CNN(0316).ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOxQvw6kvPxN/Pf/Eo+F3F9",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/cnddlf/2022_1_Study_AI/blob/main/CNN(0316).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4kewoQGwI6HT"
      },
      "outputs": [],
      "source": [
        "# 배달 시간 = 거리 * w+b\n",
        "import tensorflow as tf\n",
        "\n",
        "# 배달 시간\n",
        "label = 33\n",
        "distance=850\n",
        "\n",
        "# tensorflow 형식의 변수 지정\n",
        "# 실수 0으로 지정\n",
        "w=tf.Variable(0.)\n",
        "b=tf.Variable(0.)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "w"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6bC6rrYXJLTU",
        "outputId": "e30497a8-6fc1-4398-c0ca-b215c72d9155"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Variable 'Variable:0' shape=() dtype=float32, numpy=0.0>"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b.numpy()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oVjjEv2zJkFb",
        "outputId": "7b65f765-00d7-48fb-f75d-53751eb0e74b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred = distance * w + b\n",
        "pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "frqlrW82J8Cn",
        "outputId": "eb2462f2-19c5-4dfa-86b2-8bd1e3461469"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=float32, numpy=0.0>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred-label"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ak8AGJWsKxRK",
        "outputId": "10502e06-6122-49c6-f535-4e900171c01f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=float32, numpy=-33.0>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.square(pred-label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZzYTgzbrKzI2",
        "outputId": "830a436b-ec22-4d50-d7d0-62fbcfb81862"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=float32, numpy=1089.0>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 손실 함수는 예측값과 실제값의 차이를 줄이기 위한 함수이다.\n",
        "def loss_func():\n",
        "  # 예측값 계산\n",
        "  pred = distance * w + b\n",
        "  # 실 결과인 label과 예측값 사이의 손실 정도를 계산하여 0과 가까워 질때 까지 반복\n",
        "  return tf.square(pred-label)"
      ],
      "metadata": {
        "id": "37ZMirv0K2y8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "opt = tf.keras.optimizers.Adam(learning_rate=0.1)\n",
        "\n",
        "for i in range(1000):\n",
        "  opt.minimize(loss_func, var_list=[w,b])\n",
        "  print(w.numpy(), ' : ', b.numpy())"
      ],
      "metadata": {
        "id": "2k9EAtT7Qs2X",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd635bb8-4707-49c9-eda7-64d54cd2419b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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            "0.03877789  :  0.038777817\n",
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            "0.03877789  :  0.038777817\n",
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            "0.03877789  :  0.038777817\n",
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            "0.038777914  :  0.03877784\n",
            "0.03877789  :  0.038777817\n",
            "0.038777925  :  0.03877785\n",
            "0.038777914  :  0.03877784\n",
            "0.03877789  :  0.038777817\n",
            "0.038777925  :  0.03877785\n",
            "0.038777914  :  0.03877784\n",
            "0.03877789  :  0.038777817\n",
            "0.038777925  :  0.03877785\n",
            "0.038777914  :  0.03877784\n",
            "0.03877789  :  0.038777817\n",
            "0.038777925  :  0.03877785\n",
            "0.038777914  :  0.03877784\n",
            "0.03877789  :  0.038777817\n",
            "0.038777925  :  0.03877785\n",
            "0.038777914  :  0.03877784\n",
            "0.038777888  :  0.038777813\n",
            "0.038777936  :  0.03877786\n",
            "0.038777895  :  0.03877782\n",
            "0.038777903  :  0.03877783\n",
            "0.038777936  :  0.03877786\n",
            "0.03877788  :  0.038777806\n",
            "0.038777933  :  0.038777858\n",
            "0.038777906  :  0.038777832\n",
            "0.038777895  :  0.03877782\n",
            "0.03877793  :  0.038777854\n",
            "0.038777903  :  0.03877783\n",
            "0.038777906  :  0.038777832\n",
            "0.038777925  :  0.03877785\n",
            "0.0387779  :  0.038777824\n",
            "0.038777903  :  0.03877783\n",
            "0.038777936  :  0.03877786\n",
            "0.03877788  :  0.038777806\n",
            "0.038777933  :  0.038777858\n",
            "0.038777906  :  0.038777832\n",
            "0.038777895  :  0.03877782\n",
            "0.03877793  :  0.038777854\n",
            "0.038777903  :  0.03877783\n",
            "0.038777906  :  0.038777832\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "distance * w+b"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HZa8klQMRrrX",
        "outputId": "7f64225b-80a3-4b21-8656-3d02ce0222ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=float32, numpy=32.999996>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 위의 코드를 이용하여 최적의 w, b를 구하고 그 때까지의 횟수를 구하시오."
      ],
      "metadata": {
        "id": "Ek4CaVIzR-4t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "opt = tf.keras.optimizers.Adam(learning_rate=0.1)\n",
        "\n",
        "w_back = tf.Variable(0.)\n",
        "b_back = tf.Variable(0.)\n",
        "\n",
        "cnt = tf.Variable(0.)\n",
        "\n",
        "while True:\n",
        "  opt.minimize(loss_func, var_list=[w,b])\n",
        "  pred = distance*w+b\n",
        "\n",
        "  if(w_back.numpy()==w.numpy()) and (b_back.numpy()==b.numpy()) or cnt.numpy()>1000:\n",
        "    break\n",
        "  w=tf.Variable(0.)\n",
        "  b=tf.Variable(0.)\n",
        "\n",
        "  cnt.assign_add(1)\n",
        "\n",
        "print(cnt.numpy(), ' : ', w.numpy(), ' : ', b.numpy())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rc8zGchWd1oi",
        "outputId": "2fa5d965-a56b-499d-98d9-5c44f9873721"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1001.0  :  0.2516035  :  0.2516035\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "device_name=tf.test.gpu_device_name()\n",
        "\n",
        "if device_name != '/device:GPU:0':\n",
        "  raise SystemError('GPU device not found')"
      ],
      "metadata": {
        "id": "CuFdmNSAcSnU"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}