relu_plots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def relu(x):\n",
    "    return max(0, x)\n",
    "\n",
    "def leaky_relu(x, negative_slope):\n",
    "    return max(0, x) + negative_slope * min(0, x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "relu_plot = [relu(x) for x in range(-10, 10)]\n",
    "leaky_relu_plot = [leaky_relu(x, 0.2) for x in range(-10, 10)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bot/miniconda3/envs/thesis/lib/python3.10/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(range(-10, 10), relu_plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bot/miniconda3/envs/thesis/lib/python3.10/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(range(-10, 10), leaky_relu_plot)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(relu_plot)\n",
    "len(relu_plot[10:])\n",
    "len(relu_plot[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'ReLU and Leaky ReLU Comparison')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "plt.plot(range(-10, 1), relu_plot[:11])\n",
    "plt.plot(range(-10, 1), leaky_relu_plot[:11])\n",
    "plt.plot(range(0, 10), relu_plot[10:])\n",
    "\n",
    "plt.legend(['ReLU(x)', 'Leaky ReLU(x)', 'ReLU(x) and Leaky ReLU(x)'])\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"Activation Function Output\")\n",
    "plt.title('ReLU and Leaky ReLU Comparison')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0,\n",
       " 0.1,\n",
       " 0.2,\n",
       " 0.30000000000000004,\n",
       " 0.4,\n",
       " 0.5,\n",
       " 0.6000000000000001,\n",
       " 0.7000000000000001,\n",
       " 0.8,\n",
       " 0.9]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[x for x in np.arange(0, 1, 0.1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Leaky Relu Negative Slope Tuning\n",
    "t = [[leaky_relu(x, negative_slope) for x in range(-10, 10)] for negative_slope in np.arange(0, 1, 0.1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mUntitled-1.ipynb Cell 10\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell:Untitled-1.ipynb?jupyter-notebook#ch0000011untitled?line=0'>1</a>\u001b[0m sns\u001b[39m.\u001b[39mlineplot(\u001b[39mrange\u001b[39m(\u001b[39m-\u001b[39m\u001b[39m10\u001b[39m, \u001b[39m10\u001b[39m), t[\u001b[39m10\u001b[39;49m])\n",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "sns.lineplot(range(-10, 10), t[10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "negative_slope0 = [leaky_relu(x, 0) for x in range(-10, 10)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bot/miniconda3/envs/thesis/lib/python3.10/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(range(-10, 10), negative_slope0)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.4 ('thesis')",
   "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": "3.10.4"
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