CNN classifier for the MNIST dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Libraries and MNIST dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist_data = tf.keras.datasets.mnist\n",
    "(train_images, train_labels), (test_images, test_labels) = mnist_data.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scale_mnist_data(train_images, test_images):\n",
    "    \"\"\"\n",
    "    This function takes in the training and test images as loaded in the cell above, and scales them\n",
    "    so that they have minimum and maximum values equal to 0 and 1 respectively.\n",
    "    Your function should return a tuple (train_images, test_images) of scaled training and test images.\n",
    "    \"\"\"\n",
    "    train_images=train_images/255\n",
    "    test_images=test_images/255\n",
    "    return (train_images,test_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_train_images, scaled_test_images = scale_mnist_data(train_images, test_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_train_images = scaled_train_images[..., np.newaxis]\n",
    "scaled_test_images = scaled_test_images[..., np.newaxis]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building a Convolution Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Flatten,Conv2D,MaxPooling2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_model(input_shape):\n",
    "    \"\"\"\n",
    "    This function should build a Sequential model according to the above specification. Ensure the \n",
    "    weights are initialised by providing the input_shape argument in the first layer, given by the\n",
    "    function argument.\n",
    "    Your function should return the model.\n",
    "    \"\"\"\n",
    "    \n",
    "    model=Sequential([\n",
    "        Conv2D(8,kernel_size=(3,3),padding='SAME',activation='relu',input_shape=(28,28,1)),\n",
    "        MaxPooling2D(pool_size=(2,2)),\n",
    "        Flatten(),\n",
    "        Dense(64,activation='relu'),\n",
    "        Dense(10,activation='softmax')\n",
    "    ])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = get_model(scaled_train_images[0].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compile_model(model):\n",
    "    \"\"\"\n",
    "    This function takes in the model returned from your get_model function, and compiles it with an optimiser,\n",
    "    loss function and metric.\n",
    "    Compile the model using the Adam optimiser (with default settings), the cross-entropy loss function and\n",
    "    accuracy as the only metric. \n",
    "    Your function doesn't need to return anything; the model will be compiled in-place.\n",
    "    \"\"\"\n",
    "    model.compile(\n",
    "        optimizer='adam',\n",
    "        loss='sparse_categorical_crossentropy',\n",
    "        metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "compile_model(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(model, scaled_train_images, train_labels):\n",
    "    \"\"\"\n",
    "    This function should train the model for 5 epochs on the scaled_train_images and train_labels. \n",
    "    Your function should return the training history, as returned by model.fit.\n",
    "    \"\"\"\n",
    "    batch_size = 128\n",
    "    num_epoch = 5\n",
    "    history=model.fit(scaled_train_images, train_labels,\n",
    "          batch_size=batch_size,\n",
    "          epochs=num_epoch,\n",
    "          verbose=1)\n",
    "       #   validation_data=(test_images, test_labels))\n",
    "    \n",
    "    return history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples\n",
      "Epoch 1/5\n",
      "60000/60000 [==============================] - 16s 272us/sample - loss: 0.3420 - accuracy: 0.9039- loss: 0.3445 - accuracy: \n",
      "Epoch 2/5\n",
      "60000/60000 [==============================] - 14s 232us/sample - loss: 0.1270 - accuracy: 0.9626- loss: 0.1297 - accu\n",
      "Epoch 3/5\n",
      "60000/60000 [==============================] - 15s 242us/sample - loss: 0.0865 - accuracy: 0.9746\n",
      "Epoch 4/5\n",
      "60000/60000 [==============================] - 13s 213us/sample - loss: 0.0674 - accuracy: 0.9805\n",
      "Epoch 5/5\n",
      "60000/60000 [==============================] - 14s 232us/sample - loss: 0.0561 - accuracy: 0.9830\n"
     ]
    }
   ],
   "source": [
    "history = train_model(model, scaled_train_images, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "frame = pd.DataFrame(history.history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Epochs'), Text(0, 0.5, 'Accuracy')]"
      ]
     },
     "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": [
    "acc_plot = frame.plot(y=\"accuracy\", title=\"Accuracy vs Epochs\", legend=False)\n",
    "acc_plot.set(xlabel=\"Epochs\", ylabel=\"Accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Epochs'), Text(0, 0.5, 'Loss')]"
      ]
     },
     "execution_count": 15,
     "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": [
    "acc_plot = frame.plot(y=\"loss\", title = \"Loss vs Epochs\",legend=False)\n",
    "acc_plot.set(xlabel=\"Epochs\", ylabel=\"Loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_model(model, scaled_test_images, test_labels):\n",
    "    \"\"\"\n",
    "    This function should evaluate the model on the scaled_test_images and test_labels. \n",
    "    Your function should return a tuple (test_loss, test_accuracy).\n",
    "    \"\"\"\n",
    "    loss,accuracy=model.evaluate(scaled_test_images, test_labels,verbose=2)\n",
    "    return (loss,accuracy)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 - 3s - loss: 0.0622 - accuracy: 0.9800\n",
      "Test loss: 0.06221009950283915\n",
      "Test accuracy: 0.9800000190734863\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_accuracy = evaluate_model(model, scaled_test_images, test_labels)\n",
    "print(f\"Test loss: {test_loss}\")\n",
    "print(f\"Test accuracy: {test_accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x864 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_test_images = scaled_test_images.shape[0]\n",
    "\n",
    "random_inx = np.random.choice(num_test_images, 4)\n",
    "random_test_images = scaled_test_images[random_inx, ...]\n",
    "random_test_labels = test_labels[random_inx, ...]\n",
    "\n",
    "predictions = model.predict(random_test_images)\n",
    "\n",
    "fig, axes = plt.subplots(4, 2, figsize=(16, 12))\n",
    "fig.subplots_adjust(hspace=0.4, wspace=-0.2)\n",
    "\n",
    "for i, (prediction, image, label) in enumerate(zip(predictions, random_test_images, random_test_labels)):\n",
    "    axes[i, 0].imshow(np.squeeze(image))\n",
    "    axes[i, 0].get_xaxis().set_visible(False)\n",
    "    axes[i, 0].get_yaxis().set_visible(False)\n",
    "    axes[i, 0].text(10., -1.5, f'Digit {label}')\n",
    "    axes[i, 1].bar(np.arange(len(prediction)), prediction)\n",
    "    axes[i, 1].set_xticks(np.arange(len(prediction)))\n",
    "    axes[i, 1].set_title(f\"Categorical distribution. Model prediction: {np.argmax(prediction)}\")\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}