Deep Learning - (mnist).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "da4987a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f2362835",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80c9e02b",
   "metadata": {},
   "source": [
    "## **Load Dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c8a41b72",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = tf.keras.datasets.mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c1bc388b",
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train, y_train), (x_test, y_test) = data.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "41805b34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape = (60000, 28, 28)\n",
      "y_train shape = (60000,)\n"
     ]
    }
   ],
   "source": [
    "print('X_train shape =', x_train.shape)\n",
    "print('y_train shape =', y_train.shape) #label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d7879984",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_test shape = (10000, 28, 28)\n",
      "y_test shape = (10000,)\n"
     ]
    }
   ],
   "source": [
    "print('X_test shape =', x_test.shape)\n",
    "print('y_test shape =', y_test.shape) #label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6e073b68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1a614488e10>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualize image from train data\n",
    "plt.imshow(x_train[2000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9a8f1727",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[2000]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36833645",
   "metadata": {},
   "source": [
    "RGB Image:\n",
    "    1- Red ---> (255, 0, 0)\n",
    "    2- Green ---> (0, 255, 0)\n",
    "    3- Blue ---> (0, 0, 255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d2ce6839",
   "metadata": {},
   "outputs": [],
   "source": [
    "#scaling images ---> divide image by 255 ---> 0:1\n",
    "x_train = x_train / 255 \n",
    "x_test = x_test / 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "979841e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train[2000]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb299098",
   "metadata": {},
   "source": [
    "## **Model Building**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a347439c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "14a34e70",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential() #model layers are connected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ae649c81",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\reshaping\\flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    }
   ],
   "source": [
    "#input layer ----> Hidden Layers ----> Output layer\n",
    "model.add(tf.keras.layers.Flatten(input_shape = (28,28))) #input layer ---> to take input images\n",
    "\n",
    "#hidden layers ---> Dense Layer \n",
    "model.add(tf.keras.layers.Dense(128, activation = tf.keras.activations.relu))\n",
    "model.add(tf.keras.layers.Dense(128, activation = tf.keras.activations.relu))\n",
    "\n",
    "#Output Layer ---> Dense Layer ---> softmax activation (multi-class classification) 10 classes\n",
    "model.add(tf.keras.layers.Dense(10, activation = tf.keras.activations.softmax))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8eed2e7",
   "metadata": {},
   "source": [
    "1- model building\n",
    "2- compile model\n",
    "3- fit model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b2a84a05",
   "metadata": {},
   "outputs": [],
   "source": [
    "#model compile\n",
    "model.compile(optimizer = 'adam',\n",
    "             loss = 'sparse_categorical_crossentropy',\n",
    "             metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e96807ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 935us/step - accuracy: 0.9901 - loss: 0.0303\n",
      "Epoch 2/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 949us/step - accuracy: 0.9926 - loss: 0.0237\n",
      "Epoch 3/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 914us/step - accuracy: 0.9927 - loss: 0.0227\n",
      "Epoch 4/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 908us/step - accuracy: 0.9935 - loss: 0.0184\n",
      "Epoch 5/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 984us/step - accuracy: 0.9947 - loss: 0.0156\n",
      "Epoch 6/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 953us/step - accuracy: 0.9947 - loss: 0.0170\n",
      "Epoch 7/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 945us/step - accuracy: 0.9952 - loss: 0.0152\n",
      "Epoch 8/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 954us/step - accuracy: 0.9954 - loss: 0.0145\n",
      "Epoch 9/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 919us/step - accuracy: 0.9971 - loss: 0.0088\n",
      "Epoch 10/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 947us/step - accuracy: 0.9968 - loss: 0.0096\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x1a61448a190>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fit model\n",
    "model.fit(x_train, y_train, epochs = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8c432dc5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 617us/step - accuracy: 0.9741 - loss: 0.1228\n"
     ]
    }
   ],
   "source": [
    "#test error, test accuracy\n",
    "test_loss, test_accuracy = model.evaluate(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d5e3f0cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    }
   ],
   "source": [
    "model.save('minst_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f775cc4d-42b3-439d-956b-8953f8eb2e94",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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