digit_recognition.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b9e22d09-03c9-4080-bf6f-b1831ce71644",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from tensorflow import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8bde8c12-3683-4193-a1d6-fe352fe55249",
   "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",
       "2      1       0       0       0       0       0       0       0       0   \n",
       "3      4       0       0       0       0       0       0       0       0   \n",
       "4      0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   pixel8  ...  pixel774  pixel775  pixel776  pixel777  pixel778  pixel779  \\\n",
       "0       0  ...         0         0         0         0         0         0   \n",
       "1       0  ...         0         0         0         0         0         0   \n",
       "2       0  ...         0         0         0         0         0         0   \n",
       "3       0  ...         0         0         0         0         0         0   \n",
       "4       0  ...         0         0         0         0         0         0   \n",
       "\n",
       "   pixel780  pixel781  pixel782  pixel783  \n",
       "0         0         0         0         0  \n",
       "1         0         0         0         0  \n",
       "2         0         0         0         0  \n",
       "3         0         0         0         0  \n",
       "4         0         0         0         0  \n",
       "\n",
       "[5 rows x 785 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_set=pd.read_csv('training_set.csv')\n",
    "test_set=pd.read_csv('test_set.csv')\n",
    "submission=pd.read_csv('sample_submission.csv')\n",
    "training_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e1e0e603-46ef-4dae-980d-1b37b9a90366",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label       0\n",
       "pixel0      0\n",
       "pixel1      0\n",
       "pixel2      0\n",
       "pixel3      0\n",
       "           ..\n",
       "pixel779    0\n",
       "pixel780    0\n",
       "pixel781    0\n",
       "pixel782    0\n",
       "pixel783    0\n",
       "Length: 785, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_set.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "659dac40-b180-41ee-ab7e-67ea5f08f5ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "0    4132\n",
       "1    4684\n",
       "2    4177\n",
       "3    4351\n",
       "4    4072\n",
       "5    3795\n",
       "6    4137\n",
       "7    4401\n",
       "8    4063\n",
       "9    4188\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=training_set.groupby('label').size()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2732ef2b-beea-41c1-9492-c6c68d3464f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " [Text(0.5890009661133138, 0.3782563441868266, '9.8%'),\n",
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       "  Text(0.6990732728983415, -0.036007764707084486, '10.0%')])"
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     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "label='0','1','2','3','4','5','6','7','8','9'\n",
    "my_explode=(0.1, 0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,)\n",
    "plt.figure(figsize=(7,7))\n",
    "plt.pie(df,labels=label, autopct='%1.1f%%',startangle=15, shadow=True ,explode=my_explode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ed9be0f7-f4bf-4e3b-98ce-5cba47562314",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 10 artists>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "colors=['#d580ff','#b3b3ff','#9fff80','#002699','#cc80ff','#70db70','#ffd24d','#ff6666','#b3b3ff','#b3b300']\n",
    "plt.bar(label,df,color=colors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a568684e-a276-4d50-907b-031af8d05b6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=training_set['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ce8bea0f-3034-4f7a-856d-d1c59e708fdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.utils.np_utils import to_categorical \n",
    "y= to_categorical(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "991a22d1-68a0-4d64-94f3-1ee4a2d22092",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=training_set.drop(labels='label',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "37593c0a-da8a-4ed8-a4d2-f0bb3a711980",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAHMAAABzCAYAAACrQz3mAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAIKklEQVR4nO2dT2gc1xnAf9+83dnV6t9KlmzFsmJHxDHkULDrxoH0kDYETC/pqSSU4kLBlxZa6KEhl9JD25xKz4Ga5lASUlpIKIESTKF/KK7dEHBtx6ns2ER2LKmStZL3j3Z35uthJ0ayvdZ6d7W78/b9QGjmzSzv2/3NvJn3Zr4ZUVUcduB1OwBH+3AyLcLJtAgn0yKcTItwMi2iJZkiclxELovInIi82q6gHM0hzfYzRcQAnwAvAvPAWeAVVb3YvvAcj0Kihc8+A8yp6lUAEXkbeAmoK9OXlKYZbKFKR4k8Zd2QBy1rReY08Nmm+Xng2MM+kGaQY/JCC1U6zujpustakdkQInISOAmQJrPT1fU1rZwA3QBmNs3vi8q2oKpvqOpRVT2aJNVCdY7taEXmWeCgiDwhIj7wMvBee8JyNEPTzayqVkXkB8CfAQOcUtULbYvM8ci0dMxU1feB99sUi6NF3AiQRTiZFuFkWsSO9zNjgWfuL9MQYnZLTX/LFMFks4RP7mNjvNYHlsifv1LCu3KDYHU1NlL7V6YIiAe7d7FwbJj1/XpXJArD15JM5cYgtwbEYy/tY5ke4gmaSlAegWCiDCoQOSvf9sFP1tYJPdCgu/E2QH/KFEE8AWMIhtMUDlR47tAVAEKtXZD4Z/kg1ZE0njEIARp2M+DG6E+ZAMYgiQTlbJJDT97kF/v+tGXxicK3qYzuJm1MbWcNgp5vavtWpoiA5xEmhGyqyLTZekVn1C+x+oCT3F7G9TMtwsm0CCezDgkvIPA9JDOA+H6tG9Pj9H6EXWLvQI7crKHwlVn00H7MUO/fu9S3J0DbMZNe4c6BAPWSjFzzyM4PwNpat8N6KG7PrEPGK6MDAdVBqKY9SPT+dt/7EXaJ6eRt9uxdZdEfoVBIo2m/2yFti9sz6zBlchyZnOepfQuUJhRSTmZs8STE96r4JkC93h75+QIn0yK2lSkip0RkUUT+s6lsXEQ+EJH/Rv/HdjZMRyM0smf+Fjh+T9mrwGlVPQicjuYdXWZbmar6V2DlnuKXgDej6TeBb7Y3LEczNHvM3KOqn0fTt4A9bYrH0QItnwBpLcGz7umeiJwUkXMicq7CRqvVtQ1VhTDEqyqrGwPcCAosh0Wq9P4dBfVoVuaCiDwGEP1frLdizyYOhQqqmGLI1YUJ3sod5oPC4ywFvbPBPSrNynwPOBFNnwDebU84HSYMMRsBlVyKs6v7uVCYJhfG7Ir0JrYdzhORt4DngQkRmQd+CrwOvCMi3wOuA9/aySDbjipoiAbglQMSa4ZPV8fxvYB8Nr4jnNtGrqqv1FkU6xRoDQIgwKzmGb46xHqwi3/tz3BtcoIv+7e7HV5TxHczbJXo5iwplBhcDFHPkMukWakOAfGU6YbzEobKgFAdAk0HpL1KtyNqmv7dM6P8Es2kKe72yE+HDE4WGDd3uhxY8/T1ninGoElDZRAYrZDNFBn0+q9rEnvEE/CEMJ1kY1fI3qnbHMoukvWK3Q6tafqzmZVaaoIYw8ZYil1PLfOzg++S9YrMJqrE9WeJZ9RtQCKhQdpjNrvM8+kKtZ8jvj9J3zazNuJkWoSTaRFOpkU4mRbhZFqEk2kRTqZFOJkW4WRahJNpEf0pUzxIJhE/SZgQvPp3isaK+I4qt4AZG6X8pQPkp3xWD3m8MFT3TtFY0Uji0IyI/EVELorIBRH5YVQe3+ShsVEWj6S59fWAgSPLHM5c73ZEbaGRZrYK/FhVnwaeBb4vIk8T5+QhY6hmIDVaYnIwT0Y2MOLd/YsrjSQOfa6qH0bT68Alai+oiW/ykPEI0srIYImxdOG+W0WMeJgYHkcfaTMUkQPAYeAMMU4e0kRN5tTQOlPpNQYlvnfkbaZhmSIyBPwB+JGqbnmGysOSh3o2cciDhAQYYvC4ygZpSKaIJKmJ/J2q/jEqbih5qGcThyykkbNZAX4DXFLVX21aZEfyUB0CHvgivJ6mkX7mc8B3gPMi8lFU9hpxTx66hyAOTwfehkYSh/4OdTfTWCcP2UZ8O1WO++jL4TwJFFMSFgrDZBIV8pq8b52SJrlVGmExP4QpCYS93wz3p8y1PNnL4yxX9nBzZoyvjX3Ms6mbW9Y5X5rhzMezpOZ9slcUWS90KdrG6UuZeifP6FwRfz3FykaKTw9PAltlXi1OkpnzGb8cMHCrhN7p/eywvpRJEGCKFfz1BKmc4R9Ls/x+YH7LKmeWDpBaVZJrVUyhggaume1JwmIJb36JgeU0/soIt3N7+eXE1mz/gaWQ3ZfWMMvraLFEWOqd0at69KVMrZQJFhZr2WDXYewjc/8z2DVEg4Aq9Pz7TL6gL2XeJZKk1WqXA2kPrp9pEU6mRTiZFuFkWoSTaRFOpkU4mRbhZFqEk2kRoh0cqhKRJSAP/K9jlXaXCdr/Xfer6uSDFnRUJoCInFPVox2ttEt0+ru6ZtYinEyL6IbMN7pQZ7fo6Hft+DHTsXO4ZtYiOipTRI6LyGURmROR+ORzbkOvJCR3rJkVEQN8ArwIzANngVdU9WJHAthBosSpx1T1QxEZBv5NLV/1u8CKqr4ebbxjqvqTnYqjk3vmM8Ccql5V1TLwNrWE3djTKwnJnZQ5DXy2aX4+KrOKbiYkuxOgNtJsQnK76KTMG8DMpvl9UZkVtJKQ3C46KfMscFBEnhARH3iZWsJu7OmVhOROXzX5BvBrwACnVPXnHat8BxGRrwJ/A87D3YckvEbtuPkO8DhRQrKq3vvK5/bF4UaA7MGdAFmEk2kRTqZFOJkW4WRahJNpEU6mRTiZFvF/hGXE4CDCo5QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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B9ZGd9Gg5wfC/6+OqunCvHSMVE0BEXlbVp0d60iNi1N81b2aniFzMKeIoxHz+CM55VIz0u478mplzeOTN7BQxUjFF5FkReVNE3h48EG4qGJeE5JE1syJigfPAZ4Fl4CXgOVV9fSQVOEQGiVMnVfUVEakCPyfLV/1T4OaupxnWVfXQHoA3Ssv8JPC2ql5Q1Qj4LlnC7sQzLgnJoxTzFHB11/byoGyqOMqE5NwBGiIPm5A8LEYp5grw2K7t04OyqeAgCcnDYpRivgQ8KSJPiEgAfJEsYXfiGZeE5FGPmvwh8Pdkz/H+lqr+7chOfoiIyO8CPwVeg1uTE75Bdt38PnCGQUKyqt48tHrkEaDpIXeApohczCkiF3OKyMWcInIxp4hczCkiF3OKyMWcIv4flIeXzRzX180AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(0,8):\n",
    "    digit1=(x.iloc[i:i+1,:].to_numpy()).reshape(28,28,1)\n",
    "    plt.figure(figsize=(1.5,1.5))\n",
    "    plt.imshow(digit1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "1e222fb0-289c-4a6c-932d-5be09e266556",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test=train_test_split(x,y,test_size=0.2,random_state=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59d33f77-58e7-4906-b097-b18a802605a3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4b691a82-b2b9-4c48-9b2c-346d92510182",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(33600, 784)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "9cfc353b-7cd2-4456-aea9-521ae3095caa",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train=x_train.values.reshape(len(x_train),28,28,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ef0b055b-4ad8-489a-ae0b-6362249a4033",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test=x_test.values.reshape(len(x_test),28,28,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b4f54bf7-6f9a-4b8d-a46e-768f3e7e6de3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 1.]\n",
      " ...\n",
      " [0. 0. 1. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 1. 0. 0.]\n",
      " [0. 0. 1. ... 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "0880b8f3-7995-49ad-a6aa-d2a35085eae1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8400, 10)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "2c047d63-b348-4670-aa30-4ba2dc674683",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(33600, 10)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "d6349da8-e872-4a87-81c1-d56efba45a48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 0. 0. 1.]\n",
      " [1. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 1.]]\n"
     ]
    }
   ],
   "source": [
    "print(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "3b8067ff-d17e-422e-934f-d0aee4d9b98d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d362e29f-5907-4045-a172-6415c00f8061",
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn=tf.keras.models.Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "69d17c51-bc49-47b3-8e0b-9e7a42fd3dd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn.add(tf.keras.layers.Conv2D(filters=32,kernel_size=(4,4), activation='relu', input_shape=(28, 28, 1)))\n",
    "cnn.add(tf.keras.layers.MaxPool2D(pool_size=2,strides=2))\n",
    "\n",
    "cnn.add(tf.keras.layers.Conv2D(filters=32, kernel_size=(4,4), activation='relu', input_shape=(28, 28, 1)))\n",
    "cnn.add(tf.keras.layers.MaxPool2D(pool_size=2,strides=2))\n",
    "\n",
    "\n",
    "cnn.add(tf.keras.layers.Flatten())\n",
    "cnn.add(tf.keras.layers.Dense(units=10 ,activation='softmax'))\n",
    "cnn.compile(optimizer= 'adam', loss= 'categorical_crossentropy', metrics = ['accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "e2dcc07c-4868-42cf-8668-db92b1e1432b",
   "metadata": {},
   "outputs": [],
   "source": [
    "reduce_lr=tf.keras.callbacks.ReduceLROnPlateau(monitor='accuracy',factor=0.2,patience=5,min_lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "f34a63bc-f061-4835-9b06-9eee4faccca9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/32\n",
      "1050/1050 [==============================] - 15s 14ms/step - loss: 0.5861 - accuracy: 0.9013 - val_loss: 0.1658 - val_accuracy: 0.9521\n",
      "Epoch 2/32\n",
      "1050/1050 [==============================] - 13s 13ms/step - loss: 0.0986 - accuracy: 0.9700 - val_loss: 0.1093 - val_accuracy: 0.9713\n",
      "Epoch 3/32\n",
      "1050/1050 [==============================] - 12s 11ms/step - loss: 0.0741 - accuracy: 0.9776 - val_loss: 0.1290 - val_accuracy: 0.9658\n",
      "Epoch 4/32\n",
      "1050/1050 [==============================] - 12s 11ms/step - loss: 0.0646 - accuracy: 0.9809 - val_loss: 0.0831 - val_accuracy: 0.9758\n",
      "Epoch 5/32\n",
      "1050/1050 [==============================] - 11s 11ms/step - loss: 0.0538 - accuracy: 0.9841 - val_loss: 0.0863 - val_accuracy: 0.9769\n",
      "Epoch 6/32\n",
      "1050/1050 [==============================] - 11s 11ms/step - loss: 0.0455 - accuracy: 0.9870 - val_loss: 0.1356 - val_accuracy: 0.9738\n",
      "Epoch 7/32\n",
      "1050/1050 [==============================] - 12s 11ms/step - loss: 0.0504 - accuracy: 0.9863 - val_loss: 0.0992 - val_accuracy: 0.9756\n",
      "Epoch 8/32\n",
      "1050/1050 [==============================] - 11s 11ms/step - loss: 0.0441 - accuracy: 0.9875 - val_loss: 0.0879 - val_accuracy: 0.9786\n",
      "Epoch 9/32\n",
      "1050/1050 [==============================] - 11s 11ms/step - loss: 0.0420 - accuracy: 0.9872 - val_loss: 0.0822 - val_accuracy: 0.9824\n",
      "Epoch 10/32\n",
      "1050/1050 [==============================] - 11s 11ms/step - loss: 0.0361 - accuracy: 0.9898 - val_loss: 0.1002 - val_accuracy: 0.9788\n",
      "Epoch 11/32\n",
      "1050/1050 [==============================] - 16s 15ms/step - loss: 0.0308 - accuracy: 0.9913 - val_loss: 0.1224 - val_accuracy: 0.9804\n",
      "Epoch 12/32\n",
      "1050/1050 [==============================] - 16s 15ms/step - loss: 0.0350 - accuracy: 0.9906 - val_loss: 0.0954 - val_accuracy: 0.9823\n",
      "Epoch 13/32\n",
      "1050/1050 [==============================] - 12s 12ms/step - loss: 0.0315 - accuracy: 0.9920 - val_loss: 0.1116 - val_accuracy: 0.9801\n",
      "Epoch 14/32\n",
      "1050/1050 [==============================] - 12s 12ms/step - loss: 0.0276 - accuracy: 0.9923 - val_loss: 0.1191 - val_accuracy: 0.9821\n",
      "Epoch 15/32\n",
      "1050/1050 [==============================] - 14s 13ms/step - loss: 0.0318 - accuracy: 0.9917 - val_loss: 0.1326 - val_accuracy: 0.9775\n",
      "Epoch 16/32\n",
      "1050/1050 [==============================] - 15s 14ms/step - loss: 0.0275 - accuracy: 0.9928 - val_loss: 0.1501 - val_accuracy: 0.9769\n",
      "Epoch 17/32\n",
      "1050/1050 [==============================] - 15s 14ms/step - loss: 0.0362 - accuracy: 0.9920 - val_loss: 0.1746 - val_accuracy: 0.9811\n",
      "Epoch 18/32\n",
      "1050/1050 [==============================] - 13s 12ms/step - loss: 0.0333 - accuracy: 0.9929 - val_loss: 0.1804 - val_accuracy: 0.9789\n",
      "Epoch 19/32\n",
      "1050/1050 [==============================] - 14s 13ms/step - loss: 0.0273 - accuracy: 0.9942 - val_loss: 0.1555 - val_accuracy: 0.9826\n",
      "Epoch 20/32\n",
      "1050/1050 [==============================] - 13s 12ms/step - loss: 0.0319 - accuracy: 0.9935 - val_loss: 0.1295 - val_accuracy: 0.9846\n",
      "Epoch 21/32\n",
      "1050/1050 [==============================] - 13s 13ms/step - loss: 0.0277 - accuracy: 0.9944 - val_loss: 0.1712 - val_accuracy: 0.9823\n",
      "Epoch 22/32\n",
      "1050/1050 [==============================] - 13s 12ms/step - loss: 0.0357 - accuracy: 0.9940 - val_loss: 0.1351 - val_accuracy: 0.9858\n",
      "Epoch 23/32\n",
      "1050/1050 [==============================] - 12s 11ms/step - loss: 0.0228 - accuracy: 0.9954 - val_loss: 0.1833 - val_accuracy: 0.9844\n",
      "Epoch 24/32\n",
      "1050/1050 [==============================] - 17s 16ms/step - loss: 0.0258 - accuracy: 0.9948 - val_loss: 0.1398 - val_accuracy: 0.9870\n",
      "Epoch 25/32\n",
      "1050/1050 [==============================] - 16s 16ms/step - loss: 0.0259 - accuracy: 0.9958 - val_loss: 0.2012 - val_accuracy: 0.9861\n",
      "Epoch 26/32\n",
      "1050/1050 [==============================] - 14s 14ms/step - loss: 0.0333 - accuracy: 0.9949 - val_loss: 0.2955 - val_accuracy: 0.9780\n",
      "Epoch 27/32\n",
      "1050/1050 [==============================] - 14s 13ms/step - loss: 0.0353 - accuracy: 0.9950 - val_loss: 0.2219 - val_accuracy: 0.9819\n",
      "Epoch 28/32\n",
      "1050/1050 [==============================] - 12s 11ms/step - loss: 0.0257 - accuracy: 0.9958 - val_loss: 0.2193 - val_accuracy: 0.9855\n",
      "Epoch 29/32\n",
      "1050/1050 [==============================] - 12s 12ms/step - loss: 0.0301 - accuracy: 0.9956 - val_loss: 0.1683 - val_accuracy: 0.9852\n",
      "Epoch 30/32\n",
      "1050/1050 [==============================] - 15s 15ms/step - loss: 0.0294 - accuracy: 0.9962 - val_loss: 0.2177 - val_accuracy: 0.9851\n",
      "Epoch 31/32\n",
      "1050/1050 [==============================] - 12s 11ms/step - loss: 0.0235 - accuracy: 0.9963 - val_loss: 0.2866 - val_accuracy: 0.9854\n",
      "Epoch 32/32\n",
      "1050/1050 [==============================] - 14s 13ms/step - loss: 0.0430 - accuracy: 0.9951 - val_loss: 0.3106 - val_accuracy: 0.9831\n"
     ]
    }
   ],
   "source": [
    "history=cnn.fit(x_train, y_train, validation_data=(x_test ,y_test), epochs=32, callbacks=[reduce_lr])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "567e3cd0-f9c2-4029-bf62-b6cc5cca7b44",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = cnn.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "e665977c-1eb7-46a2-8699-b6c0ae16e225",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=np.argmax(y_pred, axis=1)\n",
    "y_test=np.argmax(y_test, axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "e6440c48-bc17-47c5-8b7f-f23c0366a490",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 6, 9, ..., 2, 7, 2])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "7db4e73c-0aaa-4ecc-8f0f-f984876cd259",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 6, 9, ..., 2, 7, 2])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "430a0471-0ce6-4042-8e5a-54cdb1aecebf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[808   0   2   0   1   0   2   0   0   0]\n",
      " [  0 949   6   2   1   0   0   1   1   1]\n",
      " [  2   1 853   0   0   0   0   2   2   0]\n",
      " [  0   0   4 853   1   1   0   1   0   3]\n",
      " [  1   2   0   0 808   0   3   0   0  13]\n",
      " [  1   0   0   4   0 743   2   0   3   3]\n",
      " [  3   1   1   0   2   2 831   0   1   0]\n",
      " [  2   2  17   3   7   1   0 857   1   9]\n",
      " [  1   0   3   1   0   0   2   0 757   4]\n",
      " [  3   0   1   2   1   1   0   0   5 799]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9830952380952381"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "cn=confusion_matrix(y_test,y_pred)\n",
    "print(cn)\n",
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "c7826c2b-c1b6-4192-af66-b4eb8164cb56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy', 'lr'])\n"
     ]
    }
   ],
   "source": [
    "print(history.history.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "f436fe81-b1ff-41e5-a666-0ae5b2d423dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'], label='loss')\n",
    "plt.plot(history.history['accuracy'], label='accuracy')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "2434d817-6262-458e-97e8-a1283782413a",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_set=test_set.values.reshape(-1,28,28,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "a994e2a4-483b-494d-b82d-9ba75cc327e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_prediction=cnn.predict(test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "a0aa9b2d-270c-4081-b1b1-4677b88926c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_prediction=np.argmax(Y_prediction, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "43f9e811-0c19-4867-ac00-d8d4d7445325",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 0, 9, ..., 3, 9, 2])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "a3c1cb5f-ddc6-4854-a70f-03640c290cc4",
   "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>ImageId</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ImageId  Label\n",
       "0        1      2\n",
       "1        2      0\n",
       "2        3      9\n",
       "3        4      9\n",
       "4        5      3"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission['Label'] = Y_prediction\n",
    "submission.to_csv(\"my_submission.csv\", index=False)\n",
    "submission.head()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}