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PSO/example/pso2mnist.ipynb
jung-geun 7b81b40faf Colaboratory를 통해 생성됨
2023-07-23 19:41:15 +09:00

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"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"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>"
]
},
{
"cell_type": "markdown",
"source": [
"# 기본 설치\n",
"\n",
"아래 명령어를 통해 설치할 수 있습니다\n",
"```python\n",
"!pip install pso2keras\n",
"```\n",
"\n",
"필수 패키지로 tensorflow 가 필요하며, log 분석시에는 tensorboard 가 추가로 필요합니다"
],
"metadata": {
"id": "BCG-8NlVLab8"
}
},
{
"cell_type": "code",
"source": [
"import sys\n",
"print('python version ', sys.version)\n",
"\n",
"# !pip uninstall pso2keras\n",
"!pip install --upgrade pip\n",
"!pip install pso2keras"
],
"metadata": {
"id": "Qd4s8Pu0nYGs"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# 모델 생성\n",
"\n",
"keras 모델을 사용하여 학습하기 때문에 모델을 생성하여 입력을 해주어야 합니다."
],
"metadata": {
"id": "Bs6TLWxLMmEw"
}
},
{
"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",
"source": [
"# 학습\n",
"\n",
"학습을 위한 particle 개수와 기본 설정이 필요합니다\n",
"loss 는 tensorflow 의 loss 를 활용합니다"
],
"metadata": {
"id": "JKVXZC5fNjEr"
}
},
{
"cell_type": "code",
"source": [
"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!\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 267,
"referenced_widgets": [
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"fae612998dec4856b30ebb2e5ececdfc",
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"f1599b1fafd6424483c5f404b3c79fd6",
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"5c9870ced6f845f2909f706727ba5877",
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"09eb0575ee1c4bd288b5f0a79c506f67",
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"df7e251ff08b443f924eafcdf4627721",
"c1026663b57243d5b4fd651d06b83beb",
"fb4c68e932bc48ada671c11bd4b51fcb",
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"f20e024da5414b00becceb0541e3e45e",
"1b6fed5a1fb24d7d8fcc7dfd7e56e618",
"31537bf870cd4dbcbee4f55d1383a4f4",
"78c7e54708884ef0bf3feadf4e86b27b",
"de7b67b83e934558a122203e6314fb67",
"00e855c093c947a2bf0c7ec644b6e401",
"b4929212a2634d7ea12a77f79e4d61ff",
"cbea21e8c9004b149bd86e1b0c1e67ce",
"f1b4dc5fe13546c19c4bbfecb390e328",
"0048352cbddf4e5282be22298975efc4",
"52d399c1dfe4440baa77b540a2b5664c",
"4ce75915aeb4484e9f1d3f6a2a262c76",
"191804b6a0d64c1aafd50b1518c8e7e5",
"872b2c7612c44b15bdcee774fb9e70b7",
"b14aa26f85e148359bc9abff58951b51",
"77a9f488c18c4487ae107bee7362b643",
"b66fc8e0b5144229a69c5f2942ab796a",
"bdda4512f2ec4290b29bf69d9b4808fe",
"2fb4ef63d0824d36952f860f57e228ea",
"a637e3a3a7144f6db19eb3bbd812722f",
"8689e7f497fa4247b15f9929af81e68f",
"bbf5277f6016441baa5a21a8059944d9",
"814ff8b72e0e4f20877c85ba4ced21e6",
"c63110ef853d411db16a41cb355324af"
]
},
"id": "wXmfci5UKNm4",
"outputId": "c5bc9a2f-b949-4dac-e4d1-32942282cabf"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"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"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Initializing Particles: 0%| | 0/500 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "60384954600849c984ec832f1e0ba089"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"negative swarm : 50 / 500\n",
"mutation swarm : 20.0%\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Initializing velocity: 0%| | 0/500 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "0aa01a9086594c09b7be442781d15761"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"initial g_best_score : 0.2117999941110611\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"best 0.2118|0.1576: 0%| | 0/200 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "fae612998dec4856b30ebb2e5ececdfc"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"acc : 0.0000 loss : inf: 0%| | 0/500 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "09eb0575ee1c4bd288b5f0a79c506f67"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"acc : 0.0000 loss : inf: 0%| | 0/500 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "31537bf870cd4dbcbee4f55d1383a4f4"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"acc : 0.0000 loss : inf: 0%| | 0/500 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "872b2c7612c44b15bdcee774fb9e70b7"
}
},
"metadata": {}
}
]
}
]
}
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