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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jung-geun/PSO/blob/master/example/pso2mnist.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BCG-8NlVLab8"
      },
      "source": [
        "# 기본 설치\n",
        "\n",
        "아래 명령어를 통해 설치할 수 있습니다\n",
        "\n",
        "```python\n",
        "!pip install pso2keras\n",
        "```\n",
        "\n",
        "필수 패키지로 tensorflow 가 필요하며, log 분석시에는 tensorboard 가 추가로 필요합니다\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Qd4s8Pu0nYGs"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "print('python version ', sys.version)\n",
        "\n",
        "# !pip uninstall pso2keras\n",
        "!pip install --upgrade pip\n",
        "!pip install pso2keras"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bs6TLWxLMmEw"
      },
      "source": [
        "# 모델 생성\n",
        "\n",
        "keras 모델을 사용하여 학습하기 때문에 모델을 생성하여 입력을 해주어야 합니다.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "bVWF-rQ3j_ld"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import sys\n",
        "\n",
        "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\"\n",
        "\n",
        "import gc\n",
        "\n",
        "import tensorflow as tf\n",
        "from keras.datasets import mnist\n",
        "from keras.layers import Conv2D, Dense, Dropout, Flatten, MaxPooling2D\n",
        "from keras.models import Sequential\n",
        "from tensorflow import keras\n",
        "\n",
        "from pso import Optimizer\n",
        "\n",
        "\n",
        "def get_data():\n",
        "    (x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
        "\n",
        "    x_train, x_test = x_train / 255.0, x_test / 255.0\n",
        "    x_train = x_train.reshape((60000, 28, 28, 1))\n",
        "    x_test = x_test.reshape((10000, 28, 28, 1))\n",
        "\n",
        "    y_train, y_test = tf.one_hot(y_train, 10), tf.one_hot(y_test, 10)\n",
        "\n",
        "    x_train, x_test = tf.convert_to_tensor(x_train), tf.convert_to_tensor(x_test)\n",
        "    y_train, y_test = tf.convert_to_tensor(y_train), tf.convert_to_tensor(y_test)\n",
        "\n",
        "    print(f\"x_train : {x_train[0].shape} | y_train : {y_train[0].shape}\")\n",
        "    print(f\"x_test : {x_test[0].shape} | y_test : {y_test[0].shape}\")\n",
        "\n",
        "    return x_train, y_train, x_test, y_test\n",
        "\n",
        "\n",
        "def get_data_test():\n",
        "    (x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
        "    x_test = x_test / 255.0\n",
        "    x_test = x_test.reshape((10000, 28, 28, 1))\n",
        "\n",
        "    y_test = tf.one_hot(y_test, 10)\n",
        "\n",
        "    x_test = tf.convert_to_tensor(x_test)\n",
        "    y_test = tf.convert_to_tensor(y_test)\n",
        "\n",
        "    print(f\"x_test : {x_test[0].shape} | y_test : {y_test[0].shape}\")\n",
        "\n",
        "    return x_test, y_test\n",
        "\n",
        "\n",
        "def make_model():\n",
        "    model = Sequential()\n",
        "    model.add(\n",
        "        Conv2D(32, kernel_size=(5, 5), activation=\"relu\", input_shape=(28, 28, 1))\n",
        "    )\n",
        "    model.add(MaxPooling2D(pool_size=(3, 3)))\n",
        "    model.add(Conv2D(64, kernel_size=(3, 3), activation=\"relu\"))\n",
        "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "    model.add(Dropout(0.25))\n",
        "    model.add(Flatten())\n",
        "    model.add(Dense(128, activation=\"relu\"))\n",
        "    model.add(Dense(10, activation=\"softmax\"))\n",
        "\n",
        "    return model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JKVXZC5fNjEr"
      },
      "source": [
        "# 학습\n",
        "\n",
        "학습을 위한 particle 개수와 기본 설정이 필요합니다\n",
        "loss 는 tensorflow 의 loss 를 활용합니다\n"
      ]
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          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11490434/11490434 [==============================] - 0s 0us/step\n",
            "x_test : (28, 28, 1) | y_test : (10,)\n",
            "start running time : 20230723-095443\n"
          ]
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              "Initializing Particles:   0%|          | 0/500 [00:00<?, ?it/s]"
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          "text": [
            "negative swarm : 50 / 500\n",
            "mutation swarm : 20.0%\n"
          ]
        },
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              "Initializing velocity:   0%|          | 0/500 [00:00<?, ?it/s]"
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            "initial g_best_score : 0.2117999941110611\n"
          ]
        },
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              "best 0.2118|0.1576:   0%|          | 0/200 [00:00<?, ?it/s]"
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              "acc : 0.0000 loss : inf:   0%|          | 0/500 [00:00<?, ?it/s]"
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        "model = make_model()\n",
        "x_train, y_train = get_data_test()\n",
        "\n",
        "loss = \"mean_squared_error\"\n",
        "\n",
        "pso_mnist = Optimizer(\n",
        "    model,\n",
        "    loss=loss,\n",
        "    n_particles=500,\n",
        "    c0=0.35,\n",
        "    c1=0.8,\n",
        "    w_min=0.6,\n",
        "    w_max=1.2,\n",
        "    negative_swarm=0.1,\n",
        "    mutation_swarm=0.2,\n",
        "    particle_min=-5,\n",
        "    particle_max=5,\n",
        ")\n",
        "\n",
        "best_score = pso_mnist.fit(\n",
        "    x_train,\n",
        "    y_train,\n",
        "    epochs=200,\n",
        "    save_info=True,\n",
        "    log=2,\n",
        "    log_name=\"mnist\",\n",
        "    save_path=\"./result/mnist\",\n",
        "    renewal=\"acc\",\n",
        "    check_point=25,\n",
        ")\n",
        "\n",
        "print(\"Done!\")"
      ]
    }
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