Lab02/Lab02_LEEJOOYOUNG.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Lab02_LEEJOOYOUNG.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "an67MSu7UvuE",
        "outputId": "ed62368b-003a-457d-dcbf-e2fc6391baea"
      },
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "\n",
        "print(tf.__version__)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.6.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vftGtnYdX6WJ"
      },
      "source": [
        "x_data = [1, 2, 3, 4, 5]\n",
        "y_data = [1, 2, 3, 4, 5]"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "FIplWDOOX9Df",
        "outputId": "bdb2beff-3872-4158-c935-63db32a47f97"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(x_data, y_data, 'o')\n",
        "plt.ylim(0, 8)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 8.0)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gQ4STZnGYNMR",
        "outputId": "28615272-61ce-4f19-d41b-35fb5f614002"
      },
      "source": [
        "v =[1., 2., 3., 4.]\n",
        "tf.reduce_mean(v) # 2.5"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=float32, numpy=2.5>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5HTfe3cxYWVs",
        "outputId": "d1f0704c-23f9-4222-b6b3-37ebeaa5aefc"
      },
      "source": [
        "tf.square(3) # 9"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=int32, numpy=9>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iOPbv2cVYZIc"
      },
      "source": [
        "x_data = [1, 2, 3, 4, 5]\n",
        "y_data = [1, 2, 3, 4, 5]\n",
        "\n",
        "W = tf.Variable(2.0)\n",
        "b = tf.Variable(0.5)\n",
        "\n",
        "hypothesis = W * x_data + b"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IR7LVk2KYii0",
        "outputId": "b8e3c7f9-fbb6-437c-b4d2-284898eb3263"
      },
      "source": [
        "W.numpy(), b.numpy()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2.0, 0.5)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7kSK7BLSYoUj",
        "outputId": "9f41b363-4910-4590-bb38-002003e9941a"
      },
      "source": [
        "hypothesis.numpy()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 2.5,  4.5,  6.5,  8.5, 10.5], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "xli_ekmlYv1D",
        "outputId": "8dbc841d-fcab-4029-b5ef-74711fc60b4d"
      },
      "source": [
        "plt.plot(x_data, hypothesis.numpy(), 'r-')\n",
        "plt.plot(x_data, y_data, 'o')\n",
        "plt.ylim(0, 8)\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ElwppBPkYzYp"
      },
      "source": [
        "cost = tf.reduce_mean(tf.square(hypothesis - y_data))"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GLPZq3oTY5FD",
        "outputId": "e73b5f32-6249-448e-c28a-0d213bbb2c9b"
      },
      "source": [
        "with tf.GradientTape() as tape:\n",
        "    hypothesis = W * x_data + b\n",
        "    cost = tf.reduce_mean(tf.square(hypothesis - y_data))\n",
        "\n",
        "W_grad, b_grad = tape.gradient(cost, [W, b])\n",
        "W_grad.numpy(), b_grad.numpy()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(25.0, 7.0)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-_0-7NuNY-Fz",
        "outputId": "e1bd3d62-cc55-4610-a032-e7013de02684"
      },
      "source": [
        "learning_rate = 0.01\n",
        "\n",
        "W.assign_sub(learning_rate * W_grad)\n",
        "b.assign_sub(learning_rate * b_grad)\n",
        "\n",
        "W.numpy(), b.numpy()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1.75, 0.43)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "A2FW-lzTZDJP",
        "outputId": "15f3ae4a-0923-4c02-ffa5-507bcd856f4e"
      },
      "source": [
        "plt.plot(x_data, hypothesis.numpy(), 'r-')\n",
        "plt.plot(x_data, y_data, 'o')\n",
        "plt.ylim(0, 8)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 8.0)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        },
        "id": "cBLNVWNiZL_t",
        "outputId": "2c49f284-404c-4d54-df51-b2aeb11b67eb"
      },
      "source": [
        "W = tf.Variable(2.9)\n",
        "b = tf.Variable(0.5)\n",
        "\n",
        "for i in range(100):\n",
        "    with tf.GradientTape() as tape:\n",
        "        hypothesis = W * x_data + b\n",
        "        cost = tf.reduce_mean(tf.square(hypothesis - y_data))\n",
        "    W_grad, b_grad = tape.gradient(cost, [W, b])\n",
        "    W.assign_sub(learning_rate * W_grad)\n",
        "    b.assign_sub(learning_rate * b_grad)\n",
        "    if i % 10 == 0:\n",
        "      print(\"{:5}|{:10.4f}|{:10.4f}|{:10.6f}\".format(i, W.numpy(), b.numpy(), cost))\n",
        "\n",
        "plt.plot(x_data, y_data, 'o')\n",
        "plt.plot(x_data, hypothesis.numpy(), 'r-')\n",
        "plt.ylim(0, 8)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    0|    2.4520|    0.3760| 45.660004\n",
            "   10|    1.1036|    0.0034|  0.206336\n",
            "   20|    1.0128|   -0.0209|  0.001026\n",
            "   30|    1.0065|   -0.0218|  0.000093\n",
            "   40|    1.0059|   -0.0212|  0.000083\n",
            "   50|    1.0057|   -0.0205|  0.000077\n",
            "   60|    1.0055|   -0.0198|  0.000072\n",
            "   70|    1.0053|   -0.0192|  0.000067\n",
            "   80|    1.0051|   -0.0185|  0.000063\n",
            "   90|    1.0050|   -0.0179|  0.000059\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 8.0)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xZAVo2LsZTOU",
        "outputId": "f0444c9f-644b-4505-b187-d68b0349c69e"
      },
      "source": [
        "print(W * 5 + b)\n",
        "print(W * 2.5 + b)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tf.Tensor(5.0066934, shape=(), dtype=float32)\n",
            "tf.Tensor(2.4946523, shape=(), dtype=float32)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IsXJSKpvZVct",
        "outputId": "6a913797-8942-4e2b-9c4f-69b8bc048ff6"
      },
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "\n",
        "# Data\n",
        "x_data = [1, 2, 3, 4, 5]\n",
        "y_data = [1, 2, 3, 4, 5]\n",
        "\n",
        "# W, b initialize\n",
        "W = tf.Variable(2.9)\n",
        "b = tf.Variable(0.5)\n",
        "\n",
        "# W, b update\n",
        "for i in range(100):\n",
        "    # Gradient descent\n",
        "    with tf.GradientTape() as tape:\n",
        "        hypothesis = W * x_data + b\n",
        "        cost = tf.reduce_mean(tf.square(hypothesis - y_data))\n",
        "    W_grad, b_grad = tape.gradient(cost, [W, b])\n",
        "    W.assign_sub(learning_rate * W_grad)\n",
        "    b.assign_sub(learning_rate * b_grad)\n",
        "    if i % 10 == 0:\n",
        "      print(\"{:5}|{:10.4f}|{:10.4f}|{:10.6f}\".format(i, W.numpy(), b.numpy(), cost))\n",
        "\n",
        "print()\n",
        "\n",
        "# predict\n",
        "print(W * 5 + b)\n",
        "print(W * 2.5 + b)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    0|    2.4520|    0.3760| 45.660004\n",
            "   10|    1.1036|    0.0034|  0.206336\n",
            "   20|    1.0128|   -0.0209|  0.001026\n",
            "   30|    1.0065|   -0.0218|  0.000093\n",
            "   40|    1.0059|   -0.0212|  0.000083\n",
            "   50|    1.0057|   -0.0205|  0.000077\n",
            "   60|    1.0055|   -0.0198|  0.000072\n",
            "   70|    1.0053|   -0.0192|  0.000067\n",
            "   80|    1.0051|   -0.0185|  0.000063\n",
            "   90|    1.0050|   -0.0179|  0.000059\n",
            "\n",
            "tf.Tensor(5.0066934, shape=(), dtype=float32)\n",
            "tf.Tensor(2.4946523, shape=(), dtype=float32)\n"
          ]
        }
      ]
    }
  ]
}