Linear_Regression__Keras.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f5f36832",
   "metadata": {},
   "source": [
    "# Keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "09c972d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e0599c0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainSize: 80\n",
      "X_train.size: (80,); X_train: [1.84374847 1.77442549 0.39934756 0.06877704 1.97377387 0.36680902\n",
      " 0.24407647 1.04546566 1.41614516 0.01104423 1.9398197  1.04013604\n",
      " 0.65036664 1.18482914 1.1225544  0.31198904 0.28184845 1.08539217\n",
      " 1.6167947  0.60848449 0.36970891 1.93126407 1.73235229 0.98759119\n",
      " 1.02846888 0.58245828 1.24659625 1.57035192 1.65747502 0.42467822\n",
      " 0.91213997 1.19731697 1.04951286 1.36846605 1.20223002 0.71693146\n",
      " 0.11616722 0.56186902 0.60922754 0.94442985 0.176985   0.21578285\n",
      " 1.19579996 1.8186408  1.93916926 1.60439396 0.62342215 0.14910129\n",
      " 0.54269806 0.85508204 0.23173812 0.99035382 0.09045458 1.09342056\n",
      " 0.05083825 0.39196572 1.45801434 0.13010319 1.54193436 0.65066066\n",
      " 0.71350665 0.62196464 0.19534423 0.09290083 1.90142861 1.87899788\n",
      " 0.27898772 1.46398788 0.73272369 1.27511494 1.42648957 1.41371469\n",
      " 1.45921236 0.66179605 1.22370579 0.77735458 0.39743136 0.36364993\n",
      " 1.5215701  1.55026565]\n",
      "X_test.size: (20,); X_test: [0.1271167  1.7896547  1.54448954 1.32504457 0.51755996 0.88030499\n",
      " 0.5842893  1.72620685 0.04116899 0.74908024 0.86389004 1.2150897\n",
      " 1.63092286 1.89777107 0.23918849 0.31203728 1.54254069 0.1480893\n",
      " 1.66488528 0.34104825]\n",
      "y_train.size: (80,); y_train: [ 9.59980838 10.28665258  4.85532818  5.34915394 10.70314449  6.06907205\n",
      "  4.69751764  7.05929527  7.74667842  4.55507427 10.14821022  5.71855706\n",
      "  6.73818454  7.3932017   6.61392702  5.29307969  3.23806212  8.56331925\n",
      "  8.82387021  5.12340036  7.29958236  7.87502098  8.67878666  6.14696329\n",
      "  6.28312936  4.28385989  8.21362168  8.29041045  9.78594227  5.7873021\n",
      "  5.32104916  5.60438199  6.82087644  7.91303719  6.79819647  6.44386684\n",
      "  5.82639572  5.91306699  5.88791282  7.24607048  3.4686513   5.47453181\n",
      "  6.03673644 10.20785545  8.82697144  8.99781574  7.27306077  4.70718666\n",
      "  5.30603267  6.90639808  3.98086294  5.80238342  3.3519395   7.86711877\n",
      "  4.42920556  5.64948961  8.72049122  4.64785995  7.73628865  7.50191639\n",
      "  4.90965564  4.89121226  7.0492748   4.68275333  9.40527849  9.73664501\n",
      "  5.09801844  8.48372443  5.96358393  7.00466251 10.17626171  8.49163691\n",
      "  9.53623265  4.79408465  7.96723765  5.54881045  3.87183748  5.18802735\n",
      "  8.31932218  8.08449921]\n",
      "y_test.size: (20,); y_test: [ 5.03790371  8.86548845  7.3965179   7.06574625  6.34371184  6.9424623\n",
      "  5.75798135 11.04439507  5.03890908  6.33428778  6.19956196  9.53145501\n",
      "  9.18975324  9.61886731  5.53962564  4.71643995  7.94759736  4.67652161\n",
      "  8.46489564  5.19772255]\n",
      "WARNING:tensorflow:5 out of the last 9 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x7f7ed5c17af0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 37ms/stepWARNING:tensorflow:6 out of the last 11 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x7f7ed5c17af0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
      "Theta: [4.139746 2.795875]\n",
      "Mean Squared Error (MSE): 0.6562590790278945\n"
     ]
    }
   ],
   "source": [
    "# Seed for reproducibility\n",
    "np.random.seed(42)\n",
    "\n",
    "# Generate dataset\n",
    "X = np.array([2 * np.random.random() for _ in range(100)])\n",
    "y = 4 + 3 * X + np.random.normal(0, 1, size=X.shape)\n",
    "\n",
    "# Split into training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Print dataset sizes\n",
    "print(f\"trainSize: {len(X_train)}\")\n",
    "print(f\"X_train.size: {X_train.shape}; X_train: {X_train}\")\n",
    "print(f\"X_test.size: {X_test.shape}; X_test: {X_test}\")\n",
    "print(f\"y_train.size: {y_train.shape}; y_train: {y_train}\")\n",
    "print(f\"y_test.size: {y_test.shape}; y_test: {y_test}\")\n",
    "\n",
    "# Add bias term to X_train and X_test\n",
    "X_train_b = np.c_[np.ones(X_train.shape[0]), X_train]\n",
    "X_test_b = np.c_[np.ones(X_test.shape[0]), X_test]\n",
    "\n",
    "# Build model\n",
    "model = keras.Sequential([\n",
    "    layers.InputLayer(shape=(X_train_b.shape[1],)),\n",
    "    layers.Dense(1, use_bias=False, kernel_initializer=tf.keras.initializers.RandomNormal(seed=42))\n",
    "])\n",
    "\n",
    "# Compile model\n",
    "model.compile(optimizer=tf.keras.optimizers.SGD(learning_rate=0.01), loss='mean_squared_error')\n",
    "\n",
    "# Train model\n",
    "history = model.fit(X_train_b, y_train, epochs=1000, verbose=0)\n",
    "\n",
    "# Make predictions\n",
    "y_train_pred = model.predict(X_train_b)\n",
    "y_test_pred = model.predict(X_test_b)\n",
    "\n",
    "# Calculate Mean Squared Error (MSE)\n",
    "mse = np.mean((y_test - y_test_pred.flatten())**2)\n",
    "\n",
    "# Output results\n",
    "weights = model.layers[0].get_weights()[0].flatten()\n",
    "print(f\"Theta: {weights}\")\n",
    "print(f\"Mean Squared Error (MSE): {mse}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c69b1d11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test, y_test_pred)\n",
    "plt.xlim(0,12)\n",
    "plt.ylim(0,12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad132698",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55488ee0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b139b3b3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee3dfcd5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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