Clothing Classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "98461b29",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tensorflow.keras.datasets import fashion_mnist\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "47ebbb8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "30d14fb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train.astype('float32') / 255\n",
    "x_test = x_test.astype('float32') / 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cd39190c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train.reshape((-1, 28, 28, 1))\n",
    "x_test = x_test.reshape((-1, 28, 28, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8653fbe8",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = to_categorical(y_train, num_classes=10)\n",
    "y_test = to_categorical(y_test, num_classes=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ec97b223",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential([\n",
    "    Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=(28, 28, 1)),\n",
    "    MaxPooling2D(pool_size=(2, 2)),\n",
    "    \n",
    "    Conv2D(64, (3, 3), activation='relu', padding='same'),\n",
    "    MaxPooling2D(pool_size=(2, 2)),\n",
    "    \n",
    "    Flatten(),\n",
    "    \n",
    "    Dense(128, activation=\"relu\"),\n",
    "    Dense(10, activation=\"softmax\")\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f118efe2",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "841c9415",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-01 10:43:29.293082: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "469/469 [==============================] - 13s 27ms/step - loss: 0.4782 - accuracy: 0.8287 - val_loss: 0.3555 - val_accuracy: 0.8768\n",
      "Epoch 2/10\n",
      "469/469 [==============================] - 13s 27ms/step - loss: 0.3063 - accuracy: 0.8899 - val_loss: 0.3087 - val_accuracy: 0.8928\n",
      "Epoch 3/10\n",
      "469/469 [==============================] - 13s 28ms/step - loss: 0.2621 - accuracy: 0.9057 - val_loss: 0.2707 - val_accuracy: 0.8999\n",
      "Epoch 4/10\n",
      "469/469 [==============================] - 13s 29ms/step - loss: 0.2349 - accuracy: 0.9136 - val_loss: 0.2904 - val_accuracy: 0.8953\n",
      "Epoch 5/10\n",
      "469/469 [==============================] - 13s 29ms/step - loss: 0.2094 - accuracy: 0.9230 - val_loss: 0.2476 - val_accuracy: 0.9139\n",
      "Epoch 6/10\n",
      "469/469 [==============================] - 14s 29ms/step - loss: 0.1892 - accuracy: 0.9298 - val_loss: 0.2322 - val_accuracy: 0.9169\n",
      "Epoch 7/10\n",
      "469/469 [==============================] - 14s 30ms/step - loss: 0.1735 - accuracy: 0.9374 - val_loss: 0.2299 - val_accuracy: 0.9203\n",
      "Epoch 8/10\n",
      "469/469 [==============================] - 14s 30ms/step - loss: 0.1556 - accuracy: 0.9427 - val_loss: 0.2388 - val_accuracy: 0.9166\n",
      "Epoch 9/10\n",
      "469/469 [==============================] - 14s 30ms/step - loss: 0.1413 - accuracy: 0.9485 - val_loss: 0.2360 - val_accuracy: 0.9193\n",
      "Epoch 10/10\n",
      "469/469 [==============================] - 14s 31ms/step - loss: 0.1265 - accuracy: 0.9533 - val_loss: 0.2337 - val_accuracy: 0.9209\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x_train, y_train, batch_size=128, epochs=10, validation_data=(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1194d1ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"clothing.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2179da34",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_accuracy</th>\n",
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       "      <td>0.828667</td>\n",
       "      <td>0.355455</td>\n",
       "      <td>0.8768</td>\n",
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       "      <td>0.889950</td>\n",
       "      <td>0.308745</td>\n",
       "      <td>0.8928</td>\n",
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       "      <td>0.905717</td>\n",
       "      <td>0.270653</td>\n",
       "      <td>0.8999</td>\n",
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       "      <td>0.913583</td>\n",
       "      <td>0.290437</td>\n",
       "      <td>0.8953</td>\n",
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       "      <td>0.209395</td>\n",
       "      <td>0.923033</td>\n",
       "      <td>0.247632</td>\n",
       "      <td>0.9139</td>\n",
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      "text/plain": [
       "       loss  accuracy  val_loss  val_accuracy\n",
       "0  0.478243  0.828667  0.355455        0.8768\n",
       "1  0.306271  0.889950  0.308745        0.8928\n",
       "2  0.262107  0.905717  0.270653        0.8999\n",
       "3  0.234857  0.913583  0.290437        0.8953\n",
       "4  0.209395  0.923033  0.247632        0.9139"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history_df = pd.DataFrame(history.history)\n",
    "history_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4081485d",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(history.history[\"accuracy\"])\n",
    "plt.plot(history.history[\"val_accuracy\"])\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend([\"train\", \"val\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3d6a943e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(history.history[\"loss\"])\n",
    "plt.plot(history.history[\"val_loss\"])\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend([\"train\", \"val\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c6810414",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 4ms/step - loss: 0.2337 - accuracy: 0.9209\n",
      "Test accuracy: 0.9208999872207642\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(x_test, y_test)\n",
    "print('Test accuracy:', test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "260a05a0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}