fashion_cnn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n",
    "from tensorflow.keras.datasets import fashion_mnist\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()\n",
    "\n",
    "x_train, x_test = x_train / 255.0, x_test / 255.0\n",
    "\n",
    "x_train = x_train.reshape(-1, 28, 28, 1)\n",
    "x_test = x_test.reshape(-1, 28, 28, 1)\n",
    "\n",
    "y_train = to_categorical(y_train, 10)\n",
    "y_test = to_categorical(y_test, 10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential([\n",
    "    Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)),\n",
    "    MaxPooling2D(pool_size=(2, 2)),\n",
    "    Conv2D(64, kernel_size=(3, 3), activation='relu'),\n",
    "    MaxPooling2D(pool_size=(2, 2)),\n",
    "    Flatten(),\n",
    "    Dense(128, activation='relu'),\n",
    "    Dropout(0.5),\n",
    "    Dense(10, activation='softmax')\n",
    "])\n",
    "\n",
    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 10ms/step - accuracy: 0.6706 - loss: 0.9214 - val_accuracy: 0.8440 - val_loss: 0.4343\n",
      "Epoch 2/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 9ms/step - accuracy: 0.8429 - loss: 0.4428 - val_accuracy: 0.8660 - val_loss: 0.3610\n",
      "Epoch 3/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 9ms/step - accuracy: 0.8601 - loss: 0.3888 - val_accuracy: 0.8776 - val_loss: 0.3362\n",
      "Epoch 4/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 9ms/step - accuracy: 0.8733 - loss: 0.3518 - val_accuracy: 0.8863 - val_loss: 0.3149\n",
      "Epoch 5/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 9ms/step - accuracy: 0.8818 - loss: 0.3327 - val_accuracy: 0.8802 - val_loss: 0.3164\n",
      "Epoch 6/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 9ms/step - accuracy: 0.8892 - loss: 0.3074 - val_accuracy: 0.8898 - val_loss: 0.2933\n",
      "Epoch 7/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 9ms/step - accuracy: 0.8969 - loss: 0.2822 - val_accuracy: 0.8949 - val_loss: 0.2850\n",
      "Epoch 8/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 9ms/step - accuracy: 0.9010 - loss: 0.2711 - val_accuracy: 0.9045 - val_loss: 0.2663\n",
      "Epoch 9/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 10ms/step - accuracy: 0.9059 - loss: 0.2560 - val_accuracy: 0.9074 - val_loss: 0.2571\n",
      "Epoch 10/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9078 - loss: 0.2482 - val_accuracy: 0.9048 - val_loss: 0.2690\n",
      "Epoch 11/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9155 - loss: 0.2310 - val_accuracy: 0.9076 - val_loss: 0.2538\n",
      "Epoch 12/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9180 - loss: 0.2200 - val_accuracy: 0.9071 - val_loss: 0.2524\n",
      "Epoch 13/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9175 - loss: 0.2182 - val_accuracy: 0.9113 - val_loss: 0.2457\n",
      "Epoch 14/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9238 - loss: 0.2032 - val_accuracy: 0.9124 - val_loss: 0.2490\n",
      "Epoch 15/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9277 - loss: 0.1970 - val_accuracy: 0.9134 - val_loss: 0.2429\n",
      "Epoch 16/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9284 - loss: 0.1865 - val_accuracy: 0.9133 - val_loss: 0.2458\n",
      "Epoch 17/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9316 - loss: 0.1812 - val_accuracy: 0.9133 - val_loss: 0.2501\n",
      "Epoch 18/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9323 - loss: 0.1763 - val_accuracy: 0.9099 - val_loss: 0.2629\n",
      "Epoch 19/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9356 - loss: 0.1704 - val_accuracy: 0.9150 - val_loss: 0.2465\n",
      "Epoch 20/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9382 - loss: 0.1662 - val_accuracy: 0.9145 - val_loss: 0.2491\n",
      "Epoch 21/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9389 - loss: 0.1602 - val_accuracy: 0.9150 - val_loss: 0.2410\n",
      "Epoch 22/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9425 - loss: 0.1525 - val_accuracy: 0.9190 - val_loss: 0.2458\n",
      "Epoch 23/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9435 - loss: 0.1509 - val_accuracy: 0.9152 - val_loss: 0.2647\n",
      "Epoch 24/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9430 - loss: 0.1463 - val_accuracy: 0.9165 - val_loss: 0.2664\n",
      "Epoch 25/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9454 - loss: 0.1409 - val_accuracy: 0.9170 - val_loss: 0.2675\n",
      "Epoch 26/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9488 - loss: 0.1324 - val_accuracy: 0.9169 - val_loss: 0.2691\n",
      "Epoch 27/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9493 - loss: 0.1314 - val_accuracy: 0.9172 - val_loss: 0.2881\n",
      "Epoch 28/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9491 - loss: 0.1315 - val_accuracy: 0.9165 - val_loss: 0.2874\n",
      "Epoch 29/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9523 - loss: 0.1224 - val_accuracy: 0.9192 - val_loss: 0.2759\n",
      "Epoch 30/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 11ms/step - accuracy: 0.9542 - loss: 0.1156 - val_accuracy: 0.9164 - val_loss: 0.2944\n",
      "Epoch 31/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9531 - loss: 0.1211 - val_accuracy: 0.9195 - val_loss: 0.2749\n",
      "Epoch 32/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9566 - loss: 0.1128 - val_accuracy: 0.9170 - val_loss: 0.2996\n",
      "Epoch 33/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9543 - loss: 0.1155 - val_accuracy: 0.9191 - val_loss: 0.2929\n",
      "Epoch 34/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9589 - loss: 0.1043 - val_accuracy: 0.9162 - val_loss: 0.3060\n",
      "Epoch 35/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9600 - loss: 0.1025 - val_accuracy: 0.9160 - val_loss: 0.3172\n",
      "Epoch 36/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9593 - loss: 0.1026 - val_accuracy: 0.9155 - val_loss: 0.3211\n",
      "Epoch 37/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9605 - loss: 0.1001 - val_accuracy: 0.9174 - val_loss: 0.3209\n",
      "Epoch 38/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9618 - loss: 0.0975 - val_accuracy: 0.9133 - val_loss: 0.3334\n",
      "Epoch 39/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9625 - loss: 0.0978 - val_accuracy: 0.9158 - val_loss: 0.3225\n",
      "Epoch 40/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9637 - loss: 0.0932 - val_accuracy: 0.9163 - val_loss: 0.3464\n",
      "Epoch 41/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9636 - loss: 0.0926 - val_accuracy: 0.9181 - val_loss: 0.3252\n",
      "Epoch 42/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9652 - loss: 0.0863 - val_accuracy: 0.9165 - val_loss: 0.3533\n",
      "Epoch 43/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9659 - loss: 0.0840 - val_accuracy: 0.9136 - val_loss: 0.3375\n",
      "Epoch 44/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9641 - loss: 0.0901 - val_accuracy: 0.9152 - val_loss: 0.3422\n",
      "Epoch 45/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9668 - loss: 0.0836 - val_accuracy: 0.9161 - val_loss: 0.3700\n",
      "Epoch 46/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9685 - loss: 0.0817 - val_accuracy: 0.9170 - val_loss: 0.3545\n",
      "Epoch 47/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9677 - loss: 0.0793 - val_accuracy: 0.9164 - val_loss: 0.3645\n",
      "Epoch 48/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9673 - loss: 0.0804 - val_accuracy: 0.9179 - val_loss: 0.3696\n",
      "Epoch 49/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9706 - loss: 0.0772 - val_accuracy: 0.9169 - val_loss: 0.3384\n",
      "Epoch 50/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9688 - loss: 0.0774 - val_accuracy: 0.9162 - val_loss: 0.3889\n",
      "Epoch 51/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9697 - loss: 0.0755 - val_accuracy: 0.9187 - val_loss: 0.3930\n",
      "Epoch 52/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9697 - loss: 0.0745 - val_accuracy: 0.9153 - val_loss: 0.4169\n",
      "Epoch 53/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9716 - loss: 0.0705 - val_accuracy: 0.9180 - val_loss: 0.3837\n",
      "Epoch 54/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 11ms/step - accuracy: 0.9721 - loss: 0.0710 - val_accuracy: 0.9171 - val_loss: 0.3877\n",
      "Epoch 55/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 11ms/step - accuracy: 0.9712 - loss: 0.0699 - val_accuracy: 0.9178 - val_loss: 0.4019\n",
      "Epoch 56/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9711 - loss: 0.0695 - val_accuracy: 0.9152 - val_loss: 0.4121\n",
      "Epoch 57/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9713 - loss: 0.0687 - val_accuracy: 0.9182 - val_loss: 0.4083\n",
      "Epoch 58/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9727 - loss: 0.0667 - val_accuracy: 0.9164 - val_loss: 0.4354\n",
      "Epoch 59/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9720 - loss: 0.0664 - val_accuracy: 0.9130 - val_loss: 0.4443\n",
      "Epoch 60/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9714 - loss: 0.0691 - val_accuracy: 0.9170 - val_loss: 0.4396\n",
      "Epoch 61/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9756 - loss: 0.0624 - val_accuracy: 0.9165 - val_loss: 0.4473\n",
      "Epoch 62/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9729 - loss: 0.0672 - val_accuracy: 0.9144 - val_loss: 0.4563\n",
      "Epoch 63/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9723 - loss: 0.0674 - val_accuracy: 0.9165 - val_loss: 0.4457\n",
      "Epoch 64/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9721 - loss: 0.0656 - val_accuracy: 0.9133 - val_loss: 0.4474\n",
      "Epoch 65/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9733 - loss: 0.0647 - val_accuracy: 0.9151 - val_loss: 0.4453\n",
      "Epoch 66/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9723 - loss: 0.0667 - val_accuracy: 0.9177 - val_loss: 0.4410\n",
      "Epoch 67/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9747 - loss: 0.0599 - val_accuracy: 0.9149 - val_loss: 0.4501\n",
      "Epoch 68/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9741 - loss: 0.0625 - val_accuracy: 0.9188 - val_loss: 0.4540\n",
      "Epoch 69/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9747 - loss: 0.0633 - val_accuracy: 0.9152 - val_loss: 0.4792\n",
      "Epoch 70/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9758 - loss: 0.0576 - val_accuracy: 0.9208 - val_loss: 0.4662\n",
      "Epoch 71/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9772 - loss: 0.0571 - val_accuracy: 0.9171 - val_loss: 0.4685\n",
      "Epoch 72/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9762 - loss: 0.0577 - val_accuracy: 0.9164 - val_loss: 0.5011\n",
      "Epoch 73/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9773 - loss: 0.0548 - val_accuracy: 0.9184 - val_loss: 0.4698\n",
      "Epoch 74/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9756 - loss: 0.0596 - val_accuracy: 0.9138 - val_loss: 0.5076\n",
      "Epoch 75/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9759 - loss: 0.0590 - val_accuracy: 0.9170 - val_loss: 0.4633\n",
      "Epoch 76/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9777 - loss: 0.0548 - val_accuracy: 0.9171 - val_loss: 0.4902\n",
      "Epoch 77/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9775 - loss: 0.0569 - val_accuracy: 0.9193 - val_loss: 0.4944\n",
      "Epoch 78/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9782 - loss: 0.0531 - val_accuracy: 0.9118 - val_loss: 0.5289\n",
      "Epoch 79/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9778 - loss: 0.0577 - val_accuracy: 0.9162 - val_loss: 0.5105\n",
      "Epoch 80/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9779 - loss: 0.0561 - val_accuracy: 0.9183 - val_loss: 0.5507\n",
      "Epoch 81/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9784 - loss: 0.0539 - val_accuracy: 0.9152 - val_loss: 0.4991\n",
      "Epoch 82/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9767 - loss: 0.0569 - val_accuracy: 0.9151 - val_loss: 0.5432\n",
      "Epoch 83/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9780 - loss: 0.0525 - val_accuracy: 0.9160 - val_loss: 0.5250\n",
      "Epoch 84/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9789 - loss: 0.0520 - val_accuracy: 0.9133 - val_loss: 0.5534\n",
      "Epoch 85/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9776 - loss: 0.0547 - val_accuracy: 0.9150 - val_loss: 0.5320\n",
      "Epoch 86/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9800 - loss: 0.0495 - val_accuracy: 0.9187 - val_loss: 0.5397\n",
      "Epoch 87/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9790 - loss: 0.0527 - val_accuracy: 0.9164 - val_loss: 0.5220\n",
      "Epoch 88/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9795 - loss: 0.0492 - val_accuracy: 0.9163 - val_loss: 0.5193\n",
      "Epoch 89/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9788 - loss: 0.0529 - val_accuracy: 0.9139 - val_loss: 0.5420\n",
      "Epoch 90/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9801 - loss: 0.0478 - val_accuracy: 0.9191 - val_loss: 0.5211\n",
      "Epoch 91/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9795 - loss: 0.0496 - val_accuracy: 0.9153 - val_loss: 0.5094\n",
      "Epoch 92/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9778 - loss: 0.0532 - val_accuracy: 0.9185 - val_loss: 0.5613\n",
      "Epoch 93/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9803 - loss: 0.0490 - val_accuracy: 0.9151 - val_loss: 0.5397\n",
      "Epoch 94/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9798 - loss: 0.0502 - val_accuracy: 0.9150 - val_loss: 0.5493\n",
      "Epoch 95/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9805 - loss: 0.0483 - val_accuracy: 0.9153 - val_loss: 0.5627\n",
      "Epoch 96/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9802 - loss: 0.0487 - val_accuracy: 0.9178 - val_loss: 0.5558\n",
      "Epoch 97/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9803 - loss: 0.0496 - val_accuracy: 0.9155 - val_loss: 0.5732\n",
      "Epoch 98/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9802 - loss: 0.0485 - val_accuracy: 0.9156 - val_loss: 0.5707\n",
      "Epoch 99/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9813 - loss: 0.0487 - val_accuracy: 0.9171 - val_loss: 0.5715\n",
      "Epoch 100/100\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9784 - loss: 0.0531 - val_accuracy: 0.9166 - val_loss: 0.5464\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=100, batch_size=128)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 0.9166\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(x_test, y_test, verbose=0)\n",
    "print(f'Test accuracy: {test_acc:.4f}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['accuracy'], label='accuracy')\n",
    "plt.plot(history.history['val_accuracy'], label='val_accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}