Basic-Image-Classification-with-TensorFlow.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 1: Introduction\n",
    "\n",
    "Welcome to Basic Image Classification with TensorFlow.\n",
    "\n",
    "This graph describes the problem that we are trying to solve visually. We want to create and train a model that takes an image of a hand written digit as input and predicts the class of that digit, that is, it predicts the digit or it predicts the class of the input image.\n",
    "\n",
    "![Hand Written Digits Classification](images/1_1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import TensorFlow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using TensorFlow version 2.10.1\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "tf.get_logger().setLevel('ERROR')\n",
    "print('Using TensorFlow version', tf.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 2: The Dataset\n",
    "### Import MNIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
      "11490434/11490434 [==============================] - 5s 0us/step\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.datasets import mnist\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shapes of Imported Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train shape:  (60000, 28, 28)\n",
      "y_train shape:  (60000,)\n",
      "x_test shape:  (10000, 28, 28)\n",
      "y_test shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "print('x_train shape: ', x_train.shape)\n",
    "print('y_train shape: ', y_train.shape)\n",
    "print('x_test shape: ', x_test.shape)\n",
    "print('y_test shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot an Image Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x276229b6980>"
      ]
     },
     "execution_count": 15,
     "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": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.imshow(x_train[0], cmap = 'binary')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0, 1, 2, 3, 4, 5, 6, 7, 8, 9}\n"
     ]
    }
   ],
   "source": [
    "print(set(y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 3: One Hot Encoding\n",
    "After this encoding, every label will be converted to a list with 10 elements and the element at index to the corresponding class will be set to 1, rest will be set to 0:\n",
    "\n",
    "| original label | one-hot encoded label |\n",
    "|------|------|\n",
    "| 5 | [0, 0, 0, 0, 0, 1, 0, 0, 0, 0] |\n",
    "| 7 | [0, 0, 0, 0, 0, 0, 0, 1, 0, 0] |\n",
    "| 1 | [0, 1, 0, 0, 0, 0, 0, 0, 0, 0] |\n",
    "\n",
    "### Encoding Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.utils import to_categorical\n",
    "\n",
    "y_train_encoded = to_categorical(y_train)\n",
    "y_test_encoded = to_categorical(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Validated Shapes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_train_encoded shape:  (60000, 10)\n",
      "y_test_encoded shape:  (10000, 10)\n"
     ]
    }
   ],
   "source": [
    "print('y_train_encoded shape: ', y_train_encoded.shape)\n",
    "print('y_test_encoded shape: ', y_test_encoded.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display Encoded Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 1., 0., 0.], dtype=float32)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_encoded[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 4: Neural Networks\n",
    "\n",
    "### Linear Equations\n",
    "\n",
    "![Single Neuron](images/1_2.png)\n",
    "\n",
    "The above graph simply represents the equation:\n",
    "\n",
    "\\begin{equation}\n",
    "y = w1 * x1 + w2 * x2 + w3 * x3 + b\n",
    "\\end{equation}\n",
    "\n",
    "Where the `w1, w2, w3` are called the weights and `b` is an intercept term called bias. The equation can also be *vectorised* like this:\n",
    "\n",
    "\\begin{equation}\n",
    "y = W . X + b\n",
    "\\end{equation}\n",
    "\n",
    "Where `X = [x1, x2, x3]` and `W = [w1, w2, w3].T`. The .T means *transpose*. This is because we want the dot product to give us the result we want i.e. `w1 * x1 + w2 * x2 + w3 * x3`. This gives us the vectorised version of our linear equation.\n",
    "\n",
    "A simple, linear approach to solving hand-written image classification problem - could it work?\n",
    "\n",
    "![Single Neuron with 784 features](images/1_3.png)\n",
    "\n",
    "### Neural Networks\n",
    "\n",
    "![Neural Network with 2 hidden layers](images/1_4.png)\n",
    "\n",
    "This model is much more likely to solve the problem as it can learn more complex function mapping for the inputs and outputs in our dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 5: Preprocessing the Examples\n",
    "\n",
    "### Unrolling N-dimensional Arrays to Vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train_reshaped shape:  (60000, 784)\n",
      "x_test_reshaped shape:  (10000, 784)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "x_train_reshaped = np.reshape(x_train, (60000, 784))\n",
    "x_test_reshaped = np.reshape(x_test, (10000, 784))\n",
    "\n",
    "print('x_train_reshaped shape: ', x_train_reshaped.shape)\n",
    "print('x_test_reshaped shape: ', x_test_reshaped.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display Pixel Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0, 1, 2, 3, 9, 11, 14, 16, 18, 23, 24, 25, 26, 27, 30, 35, 36, 39, 43, 45, 46, 49, 55, 56, 64, 66, 70, 78, 80, 81, 82, 90, 93, 94, 107, 108, 114, 119, 126, 127, 130, 132, 133, 135, 136, 139, 148, 150, 154, 156, 160, 166, 170, 171, 172, 175, 182, 183, 186, 187, 190, 195, 198, 201, 205, 207, 212, 213, 219, 221, 225, 226, 229, 238, 240, 241, 242, 244, 247, 249, 250, 251, 252, 253, 255}\n"
     ]
    }
   ],
   "source": [
    "print(set(x_train_reshaped[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_mean = np.mean(x_test_reshaped)\n",
    "x_std = np.std(x_test_reshaped)\n",
    "\n",
    "epsilon = 1e-10\n",
    "\n",
    "x_train_norm = (x_train_reshaped - x_mean) / (x_std + epsilon)\n",
    "x_test_norm = (x_test_reshaped - x_mean) / (x_std + epsilon)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display Normalized Pixel Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0.38155659531965136, 1.1646571517069024, 1.2909636930596848, 1.7835592043355364, 1.6698833171180323, 2.794011535157796, 2.69296630207557, 1.1772878058421807, 1.720405933659145, 2.7687502268872395, 2.4150919110994487, 2.6298130313991788, 2.7182276103461263, 0.027898279531860533, 1.7330365877944234, 1.2404410765185718, 0.11631285847880825, 0.7478455652427205, 2.4656145276405614, 1.97301901636471, -0.19945349490314787, 0.15420482088464296, 2.3393079862877793, 1.5940993923063627, 0.06579024193769527, 0.6089083697546598, -0.11103891595620015, 1.4677928509535803, 2.579290414858066, -0.42680526933815627, 0.2805113622374254, -0.24997611144426085, 0.4573405201313208, -0.28786807385009555, 1.2151797682480154, 2.074064249446936, 0.5962777156193815, 0.01526762539658229, 1.3288556554655195, 2.18774013666444, 0.19209678329047772, 1.0762425727599547, 2.4277225652347267, 1.9351270539588754, 1.4425315426830239, -0.1236695700914784, 0.26788070810214715, -0.3889133069323215, 0.7604762193779987, 0.40681790359020786, 0.9246747231366158, 0.7099536028368857, 0.5836470614841033, -0.414174615202878, 0.937305377271894, 1.922496399823597, 0.14157416674936474, 1.5435767757652497, 2.036172287041101, 2.162478828393884, 2.6550743396697354, 0.5583857532135468, -0.08577760768564366, 1.2783330389244065, -0.04788564527980893, -0.40154396106759976, -0.22471480317370435, -0.13630022422675664, -0.31312938212065206, 2.7561195727519614, 2.26352406147611, 1.5183154674946933, 1.8846044374177624, 2.743488918616683, 2.2508934073408313, -0.09840826182092191, 1.0130893020835634, 2.6171823772639007, 2.3645692945583354, 1.871973783282484, 1.7456672419297017, 1.2530717306538501, 2.604551723128622, 2.730858264481405, 2.1119562118527706}\n"
     ]
    }
   ],
   "source": [
    "print(set(x_train_norm[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 6: Creating a Model\n",
    "### Creating the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "model = Sequential([\n",
    "    Dense(128, activation = 'relu', input_shape=(784,)),\n",
    "    Dense(128, activation = 'relu'),\n",
    "    Dense(10, activation = 'softmax')          \n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Activation Functions\n",
    "\n",
    "The first step in the node is the linear sum of the inputs:\n",
    "\\begin{equation}\n",
    "Z = W . X + b\n",
    "\\end{equation}\n",
    "\n",
    "The second step in the node is the activation function output:\n",
    "\n",
    "\\begin{equation}\n",
    "A = f(Z)\n",
    "\\end{equation}\n",
    "\n",
    "Graphical representation of a node where the two operations are performed:\n",
    "\n",
    "![ReLU](images/1_5.png)\n",
    "\n",
    "### Compiling the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 128)               100480    \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 128)               16512     \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 10)                1290      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 118,282\n",
      "Trainable params: 118,282\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.compile(\n",
    "    optimizer = 'sgd',\n",
    "    loss = 'categorical_crossentropy',\n",
    "    metrics = ['accuracy']\n",
    ")\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Enable GPU as the physical device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.config.list_physical_devices('GPU')\n",
    "\n",
    "tf.test.is_gpu_available()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 7: Training the Model\n",
    "\n",
    "### Training the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0327 - accuracy: 0.9912\n",
      "Epoch 2/25\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0295 - accuracy: 0.9924\n",
      "Epoch 3/25\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0267 - accuracy: 0.9934\n",
      "Epoch 4/25\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0240 - accuracy: 0.9943\n",
      "Epoch 5/25\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0217 - accuracy: 0.9949\n",
      "Epoch 6/25\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0198 - accuracy: 0.9959\n",
      "Epoch 7/25\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0177 - accuracy: 0.9967\n",
      "Epoch 8/25\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0161 - accuracy: 0.9972\n",
      "Epoch 9/25\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0148 - accuracy: 0.9975\n",
      "Epoch 10/25\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0135 - accuracy: 0.9980\n",
      "Epoch 11/25\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0122 - accuracy: 0.9981\n",
      "Epoch 12/25\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0113 - accuracy: 0.9987\n",
      "Epoch 13/25\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0102 - accuracy: 0.9990\n",
      "Epoch 14/25\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0093 - accuracy: 0.9990\n",
      "Epoch 15/25\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0086 - accuracy: 0.9992\n",
      "Epoch 16/25\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0079 - accuracy: 0.9993\n",
      "Epoch 17/25\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0073 - accuracy: 0.9993\n",
      "Epoch 18/25\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0067 - accuracy: 0.9995\n",
      "Epoch 19/25\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0063 - accuracy: 0.9995\n",
      "Epoch 20/25\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0058 - accuracy: 0.9998\n",
      "Epoch 21/25\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0053 - accuracy: 0.9997\n",
      "Epoch 22/25\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0050 - accuracy: 0.9998\n",
      "Epoch 23/25\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0047 - accuracy: 0.9998\n",
      "Epoch 24/25\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0043 - accuracy: 0.9998\n",
      "Epoch 25/25\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0041 - accuracy: 0.9999\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x276616e20e0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train_norm, y_train_encoded, epochs=25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluating the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 3ms/step - loss: 0.0835 - accuracy: 0.9770\n",
      "Test set accuracy:  97.69999980926514\n"
     ]
    }
   ],
   "source": [
    "loss, accuracy = model.evaluate(x_test_norm, y_test_encoded)\n",
    "print('Test set accuracy: ', accuracy*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 8: Predictions\n",
    "\n",
    "### Predictions on Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 2ms/step\n",
      "Shape of preds:  (10000, 10)\n"
     ]
    }
   ],
   "source": [
    "preds = model.predict(x_test_norm)\n",
    "print(\"Shape of preds: \", preds.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 12))\n",
    "\n",
    "start_index = 0\n",
    "\n",
    "for i in range(25):\n",
    "    plt.subplot(5, 5, i+1)\n",
    "    plt.grid(False)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "\n",
    "    pred = np.argmax(preds[start_index+i])\n",
    "    gt = y_test[start_index+i]\n",
    "\n",
    "    col = 'g'\n",
    "    if pred != gt:\n",
    "        col = 'r'\n",
    "    plt.xlabel('i={}, pred = {}, gt = {}'.format(start_index+i, pred, gt), color = col)\n",
    "    plt.imshow(x_test[start_index+i], cmap = 'binary')\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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(preds[8])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}