version20230901.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPSr8aS1Fx1Ajdjow9KTeI6",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Pritchardcodeonly/annotated_deep_learning_paper_implementations/blob/master/version20230901.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": "w9kq2iJR1TIY"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "source": [
        "from SOTT_NN import NN_us\n",
        "from NetworkInformation import print_network_architecture\n",
        "from sklearn.datasets import load_breast_cancer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# 加载数据\n",
        "data = load_breast_cancer()\n",
        "X, y = data.data, data.target\n",
        "\n",
        "# 划分数据集\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# 数据标准化\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "# print(X_train.shape)\n",
        "print(type(X_train))\n",
        "print(X_train)\n",
        "print(type(y_train))\n",
        "print(y_train)\n",
        "\n",
        "nn = NN_us()\n",
        "\n",
        "nn.compile(optimizer='adam', learning_rate=0.001, loss='binary_crossentropy')\n",
        "\n",
        "nn.add_layer(input_dim= X_train.shape[1], output_dim=16)\n",
        "nn.add_activation('relu')\n",
        "nn.add_layer(16,1)\n",
        "nn.add_activation('sigmoid')\n",
        "\n",
        "nn.fit(X_train, y_train, batch_size=1, epochs=1000)\n",
        "\n",
        "y_pred = nn.forward(X_test)\n",
        "print(type(y_pred))\n",
        "print(type(y_test))\n",
        "nn.evaluate_metrics(y_test, y_pred)\n",
        "\n",
        "nn.loss_show()\n",
        "\n",
        "print_network_architecture(nn)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "bnsBrk1L1jbo",
        "outputId": "59a5433b-560e-45e4-d6e3-c51d728eca66"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "[[ 0.57127137]\n",
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            " [ 0.39518552]\n",
            " [ 0.08851597]\n",
            " [-0.0437649 ]\n",
            " [-0.11954901]\n",
            " [ 0.69521577]\n",
            " [-0.43737339]\n",
            " [ 0.166841  ]\n",
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            " [ 0.00730649]\n",
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            " [-0.1890891 ]\n",
            " [-0.08966355]]\n",
            "正在训练中，喝点水休息一下吧。\n",
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            "SOTT正在疯狂运作中，老爷您休息会！！-----------------(693/1000)\n",
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            "SOTT正在疯狂运作中，老爷您休息会！！-----------------(704/1000)\n",
            "SOTT正在疯狂运作中，老爷您休息会！！-----------------(705/1000)\n",
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            "SOTT正在疯狂运作中，老爷您休息会！！-----------------(727/1000)\n",
            "SOTT正在疯狂运作中，老爷您休息会！！-----------------(728/1000)\n",
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            "SOTT正在疯狂运作中，老爷您休息会！！-----------------(730/1000)\n",
            "SOTT正在疯狂运作中，老爷您休息会！！-----------------(731/1000)\n",
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            "SOTT正在疯狂运作中，老爷您休息会！！-----------------(733/1000)\n",
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          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SOTT正在疯狂运作中，老爷您休息会！！-----------------(1000/1000)\n",
            "<class 'numpy.ndarray'>\n",
            "<class 'numpy.ndarray'>\n",
            "y_true type: <class 'numpy.ndarray'>\n",
            "y_pred type: <class 'numpy.ndarray'>\n",
            "评估指标:\n",
            "准确率: 0.5302\n",
            "精确率: 0.9627\n",
            "召回率: 0.9627\n",
            "F1值: 0.9627\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
            "WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "训练完成\n",
            "Network Architecture:\n",
            "Layer 1: FullyConnectedLayer\n",
            "   Input Dimension: 30\n",
            "   Output Dimension: 16\n",
            "   Batch Normalize: False\n",
            "\n",
            "Layer 2: Activation\n",
            "   Activation Function: relu\n",
            "\n",
            "Layer 3: FullyConnectedLayer\n",
            "   Input Dimension: 16\n",
            "   Output Dimension: 1\n",
            "   Batch Normalize: False\n",
            "\n",
            "Layer 4: Activation\n",
            "   Activation Function: sigmoid\n",
            "\n"
          ]
        }
      ]
    }
  ]
}