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"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": [ + "60384954600849c984ec832f1e0ba089", + "f4493e24f41f46d98a5c6307d4381858", + "244e9d76fe934af2b536d4a41bd9ebc5", + "64ee09f226ae41e8a8d7203db2370f64", + "62129a4ae85b4aabb44cbb4c594ae7e5", + 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"5c9870ced6f845f2909f706727ba5877", + "456cfb7462d74aeab02f9a0af32f769f", + "09eb0575ee1c4bd288b5f0a79c506f67", + "cb35fee4c8a94812b209a61499687824", + "bbca06083a094719a58367dce36c5fe0", + "df7e251ff08b443f924eafcdf4627721", + "c1026663b57243d5b4fd651d06b83beb", + "fb4c68e932bc48ada671c11bd4b51fcb", + "0efa7087787445f993e83a41ebc5136d", + "95840eaab18b4dd6ba90acd7602112c1", + "c1525b47e0d9487fb54849863ebaa4e0", + "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