밑시딥2.ipynb

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    "colab": {
      "name": "밑시딥2.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMtWCquQcymPc3FcdMFAms1",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gaeunfalo16/Deep_Learning/blob/master/%EB%B0%91%EC%8B%9C%EB%94%A52.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fJ66EgjW8ZBy"
      },
      "source": [
        "def AND(x1, x2) :\n",
        "    w1, w2, theta = 0.5, 0.5, 0.7\n",
        "    tmp = x1*w1 + x2 * w2\n",
        "    if tmp <= theta :\n",
        "        return 0\n",
        "    elif tmp > theta :\n",
        "        return 1"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I_EjbjXV8wfP",
        "outputId": "db0d7cef-613e-4c1f-ee1a-1cf516c47627",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        }
      },
      "source": [
        "print(AND(0, 0))\n",
        "print(AND(0, 1))\n",
        "print(AND(1, 0))\n",
        "print(AND(1, 1))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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    {
      "cell_type": "code",
      "metadata": {
        "id": "RRHNAOHp89U5",
        "outputId": "ce1fd960-1a6a-4d37-fce9-516698c1d118",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "x = np.array([0, 1])\n",
        "w = np.array([0.5, 0.5])\n",
        "b = -0.7\n",
        "w * x"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0. , 0.5])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ryleNuQJ9d3w",
        "outputId": "f47c3669-d064-4488-c690-0a7f597a08f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "np.sum(w * x)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W-rsbyY99zrb",
        "outputId": "7a5216d9-16a6-4664-a5c0-03a72e48b714",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "np.sum(w * x) + b"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.19999999999999996"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K7yZzFwC96Rs"
      },
      "source": [
        "def AND(x1, x2) :\n",
        "    x = np.array([x1, x2])\n",
        "    w = np.array([0.5, 0.5])\n",
        "    b = -0.7\n",
        "    tmp = np.sum(w * x) + b\n",
        "    if tmp <= 0 :\n",
        "        return 0\n",
        "    elif tmp > 0 :\n",
        "        return 1"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JEgWwjxa-MXt",
        "outputId": "0751cf05-31fa-4867-ff59-f6bdb4a53c05",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        }
      },
      "source": [
        "print(AND(0, 0))\n",
        "print(AND(0, 1))\n",
        "print(AND(1, 0))\n",
        "print(AND(1, 1))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RUwnmBo5-gip"
      },
      "source": [
        "def NAND(x1, x2) :\n",
        "    x = np.array([x1, x2])\n",
        "    w = np.array([-0.5, -0.5])\n",
        "    b = 0.7\n",
        "    tmp = np.sum(w * x) + b\n",
        "    if tmp <= 0 :\n",
        "        return 0\n",
        "    else :\n",
        "        return 1"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ENFfqtOT-vXo",
        "outputId": "82e8b98a-c2f9-4195-89ed-17d14e0ee77b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        }
      },
      "source": [
        "print(NAND(0, 0))\n",
        "print(NAND(0, 1))\n",
        "print(NAND(1, 0))\n",
        "print(NAND(1, 1))"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "1\n",
            "1\n",
            "0\n"
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        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kQaLlJB2-9qC"
      },
      "source": [
        "def OR(x1, x2) : \n",
        "    x = np.array([x1, x2])\n",
        "    w = np.array([0.5, 0.5])\n",
        "    b = -0.2\n",
        "    tmp = np.sum(w * x) + b\n",
        "    if tmp <= 0 :\n",
        "        return 0\n",
        "    else :\n",
        "        return 1"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JcVcQOKr_QxR",
        "outputId": "288fd259-b248-4486-96ca-823fd2e79ad3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        }
      },
      "source": [
        "print(OR(0, 0))\n",
        "print(OR(0, 1))\n",
        "print(OR(1, 0))\n",
        "print(OR(1, 1))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "1\n",
            "1\n",
            "1\n"
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          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2vZKAAOjAEFA"
      },
      "source": [
        "def XOR(x1, x2) :\n",
        "    s1 = NAND(x1, x2)\n",
        "    s2 = OR(x1, x2)\n",
        "    return AND(s1, s2)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yLEuWlUmAQ4-",
        "outputId": "13dd1df8-0e8f-474d-d911-08845239f76f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        }
      },
      "source": [
        "print(XOR(0, 0))\n",
        "print(XOR(0, 1))\n",
        "print(XOR(1, 0))\n",
        "print(XOR(1, 1))"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "1\n",
            "1\n",
            "0\n"
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        }
      ]
    }
  ]
}