Keras Basics .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "65ad565e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "88afb22b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "024ca2bd",
   "metadata": {},
   "source": [
    "# Loading the MNIST Dataset \n",
    "\n",
    "label - Description\n",
    "\n",
    "0     - T-shirt/top\n",
    "\n",
    "1\t  - Trouser\n",
    "\n",
    "2\t  - Pullover\n",
    "\n",
    "3\t  - Dress\n",
    "\n",
    "4\t  - Coat\n",
    "\n",
    "5\t  - Sandal\n",
    "\n",
    "6\t  - Shirt\n",
    "\n",
    "7\t  - Sneaker\n",
    "\n",
    "8\t  - Bag\n",
    "\n",
    "9\t  - Ankle boot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d88e8e2a",
   "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",
      "32768/29515 [=================================] - 0s 0us/step\n",
      "40960/29515 [=========================================] - 0s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
      "26427392/26421880 [==============================] - 0s 0us/step\n",
      "26435584/26421880 [==============================] - 0s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
      "16384/5148 [===============================================================================================] - 0s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
      "4423680/4422102 [==============================] - 0s 0us/step\n",
      "4431872/4422102 [==============================] - 0s 0us/step\n"
     ]
    }
   ],
   "source": [
    "fashion_mnist = keras.datasets.fashion_mnist\n",
    "(X_train_full, y_train_full), (X_test,y_test) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9cf1cf88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff8fa3a2550>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.imshow(X_train_full[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b188691d",
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = [\"T-shirt/top\",\"Trouser\",\"Pullover\",\"Dress\",\"Coat\",\"Sandal\",\"Shirt\",\"Sneaker\",\"Bag\",\"Ankle boot\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d316683a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'T-shirt/top'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_names[y_train_full[10]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7520c409",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff8f5f0cda0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.imshow(X_train_full[10])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcbc670f",
   "metadata": {},
   "source": [
    "## Data normaliztion \n",
    "\n",
    "We make the data dimensions so that they are of approximately the same scale "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "60a20634",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_n = X_train_full/255.\n",
    "X_test_n = X_test / 255."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "856a0bfc",
   "metadata": {},
   "source": [
    "## Split the data into train/validation datasets \n",
    "\n",
    "Validation dataset - used for tuning the hyperparameters and evaluate the models "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8bfacd20",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_valid, X_train = X_train_n[:5000],X_train_n[5000:]\n",
    "y_valid, y_train = y_train_full[:5000], y_train_full[5000:]\n",
    "X_test = X_test_n"
   ]
  }
 ],
 "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}