Deep Learning MNIST prediction model with Keras.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The MNIST (Modified National Institute of Standards and Technology) dataset consists of images of handwritten digits that is used for training and testing image processing systems"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a multiclass classification problem in which the goal is to predict a single discrete label (0,1,2,3,4,5,6,7,8,9)\n",
    "\n",
    "This notebook is used to generate a predictive model for the production system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import matplotlib.pyplot as plt  # plotting\n",
    "import numpy as np  # linear algebra\n",
    "from skimage import io, transform, util\n",
    "from tensorflow.keras import layers, models\n",
    "from tensorflow.keras.datasets import mnist  # mnist dataset\n",
    "from tensorflow.keras.models import load_model\n",
    "from tensorflow.keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading MNIST dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_images.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linear model definition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output of the network is a layer of size 10 with a probability distribution over the 10 different classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = models.Sequential()\n",
    "model.add(layers.Dense(512, activation=\"relu\", input_shape=(28 * 28,)))\n",
    "model.add(layers.Dense(10, activation=\"softmax\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compiling the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The chosen loss function is categorical_crossentropy because is a multiclass classification problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=\"rmsprop\", loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Encoding the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images_prepared = train_images.reshape((60000, 28 * 28))\n",
    "train_images_prepared = train_images_prepared.astype(\"float32\") / 255\n",
    "\n",
    "test_images_prepared = test_images.reshape((10000, 28 * 28))\n",
    "test_images_prepared = test_images_prepared.astype(\"float32\") / 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_labels_one_hot = to_categorical(train_labels)\n",
    "test_labels_one_hot = to_categorical(test_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting a validation set of 6000 samples from 60000 training images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images_val = train_images_prepared[:6000]\n",
    "train_images_partial = train_images_prepared[6000:]\n",
    "\n",
    "train_labels_val = train_labels_one_hot[:6000]\n",
    "train_labels_partial = train_labels_one_hot[6000:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training the model for 10 epochs or passes over the entire dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-03-31 21:18:56.862940: W external/local_tsl/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 169344000 exceeds 10% of free system memory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m422/422\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8670 - loss: 0.4630 - val_accuracy: 0.9535 - val_loss: 0.1597\n",
      "Epoch 2/10\n",
      "\u001b[1m422/422\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9642 - loss: 0.1230 - val_accuracy: 0.9687 - val_loss: 0.1054\n",
      "Epoch 3/10\n",
      "\u001b[1m422/422\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9777 - loss: 0.0754 - val_accuracy: 0.9762 - val_loss: 0.0829\n",
      "Epoch 4/10\n",
      "\u001b[1m422/422\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9848 - loss: 0.0531 - val_accuracy: 0.9810 - val_loss: 0.0675\n",
      "Epoch 5/10\n",
      "\u001b[1m422/422\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9887 - loss: 0.0384 - val_accuracy: 0.9807 - val_loss: 0.0637\n",
      "Epoch 6/10\n",
      "\u001b[1m422/422\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9917 - loss: 0.0285 - val_accuracy: 0.9803 - val_loss: 0.0656\n",
      "Epoch 7/10\n",
      "\u001b[1m422/422\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9940 - loss: 0.0220 - val_accuracy: 0.9805 - val_loss: 0.0666\n",
      "Epoch 8/10\n",
      "\u001b[1m422/422\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9960 - loss: 0.0152 - val_accuracy: 0.9813 - val_loss: 0.0594\n",
      "Epoch 9/10\n",
      "\u001b[1m422/422\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9970 - loss: 0.0121 - val_accuracy: 0.9783 - val_loss: 0.0678\n",
      "Epoch 10/10\n",
      "\u001b[1m422/422\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9983 - loss: 0.0086 - val_accuracy: 0.9822 - val_loss: 0.0673\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    train_images_partial,\n",
    "    train_labels_partial,\n",
    "    epochs=10,\n",
    "    batch_size=128,\n",
    "    validation_data=(train_images_val, train_labels_val),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting training and validation loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss = history.history[\"loss\"]\n",
    "validation_loss = history.history[\"val_loss\"]\n",
    "epochs = range(1, len(loss) + 1)\n",
    "plt.plot(epochs, loss, \"bo\", label=\"Training loss\")\n",
    "plt.plot(epochs, validation_loss, \"b\", label=\"Validation loss\")\n",
    "plt.title(\"Training and validation loss\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting training and validation accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc = history.history[\"accuracy\"]\n",
    "validation_acc = history.history[\"val_accuracy\"]\n",
    "\n",
    "plt.plot(epochs, acc, \"bo\", label=\"Training accuracy\")\n",
    "plt.plot(epochs, validation_acc, \"b\", label=\"Validation accuracy\")\n",
    "plt.title(\"Training and validation accuracy\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overfit begins after 5 epochs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Retraining the model from scratch for 5 epochs to avoid overfit. Note that validation set is not used for this time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "\u001b[1m  1/469\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:26\u001b[0m 186ms/step - accuracy: 0.0781 - loss: 2.4094"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-03-31 21:19:29.842693: W external/local_tsl/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 188160000 exceeds 10% of free system memory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8703 - loss: 0.4504\n",
      "Epoch 2/5\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9659 - loss: 0.1192\n",
      "Epoch 3/5\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9788 - loss: 0.0717\n",
      "Epoch 4/5\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9858 - loss: 0.0498\n",
      "Epoch 5/5\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9893 - loss: 0.0373\n"
     ]
    }
   ],
   "source": [
    "model = models.Sequential()\n",
    "model.add(layers.Dense(512, activation=\"relu\", input_shape=(28 * 28,)))\n",
    "model.add(layers.Dense(10, activation=\"softmax\"))\n",
    "model.compile(optimizer=\"rmsprop\", loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
    "history = model.fit(train_images_prepared, train_labels_one_hot, epochs=5, batch_size=128)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save MNIST model for production system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"app/mnist_model.keras\")\n",
    "del model\n",
    "model = load_model(\"app/mnist_model.keras\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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 699us/step - accuracy: 0.9745 - loss: 0.0776\n"
     ]
    }
   ],
   "source": [
    "final_loss, final_acc = model.evaluate(test_images_prepared, test_labels_one_hot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Final results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final loss: 0.06699982285499573\n",
      "Final accuracy: 0.9779999852180481\n"
     ]
    }
   ],
   "source": [
    "print(f\"Final loss: {final_loss}\")\n",
    "print(f\"Final accuracy: {final_acc}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generating predictions for two samples of numbers 4 and 9 from test images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First sample is a number 4 from test images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "digit = test_images[4]\n",
    "\n",
    "plt.imshow(digit, cmap=plt.cm.binary)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "[[5.1081292e-07 3.1363423e-09 2.1023111e-06 7.7408027e-09 9.9883264e-01\n",
      "  4.4669033e-08 1.4959137e-05 2.1703441e-05 2.2524851e-07 1.1278475e-03]]\n"
     ]
    }
   ],
   "source": [
    "prediction = model.predict(test_images_prepared[4:5], batch_size=784)\n",
    "print(prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The highest likelihood is above 0.99 for number 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate predictions for example image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "APP_ROOT = os.path.dirname(os.path.abspath(\"Deep Learning MNIST prediction model with Keras.ipynb\"))\n",
    "APP_STATIC = os.path.join(APP_ROOT, \"app/app/static\")\n",
    "\n",
    "filename = \"4.jpg\"\n",
    "path_to_file = os.path.join(APP_STATIC, filename)\n",
    "image = io.imread(path_to_file, as_gray=True)  # read as grayscale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_image(image):\n",
    "    # invert grayscale image\n",
    "    image = util.invert(image)\n",
    "    # resize image and prepare it for model\n",
    "    image = transform.resize(image, (28, 28), anti_aliasing=True, mode=\"constant\")\n",
    "    image = np.array(image)\n",
    "    plt.imshow(image, cmap=plt.cm.binary)\n",
    "    plt.show()\n",
    "    image = image.reshape((1, 28 * 28))\n",
    "\n",
    "    return image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "preprocessed_image = preprocess_image(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[8.2700979e-08, 1.6669065e-04, 4.8218979e-05, 2.3290573e-05,\n",
       "        9.9914443e-01, 1.4074722e-06, 2.4940262e-05, 4.9085240e-04,\n",
       "        4.4384862e-05, 5.5692170e-05]], dtype=float32)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds = model.predict(preprocessed_image)\n",
    "preds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The highest likelihood is above 0.99 for number 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(preds)"
   ]
  }
 ],
 "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}