lab_3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Flatten\n",
    "from tensorflow.keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and Preprocess the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "with np.load(\"Datasets\\\\mnist.npz\") as data:\n",
    "    x_train = data['x_train']\n",
    "    y_train = data['y_train']\n",
    "    x_test = data['x_test']\n",
    "    y_test = data['y_test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_1 = x_train[0]\n",
    "x_train[500].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x276989eeba0>"
      ]
     },
     "execution_count": 28,
     "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": [
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(img_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize the images to a pixel value range of 0 to 1\n",
    "x_train = x_train.astype('float32') / 255.0\n",
    "x_test = x_test.astype('float32') / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# One-hot encode the labels\n",
    "y_train = to_categorical(y_train, 10)\n",
    "y_test = to_categorical(y_test, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 1., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Abdul Saboor\\Programing\\Virtual Enviroments\\NLPvenv\\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": [
    "# 2. Build the MLP Model\n",
    "model = Sequential()\n",
    "model.add(Flatten(input_shape=(28, 28)))  # Flatten the 28x28 images to a 1D vector\n",
    "model.add(Dense(512, activation='relu'))  # Fully connected layer with 512 neurons\n",
    "model.add(Dense(256, activation='relu'))  # Fully connected layer with 256 neurons\n",
    "model.add(Dense(10, activation='softmax'))  # Output layer with 10 units (one for each class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ flatten_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">784</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">401,920</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">131,328</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,570</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ flatten_1 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m784\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)            │       \u001b[38;5;34m401,920\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │       \u001b[38;5;34m131,328\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_5 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m2,570\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">535,818</span> (2.04 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m535,818\u001b[0m (2.04 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">535,818</span> (2.04 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m535,818\u001b[0m (2.04 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You must install pydot (`pip install pydot`) for `plot_model` to work.\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.utils import plot_model\n",
    "plot_model(model=model, show_shapes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compile the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3. Compile the Model\n",
    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# tf.keras.optimizers.Adam(learning_rate=0.001)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.8937 - loss: 0.3442 - val_accuracy: 0.9607 - val_loss: 0.1314\n",
      "Epoch 2/10\n",
      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 4ms/step - accuracy: 0.9728 - loss: 0.0869 - val_accuracy: 0.9634 - val_loss: 0.1135\n",
      "Epoch 3/10\n",
      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9824 - loss: 0.0525 - val_accuracy: 0.9699 - val_loss: 0.1072\n",
      "Epoch 4/10\n",
      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 4ms/step - accuracy: 0.9872 - loss: 0.0388 - val_accuracy: 0.9743 - val_loss: 0.0971\n",
      "Epoch 5/10\n",
      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 4ms/step - accuracy: 0.9901 - loss: 0.0290 - val_accuracy: 0.9737 - val_loss: 0.1049\n",
      "Epoch 6/10\n",
      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 4ms/step - accuracy: 0.9917 - loss: 0.0259 - val_accuracy: 0.9756 - val_loss: 0.1039\n",
      "Epoch 7/10\n",
      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 4ms/step - accuracy: 0.9932 - loss: 0.0199 - val_accuracy: 0.9781 - val_loss: 0.0966\n",
      "Epoch 8/10\n",
      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 4ms/step - accuracy: 0.9940 - loss: 0.0179 - val_accuracy: 0.9794 - val_loss: 0.0957\n",
      "Epoch 9/10\n",
      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 4ms/step - accuracy: 0.9956 - loss: 0.0133 - val_accuracy: 0.9758 - val_loss: 0.1199\n",
      "Epoch 10/10\n",
      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.9946 - loss: 0.0184 - val_accuracy: 0.9728 - val_loss: 0.1413\n"
     ]
    }
   ],
   "source": [
    "# 4. Train the Model\n",
    "history = model.fit(x_train, y_train, epochs=10, batch_size=32, validation_split=0.2, verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ploting Loss Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PLot the validation and training curvees seperately\n",
    "def plot_loss_curves(history):\n",
    "  \"\"\"\n",
    "  Returns seperate Loss curves for training and validation metrics\n",
    "  \"\"\"\n",
    "  loss= history.history[\"loss\"]\n",
    "  val_loss= history.history[\"val_loss\"]\n",
    "\n",
    "  accuracy= history.history[\"accuracy\"]\n",
    "  val_accuracy= history.history[\"val_accuracy\"]\n",
    "\n",
    "  epochs = range(len(history.history[\"loss\"]))\n",
    "\n",
    "  # PLot Loss\n",
    "  plt.plot(epochs, loss, label=\"training_loss\")\n",
    "  plt.plot(epochs, val_loss, label=\"val_loss\")\n",
    "  plt.title(\"loss\")\n",
    "  plt.xlabel(\"epochs\")\n",
    "  plt.legend()\n",
    "\n",
    "  # PLot accuracy\n",
    "  plt.figure() # Create a new figure\n",
    "  plt.plot(epochs, accuracy, label=\"training_accuracy\")\n",
    "  plt.plot(epochs, val_accuracy, label=\"val_accuracy\")\n",
    "  plt.title(\"accuracy\")\n",
    "  plt.xlabel(\"epochs\")\n",
    "  plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss_curves(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 1., 0., 0.])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1000 with 50 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 1. Select 50 random images from the test set\n",
    "num_images = 50\n",
    "random_indices = np.random.choice(x_test.shape[0], num_images, replace=False)\n",
    "test_images = x_test[random_indices]\n",
    "true_labels = np.argmax(y_test[random_indices], axis=1)\n",
    "\n",
    "# 2. Predict the labels for these images\n",
    "predicted_labels = np.argmax(model.predict(test_images), axis=1)\n",
    "\n",
    "# 3. Plot the images with their predicted and true labels\n",
    "plt.figure(figsize=(20, 10))\n",
    "rows = num_images // 10\n",
    "for i in range(num_images):\n",
    "    plt.subplot(rows, 10, i + 1)\n",
    "    plt.imshow(test_images[i], cmap='gray')\n",
    "    plt.title(f\"True: {true_labels[i]}\\nPred: {predicted_labels[i]}\")\n",
    "    plt.axis('off')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "NLPvenv",
   "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": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}