diff --git a/README.md b/README.md index d3d7553..16cf89f 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,5 @@ [![Python Package Index publish](https://github.com/jung-geun/PSO/actions/workflows/pypi.yml/badge.svg?event=push)](https://github.com/jung-geun/PSO/actions/workflows/pypi.yml) +Open In Colab # PSO 알고리즘 구현 및 새로운 시도 @@ -41,13 +42,22 @@ pip install pso2keras==0.1.4 위의 패키지를 사용하기 위해서는 tensorflow 와 tensorboard 가 설치되어 있어야 합니다 +python 패키지를 사용하기 위한 라이브러리는 아래 코드를 사용합니다 + +```python +from pso import Optimizer + +pso_model = Optimizer(...) +pso_model.fit(...) +``` + # 현재 진행 상황 ## 1. PSO 알고리즘 구현 ### 파일 구조 -```plain text +```plain |-- /conda_env # conda 환경 설정 파일 | |-- environment.yaml # conda 환경 설정 파일 |-- /metacode # pso 기본 코드 @@ -212,7 +222,7 @@ best_score = pso_mnist.fit( > 머신러닝 분류 방식에 존재하는 random forest 방식을 이용하여, 오차역전파 함수를 최적화 하는 방법이 있을것 같습니다 > > > pso 와 random forest 방식이 매우 유사하다고 생각하여 학습할 때 뿐만 아니라 예측 할 때도 이러한 방식으로 사용할 수 있을 것 같습니다 -> +> > 각 # 참고 자료 diff --git a/mnist.py b/mnist.py index e9cbdfc..d0ce923 100644 --- a/mnist.py +++ b/mnist.py @@ -106,11 +106,11 @@ loss = [ pso_mnist = Optimizer( model, loss=loss[0], - n_particles=100, - c0=0.25, - c1=0.4, - w_min=0.3, - w_max=0.9, + n_particles=1000, + c0=0.4, + c1=0.6, + w_min=0.5, + w_max=0.8, negative_swarm=0.1, mutation_swarm=0.2, particle_min=-5, diff --git a/pso/__init__.py b/pso/__init__.py index 2cf034b..1962161 100644 --- a/pso/__init__.py +++ b/pso/__init__.py @@ -1,7 +1,7 @@ from .optimizer import Optimizer from .particle import Particle -__version__ = "0.1.5" +__version__ = "0.1.6" __all__ = [ "Optimizer", diff --git a/pso/optimizer.py b/pso/optimizer.py index eec8e0f..65bac56 100644 --- a/pso/optimizer.py +++ b/pso/optimizer.py @@ -95,16 +95,14 @@ class Optimizer: print(f"start running time : {self.day}") for i in tqdm(range(self.n_particles), desc="Initializing Particles"): - m = keras.models.model_from_json(model.to_json()) - init_weights = m.get_weights() - - w_, sh_, len_ = self._encode(init_weights) + model_ = keras.models.model_from_json(model.to_json()) + w_, sh_, len_ = self._encode(model_.get_weights()) w_ = np.random.uniform(particle_min, particle_max, len(w_)) - m.set_weights(self._decode(w_, sh_, len_)) + model_.set_weights(self._decode(w_, sh_, len_)) - m.compile(loss=self.loss, optimizer="sgd", metrics=["accuracy"]) + model_.compile(loss=self.loss, optimizer="sgd", metrics=["accuracy"]) self.particles[i] = Particle( - m, + model_, loss, negative=True if i < negative_swarm * self.n_particles else False, mutation=mutation_swarm, @@ -112,6 +110,9 @@ class Optimizer: if i < negative_swarm * self.n_particles: negative_count += 1 # del m, init_weights, w_, sh_, len_ + gc.collect() + tf.keras.backend.reset_uids() + tf.keras.backend.clear_session() print(f"negative swarm : {negative_count} / {self.n_particles}") print(f"mutation swarm : {mutation_swarm * 100}%") @@ -202,7 +203,7 @@ class Optimizer: (float): 목적 함수 값 """ self.model.set_weights(weights) - self.model.compile(loss=self.loss, optimizer="sgd", metrics=["accuracy"]) + # self.model.compile(loss=self.loss, optimizer="sgd", metrics=["accuracy"]) score = self.model.evaluate(x, y, verbose=0)[1] if score > 0: return 1 / (1 + score) @@ -252,11 +253,8 @@ class Optimizer: self.train_summary_writer[i] = tf.summary.create_file_writer( train_log_dir + f"/{i}" ) - except AssertionError as e: - print(e) - sys.exit(1) - try: - if check_point is not None or log == 1: + + elif check_point is not None or log == 1: if save_path is None: raise ValueError("save_path is None") else: @@ -290,12 +288,6 @@ class Optimizer: self.g_best = p.get_best_weights() self.g_best_ = p.get_best_weights() - if local_score[0] == None: - local_score[0] = np.inf - - if local_score[1] == None: - local_score[1] = 0 - if log == 1: with open( f"./{save_path}/{self.day}_{self.n_particles}_{epochs}_{self.c0}_{self.c1}_{self.w_min}_{renewal}.csv", @@ -306,10 +298,12 @@ class Optimizer: f.write(", ") else: f.write("\n") - 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"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. 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It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", - "\u001b[0m" - ] - } - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 458, - "referenced_widgets": [ - "52249d81446e4ae29b376ade96d15636", - "6dafb96228714fc19c62b97d43c3835c", - "a1c7d30b133c4015a7f6ae5aa9b79705", - "32b01d1a0d9c4c27b1f4862479e96c86", - "f44dfe5c3d7d48f99a7071c94ee61b0b", - "e1622198e36d4d23a87ddc05d4466713", - "3ebf57ddd70642ef8cbda4b25a375e64", - "b517359a6556481392beb0744b6b7127", - "2dc41c24eb5243dbbcff659b6e635da1", - "85347ee2ed11446ba3a5a4b39578b1b8", - "6174b44f218448c98098c7bde09f3fcf" - ] - }, - "id": "bVWF-rQ3j_ld", - "outputId": "303830cd-14b4-4fe5-f75c-a5280ad03f35" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "x_test : (28, 28, 1) | y_test : (10,)\n", - "start running time : 20230721-063118\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "Initializing Particles: 0%| | 0/100 [00:00\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'mean_squared_error'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m pso_mnist = Optimizer(\n\u001b[0m\u001b[1;32m 74\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 75\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pso/optimizer.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, model, loss, n_particles, c0, c1, w_min, w_max, negative_swarm, mutation_swarm, np_seed, tf_seed, random_state, particle_min, particle_max)\u001b[0m\n\u001b[1;32m 101\u001b[0m \u001b[0mw_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msh_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_encode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minit_weights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 102\u001b[0m \u001b[0mw_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muniform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparticle_min\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparticle_max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msh_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 105\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"sgd\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"accuracy\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/engine/base_layer.py\u001b[0m in \u001b[0;36mset_weights\u001b[0;34m(self, weights)\u001b[0m\n\u001b[1;32m 1833\u001b[0m \u001b[0mweight_index\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1834\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1835\u001b[0;31m \u001b[0mbackend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch_set_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweight_value_tuples\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1836\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1837\u001b[0m \u001b[0;31m# Perform any layer defined finalization of the layer state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/util/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 149\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 151\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 152\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - 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"\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "# %%\n", - "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\n", - "\n", - "\n", - "# %%\n", - "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=100,\n", - " c0=0.3,\n", - " c1=0.5,\n", - " w_min=0.4,\n", - " w_max=0.7,\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!\")\n", - "\n", - "gc.collect()\n", - "sys.exit(0)\n" - ] - } - ] -} \ No newline at end of file