budgetkeras_sample.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from budgetkeras import *\n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "titanic_X = pd.read_csv('titanic_X.csv', index_col='PassengerId')\n",
    "titanic_Y = pd.read_csv('titanic_Y.csv', index_col='PassengerId')\n",
    "\n",
    "titanic_train_Y = titanic_Y.to_numpy().T\n",
    "titanic_train_X = titanic_X.to_numpy() \n",
    "\n",
    "X = np.array([[0,0],\n",
    "              [0,1],\n",
    "              [1,0],\n",
    "              [1,1]])\n",
    "Y = np.array([[0,1,1,0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "add, compile_model, summary = sequential()\n",
    "add(dense(2, input_shape=(2,), activation=relu, initializer=kaiming))\n",
    "# add(dense(2, activation=relu, initializer=kaiming))\n",
    "add(dense(1, activation=sigmoid, initializer=xavier))\n",
    "\n",
    "fit = compile_model(gradient_descent_with_momentum(learning_rate=0.1), binary_crossentropy) \n",
    "\n",
    "history, predict = fit(X, Y, epochs=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+------------------------------+\n",
      "|    Hidden Units    |     Activation Function      |\n",
      "+--------------------+------------------------------+\n",
      "|         2          |         relu_forward         |\n",
      "|         1          |       sigmoid_forward        |\n",
      "+--------------------+------------------------------+\n"
     ]
    }
   ],
   "source": [
    "summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='epochs', ylabel='loss'>"
      ]
     },
     "execution_count": 79,
     "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": [
    "history_data = pd.DataFrame({'loss': history, 'epochs' :list(range(1, len(history)+1))})\n",
    "\n",
    "sns.lineplot(data=history_data, x='epochs', y='loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.494, 0.494, 0.961, 0.049]])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.around(predict(X), 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "add, compile_model, summary = sequential()\n",
    "add(dense(128, input_shape=(16,), activation=tanh, initializer=xavier))\n",
    "add(dense(128, activation=tanh, initializer=xavier))\n",
    "add(dense(1, activation=sigmoid, initializer=xavier))\n",
    "\n",
    "fit = compile_model(RMSprop(learning_rate=0.01), binary_crossentropy) \n",
    "\n",
    "history, predict = fit(titanic_train_X, titanic_train_Y, epochs=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='epochs', ylabel='loss'>"
      ]
     },
     "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": [
    "history_data = pd.DataFrame({'loss': history, 'epochs' :list(range(1, len(history)+1))})\n",
    "\n",
    "sns.lineplot(data=history_data, x='epochs', y='loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+------------------------------+\n",
      "|    Hidden Units    |     Activation Function      |\n",
      "+--------------------+------------------------------+\n",
      "|        128         |         tanh_forward         |\n",
      "|        128         |         tanh_forward         |\n",
      "|         1          |       sigmoid_forward        |\n",
      "+--------------------+------------------------------+\n"
     ]
    }
   ],
   "source": [
    "summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.38 , 1.   , 0.837, 1.   , 0.009, 0.106, 0.   , 0.003, 1.   ,\n",
       "        1.   , 0.998, 1.   , 0.066, 0.   , 0.074, 0.861, 0.   , 0.318,\n",
       "        0.218, 1.   , 0.001, 0.289, 1.   , 0.969, 0.   , 0.703, 0.11 ,\n",
       "        0.001, 0.845, 0.085, 0.013, 1.   , 0.843, 0.202, 0.056, 0.502,\n",
       "        0.11 , 0.051, 0.   , 0.898, 0.002, 0.021, 0.114, 1.   , 0.645,\n",
       "        0.085, 0.   , 0.843, 0.003, 0.007, 0.   , 0.052, 1.   , 1.   ,\n",
       "        0.   , 0.702, 0.994, 0.357, 1.   , 0.   , 0.685, 1.   , 0.018,\n",
       "        0.   , 0.284, 1.   , 0.998, 0.116, 0.995, 0.001, 0.207, 0.   ,\n",
       "        0.   , 0.005, 0.969, 0.047, 0.085, 0.085, 1.   , 0.983, 0.026,\n",
       "        0.093, 0.843, 0.006, 0.999, 0.999, 0.001, 0.085, 1.   , 0.076,\n",
       "        0.096, 0.065, 0.021, 0.   , 0.   , 0.085, 0.001, 1.   , 1.   ,\n",
       "        0.046, 0.449, 0.085, 0.006, 0.156, 0.   , 0.293, 0.31 , 0.085,\n",
       "        0.021, 0.999, 0.   , 0.03 , 0.039, 0.077, 0.005, 0.051, 0.   ,\n",
       "        0.018, 0.   , 0.06 , 0.   , 0.085, 0.   , 0.995, 0.   , 1.   ,\n",
       "        0.126, 0.104, 0.997, 0.13 , 0.   , 0.061, 0.003, 1.   , 0.001,\n",
       "        0.009, 1.   , 0.013, 0.084, 0.017, 0.63 , 0.725, 0.954, 0.   ,\n",
       "        0.012, 0.001, 0.276, 0.001, 0.   , 0.   , 0.   , 1.   , 0.   ,\n",
       "        0.   , 0.085, 0.   , 0.999, 0.087, 0.085, 0.   , 0.   , 1.   ,\n",
       "        0.035, 0.113, 0.   , 1.   , 0.997, 0.001, 0.359, 0.501, 0.   ,\n",
       "        0.001, 0.999, 0.051, 0.009, 0.02 , 0.   , 0.005, 0.354, 0.007,\n",
       "        0.   , 0.331, 0.003, 1.   , 0.996, 0.001, 1.   , 0.949, 0.   ,\n",
       "        0.007, 1.   , 0.008, 0.945, 1.   , 0.997, 1.   , 0.126, 0.   ,\n",
       "        0.843, 0.645, 0.275, 0.   , 0.016, 0.004, 0.136, 0.543, 0.   ,\n",
       "        0.994, 0.999, 0.946, 0.107, 0.995, 0.045, 0.354, 0.003, 1.   ,\n",
       "        0.928, 0.   , 1.   , 0.175, 0.129, 0.   , 0.   , 0.085, 0.802,\n",
       "        0.021, 0.134, 0.055, 0.003, 0.   , 1.   , 0.096, 0.011, 0.543,\n",
       "        0.002, 0.008, 0.   , 1.   , 0.134, 0.31 , 0.   , 0.999, 0.006,\n",
       "        0.045, 0.076, 0.002, 0.158, 1.   , 0.992, 0.   , 0.085, 0.002,\n",
       "        0.002, 0.   , 0.   , 0.986, 1.   , 1.   , 1.   , 1.   , 0.126,\n",
       "        0.517, 0.001, 0.   , 0.843, 0.037, 0.   , 0.582, 0.998, 1.   ,\n",
       "        0.475, 0.952, 1.   , 0.017, 0.843, 0.999, 0.002, 0.   , 0.001,\n",
       "        0.994, 0.   , 0.293, 0.073, 0.115, 0.365, 0.   , 0.088, 0.04 ,\n",
       "        0.87 , 0.835, 0.998, 1.   , 0.001, 0.157, 0.08 , 0.284, 0.373,\n",
       "        0.002, 0.47 , 1.   , 0.843, 0.993, 0.046, 1.   , 0.085, 0.996,\n",
       "        1.   , 1.   , 0.   , 1.   , 1.   , 1.   , 0.006, 0.293, 0.   ,\n",
       "        0.836, 0.999, 0.   , 1.   , 0.993, 0.045, 0.267, 1.   , 1.   ,\n",
       "        0.   , 1.   , 0.   , 1.   , 0.986, 1.   , 1.   , 0.261, 0.001,\n",
       "        0.   , 1.   , 0.085, 0.077, 1.   , 0.19 , 0.001, 1.   , 1.   ,\n",
       "        0.   , 0.001, 0.095, 0.645, 0.545, 0.872, 1.   , 0.137, 0.009,\n",
       "        0.66 , 0.001, 0.014, 0.11 , 0.275, 0.997, 0.314, 0.845, 0.845,\n",
       "        0.   , 0.   , 0.002, 0.008, 0.   , 0.086, 1.   , 1.   , 0.843,\n",
       "        1.   , 0.994, 0.026, 0.115, 0.001, 0.002, 1.   , 0.778, 0.001,\n",
       "        0.257, 0.114, 1.   , 1.   , 0.16 , 1.   , 0.085, 0.002, 0.002,\n",
       "        0.998, 0.127, 1.   , 0.993, 0.052, 0.004, 1.   , 1.   , 0.041,\n",
       "        0.008, 0.   , 0.005, 0.999, 0.111, 0.033, 0.006, 0.001, 0.315,\n",
       "        0.018, 0.   , 1.   , 0.052, 0.   , 0.085, 0.149, 1.   , 0.   ,\n",
       "        0.148, 0.01 , 1.   , 0.997, 0.354, 0.021, 0.114, 0.   , 0.096,\n",
       "        0.001, 0.   , 0.085, 0.997, 1.   , 0.126, 0.162, 0.919, 0.872,\n",
       "        0.986, 0.155, 0.05 , 0.998, 0.   , 0.996, 0.   , 0.226, 1.   ,\n",
       "        0.066, 0.582, 1.   , 0.085, 1.   , 1.   , 0.988, 0.999, 0.994,\n",
       "        0.   , 0.   , 0.222, 0.994, 0.085, 0.349, 0.002, 1.   , 0.992,\n",
       "        0.126, 0.272, 0.023, 0.001, 0.016, 0.085, 0.015, 0.   , 0.004,\n",
       "        0.127, 0.999, 0.085, 0.03 , 1.   , 1.   , 0.241, 0.002, 0.018,\n",
       "        0.008, 0.043, 0.818, 0.   , 0.   , 0.   , 0.991, 0.948, 0.   ,\n",
       "        1.   , 0.002, 0.087, 1.   , 0.   , 0.053, 0.033, 0.001, 0.051,\n",
       "        0.003, 1.   , 0.003, 0.003, 0.082, 0.113, 0.503, 0.84 , 0.002,\n",
       "        0.993, 0.005, 0.999, 0.403, 0.   , 0.842, 0.238, 0.085, 0.993,\n",
       "        1.   , 0.091, 0.001, 1.   , 0.002, 1.   , 0.16 , 1.   , 0.04 ,\n",
       "        0.11 , 0.999, 0.11 , 0.   , 0.992, 0.004, 0.111, 0.004, 1.   ,\n",
       "        0.11 , 0.001, 0.994, 0.075, 1.   , 0.001, 1.   , 0.007, 1.   ,\n",
       "        0.999, 0.007, 0.002, 0.981, 0.001, 0.001, 1.   , 0.581, 0.   ,\n",
       "        1.   , 0.998, 0.   , 0.124, 0.684, 0.722, 0.002, 1.   , 0.   ,\n",
       "        1.   , 0.956, 0.126, 0.136, 0.001, 0.085, 0.019, 0.   , 0.115,\n",
       "        0.002, 0.11 , 0.159, 0.598, 1.   , 0.993, 0.843, 0.129, 0.003,\n",
       "        1.   , 1.   , 0.   , 0.16 , 1.   , 0.998, 0.   , 0.189, 0.116,\n",
       "        0.994, 0.004, 0.996, 0.039, 0.085, 0.008, 1.   , 0.074, 0.   ,\n",
       "        0.004, 0.   , 0.999, 0.001, 0.11 , 1.   , 0.998, 0.085, 0.013,\n",
       "        0.15 , 0.956, 0.   , 0.086, 0.978, 1.   , 1.   , 0.079, 0.085,\n",
       "        0.999, 0.126, 0.009, 1.   , 0.   , 0.134, 0.997, 0.   , 0.005,\n",
       "        0.519, 0.997, 0.051, 0.   , 0.001, 0.001, 0.992, 0.034, 0.127,\n",
       "        0.986, 0.   , 0.987, 0.001, 0.001, 1.   , 0.16 , 0.   , 0.   ,\n",
       "        0.   , 0.065, 1.   , 0.001, 0.452, 0.999, 0.998, 0.115, 0.987,\n",
       "        0.085, 0.512, 0.085, 1.   , 0.049, 0.844, 0.583, 0.   , 0.085,\n",
       "        0.002, 0.008, 0.   , 0.999, 0.001, 0.273, 0.007, 0.902, 0.   ,\n",
       "        0.001, 0.085, 0.139, 1.   , 1.   , 0.066, 0.013, 0.41 , 0.   ,\n",
       "        0.141, 0.067, 0.917, 0.001, 1.   , 0.851, 0.978, 0.068, 0.   ,\n",
       "        0.   , 0.003, 0.   , 0.069, 0.14 , 0.999, 0.854, 1.   , 0.452,\n",
       "        0.109, 0.009, 0.001, 0.15 , 0.842, 0.   , 0.137, 1.   , 0.992,\n",
       "        0.   , 0.   , 0.29 , 0.   , 0.999, 0.997, 1.   , 1.   , 1.   ,\n",
       "        0.403, 0.964, 0.093, 0.002, 0.113, 1.   , 0.998, 0.002, 0.136,\n",
       "        1.   , 0.023, 0.289, 0.003, 0.977, 0.068, 1.   , 0.842, 0.002,\n",
       "        0.   , 1.   , 0.014, 0.   , 0.008, 0.008, 0.   , 0.001, 1.   ,\n",
       "        0.085, 0.085, 0.465, 0.136, 1.   , 0.005, 0.106, 0.001, 0.   ,\n",
       "        0.996, 0.022, 0.029, 1.   , 1.   , 0.17 , 0.015, 0.997, 1.   ,\n",
       "        0.293, 0.012, 0.023, 1.   , 0.007, 0.137, 0.896, 0.997, 0.138,\n",
       "        1.   , 0.03 , 0.763, 0.001, 0.167, 0.054, 0.005, 0.102, 0.11 ,\n",
       "        1.   , 0.141, 0.126, 0.973, 0.126, 1.   , 1.   , 1.   , 0.667,\n",
       "        0.   , 0.057, 0.053, 0.685, 0.002, 1.   , 0.001, 0.126, 0.002,\n",
       "        0.   , 0.425, 0.043, 0.012, 1.   , 0.95 , 0.076, 0.   , 0.289,\n",
       "        0.993, 0.999, 1.   , 0.336, 0.105, 0.   , 0.708, 0.012, 1.   ,\n",
       "        0.034, 0.002, 0.161, 0.465, 0.093, 0.001, 0.445, 0.005, 0.133,\n",
       "        0.003, 1.   , 0.183, 0.001, 1.   , 0.   , 0.147, 0.452, 1.   ,\n",
       "        0.126, 1.   , 1.   , 1.   , 0.11 , 0.015, 0.131, 1.   , 0.047,\n",
       "        0.085, 0.969, 0.417, 0.065, 0.005, 1.   , 0.   , 0.113, 0.137,\n",
       "        0.   , 0.   , 0.   , 1.   , 0.307, 0.   , 0.019, 1.   , 0.138,\n",
       "        1.   , 1.   , 0.98 , 1.   , 0.11 , 0.   , 0.006, 1.   , 0.   ,\n",
       "        0.002, 1.   , 0.999, 0.001, 0.085, 0.999, 0.034, 1.   , 0.   ,\n",
       "        0.109, 1.   , 0.999, 0.062, 0.115, 0.085, 1.   , 1.   , 0.139,\n",
       "        0.141, 0.   , 0.057, 0.   , 0.001, 1.   , 0.   , 0.988, 0.004]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.around(predict(titanic_train_X), 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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