7_fashion_mnist_classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "aa1f681d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import display\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.compose import make_column_transformer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.losses import BinaryCrossentropy, MeanAbsoluteError\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense \n",
    "from keras import backend as K\n",
    "from keras.datasets import fashion_mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "392f2ac1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
      "29515/29515 [==============================] - 0s 1us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
      "26421880/26421880 [==============================] - 15s 1us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
      "5148/5148 [==============================] - 0s 0s/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
      "4422102/4422102 [==============================] - 3s 1us/step\n"
     ]
    }
   ],
   "source": [
    "(X_train, y_train), (X_test, y_test) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "80a8cc6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dc8b7aed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000,)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "01a4fb54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8eb64466",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000,)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c684e3d",
   "metadata": {},
   "source": [
    "### Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5833595c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize and reeshape images \n",
    "X_train_final = X_train.reshape(-1, 28*28) / 255\n",
    "X_test_final = X_test.reshape(-1, 28*28) / 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "11fbbe72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 784)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_final.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1587f689",
   "metadata": {},
   "source": [
    "### Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f2b1bb7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 512)               401920    \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 128)               65664     \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 10)                1290      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 468,874\n",
      "Trainable params: 468,874\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(512, input_dim=784, activation='relu'))\n",
    "model.add(Dense(128, activation='relu'))\n",
    "model.add(Dense(10, activation='softmax'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8c4f10fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=\"SparseCategoricalCrossentropy\", optimizer=\"Adam\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d5bada96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "1875/1875 [==============================] - 104s 51ms/step - loss: 0.4690 - accuracy: 0.8307 - val_loss: 0.4274 - val_accuracy: 0.8449\n",
      "Epoch 2/20\n",
      "1875/1875 [==============================] - 77s 41ms/step - loss: 0.3574 - accuracy: 0.8683 - val_loss: 0.3862 - val_accuracy: 0.8641\n",
      "Epoch 3/20\n",
      "1875/1875 [==============================] - 60s 32ms/step - loss: 0.3221 - accuracy: 0.8817 - val_loss: 0.3630 - val_accuracy: 0.8663\n",
      "Epoch 4/20\n",
      "1875/1875 [==============================] - 64s 34ms/step - loss: 0.2976 - accuracy: 0.8888 - val_loss: 0.3457 - val_accuracy: 0.8745\n",
      "Epoch 5/20\n",
      "1875/1875 [==============================] - 64s 34ms/step - loss: 0.2789 - accuracy: 0.8963 - val_loss: 0.3419 - val_accuracy: 0.8760\n",
      "Epoch 6/20\n",
      "1875/1875 [==============================] - 66s 35ms/step - loss: 0.2641 - accuracy: 0.9012 - val_loss: 0.3522 - val_accuracy: 0.8739\n",
      "Epoch 7/20\n",
      "1875/1875 [==============================] - 65s 34ms/step - loss: 0.2518 - accuracy: 0.9047 - val_loss: 0.3343 - val_accuracy: 0.8848\n",
      "Epoch 8/20\n",
      "1875/1875 [==============================] - 61s 33ms/step - loss: 0.2419 - accuracy: 0.9078 - val_loss: 0.3345 - val_accuracy: 0.8818\n",
      "Epoch 9/20\n",
      "1875/1875 [==============================] - 90s 48ms/step - loss: 0.2304 - accuracy: 0.9131 - val_loss: 0.3296 - val_accuracy: 0.8853\n",
      "Epoch 10/20\n",
      "1875/1875 [==============================] - 72s 39ms/step - loss: 0.2221 - accuracy: 0.9156 - val_loss: 0.3466 - val_accuracy: 0.8831\n",
      "Epoch 11/20\n",
      "1875/1875 [==============================] - 68s 36ms/step - loss: 0.2153 - accuracy: 0.9175 - val_loss: 0.3438 - val_accuracy: 0.8871\n",
      "Epoch 12/20\n",
      "1875/1875 [==============================] - 90s 48ms/step - loss: 0.2057 - accuracy: 0.9224 - val_loss: 0.3346 - val_accuracy: 0.8870\n",
      "Epoch 13/20\n",
      "1875/1875 [==============================] - 93s 49ms/step - loss: 0.1910 - accuracy: 0.9276 - val_loss: 0.3456 - val_accuracy: 0.8942\n",
      "Epoch 15/20\n",
      "1875/1875 [==============================] - 66s 35ms/step - loss: 0.1849 - accuracy: 0.9294 - val_loss: 0.3466 - val_accuracy: 0.8937\n",
      "Epoch 16/20\n",
      "1875/1875 [==============================] - 112s 60ms/step - loss: 0.1785 - accuracy: 0.9310 - val_loss: 0.3742 - val_accuracy: 0.8879\n",
      "Epoch 17/20\n",
      "1875/1875 [==============================] - 130s 69ms/step - loss: 0.1727 - accuracy: 0.9338 - val_loss: 0.3756 - val_accuracy: 0.8848\n",
      "Epoch 18/20\n",
      "1875/1875 [==============================] - 86s 46ms/step - loss: 0.1684 - accuracy: 0.9358 - val_loss: 0.3653 - val_accuracy: 0.8926\n",
      "Epoch 19/20\n",
      "1875/1875 [==============================] - 77s 41ms/step - loss: 0.1618 - accuracy: 0.9379 - val_loss: 0.3768 - val_accuracy: 0.8929\n",
      "Epoch 20/20\n",
      "1875/1875 [==============================] - 64s 34ms/step - loss: 0.1568 - accuracy: 0.9396 - val_loss: 0.4108 - val_accuracy: 0.8875\n"
     ]
    }
   ],
   "source": [
    "result = model.fit(X_train_final, y_train, verbose=1, epochs=20, validation_data=(X_test_final, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e5773bb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(result.history[\"loss\"])\n",
    "plt.plot(result.history[\"val_loss\"])\n",
    "plt.title(\"Model Loss\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.legend([\"Train\", \"Test\"], loc=\"upper right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3e9156e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(result.history[\"accuracy\"])\n",
    "plt.plot(result.history[\"val_accuracy\"])\n",
    "plt.title(\"Model Accuracy\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.xlabel(\"Accuracy\")\n",
    "plt.legend([\"Train\", \"Test\"], loc=\"upper right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f235195e",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"fashion_mnist_classification_model.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "337a320d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save_weights(\"fashion_mnist_classification_model_weights.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "788266a1",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}