hand_written_digit_recognition.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hand Written Digit Recognition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T16:42:11.521096Z",
     "iopub.status.busy": "2024-11-17T16:42:11.520674Z",
     "iopub.status.idle": "2024-11-17T16:42:23.927757Z",
     "shell.execute_reply": "2024-11-17T16:42:23.926988Z",
     "shell.execute_reply.started": "2024-11-17T16:42:11.521046Z"
    },
    "trusted": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T16:42:23.930588Z",
     "iopub.status.busy": "2024-11-17T16:42:23.929628Z",
     "iopub.status.idle": "2024-11-17T16:42:25.419522Z",
     "shell.execute_reply": "2024-11-17T16:42:25.418713Z",
     "shell.execute_reply.started": "2024-11-17T16:42:23.930538Z"
    },
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
      "\u001b[1m11490434/11490434\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 0us/step\n"
     ]
    }
   ],
   "source": [
    "(X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data()\n",
    "\n",
    "X_train, X_test = X_train / 255, X_test / 255"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seeing Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T16:42:25.421190Z",
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     "iopub.status.idle": "2024-11-17T16:42:25.521125Z",
     "shell.execute_reply": "2024-11-17T16:42:25.519673Z",
     "shell.execute_reply.started": "2024-11-17T16:42:25.421148Z"
    },
    "trusted": true
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_train[0], cmap= 'binary')\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T16:42:25.525538Z",
     "iopub.status.busy": "2024-11-17T16:42:25.524752Z",
     "iopub.status.idle": "2024-11-17T16:42:25.550425Z",
     "shell.execute_reply": "2024-11-17T16:42:25.548994Z",
     "shell.execute_reply.started": "2024-11-17T16:42:25.525474Z"
    },
    "trusted": true
   },
   "outputs": [
    {
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       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        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.        ]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T16:42:25.552434Z",
     "iopub.status.busy": "2024-11-17T16:42:25.551911Z",
     "iopub.status.idle": "2024-11-17T16:42:25.562113Z",
     "shell.execute_reply": "2024-11-17T16:42:25.560636Z",
     "shell.execute_reply.started": "2024-11-17T16:42:25.552374Z"
    },
    "trusted": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T16:42:25.564912Z",
     "iopub.status.busy": "2024-11-17T16:42:25.563871Z",
     "iopub.status.idle": "2024-11-17T16:42:25.572588Z",
     "shell.execute_reply": "2024-11-17T16:42:25.571404Z",
     "shell.execute_reply.started": "2024-11-17T16:42:25.564849Z"
    },
    "trusted": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 0, 4, ..., 5, 6, 8], dtype=uint8)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T16:42:25.575308Z",
     "iopub.status.busy": "2024-11-17T16:42:25.574092Z",
     "iopub.status.idle": "2024-11-17T16:42:27.621565Z",
     "shell.execute_reply": "2024-11-17T16:42:27.620660Z",
     "shell.execute_reply.started": "2024-11-17T16:42:25.575245Z"
    },
    "trusted": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 40 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_rows: int = 4\n",
    "n_cols: int = 10\n",
    "plt.figure(figsize= (n_cols, n_rows))\n",
    "\n",
    "for row in range(n_rows):\n",
    "    for col in range(n_cols):\n",
    "        index: int = n_cols * row + col + 1\n",
    "        plt.subplot(n_rows, n_cols, index)\n",
    "        plt.imshow(X_train[index], cmap= 'binary', interpolation= 'nearest')\n",
    "        plt.axis('off')\n",
    "        plt.title(y_train[index])\n",
    "        \n",
    "plt.subplots_adjust(wspace=0.2, hspace=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T16:42:27.623303Z",
     "iopub.status.busy": "2024-11-17T16:42:27.622915Z",
     "iopub.status.idle": "2024-11-17T16:42:27.628276Z",
     "shell.execute_reply": "2024-11-17T16:42:27.627288Z",
     "shell.execute_reply.started": "2024-11-17T16:42:27.623262Z"
    },
    "trusted": true
   },
   "outputs": [],
   "source": [
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T16:42:27.629763Z",
     "iopub.status.busy": "2024-11-17T16:42:27.629479Z",
     "iopub.status.idle": "2024-11-17T16:42:27.714769Z",
     "shell.execute_reply": "2024-11-17T16:42:27.713986Z",
     "shell.execute_reply.started": "2024-11-17T16:42:27.629733Z"
    },
    "trusted": true
   },
   "outputs": [],
   "source": [
    "tensorboard_cb = tf.keras.callbacks.TensorBoard(\n",
    "    '/kaggle/working/logs', \n",
    "    profile_batch= (100, 200), \n",
    "    histogram_freq= 1\n",
    ")\n",
    "early_stopping = tf.keras.callbacks.EarlyStopping(\n",
    "    patience= 5, \n",
    "    restore_best_weights= True\n",
    ")\n",
    "checkpoint_cb = tf.keras.callbacks.ModelCheckpoint(\n",
    "    'my_checkpoints.weights.h5', \n",
    "    save_weights_only= True,\n",
    "    save_best_only= True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T16:42:27.718222Z",
     "iopub.status.busy": "2024-11-17T16:42:27.717828Z",
     "iopub.status.idle": "2024-11-17T16:42:28.372523Z",
     "shell.execute_reply": "2024-11-17T16:42:28.371740Z",
     "shell.execute_reply.started": "2024-11-17T16:42:27.718187Z"
    },
    "trusted": true
   },
   "outputs": [],
   "source": [
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Input(shape= (28, 28, 1)),\n",
    "    tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),\n",
    "    tf.keras.layers.MaxPooling2D((2, 2)),\n",
    "    tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),\n",
    "    tf.keras.layers.MaxPooling2D((2, 2)),\n",
    "    tf.keras.layers.Flatten(),\n",
    "    tf.keras.layers.Dense(128, activation='relu'),\n",
    "    tf.keras.layers.Dropout(0.3),\n",
    "    tf.keras.layers.Dense(10, activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2024-11-17T16:42:28.397802Z",
     "shell.execute_reply": "2024-11-17T16:42:28.396907Z",
     "shell.execute_reply.started": "2024-11-17T16:42:28.373787Z"
    },
    "trusted": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">11</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">11</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1600</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">204,928</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,290</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m320\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m18,496\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1600\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │       \u001b[38;5;34m204,928\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m1,290\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">225,034</span> (879.04 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m225,034\u001b[0m (879.04 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">225,034</span> (879.04 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m225,034\u001b[0m (879.04 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
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   "execution_count": 12,
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     "shell.execute_reply": "2024-11-17T16:42:28.622027Z",
     "shell.execute_reply.started": "2024-11-17T16:42:28.399101Z"
    },
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   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.keras.utils.plot_model(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T16:42:28.626234Z",
     "iopub.status.busy": "2024-11-17T16:42:28.624535Z",
     "iopub.status.idle": "2024-11-17T16:42:28.647384Z",
     "shell.execute_reply": "2024-11-17T16:42:28.646476Z",
     "shell.execute_reply.started": "2024-11-17T16:42:28.626189Z"
    },
    "trusted": true
   },
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    optimizer='adam',\n",
    "    loss='sparse_categorical_crossentropy',\n",
    "    metrics=['accuracy']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T16:42:28.648654Z",
     "iopub.status.busy": "2024-11-17T16:42:28.648380Z",
     "iopub.status.idle": "2024-11-17T16:43:23.535341Z",
     "shell.execute_reply": "2024-11-17T16:43:23.534366Z",
     "shell.execute_reply.started": "2024-11-17T16:42:28.648624Z"
    },
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1731861751.084978      98 service.cc:145] XLA service 0x7ae9e00050a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
      "I0000 00:00:1731861751.085023      98 service.cc:153]   StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0\n",
      "I0000 00:00:1731861754.279135      98 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 5ms/step - accuracy: 0.8875 - loss: 0.3624 - val_accuracy: 0.9867 - val_loss: 0.0470\n",
      "Epoch 2/50\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9805 - loss: 0.0619 - val_accuracy: 0.9893 - val_loss: 0.0391\n",
      "Epoch 3/50\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9861 - loss: 0.0422 - val_accuracy: 0.9902 - val_loss: 0.0371\n",
      "Epoch 4/50\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9903 - loss: 0.0309 - val_accuracy: 0.9905 - val_loss: 0.0329\n",
      "Epoch 5/50\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9918 - loss: 0.0257 - val_accuracy: 0.9910 - val_loss: 0.0392\n",
      "Epoch 6/50\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9927 - loss: 0.0222 - val_accuracy: 0.9927 - val_loss: 0.0343\n",
      "Epoch 7/50\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9948 - loss: 0.0164 - val_accuracy: 0.9927 - val_loss: 0.0341\n",
      "Epoch 8/50\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9946 - loss: 0.0158 - val_accuracy: 0.9912 - val_loss: 0.0412\n",
      "Epoch 9/50\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9951 - loss: 0.0151 - val_accuracy: 0.9913 - val_loss: 0.0385\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    X_train, \n",
    "    y_train,\n",
    "    epochs= 50,\n",
    "    validation_split= 0.1,\n",
    "    callbacks= [tensorboard_cb, early_stopping, checkpoint_cb]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T16:43:23.537112Z",
     "iopub.status.busy": "2024-11-17T16:43:23.536765Z",
     "iopub.status.idle": "2024-11-17T16:43:24.150161Z",
     "shell.execute_reply": "2024-11-17T16:43:24.149335Z",
     "shell.execute_reply.started": "2024-11-17T16:43:23.537078Z"
    },
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9884 - loss: 0.0333\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T16:43:24.151796Z",
     "iopub.status.busy": "2024-11-17T16:43:24.151483Z",
     "iopub.status.idle": "2024-11-17T16:43:24.156459Z",
     "shell.execute_reply": "2024-11-17T16:43:24.155565Z",
     "shell.execute_reply.started": "2024-11-17T16:43:24.151762Z"
    },
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99.11%\n"
     ]
    }
   ],
   "source": [
    "print(f'{test_acc:.2%}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T16:43:24.157892Z",
     "iopub.status.busy": "2024-11-17T16:43:24.157592Z",
     "iopub.status.idle": "2024-11-17T16:43:25.238344Z",
     "shell.execute_reply": "2024-11-17T16:43:25.237461Z",
     "shell.execute_reply.started": "2024-11-17T16:43:24.157849Z"
    },
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "y_pred_classes = y_pred.argmax(axis= 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T16:43:25.239821Z",
     "iopub.status.busy": "2024-11-17T16:43:25.239532Z",
     "iopub.status.idle": "2024-11-17T16:43:25.780561Z",
     "shell.execute_reply": "2024-11-17T16:43:25.779665Z",
     "shell.execute_reply.started": "2024-11-17T16:43:25.239791Z"
    },
    "trusted": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7aea8936b910>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred_classes)\n",
    "\n",
    "cmd = ConfusionMatrixDisplay(confusion_matrix= cm, display_labels= range(10))\n",
    "cmd.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T16:43:25.782275Z",
     "iopub.status.busy": "2024-11-17T16:43:25.781780Z",
     "iopub.status.idle": "2024-11-17T16:43:25.819486Z",
     "shell.execute_reply": "2024-11-17T16:43:25.818774Z",
     "shell.execute_reply.started": "2024-11-17T16:43:25.782231Z"
    },
    "trusted": true
   },
   "outputs": [],
   "source": [
    "model.save('/kaggle/working/handwritten_digit_rec.keras')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T16:43:25.820794Z",
     "iopub.status.busy": "2024-11-17T16:43:25.820500Z",
     "iopub.status.idle": "2024-11-17T16:43:26.172082Z",
     "shell.execute_reply": "2024-11-17T16:43:26.171140Z",
     "shell.execute_reply.started": "2024-11-17T16:43:25.820762Z"
    },
    "trusted": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CompletedProcess(args=['zip', '-r', 'working_dir.zip', '/kaggle/working'], returncode=0)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import subprocess\n",
    "\n",
    "subprocess.run([\"zip\", \"-r\", \"working_dir.zip\", \"/kaggle/working\"], stdout= subprocess.DEVNULL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "trusted": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kaggle": {
   "accelerator": "gpu",
   "dataSources": [],
   "dockerImageVersionId": 30787,
   "isGpuEnabled": true,
   "isInternetEnabled": true,
   "language": "python",
   "sourceType": "notebook"
  },
  "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": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}