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PSO/xor.ipynb
jung-geun 91c6ec965b 23-05-29 
EBPSO 알고리즘 구현 - 선택지로 추가
random 으로 분산시키는 방법 구현 - 선택지로 추가
iris 기준 98퍼센트로 나오나 정확한 결과를 지켜봐야 할것으로 보임
2023-05-29 04:01:48 +09:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2023-05-26 10:26:11.173286: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
"/home/pieroot/miniconda3/envs/pso/lib/python3.8/site-packages/cupy/_environment.py:445: UserWarning: \n",
"--------------------------------------------------------------------------------\n",
"\n",
" CuPy may not function correctly because multiple CuPy packages are installed\n",
" in your environment:\n",
"\n",
" cupy, cupy-cuda11x\n",
"\n",
" Follow these steps to resolve this issue:\n",
"\n",
" 1. For all packages listed above, run the following command to remove all\n",
" existing CuPy installations:\n",
"\n",
" $ pip uninstall <package_name>\n",
"\n",
" If you previously installed CuPy via conda, also run the following:\n",
"\n",
" $ conda uninstall cupy\n",
"\n",
" 2. Install the appropriate CuPy package.\n",
" Refer to the Installation Guide for detailed instructions.\n",
"\n",
" https://docs.cupy.dev/en/stable/install.html\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\n",
" warnings.warn(f'''\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.10.0\n",
"[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'), PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n"
]
}
],
"source": [
"import os\n",
"os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n",
"\n",
"import tensorflow as tf\n",
"tf.random.set_seed(777) # for reproducibility\n",
"\n",
"# from pso_tf import PSO\n",
"from pso import Optimizer\n",
"from tensorflow import keras\n",
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from tqdm import tqdm\n",
"\n",
"from tensorflow import keras\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras import layers\n",
"\n",
"from datetime import datetime\n",
"\n",
"import json\n",
"\n",
"print(tf.__version__)\n",
"print(tf.config.list_physical_devices())\n",
"\n",
"def get_data():\n",
" x = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])\n",
" y = np.array([[0], [1], [1], [0]])\n",
" return x, y\n",
"\n",
"def make_model():\n",
" leyer = []\n",
" leyer.append(layers.Dense(2, activation='sigmoid', input_shape=(2,)))\n",
" leyer.append(layers.Dense(1, activation='sigmoid'))\n",
"\n",
" model = Sequential(leyer)\n",
"\n",
" return model"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
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{
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"text": [
"WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_test_function.<locals>.test_function at 0x7f8a2a586e50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n"
]
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{
"name": "stderr",
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"text": [
"WARNING:tensorflow:6 out of the last 6 calls to <function Model.make_test_function.<locals>.test_function at 0x7f8a2a509280> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n"
]
},
{
"name": "stderr",
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" [0.5473223 ]\n",
" [0.580507 ]\n",
" [0.5538927 ]]\n",
"실 데이터 > [[0]\n",
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" [0]]\n",
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{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
},
{
"data": {
"text/plain": [
"<pso.optimizer.Optimizer at 0x7f8a2b933ee0>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def fit(x, y, model:keras.models = make_model(), loss_method=\"binary_crossentropy\", n_particles=100, maxiter=50, c0=0.5, c1=1.5, w=0.75,renewal=\"acc\"):\n",
" x, y = get_data()\n",
" x_test = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])\n",
" y_test = np.array([[0], [1], [1], [0]])\n",
"\n",
" model = make_model()\n",
"\n",
" # loss = loss_method\n",
" day = datetime.now().strftime(\"%m-%d-%H-%M\")\n",
" pso_xor = Optimizer(model=model, loss=loss_method, n_particles=n_particles, c0=c0, c1=c1, w=w)\n",
"\n",
" weight, score = pso_xor.fit(x, y, epochs=maxiter, save=True, save_path=\"./result/xor\", renewal=renewal)\n",
" # pso_xor = PSO(model=model, loss_method=loss, n_particles=n_particles)\n",
"\n",
" # best_weights, score = pso_xor.optimize(x, y, maxiter=maxiter, c0=c0, c1=c1, w=w)\n",
"\n",
" model.set_weights(weight)\n",
"\n",
" y_pred = model.predict(x_test)\n",
" print(f\"추론 > {y_pred}\")\n",
" print(f\"실 데이터 > {y_test}\")\n",
"\n",
" # score_ = model.evaluate(x_test, y_test, verbose=2)\n",
" print(f\"score > {score}\")\n",
" \n",
" # pso_xor.plot_history()\n",
" \n",
" # history = pso_xor.global_history()\n",
" json_data = {\n",
" \"best score\": score,\n",
" \"epoch\": maxiter,\n",
" \"n_particles\": n_particles,\n",
" \"c0\": c0,\n",
" \"c1\": c1,\n",
" \"w\": w,\n",
" \"loss_method\": loss_method\n",
" }\n",
" \n",
" with open(f\"./result/xor/{day}_{loss_method}_{n_particles}_{maxiter}.json\", \"w\") as f:\n",
" json.dump(json_data, f, indent=4)\n",
" \n",
" return pso_xor\n",
"\n",
"fit(*get_data(), make_model(), n_particles=100, maxiter=100, c0=0.5, c1=1.5, w=0.65, renewal=\"loss\")\n",
"\n",
"# pso_=fit(*get_data(), make_model(), n_particles=10, maxiter=10, c0=0.5, c1=1.5, w=0.75)\n",
"# print(f\"history > {history}\")\n",
"# print(f\"score > {score}\")\n",
"# plt.plot(history)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "pso",
"language": "python",
"name": "pso"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.16"
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {},
"version_major": 2,
"version_minor": 0
}
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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