Assignment_2_3_MNIST_offline_dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0b020e7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import TensorFlow and relevant libraries\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4c3cab30",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the paths to the training and testing data directories\n",
    "train_data_dir = 'dataset/mnist-jpg/mnist-jpg/train'\n",
    "test_data_dir = 'dataset/mnist-jpg/mnist-jpg/test'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f1bd496a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 60000 images belonging to 10 classes.\n",
      "Found 10000 images belonging to 10 classes.\n"
     ]
    }
   ],
   "source": [
    "# Set up an ImageDataGenerator to rescale pixel values to [0, 1]\n",
    "image_data_generator = ImageDataGenerator(rescale=1.0/255)\n",
    "\n",
    "# Define batch sizes\n",
    "train_batch_size = 10000\n",
    "test_batch_size = 5000\n",
    "\n",
    "# Create data generators for training and testing\n",
    "train_generator = image_data_generator.flow_from_directory(\n",
    "    train_data_dir,\n",
    "    target_size=(28, 28),  # Resize images to 28x28 pixels\n",
    "    batch_size=train_batch_size,  # Number of images per training batch\n",
    "    class_mode='categorical',  # One-hot encoded labels\n",
    "    color_mode='grayscale',  # Convert images to grayscale\n",
    "    shuffle=True,  # Shuffle the order of images during training\n",
    ")\n",
    "\n",
    "test_generator = image_data_generator.flow_from_directory(\n",
    "    test_data_dir,\n",
    "    target_size=(28, 28),  # Resize images to 28x28 pixels\n",
    "    batch_size=test_batch_size,  # Number of images per testing batch\n",
    "    class_mode='categorical',  # One-hot encoded labels\n",
    "    color_mode='grayscale',  # Convert images to grayscale\n",
    "    shuffle=True,  # Shuffle the order of images during testing\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "42c38034",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, y_train = train_generator[0]\n",
    "x_test, y_test = test_generator[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b7b670e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X_train (10000, 28, 28, 1)\n",
      "Shape of y_train (10000, 10)\n",
      "Shape of x_test  (5000, 28, 28, 1)\n",
      "Shape of y_test  (5000, 10)\n"
     ]
    }
   ],
   "source": [
    "print(f\"Shape of X_train {x_train.shape}\")\n",
    "print(f\"Shape of y_train {y_train.shape}\")\n",
    "print(f\"Shape of x_test  {x_test.shape}\")\n",
    "print(f\"Shape of y_test  {y_test.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f777dae1",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Flatten(input_shape=(28,28)),\n",
    "    keras.layers.Dense(50,activation='relu',name='L1'),\n",
    "    keras.layers.Dense(50,activation='relu',name='L2'),\n",
    "    keras.layers.Dense(10,activation='softmax',name='L3')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a1a28156",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1000/1000 [==============================] - 4s 4ms/step - loss: 1.0949 - accuracy: 0.6830 - val_loss: 0.4657 - val_accuracy: 0.8726\n",
      "Epoch 2/10\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 0.4172 - accuracy: 0.8788 - val_loss: 0.3689 - val_accuracy: 0.8954\n",
      "Epoch 3/10\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 0.3330 - accuracy: 0.9035 - val_loss: 0.2969 - val_accuracy: 0.9142\n",
      "Epoch 4/10\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2910 - accuracy: 0.9167 - val_loss: 0.2786 - val_accuracy: 0.9226\n",
      "Epoch 5/10\n",
      "1000/1000 [==============================] - 3s 3ms/step - loss: 0.2606 - accuracy: 0.9245 - val_loss: 0.2551 - val_accuracy: 0.9236\n",
      "Epoch 6/10\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2379 - accuracy: 0.9294 - val_loss: 0.2503 - val_accuracy: 0.9256\n",
      "Epoch 7/10\n",
      "1000/1000 [==============================] - 3s 3ms/step - loss: 0.2162 - accuracy: 0.9359 - val_loss: 0.2383 - val_accuracy: 0.9326\n",
      "Epoch 8/10\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 0.1986 - accuracy: 0.9423 - val_loss: 0.2320 - val_accuracy: 0.9308\n",
      "Epoch 9/10\n",
      "1000/1000 [==============================] - 3s 3ms/step - loss: 0.1824 - accuracy: 0.9469 - val_loss: 0.2312 - val_accuracy: 0.9310\n",
      "Epoch 10/10\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 0.1681 - accuracy: 0.9509 - val_loss: 0.2181 - val_accuracy: 0.9342\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer='sgd', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "history = model.fit(x_train, y_train, epochs=10, validation_data=(x_test, y_test), batch_size=10, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3d1247f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.lineplot(model.history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "56c877f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157/157 [==============================] - 1s 2ms/step - loss: 0.2181 - accuracy: 0.9342\n",
      "Loss:  0.21812231838703156\n",
      "Accuracy:  0.9341999888420105\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(x_test, y_test)\n",
    "print(\"Loss: \", test_loss)\n",
    "print(\"Accuracy: \", test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fc1d2b2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157/157 [==============================] - 0s 2ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "predicted_value=model.predict(x_test)\n",
    "plt.imshow(x_test[15])\n",
    "plt.show()\n",
    "print(np.argmax(predicted_value[15], axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ce57b6b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "lab_env",
   "language": "python",
   "name": "lab_env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}