3859 - val_loss: 0.0810 - val_accuracy: 0.3994\n",
      "Epoch 25/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0810 - accuracy: 0.3904 - val_loss: 0.0805 - val_accuracy: 0.4042\n",
      "Epoch 26/200\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0805 - accuracy: 0.3962 - val_loss: 0.0801 - val_accuracy: 0.4106\n",
      "Epoch 27/200\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0801 - accuracy: 0.4007 - val_loss: 0.0796 - val_accuracy: 0.4164\n",
      "Epoch 28/200\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0796 - accuracy: 0.4086 - val_loss: 0.0792 - val_accuracy: 0.4233\n",
      "Epoch 29/200\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0792 - accuracy: 0.4148 - val_loss: 0.0787 - val_accuracy: 0.4294\n",
      "Epoch 30/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0787 - accuracy: 0.4220 - val_loss: 0.0782 - val_accuracy: 0.4344\n",
      "Epoch 31/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0783 - accuracy: 0.4277 - val_loss: 0.0778 - val_accuracy: 0.4404\n",
      "Epoch 32/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0778 - accuracy: 0.4346 - val_loss: 0.0773 - val_accuracy: 0.4471\n",
      "Epoch 33/200\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0773 - accuracy: 0.4402 - val_loss: 0.0768 - val_accuracy: 0.4513\n",
      "Epoch 34/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0769 - accuracy: 0.4450 - val_loss: 0.0763 - val_accuracy: 0.4561\n",
      "Epoch 35/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0764 - accuracy: 0.4507 - val_loss: 0.0759 - val_accuracy: 0.4634\n",
      "Epoch 36/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0759 - accuracy: 0.4579 - val_loss: 0.0754 - val_accuracy: 0.4672\n",
      "Epoch 37/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0755 - accuracy: 0.4624 - val_loss: 0.0749 - val_accuracy: 0.4733\n",
      "Epoch 38/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0750 - accuracy: 0.4676 - val_loss: 0.0745 - val_accuracy: 0.4783\n",
      "Epoch 39/200\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0746 - accuracy: 0.4736 - val_loss: 0.0740 - val_accuracy: 0.4825\n",
      "Epoch 40/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0741 - accuracy: 0.4792 - val_loss: 0.0735 - val_accuracy: 0.4880\n",
      "Epoch 41/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0736 - accuracy: 0.4851 - val_loss: 0.0731 - val_accuracy: 0.4937\n",
      "Epoch 42/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0732 - accuracy: 0.4891 - val_loss: 0.0726 - val_accuracy: 0.4979\n",
      "Epoch 43/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0727 - accuracy: 0.4949 - val_loss: 0.0721 - val_accuracy: 0.5035\n",
      "Epoch 44/200\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0723 - accuracy: 0.5002 - val_loss: 0.0717 - val_accuracy: 0.5076\n",
      "Epoch 45/200\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0718 - accuracy: 0.5048 - val_loss: 0.0712 - val_accuracy: 0.5121\n",
      "Epoch 46/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0714 - accuracy: 0.5103 - val_loss: 0.0707 - val_accuracy: 0.5173\n",
      "Epoch 47/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0709 - accuracy: 0.5156 - val_loss: 0.0703 - val_accuracy: 0.5217\n",
      "Epoch 48/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0705 - accuracy: 0.5207 - val_loss: 0.0698 - val_accuracy: 0.5260\n",
      "Epoch 49/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0700 - accuracy: 0.5268 - val_loss: 0.0694 - val_accuracy: 0.5308\n",
      "Epoch 50/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0696 - accuracy: 0.5314 - val_loss: 0.0689 - val_accuracy: 0.5363\n",
      "Epoch 51/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0691 - accuracy: 0.5378 - val_loss: 0.0685 - val_accuracy: 0.5415\n",
      "Epoch 52/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0687 - accuracy: 0.5433 - val_loss: 0.0680 - val_accuracy: 0.5480\n",
      "Epoch 53/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0682 - accuracy: 0.5492 - val_loss: 0.0676 - val_accuracy: 0.5535\n",
      "Epoch 54/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0678 - accuracy: 0.5551 - val_loss: 0.0671 - val_accuracy: 0.5588\n",
      "Epoch 55/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0673 - accuracy: 0.5607 - val_loss: 0.0667 - val_accuracy: 0.5659\n",
      "Epoch 56/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0669 - accuracy: 0.5669 - val_loss: 0.0662 - val_accuracy: 0.5712\n",
      "Epoch 57/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0664 - accuracy: 0.5730 - val_loss: 0.0658 - val_accuracy: 0.5779\n",
      "Epoch 58/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0660 - accuracy: 0.5791 - val_loss: 0.0653 - val_accuracy: 0.5840\n",
      "Epoch 59/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0656 - accuracy: 0.5847 - val_loss: 0.0649 - val_accuracy: 0.5883\n",
      "Epoch 60/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0651 - accuracy: 0.5896 - val_loss: 0.0644 - val_accuracy: 0.5950\n",
      "Epoch 61/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0647 - accuracy: 0.5957 - val_loss: 0.0640 - val_accuracy: 0.5998\n",
      "Epoch 62/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0642 - accuracy: 0.6017 - val_loss: 0.0635 - val_accuracy: 0.6050\n",
      "Epoch 63/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0638 - accuracy: 0.6069 - val_loss: 0.0631 - val_accuracy: 0.6104\n",
      "Epoch 64/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0634 - accuracy: 0.6115 - val_loss: 0.0627 - val_accuracy: 0.6170\n",
      "Epoch 65/200\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0629 - accuracy: 0.6162 - val_loss: 0.0622 - val_accuracy: 0.6234\n",
      "Epoch 66/200\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0625 - accuracy: 0.6215 - val_loss: 0.0618 - val_accuracy: 0.6282\n",
      "Epoch 67/200\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0620 - accuracy: 0.6265 - val_loss: 0.0613 - val_accuracy: 0.6328\n",
      "Epoch 68/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0616 - accuracy: 0.6309 - val_loss: 0.0609 - val_accuracy: 0.6370\n",
      "Epoch 69/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0612 - accuracy: 0.6355 - val_loss: 0.0604 - val_accuracy: 0.6423\n",
      "Epoch 70/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0607 - accuracy: 0.6392 - val_loss: 0.0600 - val_accuracy: 0.6474\n",
      "Epoch 71/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0603 - accuracy: 0.6436 - val_loss: 0.0596 - val_accuracy: 0.6515\n",
      "Epoch 72/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0599 - accuracy: 0.6482 - val_loss: 0.0591 - val_accuracy: 0.6560\n",
      "Epoch 73/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0594 - accuracy: 0.6515 - val_loss: 0.0587 - val_accuracy: 0.6605\n",
      "Epoch 74/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0590 - accuracy: 0.6554 - val_loss: 0.0583 - val_accuracy: 0.6649\n",
      "Epoch 75/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0586 - accuracy: 0.6587 - val_loss: 0.0578 - val_accuracy: 0.6688\n",
      "Epoch 76/200\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0582 - accuracy: 0.6629 - val_loss: 0.0574 - val_accuracy: 0.6730\n",
      "Epoch 77/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0577 - accuracy: 0.6664 - val_loss: 0.0570 - val_accuracy: 0.6763\n",
      "Epoch 78/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0573 - accuracy: 0.6704 - val_loss: 0.0566 - val_accuracy: 0.6805\n",
      "Epoch 79/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0569 - accuracy: 0.6739 - val_loss: 0.0561 - val_accuracy: 0.6835\n",
      "Epoch 80/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0565 - accuracy: 0.6771 - val_loss: 0.0557 - val_accuracy: 0.6874\n",
      "Epoch 81/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0561 - accuracy: 0.6805 - val_loss: 0.0553 - val_accuracy: 0.6920\n",
      "Epoch 82/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0556 - accuracy: 0.6847 - val_loss: 0.0549 - val_accuracy: 0.6950\n",
      "Epoch 83/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0552 - accuracy: 0.6876 - val_loss: 0.0545 - val_accuracy: 0.6983\n",
      "Epoch 84/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0548 - accuracy: 0.6905 - val_loss: 0.0540 - val_accuracy: 0.7030\n",
      "Epoch 85/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0544 - accuracy: 0.6945 - val_loss: 0.0536 - val_accuracy: 0.7064\n",
      "Epoch 86/200\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0540 - accuracy: 0.6976 - val_loss: 0.0532 - val_accuracy: 0.7081\n",
      "Epoch 87/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0536 - accuracy: 0.7006 - val_loss: 0.0528 - val_accuracy: 0.7107\n",
      "Epoch 88/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0532 - accuracy: 0.7031 - val_loss: 0.0524 - val_accuracy: 0.7138\n",
      "Epoch 89/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0528 - accuracy: 0.7057 - val_loss: 0.0520 - val_accuracy: 0.7179\n",
      "Epoch 90/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0525 - accuracy: 0.7094 - val_loss: 0.0516 - val_accuracy: 0.7225\n",
      "Epoch 91/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0521 - accuracy: 0.7132 - val_loss: 0.0513 - val_accuracy: 0.7257\n",
      "Epoch 92/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0517 - accuracy: 0.7155 - val_loss: 0.0509 - val_accuracy: 0.7291\n",
      "Epoch 93/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0513 - accuracy: 0.7186 - val_loss: 0.0505 - val_accuracy: 0.7320\n",
      "Epoch 94/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0509 - accuracy: 0.7211 - val_loss: 0.0501 - val_accuracy: 0.7344\n",
      "Epoch 95/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0506 - accuracy: 0.7240 - val_loss: 0.0497 - val_accuracy: 0.7380\n",
      "Epoch 96/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0502 - accuracy: 0.7271 - val_loss: 0.0494 - val_accuracy: 0.7408\n",
      "Epoch 97/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0498 - accuracy: 0.7292 - val_loss: 0.0490 - val_accuracy: 0.7428\n",
      "Epoch 98/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0495 - accuracy: 0.7318 - val_loss: 0.0486 - val_accuracy: 0.7445\n",
      "Epoch 99/200\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0491 - accuracy: 0.7341 - val_loss: 0.0483 - val_accuracy: 0.7466\n",
      "Epoch 100/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0488 - accuracy: 0.7365 - val_loss: 0.0479 - val_accuracy: 0.7476\n",
      "Epoch 101/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0484 - accuracy: 0.7380 - val_loss: 0.0476 - val_accuracy: 0.7493\n",
      "Epoch 102/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0481 - accuracy: 0.7401 - val_loss: 0.0472 - val_accuracy: 0.7523\n",
      "Epoch 103/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0478 - accuracy: 0.7423 - val_loss: 0.0469 - val_accuracy: 0.7542\n",
      "Epoch 104/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0474 - accuracy: 0.7445 - val_loss: 0.0466 - val_accuracy: 0.7564\n",
      "Epoch 105/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0471 - accuracy: 0.7459 - val_loss: 0.0462 - val_accuracy: 0.7589\n",
      "Epoch 106/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0468 - accuracy: 0.7479 - val_loss: 0.0459 - val_accuracy: 0.7600\n",
      "Epoch 107/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0465 - accuracy: 0.7495 - val_loss: 0.0456 - val_accuracy: 0.7609\n",
      "Epoch 108/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0461 - accuracy: 0.7514 - val_loss: 0.0453 - val_accuracy: 0.7619\n",
      "Epoch 109/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0458 - accuracy: 0.7534 - val_loss: 0.0450 - val_accuracy: 0.7630\n",
      "Epoch 110/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0455 - accuracy: 0.7545 - val_loss: 0.0446 - val_accuracy: 0.7648\n",
      "Epoch 111/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0452 - accuracy: 0.7561 - val_loss: 0.0443 - val_accuracy: 0.7664\n",
      "Epoch 112/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0449 - accuracy: 0.7575 - val_loss: 0.0440 - val_accuracy: 0.7678\n",
      "Epoch 113/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0446 - accuracy: 0.7590 - val_loss: 0.0437 - val_accuracy: 0.7697\n",
      "Epoch 114/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0443 - accuracy: 0.7606 - val_loss: 0.0434 - val_accuracy: 0.7719\n",
      "Epoch 115/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0440 - accuracy: 0.7620 - val_loss: 0.0431 - val_accuracy: 0.7741\n",
      "Epoch 116/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0437 - accuracy: 0.7634 - val_loss: 0.0428 - val_accuracy: 0.7749\n",
      "Epoch 117/200\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0435 - accuracy: 0.7647 - val_loss: 0.0426 - val_accuracy: 0.7767\n",
      "Epoch 118/200\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0432 - accuracy: 0.7661 - val_loss: 0.0423 - val_accuracy: 0.7779\n",
      "Epoch 119/200\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0429 - accuracy: 0.7672 - val_loss: 0.0420 - val_accuracy: 0.7794\n",
      "Epoch 120/200\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0426 - accuracy: 0.7689 - val_loss: 0.0417 - val_accuracy: 0.7806\n",
      "Epoch 121/200\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0424 - accuracy: 0.7702 - val_loss: 0.0415 - val_accuracy: 0.7819\n",
      "Epoch 122/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0421 - accuracy: 0.7717 - val_loss: 0.0412 - val_accuracy: 0.7834\n",
      "Epoch 123/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0418 - accuracy: 0.7730 - val_loss: 0.0409 - val_accuracy: 0.7858\n",
      "Epoch 124/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0416 - accuracy: 0.7742 - val_loss: 0.0407 - val_accuracy: 0.7871\n",
      "Epoch 125/200\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0413 - accuracy: 0.7757 - val_loss: 0.0404 - val_accuracy: 0.7883\n",
      "Epoch 126/200\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0411 - accuracy: 0.7771 - val_loss: 0.0401 - val_accuracy: 0.7908\n",
      "Epoch 127/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0408 - accuracy: 0.7787 - val_loss: 0.0399 - val_accuracy: 0.7925\n",
      "Epoch 128/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0406 - accuracy: 0.7800 - val_loss: 0.0396 - val_accuracy: 0.7939\n",
      "Epoch 129/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0403 - accuracy: 0.7817 - val_loss: 0.0394 - val_accuracy: 0.7949\n",
      "Epoch 130/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0401 - accuracy: 0.7832 - val_loss: 0.0391 - val_accuracy: 0.7959\n",
      "Epoch 131/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0398 - accuracy: 0.7850 - val_loss: 0.0389 - val_accuracy: 0.7975\n",
      "Epoch 132/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0396 - accuracy: 0.7864 - val_loss: 0.0387 - val_accuracy: 0.7990\n",
      "Epoch 133/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0394 - accuracy: 0.7879 - val_loss: 0.0384 - val_accuracy: 0.8013\n",
      "Epoch 134/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0391 - accuracy: 0.7899 - val_loss: 0.0382 - val_accuracy: 0.8026\n",
      "Epoch 135/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0389 - accuracy: 0.7917 - val_loss: 0.0380 - val_accuracy: 0.8043\n",
      "Epoch 136/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0387 - accuracy: 0.7931 - val_loss: 0.0377 - val_accuracy: 0.8062\n",
      "Epoch 137/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0384 - accuracy: 0.7949 - val_loss: 0.0375 - val_accuracy: 0.8079\n",
      "Epoch 138/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0382 - accuracy: 0.7970 - val_loss: 0.0373 - val_accuracy: 0.8088\n",
      "Epoch 139/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0380 - accuracy: 0.7992 - val_loss: 0.0371 - val_accuracy: 0.8110\n",
      "Epoch 140/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0378 - accuracy: 0.8005 - val_loss: 0.0368 - val_accuracy: 0.8127\n",
      "Epoch 141/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0376 - accuracy: 0.8021 - val_loss: 0.0366 - val_accuracy: 0.8147\n",
      "Epoch 142/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0374 - accuracy: 0.8042 - val_loss: 0.0364 - val_accuracy: 0.8166\n",
      "Epoch 143/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0372 - accuracy: 0.8061 - val_loss: 0.0362 - val_accuracy: 0.8175\n",
      "Epoch 144/200\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0369 - accuracy: 0.8077 - val_loss: 0.0360 - val_accuracy: 0.8185\n",
      "Epoch 145/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0367 - accuracy: 0.8092 - val_loss: 0.0358 - val_accuracy: 0.8200\n",
      "Epoch 146/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0365 - accuracy: 0.8109 - val_loss: 0.0356 - val_accuracy: 0.8210\n",
      "Epoch 147/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0363 - accuracy: 0.8129 - val_loss: 0.0354 - val_accuracy: 0.8224\n",
      "Epoch 148/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0361 - accuracy: 0.8145 - val_loss: 0.0352 - val_accuracy: 0.8235\n",
      "Epoch 149/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0359 - accuracy: 0.8163 - val_loss: 0.0350 - val_accuracy: 0.8251\n",
      "Epoch 150/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0357 - accuracy: 0.8176 - val_loss: 0.0348 - val_accuracy: 0.8262\n",
      "Epoch 151/200\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0355 - accuracy: 0.8191 - val_loss: 0.0346 - val_accuracy: 0.8280\n",
      "Epoch 152/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0354 - accuracy: 0.8206 - val_loss: 0.0344 - val_accuracy: 0.8293\n",
      "Epoch 153/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0352 - accuracy: 0.8218 - val_loss: 0.0342 - val_accuracy: 0.8313\n",
      "Epoch 154/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0350 - accuracy: 0.8233 - val_loss: 0.0340 - val_accuracy: 0.8322\n",
      "Epoch 155/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0348 - accuracy: 0.8246 - val_loss: 0.0338 - val_accuracy: 0.8331\n",
      "Epoch 156/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0346 - accuracy: 0.8258 - val_loss: 0.0336 - val_accuracy: 0.8350\n",
      "Epoch 157/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0344 - accuracy: 0.8273 - val_loss: 0.0334 - val_accuracy: 0.8366\n",
      "Epoch 158/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0342 - accuracy: 0.8285 - val_loss: 0.0333 - val_accuracy: 0.8380\n",
      "Epoch 159/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0341 - accuracy: 0.8294 - val_loss: 0.0331 - val_accuracy: 0.8398\n",
      "Epoch 160/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0339 - accuracy: 0.8305 - val_loss: 0.0329 - val_accuracy: 0.8414\n",
      "Epoch 161/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0337 - accuracy: 0.8316 - val_loss: 0.0327 - val_accuracy: 0.8422\n",
      "Epoch 162/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0335 - accuracy: 0.8328 - val_loss: 0.0326 - val_accuracy: 0.8433\n",
      "Epoch 163/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0334 - accuracy: 0.8339 - val_loss: 0.0324 - val_accuracy: 0.8444\n",
      "Epoch 164/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0332 - accuracy: 0.8345 - val_loss: 0.0322 - val_accuracy: 0.8456\n",
      "Epoch 165/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0330 - accuracy: 0.8353 - val_loss: 0.0320 - val_accuracy: 0.8468\n",
      "Epoch 166/200\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0329 - accuracy: 0.8368 - val_loss: 0.0319 - val_accuracy: 0.8473\n",
      "Epoch 167/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0327 - accuracy: 0.8375 - val_loss: 0.0317 - val_accuracy: 0.8481\n",
      "Epoch 168/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0326 - accuracy: 0.8385 - val_loss: 0.0316 - val_accuracy: 0.8490\n",
      "Epoch 169/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0324 - accuracy: 0.8392 - val_loss: 0.0314 - val_accuracy: 0.8505\n",
      "Epoch 170/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0322 - accuracy: 0.8399 - val_loss: 0.0312 - val_accuracy: 0.8516\n",
      "Epoch 171/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0321 - accuracy: 0.8405 - val_loss: 0.0311 - val_accuracy: 0.8523\n",
      "Epoch 172/200\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0319 - accuracy: 0.8413 - val_loss: 0.0309 - val_accuracy: 0.8530\n",
      "Epoch 173/200\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0318 - accuracy: 0.8423 - val_loss: 0.0308 - val_accuracy: 0.8537\n",
      "Epoch 174/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0316 - accuracy: 0.8432 - val_loss: 0.0306 - val_accuracy: 0.8543\n",
      "Epoch 175/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0315 - accuracy: 0.8441 - val_loss: 0.0305 - val_accuracy: 0.8548\n",
      "Epoch 176/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0313 - accuracy: 0.8448 - val_loss: 0.0303 - val_accuracy: 0.8553\n",
      "Epoch 177/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0312 - accuracy: 0.8454 - val_loss: 0.0302 - val_accuracy: 0.8563\n",
      "Epoch 178/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0310 - accuracy: 0.8463 - val_loss: 0.0300 - val_accuracy: 0.8567\n",
      "Epoch 179/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0309 - accuracy: 0.8472 - val_loss: 0.0299 - val_accuracy: 0.8573\n",
      "Epoch 180/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0308 - accuracy: 0.8481 - val_loss: 0.0297 - val_accuracy: 0.8577\n",
      "Epoch 181/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0306 - accuracy: 0.8490 - val_loss: 0.0296 - val_accuracy: 0.8585\n",
      "Epoch 182/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0305 - accuracy: 0.8497 - val_loss: 0.0295 - val_accuracy: 0.8590\n",
      "Epoch 183/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0303 - accuracy: 0.8504 - val_loss: 0.0293 - val_accuracy: 0.8601\n",
      "Epoch 184/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0302 - accuracy: 0.8513 - val_loss: 0.0292 - val_accuracy: 0.8608\n",
      "Epoch 185/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0301 - accuracy: 0.8519 - val_loss: 0.0291 - val_accuracy: 0.8612\n",
      "Epoch 186/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0299 - accuracy: 0.8522 - val_loss: 0.0289 - val_accuracy: 0.8617\n",
      "Epoch 187/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0298 - accuracy: 0.8528 - val_loss: 0.0288 - val_accuracy: 0.8624\n",
      "Epoch 188/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0297 - accuracy: 0.8536 - val_loss: 0.0287 - val_accuracy: 0.8627\n",
      "Epoch 189/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0296 - accuracy: 0.8541 - val_loss: 0.0285 - val_accuracy: 0.8629\n",
      "Epoch 190/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0294 - accuracy: 0.8546 - val_loss: 0.0284 - val_accuracy: 0.8632\n",
      "Epoch 191/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0293 - accuracy: 0.8553 - val_loss: 0.0283 - val_accuracy: 0.8640\n",
      "Epoch 192/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0292 - accuracy: 0.8556 - val_loss: 0.0282 - val_accuracy: 0.8646\n",
      "Epoch 193/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0291 - accuracy: 0.8561 - val_loss: 0.0281 - val_accuracy: 0.8651\n",
      "Epoch 194/200\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0289 - accuracy: 0.8566 - val_loss: 0.0279 - val_accuracy: 0.8655\n",
      "Epoch 195/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0288 - accuracy: 0.8571 - val_loss: 0.0278 - val_accuracy: 0.8664\n",
      "Epoch 196/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0287 - accuracy: 0.8575 - val_loss: 0.0277 - val_accuracy: 0.8669\n",
      "Epoch 197/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0286 - accuracy: 0.8579 - val_loss: 0.0276 - val_accuracy: 0.8679\n",
      "Epoch 198/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0285 - accuracy: 0.8583 - val_loss: 0.0275 - val_accuracy: 0.8681\n",
      "Epoch 199/200\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 0.0284 - accuracy: 0.8586 - val_loss: 0.0274 - val_accuracy: 0.8684\n",
      "Epoch 200/200\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 0.0283 - accuracy: 0.8590 - val_loss: 0.0272 - val_accuracy: 0.8688\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x24a3496b4d0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train, batch_size=128, epochs=200, verbose=1, validation_data=(X_valid, y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "54e429ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 2ms/step - loss: 0.0272 - accuracy: 0.8688\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.027239080518484116, 0.8687999844551086]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_valid, y_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1611d2e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_0 = X_valid[0].reshape(1, 784)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "69e9a538",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 118ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.00688593, 0.00112572, 0.00173401, 0.01063465, 0.00721964,\n",
       "        0.0200084 , 0.00153135, 0.89983237, 0.00499124, 0.04603665]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(valid_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8a9584af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 39ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([7], dtype=int64)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(valid_0).argmax(axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a235206c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}