Lesson 4: WordEmbedding_概念介紹.ipynb

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      "name": "簡短 WordEmbedding 概念介紹.ipynb",
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      "cell_type": "code",
      "metadata": {
        "id": "MlfnhHjG_PPi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import math\n",
        "\n",
        "def sigmoid(x):\n",
        "  return 1 / (1 + math.exp(-x))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "sigmoid(-100)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.7200759760208356e-44"
            ]
          },
          "metadata": {
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          "execution_count": 8
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    {
      "cell_type": "code",
      "metadata": {
        "id": "gbO3RCaSAEKG",
        "colab_type": "code",
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      },
      "source": [
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "62rNS8dIBQAf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def loss(train_samples):\n",
        "  loss = 0\n",
        "  for i in train_samples:\n",
        "    loss = loss + (1-i[2])*sigmoid(np.dot(i[0],i[1]))+(i[2])*(1-sigmoid(np.dot(i[0],i[1])))\n",
        "  return loss"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "knDNdFoT_lTQ",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "w1 = np.array([0,0,0])\n",
        "w2 = np.array([0,0,0])\n",
        "w3 = np.array([0,0,0])\n",
        "w4 = np.array([0,0,0])\n",
        "w5 = np.array([0,0,0])\n",
        "train_samples = [[w1,w2,1], [w2,w3,1], [w4,w5,1], [w5,w3,1], [w1,w3,0], [w1,w5,0], [w2,w4,0]]\n",
        "loss(train_samples)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.5"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 86
        }
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "w1 = np.array([1,0,0])\n",
        "w2 = np.array([0,0,0])\n",
        "w3 = np.array([0,0,0])\n",
        "w4 = np.array([0,0,0])\n",
        "w5 = np.array([0,0,0])\n",
        "train_samples = [[w1,w2,1], [w2,w3,1], [w4,w5,1], [w5,w3,1], [w1,w3,0], [w1,w5,0], [w2,w4,0]]\n",
        "loss(train_samples)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.5"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 87
        }
      ]
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      "cell_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "w1 = np.array([1,0,0])\n",
        "w2 = np.array([1,0,0])\n",
        "w3 = np.array([0,0,0])\n",
        "w4 = np.array([0,0,0])\n",
        "w5 = np.array([0,0,0])\n",
        "train_samples = [[w1,w2,1], [w2,w3,1], [w4,w5,1], [w5,w3,1], [w1,w3,0], [w1,w5,0], [w2,w4,0]]\n",
        "loss(train_samples)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.268941421369995"
            ]
          },
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          "execution_count": 88
        }
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      "cell_type": "code",
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        "colab_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "w1 = np.array([1,0,0])\n",
        "w2 = np.array([1,0,0])\n",
        "w3 = np.array([-1,0,0])\n",
        "w4 = np.array([0,0,0])\n",
        "w5 = np.array([0,0,0])\n",
        "train_samples = [[w1,w2,1], [w2,w3,1], [w4,w5,1], [w5,w3,1], [w1,w3,0], [w1,w5,0], [w2,w4,0]]\n",
        "loss(train_samples)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.268941421369995"
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          "execution_count": 90
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      "cell_type": "code",
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        "colab_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "w1 = np.array([1,0,0])\n",
        "w2 = np.array([1,0,0])\n",
        "w3 = np.array([-1,0,0])\n",
        "w4 = np.array([-1,0,0])\n",
        "w5 = np.array([0,0,0])\n",
        "train_samples = [[w1,w2,1], [w2,w3,1], [w4,w5,1], [w5,w3,1], [w1,w3,0], [w1,w5,0], [w2,w4,0]]\n",
        "loss(train_samples)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.0378828427399904"
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          },
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          "execution_count": 92
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      "source": [
        ""
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