neural_network/numpy_nn/softmax_numpy_4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b76a974b",
   "metadata": {},
   "source": [
    "### 1. python code"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5615c35",
   "metadata": {},
   "source": [
    "<img src='softmax.png' width=60% align=center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70d78da6",
   "metadata": {},
   "source": [
    "$$S_{i,j} = \\frac{e^{z_{i,j}}}{\\sum_{l=1}^Le^{z_{i,j}}}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9d932179",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "14179b41",
   "metadata": {},
   "outputs": [],
   "source": [
    "outputs = [2.12, 3.14, -2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ace8866d",
   "metadata": {},
   "outputs": [],
   "source": [
    "e = math.e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e0e8fcc5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[8.331137487687691, 23.10386685872218, 0.1353352832366127]\n"
     ]
    }
   ],
   "source": [
    "exp_output = []\n",
    "for output in outputs:\n",
    "    exp_output.append(e**output)\n",
    "print(exp_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cb818c38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.26389128483952834, 0.7318219293727912, 0.0042867857876804265] 0.9999999999999999\n"
     ]
    }
   ],
   "source": [
    "base = sum(exp_output)\n",
    "values = []\n",
    "for value in exp_output:\n",
    "    values.append(value / base)\n",
    "print(values, sum(values))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4cb814b",
   "metadata": {},
   "source": [
    "### 2. numpy code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6d2ec344",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "10cc6ec3",
   "metadata": {},
   "outputs": [],
   "source": [
    "e = np.exp(outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1f43e475",
   "metadata": {},
   "outputs": [],
   "source": [
    "values = e / np.sum(e, keepdims=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2c9d7f21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.26389128, 0.73182193, 0.00428679]), 1.0)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values, np.sum(values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c73bb74",
   "metadata": {},
   "source": [
    "### 3.batch size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3e93a7e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "outputs = [[2.7,3.05,4.2],\n",
    "          [2.5, 4.2, 3],\n",
    "          [5.0, 1.2, 2.1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d3b150e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "exp_outputs = np.exp(outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0f466cc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 14.87973172,  21.11534442,  66.68633104],\n",
       "       [ 12.18249396,  66.68633104,  20.08553692],\n",
       "       [148.4131591 ,   3.32011692,   8.16616991]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp_outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3e3e9f8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "bases = np.sum(exp_outputs, axis=1, keepdims=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "822c0d2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[102.68140719],\n",
       "       [ 98.95436192],\n",
       "       [159.89944594]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "89f8c6e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[0.14491165, 0.20563941, 0.64944894],\n",
       "        [0.12311225, 0.67390997, 0.20297778],\n",
       "        [0.92816556, 0.02076378, 0.05107066]]),\n",
       " array([1., 1., 1.]))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soft_outputs = exp_outputs / bases\n",
    "soft_outputs, np.sum(soft_outputs, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2f755cf",
   "metadata": {},
   "source": [
    "### 4.overflow prevention"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4356909",
   "metadata": {},
   "source": [
    "$$v = u - max(u)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "958e2993",
   "metadata": {},
   "source": [
    "如果遇到值很大的情况。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "31d758fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\envs\\stark-lin\\lib\\site-packages\\ipykernel_launcher.py:1: RuntimeWarning: overflow encountered in exp\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "inf"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea69745c",
   "metadata": {},
   "source": [
    "所以需要归一化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6ab89eb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "outputs = [[2.7,3.05,1000],\n",
    "          [2.5, 4.2, 3],\n",
    "          [5.0, 1.2, 2.1]]\n",
    "outputs = np.array(outputs)\n",
    "minus_outputs = outputs - np.max(outputs, axis=1, keepdims=True)\n",
    "exp_outputs = np.exp(minus_outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3a8dc68f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[0.        , 0.        , 1.        ],\n",
       "        [0.12311225, 0.67390997, 0.20297778],\n",
       "        [0.92816556, 0.02076378, 0.05107066]]),\n",
       " array([1., 1., 1.]))"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minus_softmax = exp_outputs / np.sum(exp_outputs, axis=1, keepdims=True)\n",
    "minus_softmax, np.sum(minus_softmax, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5081448",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b223fc9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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