1_FFN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 활성화함수 구현하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.,  2.,  2.])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "x=np.array([-1.0, 2.0, 2.0])\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False,  True,  True], dtype=bool)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=x>0\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y= y.astype(np.int) #y의 원소를 bool에서 int형으로 변환\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "그래프 그리기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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S8XXXHJd0Z2/VzI2SvjmOfjsAYDRDZ+4Rkdk+KOmEpI6koxFx2vaB3uMLkhYl3SppSdKr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\n1WIQXfsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107b20dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "\n",
    "def step_function(x):\n",
    "    return np.array(x > 0, dtype=np.int)\n",
    "\n",
    "x=np.arange(-5.0, 5.0, 0.1)\n",
    "y=step_function(x)\n",
    "plt.plot(x,y)\n",
    "plt.ylim(-0.1, 1.1) #y축의 범위 지정\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def sigmoid(x):\n",
    "    return 1/ (1+np.exp(-x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.26894142,  0.73105858,  0.88079708])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=np.array([-1.0, 1.0, 2.0])\n",
    "sigmoid(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CkMpdRCQMqdxFRMKQyl1EJAyp3EVEwpDKXUQkDKncRUTCkLUu6OjBG5uVA7s9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\nVO4iImFI5S4iEob+DxH15vm8DFuTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115058240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def sigmoid(x):\n",
    "    return 1/ (1+np.exp(-x))\n",
    "x=np.arange(-5.0, 5.0, 0.1)\n",
    "y=sigmoid(x)\n",
    "plt.plot(x,y)\n",
    "plt.ylim(-0.1,1.1) #y축의 범위 지정\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vXe2A7UO2j9k+Nj4+3niVQEHMrSlzO1fuKLhMb6ja/i3NhvsnrnY8Io5ExHBEDA8MDGT5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115204cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def relu(x):\n",
    "    return np.maximum(0,x) #두값중 큰값을 선택반환\n",
    "x=np.arange(-5.0, 5.0, 0.1)\n",
    "y=relu(x)\n",
    "plt.plot(x,y)\n",
    "plt.ylim(-0.1,1.1) #y축의 범위 지정\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 다차원 배열의 계산"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "A = np.array([1,2,3,4])\n",
    "print(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ndim(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4,)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2]\n",
      " [3 4]\n",
      " [5 6]]\n"
     ]
    }
   ],
   "source": [
    "B = np.array([[1,2],[3,4],[5,6]])\n",
    "print(B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ndim(B)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$B=\\begin{pmatrix}\n",
    "1&2\\\\\n",
    "3&4\\\\\n",
    "5&6\n",
    "\\end{pmatrix}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 2)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 행렬의 내적(곱)\n",
    "\n",
    "$$\\begin{pmatrix}\n",
    "1&2\\\\\n",
    "3&4\n",
    "\\end{pmatrix} \\begin{pmatrix}\n",
    "5&6\\\\\n",
    "7&8\n",
    "\\end{pmatrix} =\\begin{pmatrix}\n",
    "19&22\\\\\n",
    "43&50\n",
    "\\end{pmatrix}$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 2)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A=np.array([[1,2],[3,4]])\n",
    "A.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 2)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B= np.array([[5,6],[7,8]])\n",
    "B.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[19, 22],\n",
       "       [43, 50]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(A,B)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 신경망내적 \n",
    "\n",
    "X W = Y\n",
    "\n",
    "2  2$\\times$3   3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2,)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=np.array([1,2])\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 3 5]\n",
      " [2 4 6]]\n"
     ]
    }
   ],
   "source": [
    "W=np.array([[1,3,5], [2,4,6]])\n",
    "print(W)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "W.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 5 11 17]\n"
     ]
    }
   ],
   "source": [
    "Y=np.dot(X,W) #np.dot 은 for문 없이 Y의 값이 여러개여도 한 번의 연산으로 가능\n",
    "print(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example 4 pixels 3 classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ -97.9   434.7    61.95]\n"
     ]
    }
   ],
   "source": [
    "X=np.array([56,231,24,2])\n",
    "W=np.array([[0.2,-0.5,0.1,2.0],[1.5,1.3,2.1,0.0],[0,0.25,0.2,-0.3]])\n",
    "X1=np.dot(W,X)\n",
    "print(X1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ -96.8   437.9    60.75]\n"
     ]
    }
   ],
   "source": [
    "b=np.array([1.1,3.2,-1.2])\n",
    "Y=X1+b\n",
    "print(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 각 층의 신호 전달 구현\n",
    "\n",
    "1계층.   $a_1 ^{(1)} = w_{11} ^{(1)}x_1 + w_{12} ^{(1)}x_2 + b_1 ^{(1)} $\n",
    "\n",
    "행렬 내적 이용하여, '가중치 부분' 간소화하면 \n",
    "\n",
    "$\\mathbb{A^{(1)}} =  \\ \\mathbb{XW^{(1)}} + \\mathbb{B^{(1)}}$\n",
    "\n",
    "$\\mathbb{A^{(1)}} (a_1 ^{(1)}  a_2 ^{(1)}  a_3 ^{(1)} )$, $\\mathbb{X} = (x_1 x_2)$, $\\mathbb{B^{(1)}} = (b_1 ^{(1)}  b_2 ^{(1)}  b_3 ^{(1)} )$\n",
    "\n",
    "$\\mathbb{W^{(1)}}\\begin{pmatrix}\n",
    "w_{11} ^{(1)}  & w_{21} ^{(1)} & w_{31} ^{(1)}\\\\\n",
    "w_{12} ^{(1)}  & w_{22} ^{(1)} & w_{32} ^{(1)}\n",
    "\\end{pmatrix}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 3)\n",
      "(2,)\n",
      "(1, 3)\n"
     ]
    }
   ],
   "source": [
    "X=np.array([1.0, 0.5])\n",
    "W1=np.array([[0.1,0.3,0.5], [0.2,0.4,0.6]])\n",
    "B1=np.array([[0.1, 0.2, 0.3]])\n",
    "\n",
    "print(W1.shape)  # (2,3)\n",
    "print(X.shape)  # (2,)\n",
    "print(B1.shape)  # (3,)\n",
    "A1 = np.dot (X, W1) + B1 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 은닉층에서의 가중치 합을 활성화 함수: 시그모이드 함수 사용해서 적용"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.3  0.7  1.1]]\n",
      "[[ 0.57444252  0.66818777  0.75026011]]\n"
     ]
    }
   ],
   "source": [
    "Z1=sigmoid(A1)\n",
    "print(A1)\n",
    "print(Z1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1층에서 2층으로 신호 전달"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 3)\n",
      "(3, 2)\n",
      "(1, 2)\n",
      "[[ 0.51615984  1.21402696]]\n",
      "[[ 0.62624937  0.7710107 ]]\n"
     ]
    }
   ],
   "source": [
    "W2=np.array([[0.1,0.4], [0.2,0.5], [0.3,0.6]])\n",
    "B2=np.array([[0.1, 0.2]])\n",
    "\n",
    "print(Z1.shape)  # (2,3)\n",
    "print(W2.shape)  # (2,)\n",
    "print(B2.shape)  # (3,)\n",
    "\n",
    "A2 = np.dot (Z1, W2) + B2\n",
    "Z2 = sigmoid(A2)\n",
    "print(A2)\n",
    "print(Z2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2층에서 출력층으로의 신호 전달"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.31682708  0.69627909]]\n",
      "[[ 0.31682708  0.69627909]]\n"
     ]
    }
   ],
   "source": [
    "def identity_function(x):\n",
    "    return x\n",
    "\n",
    "W3=np.array([[0.1,0.3], [0.2,0.4]])\n",
    "B3=np.array([[0.1, 0.2]])\n",
    "\n",
    "A3 = np.dot (Z2, W3) + B3\n",
    "Y = identity_function(A3) \n",
    "\n",
    "print(A3)\n",
    "print(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 구현정리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.31682708  0.69627909]]\n"
     ]
    }
   ],
   "source": [
    "def init_network():\n",
    "    network ={}\n",
    "    network['W1'] = np.array([[0.1,0.3,0.5], [0.2,0.4,0.6]])\n",
    "    network['b1'] = np.array([[0.1, 0.2, 0.3]])\n",
    "    network['W2'] = np.array([[0.1,0.4], [0.2,0.5], [0.3,0.6]])\n",
    "    network['b2'] = np.array([[0.1, 0.2]])\n",
    "    network['W3'] = np.array([[0.1,0.3], [0.2,0.4]])\n",
    "    network['b3'] = np.array([[0.1, 0.2]])\n",
    "    \n",
    "    return network\n",
    "\n",
    "def forward(network, x):\n",
    "    W1, W2, W3 = network['W1'], network['W2'], network['W3']\n",
    "    b1, b2, b3 = network['b1'], network['b2'], network['b3']\n",
    "    \n",
    "    a1 = np.dot(x, W1) + b1\n",
    "    z1 =sigmoid(a1)\n",
    "    a2 = np.dot(z1,W2) + b2\n",
    "    z2 =sigmoid(a2)\n",
    "    a3 = np.dot(z2,W3) + b3\n",
    "    y = identity_function(a3)\n",
    "    \n",
    "    return y\n",
    "\n",
    "network =init_network()\n",
    "x = np.array([1.0 ,0.5])\n",
    "y = forward(network, x)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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