99. MNIST Classification using Tensorflow.ipynb

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  {
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   "id": "ed7133f0",
   "metadata": {},
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers, models\n",
    "from keras.callbacks import EarlyStopping\n",
    "\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "621d845d",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.read_csv('MNIST/train.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5a60142c",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "       label  pixel0  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  \\\n",
       "0          1       0       0       0       0       0       0       0       0   \n",
       "1          0       0       0       0       0       0       0       0       0   \n",
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       "4          0       0       0       0       0       0       0       0       0   \n",
       "...      ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "41995      0       0       0       0       0       0       0       0       0   \n",
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       "41999      9       0       0       0       0       0       0       0       0   \n",
       "\n",
       "       pixel8  ...  pixel774  pixel775  pixel776  pixel777  pixel778  \\\n",
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       "\n",
       "       pixel779  pixel780  pixel781  pixel782  pixel783  \n",
       "0             0         0         0         0         0  \n",
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       "...         ...       ...       ...       ...       ...  \n",
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       "\n",
       "[42000 rows x 785 columns]"
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    }
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Get the label counts from the DataFrame\n",
    "label_counts = train_df['label'].value_counts()\n",
    "\n",
    "# Create a bar chart\n",
    "plt.bar(label_counts.index, label_counts.values)\n",
    "\n",
    "# Set labels and title\n",
    "plt.xlabel('Label')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Distribution of Labels')\n",
    "\n",
    "# Set x-axis ticks\n",
    "plt.xticks(list(label_counts.index))\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7663201f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def image_row_reshaper(df, row_number, visualize=False, test_df = False):\n",
    "#  visualize --> If True then displays the raw data into 28 by 28 pixels image.\n",
    "#                If False then returns '28 by 28 pixels reshaped image data' and 'corresponding label'\n",
    "    raw_image_data = df.loc[row_number,] \n",
    "    \n",
    "    if test_df == False:\n",
    "        image_label = raw_image_data[0] #This is the label of the current particular image\n",
    "        filtered_image_data = raw_image_data[1:] #taking data after the first value as the first value is label\n",
    "    else:\n",
    "        filtered_image_data = raw_image_data\n",
    "        \n",
    "    reshaped_image_data = filtered_image_data.values.reshape((28,28)) #Reshaping the image into 28 by 28 pixes\n",
    "    \n",
    "    if visualize==True:\n",
    "        plt.imshow(reshaped_image_data, cmap='gray')\n",
    "        if test_df == False:\n",
    "            plt.title(f'Label: {image_label}')\n",
    "        plt.show()\n",
    "    else:\n",
    "        return reshaped_image_data, image_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a5564d66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ABAUEADBBAQEATFBAAAATFBAAwAQFBAAwQQEBAExQQAAAExQQAMAEBQQAMEEBAQBMuP5zDGh7Qv1TGaHcMblDhw4h7etMqaysdJ1JTEx0nUlOTnadCeWOzikpKa4zkvThhx+6ztx1112uM5s3b3ad2bt3r+sMWiaugAAAJiggAIAJCggAYIICAgCYoIAAACYoIACACQoIAGCCAgIAmKCAAAAmKCAAgAkKCABgggICAJjgZqTQL3/5y5By06ZNc51JTU0NaV9tzc6dO11nCgoKXGfWrFnjOiNJJSUlIeUAN7gCAgCYoIAAACYoIACACQoIAGCCAgIAmKCAAAAmKCAAgAkKCABgggICAJiggAAAJiggAIAJCggAYCLCcRzHeoif8vv98nq91mPgFHTu3Nl1Ji4urhkmsfX73//edeY3v/mN68zevXtdZyZPnuw6I0lffPFFSDngp3w+n6Kjo5t8nCsgAIAJCggAYIICAgCYoIAAACYoIACACQoIAGCCAgIAmKCAAAAmKCAAgAkKCABgggICAJiggAAAJrgZKWAgMjLSdWbSpEmuMw8++KDrjCQVFxe7ztx6662uM/X19a4zaD24GSkAoEWigAAAJlwX0Nq1azVq1CglJSUpIiJCy5YtC3o8NzdXERERQcuIESPCNS8AoI1wXUC1tbXKyMjQvHnzmtxmxIgRqqysDCyvv/76aQ0JAGh7znEbGDlypEaOHHnCbTwejxISEkIeCgDQ9jXLa0CFhYWKi4vTpZdeqkmTJmnfvn1NbltXVye/3x+0AADavrAX0IgRI7Ro0SKtXr1ajz/+uIqKijRy5EgdPXq00e0LCgrk9XoDS3JycrhHAgC0QK6fgjuZsWPHBj5OT0/XFVdcoQsvvFCFhYUaNmzYcdvn5+dr2rRpgc/9fj8lBABngWZ/G3ZaWppiY2NVVlbW6OMej0fR0dFBCwCg7Wv2Avr222+1b98+JSYmNveuAACtiOun4A4cOBB0NVNRUaHNmzcrJiZGMTExeuSRRzRmzBglJCSovLxc06dP10UXXaScnJywDg4AaN1cF9DGjRt17bXXBj7/8fWb8ePHa/78+dqyZYteeeUV7d+/X0lJSRo+fLj+9Kc/yePxhG9qAECrx81IW7CMjAzXmZ07d7rOfP/9964zaB169eoVUm7lypWuM7t373adueWWW1xnysvLXWdgg5uRAgBaJAoIAGCCAgIAmKCAAAAmKCAAgAkKCABgggICAJiggAAAJiggAIAJCggAYIICAgCYoIAAACYoIACACe6GfYbExcW5zmzdutV1ZujQoa4zX331lesM2rYBAwa4zvztb39znQnl//Xs7GzXmW+++cZ1BqePu2EDAFokCggAYIICAgCYoIAAACYoIACACQoIAGCCAgIAmKCAAAAmKCAAgAkKCABgggICAJiggAAAJrgZ6RmSm5vrOjN48GDXmQkTJrjOAOGQkpLiOvPhhx+6znz33XeuM6NGjXKdkaRDhw6FlMMx3IwUANAiUUAAABMUEADABAUEADBBAQEATFBAAAATFBAAwAQFBAAwQQEBAExQQAAAExQQAMAEBQQAMHGO9QBoms/nsx4BOGU7duxwnZk1a5brzBtvvOE6M3DgQNcZSVq1alVIOZwaroAAACYoIACACQoIAGCCAgIAmKCAAAAmKCAAgAkKCABgggICAJiggAAAJiggAIAJCggAYIICAgCY4GakZ0hlZaXrzN133+064/V6XWe46SmsLFu2zHVm27ZtrjNjxoxxnZG4GWlz4woIAGCCAgIAmHBVQAUFBbrmmmsUFRWluLg4jR49WiUlJUHbHD58WHl5eerevbvOPfdcjRkzRtXV1WEdGgDQ+rkqoKKiIuXl5WndunVauXKl6uvrNXz4cNXW1ga2uffee/Xee+9pyZIlKioq0q5du3TTTTeFfXAAQOvm6k0IK1asCPp84cKFiouL06ZNmzRkyBD5fD699NJLWrx4sa677jpJ0oIFC3TZZZdp3bp1GjBgQPgmBwC0aqf1GtCP756KiYmRJG3atEn19fXKzs4ObNOrVy+lpKSouLi40a9RV1cnv98ftAAA2r6QC6ihoUFTp07VwIED1adPH0lSVVWVIiMj1bVr16Bt4+PjVVVV1ejXKSgokNfrDSzJycmhjgQAaEVCLqC8vDxt3bpVb7zxxmkNkJ+fL5/PF1h27tx5Wl8PANA6hPSLqJMnT9b777+vtWvXqkePHoH1CQkJOnLkiPbv3x90FVRdXa2EhIRGv5bH45HH4wllDABAK+bqCshxHE2ePFlLly7VRx99pNTU1KDH+/btqw4dOmj16tWBdSUlJdqxY4eysrLCMzEAoE1wdQWUl5enxYsXa/ny5YqKigq8ruP1etWpUyd5vV5NmDBB06ZNU0xMjKKjo3XPPfcoKyuLd8ABAIK4KqD58+dLkoYOHRq0fsGCBcrNzZUkPf3002rXrp3GjBmjuro65eTk6Pnnnw/LsACAtiPCcRzHeoif8vv9Id1Qs6Xr3Lmz60x5ebnrzJQpU1xn3n77bdcZ6dg7IYEzbc2aNa4zP39n7qm66qqrQsrhGJ/Pp+jo6CYf515wAAATFBAAwAQFBAAwQQEBAExQQAAAExQQAMAEBQQAMEEBAQBMUEAAABMUEADABAUEADBBAQEATFBAAAATIf1FVLh38OBB15np06e7zixatMh15vLLL3edkaQ///nPrjN1dXUh7Qtt0/333+86k5GR4Trz6KOPus6g+XEFBAAwQQEBAExQQAAAExQQAMAEBQQAMEEBAQBMUEAAABMUEADABAUEADBBAQEATFBAAAATFBAAwAQ3I23B/vGPf7jOREREuM789a9/dZ2RpNGjR7vOPPjgg64zH3/8sevMgQMHXGfwP71793admTRp0hnJPPHEE64zL774ousMmh9XQAAAExQQAMAEBQQAMEEBAQBMUEAAABMUEADABAUEADBBAQEATFBAAAATFBAAwAQFBAAwQQEBAExEOI7jWA/xU36/X16v13qMs8qVV14ZUm7q1KmuM5mZma4zoZwPK1ascJ2RpCVLlrjOHDp0yHUmJSXFdWbgwIGuM8OHD3edkaTzzz/fdaasrMx15tlnn3WdmT9/vusMbPh8PkVHRzf5OFdAAAATFBAAwAQFBAAwQQEBAExQQAAAExQQAMAEBQQAMEEBAQBMUEAAABMUEADABAUEADBBAQEATHAzUpxRXbp0cZ2ZPn2668ygQYNcZyQpPT3ddebgwYOuMz179nSd+fjjj11nPvnkE9cZSfr0009dZ1auXOk6c+TIEdcZtB7cjBQA0CJRQAAAE64KqKCgQNdcc42ioqIUFxen0aNHq6SkJGiboUOHKiIiImi56667wjo0AKD1c1VARUVFysvL07p167Ry5UrV19dr+PDhqq2tDdpu4sSJqqysDCxz5swJ69AAgNbvHDcb//yvTC5cuFBxcXHatGmThgwZEljfuXNnJSQkhGdCAECbdFqvAfl8PklSTExM0PrXXntNsbGx6tOnj/Lz80/4LqG6ujr5/f6gBQDQ9rm6AvqphoYGTZ06VQMHDlSfPn0C62+77Tb17NlTSUlJ2rJlix544AGVlJTonXfeafTrFBQU6JFHHgl1DABAKxVyAeXl5Wnr1q3H/Z7BnXfeGfg4PT1diYmJGjZsmMrLy3XhhRce93Xy8/M1bdq0wOd+v1/JycmhjgUAaCVCKqDJkyfr/fff19q1a9WjR48TbpuZmSlJKisra7SAPB6PPB5PKGMAAFoxVwXkOI7uueceLV26VIWFhUpNTT1pZvPmzZKkxMTEkAYEALRNrgooLy9Pixcv1vLlyxUVFaWqqipJktfrVadOnVReXq7FixfrV7/6lbp3764tW7bo3nvv1ZAhQ3TFFVc0yzcAAGidXBXQ/PnzJR37ZdOfWrBggXJzcxUZGalVq1Zp7ty5qq2tVXJyssaMGaOHH344bAMDANoG10/BnUhycrKKiopOayAAwNmBu2EDAJoFd8MGALRIFBAAwAQFBAAwQQEBAExQQAAAExQQAMAEBQQAMEEBAQBMUEAAABMUEADABAUEADBBAQEATFBAAAATFBAAwAQFBAAwQQEBAExQQAAAExQQAMAEBQQAMEEBAQBMUEAAABMUEADABAUEADBBAQEATLS4AnIcx3oEAEAYnOzneYsroJqaGusRAABhcLKf5xFOC7vkaGho0K5duxQVFaWIiIigx/x+v5KTk7Vz505FR0cbTWiP43AMx+EYjsMxHIdjWsJxcBxHNTU1SkpKUrt2TV/nnHMGZzol7dq1U48ePU64TXR09Fl9gv2I43AMx+EYjsMxHIdjrI+D1+s96TYt7ik4AMDZgQICAJhoVQXk8Xg0a9YseTwe61FMcRyO4Tgcw3E4huNwTGs6Di3uTQgAgLNDq7oCAgC0HRQQAMAEBQQAMEEBAQBMUEAAABOtpoDmzZunCy64QB07dlRmZqY2bNhgPdIZN3v2bEVERAQtvXr1sh6r2a1du1ajRo1SUlKSIiIitGzZsqDHHcfRzJkzlZiYqE6dOik7O1ulpaU2wzajkx2H3Nzc486PESNG2AzbTAoKCnTNNdcoKipKcXFxGj16tEpKSoK2OXz4sPLy8tS9e3ede+65GjNmjKqrq40mbh6nchyGDh163Plw1113GU3cuFZRQG+++aamTZumWbNm6bPPPlNGRoZycnK0e/du69HOuMsvv1yVlZWB5ZNPPrEeqdnV1tYqIyND8+bNa/TxOXPm6JlnntELL7yg9evXq0uXLsrJydHhw4fP8KTN62THQZJGjBgRdH68/vrrZ3DC5ldUVKS8vDytW7dOK1euVH19vYYPH67a2trANvfee6/ee+89LVmyREVFRdq1a5duuukmw6nD71SOgyRNnDgx6HyYM2eO0cRNcFqB/v37O3l5eYHPjx496iQlJTkFBQWGU515s2bNcjIyMqzHMCXJWbp0aeDzhoYGJyEhwfnLX/4SWLd//37H4/E4r7/+usGEZ8bPj4PjOM748eOdG264wWQeK7t373YkOUVFRY7jHPtv36FDB2fJkiWBbb7++mtHklNcXGw1ZrP7+XFwHMf5xS9+4UyZMsVuqFPQ4q+Ajhw5ok2bNik7Ozuwrl27dsrOzlZxcbHhZDZKS0uVlJSktLQ03X777dqxY4f1SKYqKipUVVUVdH54vV5lZmaeledHYWGh4uLidOmll2rSpEnat2+f9UjNyufzSZJiYmIkSZs2bVJ9fX3Q+dCrVy+lpKS06fPh58fhR6+99ppiY2PVp08f5efn6+DBgxbjNanF3Q375/bu3aujR48qPj4+aH18fLy2bdtmNJWNzMxMLVy4UJdeeqkqKyv1yCOPaPDgwdq6dauioqKsxzNRVVUlSY2eHz8+drYYMWKEbrrpJqWmpqq8vFwPPfSQRo4cqeLiYrVv3956vLBraGjQ1KlTNXDgQPXp00fSsfMhMjJSXbt2Ddq2LZ8PjR0HSbrtttvUs2dPJSUlacuWLXrggQdUUlKid955x3DaYC2+gPA/I0eODHx8xRVXKDMzUz179tRbb72lCRMmGE6GlmDs2LGBj9PT03XFFVfowgsvVGFhoYYNG2Y4WfPIy8vT1q1bz4rXQU+kqeNw5513Bj5OT09XYmKihg0bpvLycl144YVnesxGtfin4GJjY9W+ffvj3sVSXV2thIQEo6lahq5du+qSSy5RWVmZ9ShmfjwHOD+Ol5aWptjY2DZ5fkyePFnvv/++1qxZE/T3wxISEnTkyBHt378/aPu2ej40dRwak5mZKUkt6nxo8QUUGRmpvn37avXq1YF1DQ0NWr16tbKysgwns3fgwAGVl5crMTHRehQzqampSkhICDo//H6/1q9ff9afH99++6327dvXps4Px3E0efJkLV26VB999JFSU1ODHu/bt686dOgQdD6UlJRox44dbep8ONlxaMzmzZslqWWdD9bvgjgVb7zxhuPxeJyFCxc6X331lXPnnXc6Xbt2daqqqqxHO6P++Mc/OoWFhU5FRYXz6aefOtnZ2U5sbKyze/du69GaVU1NjfP55587n3/+uSPJeeqpp5zPP//c+e9//+s4juM89thjTteuXZ3ly5c7W7ZscW644QYnNTXVOXTokPHk4XWi41BTU+Pcd999TnFxsVNRUeGsWrXKufrqq52LL77YOXz4sPXoYTNp0iTH6/U6hYWFTmVlZWA5ePBgYJu77rrLSUlJcT766CNn48aNTlZWlpOVlWU4dfid7DiUlZU5jz76qLNx40anoqLCWb58uZOWluYMGTLEePJgraKAHMdxnn32WSclJcWJjIx0+vfv76xbt856pDPu1ltvdRITE53IyEjn/PPPd2699VanrKzMeqxmt2bNGkfSccv48eMdxzn2VuwZM2Y48fHxjsfjcYYNG+aUlJTYDt0MTnQcDh486AwfPtw577zznA4dOjg9e/Z0Jk6c2Ob+kdbY9y/JWbBgQWCbQ4cOOXfffbfTrVs3p3Pnzs6NN97oVFZW2g3dDE52HHbs2OEMGTLEiYmJcTwej3PRRRc5999/v+Pz+WwH/xn+HhAAwESLfw0IANA2UUAAABMUEADABAUEADBBAQEATFBAAAATFBAAwAQFBAAwQQEBAExQQAAAExQQAMDE/wNQif6sGFHv8wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_row_reshaper(train_df, 3, visualize=True)\n",
    "image_row_reshaper(train_df, 4, visualize=True)\n",
    "image_row_reshaper(train_df, 7, visualize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b5f5fea2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42000, 785)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c8145ce9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def dataframe_formatter(df, isTest=False):\n",
    "    \n",
    "    if isTest==False:\n",
    "        y = df['label'] # extracting just the labels into a scalar form\n",
    "        X = train_df.loc[:,'pixel0':'pixel783'] # extracting just the pixels data\n",
    "        X = X.apply(lambda x: x/255) # normalizing the values between 0 and 1\n",
    "        X = X.values.reshape(-1, 28, 28, 1)\n",
    "        return X, y\n",
    "        \n",
    "    elif isTest==True:\n",
    "        X = df\n",
    "        X = X.apply(lambda x: x/255) # normalizing the values between 0 and 1\n",
    "        X = X.values.reshape(-1, 28, 28, 1)\n",
    "        return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "86e36703",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train = dataframe_formatter(train_df, isTest=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0da3a797",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into a train and validation set\n",
    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b1bcd954",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 26, 26, 20)        200       \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 13, 13, 20)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 11, 11, 32)        5792      \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 5, 5, 32)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 4, 4, 32)          4128      \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 2, 2, 32)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 128)               0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 200)               25800     \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 200)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 10)                2010      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 37,930\n",
      "Trainable params: 37,930\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = models.Sequential(\n",
    "    [\n",
    "        layers.Conv2D(20, (3, 3), activation='relu', input_shape=(28, 28, 1)),\n",
    "        layers.MaxPooling2D((2, 2)),\n",
    "        layers.Conv2D(32, (3, 3), activation='relu'),\n",
    "        layers.MaxPooling2D((2, 2)),\n",
    "        layers.Conv2D(32, (2, 2), activation='relu'),\n",
    "        layers.MaxPooling2D((2, 2)),\n",
    "        layers.Flatten(),\n",
    "        layers.Dense(units=200, activation='relu'),\n",
    "        layers.Dropout(0.2),\n",
    "        layers.Dense(units=10, activation='sigmoid')\n",
    "    ]\n",
    ")\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8c09da62",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    optimizer='adam',\n",
    "    loss='sparse_categorical_crossentropy',\n",
    "    metrics=['accuracy']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "61c822eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Early stopping in case of 3 bad epochs\n",
    "stop_training = EarlyStopping(monitor='val_loss', patience=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "62cf670d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1050/1050 [==============================] - 29s 25ms/step - loss: 0.2863 - accuracy: 0.9110 - val_loss: 0.1228 - val_accuracy: 0.9606\n",
      "Epoch 2/10\n",
      "1050/1050 [==============================] - 24s 23ms/step - loss: 0.0935 - accuracy: 0.9705 - val_loss: 0.0752 - val_accuracy: 0.9752\n",
      "Epoch 3/10\n",
      "1050/1050 [==============================] - 25s 24ms/step - loss: 0.0663 - accuracy: 0.9788 - val_loss: 0.0577 - val_accuracy: 0.9814\n",
      "Epoch 4/10\n",
      "1050/1050 [==============================] - 37s 35ms/step - loss: 0.0508 - accuracy: 0.9836 - val_loss: 0.0766 - val_accuracy: 0.9727\n",
      "Epoch 5/10\n",
      "1050/1050 [==============================] - 24s 23ms/step - loss: 0.0431 - accuracy: 0.9863 - val_loss: 0.0577 - val_accuracy: 0.9829\n",
      "Epoch 6/10\n",
      "1050/1050 [==============================] - 26s 25ms/step - loss: 0.0387 - accuracy: 0.9871 - val_loss: 0.0577 - val_accuracy: 0.9804\n",
      "Epoch 7/10\n",
      "1050/1050 [==============================] - 24s 23ms/step - loss: 0.0321 - accuracy: 0.9899 - val_loss: 0.0544 - val_accuracy: 0.9839\n",
      "Epoch 8/10\n",
      "1050/1050 [==============================] - 37s 35ms/step - loss: 0.0288 - accuracy: 0.9908 - val_loss: 0.0494 - val_accuracy: 0.9854\n",
      "Epoch 9/10\n",
      "1050/1050 [==============================] - 23s 22ms/step - loss: 0.0257 - accuracy: 0.9911 - val_loss: 0.0526 - val_accuracy: 0.9844\n",
      "Epoch 10/10\n",
      "1050/1050 [==============================] - 24s 23ms/step - loss: 0.0211 - accuracy: 0.9933 - val_loss: 0.0752 - val_accuracy: 0.9801\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1c1bf9f1030>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training the model\n",
    "model.fit(\n",
    "    X_train,\n",
    "    y_train,\n",
    "    epochs=10,\n",
    "    validation_data=(X_val, y_val),\n",
    "    callbacks=stop_training\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b30bf27c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3093aa8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df = pd.read_csv('MNIST/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3f64b074",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pixel0</th>\n",
       "      <th>pixel1</th>\n",
       "      <th>pixel2</th>\n",
       "      <th>pixel3</th>\n",
       "      <th>pixel4</th>\n",
       "      <th>pixel5</th>\n",
       "      <th>pixel6</th>\n",
       "      <th>pixel7</th>\n",
       "      <th>pixel8</th>\n",
       "      <th>pixel9</th>\n",
       "      <th>...</th>\n",
       "      <th>pixel774</th>\n",
       "      <th>pixel775</th>\n",
       "      <th>pixel776</th>\n",
       "      <th>pixel777</th>\n",
       "      <th>pixel778</th>\n",
       "      <th>pixel779</th>\n",
       "      <th>pixel780</th>\n",
       "      <th>pixel781</th>\n",
       "      <th>pixel782</th>\n",
       "      <th>pixel783</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "</table>\n",
       "<p>28000 rows × 784 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       pixel0  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  \\\n",
       "0           0       0       0       0       0       0       0       0       0   \n",
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       "27999       0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "       pixel9  ...  pixel774  pixel775  pixel776  pixel777  pixel778  \\\n",
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       "\n",
       "       pixel779  pixel780  pixel781  pixel782  pixel783  \n",
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       "27997         0         0         0         0         0  \n",
       "27998         0         0         0         0         0  \n",
       "27999         0         0         0         0         0  \n",
       "\n",
       "[28000 rows x 784 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9c30e729",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28000, 784)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "83457040",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = dataframe_formatter(test_df, isTest=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "de88288b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28000, 28, 28, 1)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0d334c22",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform predictions for all rows of X_test at once\n",
    "predictions = model.predict(X_test, verbose=0)\n",
    "\n",
    "# Get the predicted class labels by finding the indices of the maximum values\n",
    "predicted_labels = np.argmax(predictions, axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "377c1750",
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_model(row):\n",
    "    image_row_reshaper(test_df, row, visualize=True, test_df = True)\n",
    "    print(f'Predicted Label: {predicted_labels[row]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "cfc16dcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Label: 2\n"
     ]
    }
   ],
   "source": [
    "# test_model(row) where row can be any values from 0 to 28000 only!\n",
    "\n",
    "test_model(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b03edfdc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1050/1050 [==============================] - 10s 9ms/step - loss: 0.0253 - accuracy: 0.9918\n",
      "Train Accuracy: 0.9918452501296997\n",
      "263/263 [==============================] - 2s 9ms/step - loss: 0.0752 - accuracy: 0.9801\n",
      "Validation Accuracy: 0.9801190495491028\n"
     ]
    }
   ],
   "source": [
    "# Trainig and validation Accuray\n",
    "loss, accuracy = model.evaluate(X_train, y_train, verbose=1)\n",
    "print(\"Train Accuracy:\", accuracy)\n",
    "\n",
    "loss, accuracy = model.evaluate(X_val, y_val, verbose=1)\n",
    "print(\"Validation Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86613ee6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "275fc5c5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61596867",
   "metadata": {},
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