Files
PSO/test.ipynb
jung-geun 0d99329a43 23-06-03
tensorflow gpu 의 메모리 용량 제한을 추가
readme에 분류 문제별 해결 현황 추가
2023-06-03 17:25:30 +09:00

419 lines
142 KiB
Plaintext

{
"cells": [
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"import matplotlib.pyplot as plt\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(100, 200)\n",
" 0 1 2 3 4 5 6 \n",
"0 1.065477 0.341667 1.620457 0.333333 0.973868 0.733333 1.119378 \\\n",
"1 2.018898 0.350000 1.063357 0.333333 5.985742 0.333333 1.492205 \n",
"2 3.812844 0.441667 0.973291 0.391667 0.982345 0.375000 1.206667 \n",
"3 0.992694 0.666667 0.919116 0.583333 1.219213 0.333333 0.904292 \n",
"4 1.111336 0.333333 0.874328 0.433333 1.070013 0.308333 0.974060 \n",
"\n",
" 7 8 9 ... 190 191 192 193 \n",
"0 0.333333 1.191749 0.375000 ... 1.764640 0.333333 1.150359 0.333333 \\\n",
"1 0.408333 17.772097 0.333333 ... 1.721332 0.333333 1.078496 0.383333 \n",
"2 0.525000 1.002070 0.391667 ... 1.320217 0.333333 0.978259 0.450000 \n",
"3 0.475000 2.042726 0.000000 ... 1.152177 0.333333 2.090459 0.333333 \n",
"4 0.633333 1.245026 0.333333 ... 0.967579 0.825000 2.405662 0.333333 \n",
"\n",
" 194 195 196 197 198 199 \n",
"0 1.353020 0.333333 1.798916 0.000000 0.980328 0.441667 \n",
"1 1.102060 0.333333 0.856502 0.333333 0.674042 0.375000 \n",
"2 1.075823 0.333333 1.588661 0.650000 0.701815 0.483333 \n",
"3 0.885551 0.666667 2.373989 0.583333 0.842598 0.666667 \n",
"4 0.876492 0.666667 0.979572 0.516667 1.128002 0.333333 \n",
"\n",
"[5 rows x 200 columns]\n",
"100\n",
"100\n"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"iris = pd.read_csv(\"./result/iris/05-26-07-03_100_100_0.5_1.5_0.75.csv\", header=None)\n",
"print(iris.shape)\n",
"print(iris.head())\n",
"\n",
"loss = []\n",
"acc = []\n",
"for i in range(len(iris.iloc[0])):\n",
" if i % 2 == 0:\n",
" loss.append(iris[i])\n",
" else:\n",
" acc.append(iris[i])\n",
"\n",
"print(len(loss))\n",
"print(len(acc))\n",
"\n",
"plt.subplot(2,1,1)\n",
"for i in range(len(loss)):\n",
" plt.plot(loss[i], label=f\"loss_{i}\")\n",
"\n",
"plt.subplot(2,1,2)\n",
"for i in range(len(acc)):\n",
" plt.plot(acc[i], label=f\"acc_{i}\")\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2023-05-26 03:53:32.688348: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n",
"To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
"2023-05-26 03:53:32.796903: 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",
"2023-05-26 03:53:33.196478: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvrtc.so.11.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-11.2/lib64:/usr/local/TensorRT/lib:/usr/local/cuda-11.2/lib64:/usr/local/TensorRT/lib:\n",
"2023-05-26 03:53:33.196616: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvrtc.so.11.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-11.2/lib64:/usr/local/TensorRT/lib:/usr/local/cuda-11.2/lib64:/usr/local/TensorRT/lib:\n",
"2023-05-26 03:53:33.196622: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
]
}
],
"source": [
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Dropout\n",
"\n",
"import numpy as np\n",
"\n",
"def make_model():\n",
" model = Sequential()\n",
" model.add(Conv2D(32, kernel_size=(5, 5), activation='relu', input_shape=(28,28,1)))\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",
" # model.summary()\n",
"\n",
" return model"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2023-05-26 03:53:33.924839: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2023-05-26 03:53:33.928891: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2023-05-26 03:53:33.929032: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2023-05-26 03:53:33.929450: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n",
"To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
"2023-05-26 03:53:33.929902: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2023-05-26 03:53:33.930018: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2023-05-26 03:53:33.930117: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2023-05-26 03:53:34.287172: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2023-05-26 03:53:34.287322: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2023-05-26 03:53:34.287430: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2023-05-26 03:53:34.287524: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 10109 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060, pci bus id: 0000:09:00.0, compute capability: 8.6\n"
]
}
],
"source": [
"model = make_model()\n",
"# json_ = model.to_json()\n",
"# print(json_)\n",
"# for layer in model.get_weights():\n",
" # print(layer.shape)\n",
"weight = model.get_weights()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 ~ 800\n",
"(5, 5, 1, 32)\n",
"800 ~ 832\n",
"(32,)\n",
"832 ~ 19264\n",
"(3, 3, 32, 64)\n",
"19264 ~ 19328\n",
"(64,)\n",
"19328 ~ 93056\n",
"(576, 128)\n",
"93056 ~ 93184\n",
"(128,)\n",
"93184 ~ 94464\n",
"(128, 10)\n",
"94464 ~ 94474\n",
"(10,)\n",
"[800, 32, 18432, 64, 73728, 128, 1280, 10]\n",
"[(5, 5, 1, 32), (32,), (3, 3, 32, 64), (64,), (576, 128), (128,), (128, 10), (10,)]\n"
]
}
],
"source": [
"from time import time\n",
"import cupy as cp\n",
"\n",
"def encode(weights):\n",
" w_gpu = cp.array([])\n",
" lenght = []\n",
" shape = []\n",
" for layer in weights:\n",
" shape.append(layer.shape)\n",
" w_ = layer.reshape(-1)\n",
" lenght.append(len(w_))\n",
" w_gpu = cp.append(w_gpu, w_)\n",
" \n",
" return w_gpu, shape, lenght\n",
"\n",
"def decode(weight, shape, lenght):\n",
" weights = []\n",
" start = 0\n",
" for i in range(len(shape)):\n",
" end = start + lenght[i]\n",
" print(f\"{start} ~ {end}\")\n",
" print(f\"{shape[i]}\")\n",
" w_ = weight[start:end]\n",
" w_ = w_.reshape(shape[i])\n",
" weights.append(w_)\n",
" start = end\n",
"\n",
" return weights\n",
"\n",
"w = 0.8\n",
"v,_,_ = encode(weight)\n",
"c0 = 0.5\n",
"c1 = 1.5\n",
"r0 = 0.2\n",
"r1 = 0.8\n",
"p_best,_,_ = encode(weight)\n",
"g_best,_,_ = encode(weight)\n",
"layer,shape,leng = encode(weight)\n",
"\n",
"# new_v = w*v[i]\n",
"# new_v = new_v + c0*r0*(p_best[i] - layer)\n",
"# new_v = new_v + c1*r1*(self.g_best[i] - layer)\n",
"\n",
"start = time()\n",
"new_velocity = w * v + c0 * r0 * (p_best - layer) + c1 * r1 * (g_best - layer)\n",
"\n",
"# print(new_velocity)\n",
"\n",
"we2 = decode(new_velocity, shape, leng)\n",
"# print(we2)\n",
"\n",
"\n",
"# # s= [1,2]\n",
"# print(w)\n",
"print(leng)\n",
"print(shape)\n",
"\n",
"# w2 = w\n",
"# c1 = c\n",
"\n",
"# tf_start = time()\n",
"# w3 = tf.multiply(w2, w)\n",
"# tf_end = time()\n",
"# mul_start = time()\n",
"# w4 = w2 * w\n",
"# mul_end = time()\n",
"# cuda_start = time()\n",
"# w5 = c1 * c\n",
"# cuda_end = time()\n",
"\n",
"# print(f\"tf 연산 > {tf_end-tf_start} | {w3}\")\n",
"# print(f\"곱셈 연산 > {mul_end-mul_start} | {w4}\")\n",
"# print(f\"cuda 연산 > {cuda_end-cuda_start} | {w5}\")\n",
"\n",
"# for i in range(len(w)):\n",
"# if w[i] != w2[i]:\n",
"# print(\"not same\")\n",
"# break\n",
"# else:\n",
"# print(\"same\")\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1/1 [==============================] - 0s 452ms/step\n",
"[[0.0000000e+00 1.0000000e+00 8.5117706e-28]\n",
" [0.0000000e+00 1.0000000e+00 0.0000000e+00]\n",
" [1.0000000e+00 3.3700031e-35 0.0000000e+00]\n",
" [1.0000000e+00 1.3158974e-19 0.0000000e+00]\n",
" [0.0000000e+00 1.0000000e+00 0.0000000e+00]\n",
" [0.0000000e+00 0.0000000e+00 1.0000000e+00]\n",
" [1.0000000e+00 1.4602315e-27 0.0000000e+00]\n",
" [0.0000000e+00 0.0000000e+00 1.0000000e+00]\n",
" [1.0000000e+00 2.4845295e-16 0.0000000e+00]\n",
" [0.0000000e+00 1.0000000e+00 1.6942224e-33]\n",
" [1.0000000e+00 0.0000000e+00 0.0000000e+00]\n",
" [0.0000000e+00 1.0000000e+00 0.0000000e+00]\n",
" [1.0000000e+00 9.0455008e-36 0.0000000e+00]\n",
" [1.0000000e+00 0.0000000e+00 0.0000000e+00]\n",
" [0.0000000e+00 1.8117375e-33 1.0000000e+00]\n",
" [0.0000000e+00 1.0000000e+00 6.7984806e-36]\n",
" [0.0000000e+00 1.7472901e-25 1.0000000e+00]\n",
" [0.0000000e+00 6.2991115e-37 1.0000000e+00]\n",
" [0.0000000e+00 0.0000000e+00 1.0000000e+00]\n",
" [0.0000000e+00 1.0598510e-30 1.0000000e+00]\n",
" [1.0000000e+00 1.7519910e-30 0.0000000e+00]\n",
" [0.0000000e+00 1.0000000e+00 0.0000000e+00]\n",
" [0.0000000e+00 1.0000000e+00 0.0000000e+00]\n",
" [1.0000000e+00 7.4562871e-27 0.0000000e+00]\n",
" [0.0000000e+00 0.0000000e+00 1.0000000e+00]\n",
" [0.0000000e+00 0.0000000e+00 1.0000000e+00]\n",
" [0.0000000e+00 0.0000000e+00 1.0000000e+00]\n",
" [0.0000000e+00 1.0000000e+00 0.0000000e+00]\n",
" [0.0000000e+00 1.0000000e+00 0.0000000e+00]\n",
" [1.0000000e+00 0.0000000e+00 0.0000000e+00]]\n",
"[[0. 1. 0.]\n",
" [0. 1. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [0. 1. 0.]\n",
" [0. 0. 1.]\n",
" [1. 0. 0.]\n",
" [0. 0. 1.]\n",
" [1. 0. 0.]\n",
" [0. 1. 0.]\n",
" [1. 0. 0.]\n",
" [0. 1. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [0. 0. 1.]\n",
" [0. 1. 0.]\n",
" [0. 0. 1.]\n",
" [0. 0. 1.]\n",
" [0. 0. 1.]\n",
" [0. 0. 1.]\n",
" [1. 0. 0.]\n",
" [0. 1. 0.]\n",
" [0. 1. 0.]\n",
" [1. 0. 0.]\n",
" [0. 0. 1.]\n",
" [0. 0. 1.]\n",
" [0. 0. 1.]\n",
" [0. 1. 0.]\n",
" [0. 1. 0.]\n",
" [1. 0. 0.]]\n",
"1/1 [==============================] - 0s 88ms/step - loss: 0.0000e+00 - accuracy: 1.0000\n",
"[0.0, 1.0]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2023-06-02 14:34:49.851147: I tensorflow/stream_executor/cuda/cuda_blas.cc:1614] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\n"
]
}
],
"source": [
"import numpy as np\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"from tensorflow.keras import layers\n",
"from tensorflow.keras.models import Sequential\n",
"\n",
"from sklearn.datasets import load_iris\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"def get_xor():\n",
" x = np.array([[0,0],[0,1],[1,0],[1,1]])\n",
" y = np.array([[0],[1],[1],[0]])\n",
"\n",
" return x,y\n",
"\n",
"def get_iris():\n",
" iris = load_iris()\n",
" x = iris.data\n",
" y = iris.target\n",
"\n",
" y = keras.utils.to_categorical(y, 3)\n",
"\n",
" x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, shuffle=True, stratify=y)\n",
"\n",
" return x_train, x_test, y_train, y_test\n",
"\n",
"# model = keras.models.load_model(\"./result/xor/06-02-13-31/75_0.35_0.8_0.6.h5\")\n",
"model = keras.models.load_model(\"./result/iris/06-02-13-48/50_0.4_0.8_0.7.h5\")\n",
"# x,y = get_xor()\n",
"x_train, x_test, y_train, y_test = get_iris()\n",
"\n",
"print(model.predict(x_test))\n",
"print(y_test)\n",
"print(model.evaluate(x_test,y_test))"
]
}
],
"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"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}