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23-07-23

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메모리 누수 해결 - 완전한 해결은 아니라 대량의 메모리가 필요
mnist 최적값을 찾는 파티클 개수 찾아야 함
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jung-geun
2023-07-23 18:37:20 +09:00
parent 7d1161aa95
commit f692ff7b4a
6 changed files with 33 additions and 649 deletions
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12
README.md
View File

@@ -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)
<a href="https://colab.research.google.com/github/jung-geun/PSO/blob/master/pso2keras.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# 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

10
mnist.py
View File

@@ -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,

2
pso/__init__.py
View File

@@ -1,7 +1,7 @@
from .optimizer import Optimizer
from .particle import Particle
__version__ = "0.1.5"
__version__ = "0.1.6"
__all__ = [
"Optimizer",

34
pso/optimizer.py
View File

@@ -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")
if log == 2:
elif log == 2:
with self.train_summary_writer[i].as_default():
tf.summary.scalar("loss", local_score[0], step=0)
tf.summary.scalar("accuracy", local_score[1], step=0)
del local_score
gc.collect()
tf.keras.backend.reset_uids()

2
pso/particle.py
View File

@@ -107,7 +107,7 @@ class Particle:
Returns:
(float): 점수
"""
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, use_multiprocessing=True)
if renewal == "acc":
if score[1] > self.best_score:

620
pso2keras.ipynb
View File

@@ -1,620 +0,0 @@
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"cells": [
{
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"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/jung-geun/PSO/blob/master/pso2keras.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"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==0.1.5"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Qd4s8Pu0nYGs",
"outputId": "6390f505-8f70-4a26-f5e2-5c534732c0fc"
},
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"python version 3.10.6 (main, May 29 2023, 11:10:38) [GCC 11.3.0]\n",
"Requirement already satisfied: pip in /usr/local/lib/python3.10/dist-packages (23.2)\n",
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0mCollecting pso2keras==0.1.5\n",
" Obtaining dependency information for pso2keras==0.1.5 from https://files.pythonhosted.org/packages/cc/e9/6694b997be42496d097288cad18e140fc083221b581b5bba4d7c187b48cd/pso2keras-0.1.5-py3-none-any.whl.metadata\n",
" Downloading pso2keras-0.1.5-py3-none-any.whl.metadata (8.6 kB)\n",
"Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from pso2keras==0.1.5) (4.65.0)\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from pso2keras==0.1.5) (1.22.4)\n",
"Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pso2keras==0.1.5) (1.5.3)\n",
"Requirement already satisfied: ipython in /usr/local/lib/python3.10/dist-packages (from pso2keras==0.1.5) (7.34.0)\n",
"Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.10/dist-packages (from ipython->pso2keras==0.1.5) (67.7.2)\n",
"Collecting jedi>=0.16 (from ipython->pso2keras==0.1.5)\n",
" Downloading jedi-0.18.2-py2.py3-none-any.whl (1.6 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m10.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: decorator in /usr/local/lib/python3.10/dist-packages (from ipython->pso2keras==0.1.5) (4.4.2)\n",
"Requirement already satisfied: pickleshare in /usr/local/lib/python3.10/dist-packages (from ipython->pso2keras==0.1.5) (0.7.5)\n",
"Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.10/dist-packages (from ipython->pso2keras==0.1.5) (5.7.1)\n",
"Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from ipython->pso2keras==0.1.5) (3.0.39)\n",
"Requirement already satisfied: pygments in /usr/local/lib/python3.10/dist-packages (from ipython->pso2keras==0.1.5) (2.14.0)\n",
"Requirement already satisfied: backcall in /usr/local/lib/python3.10/dist-packages (from ipython->pso2keras==0.1.5) (0.2.0)\n",
"Requirement already satisfied: matplotlib-inline in /usr/local/lib/python3.10/dist-packages (from ipython->pso2keras==0.1.5) (0.1.6)\n",
"Requirement already satisfied: pexpect>4.3 in /usr/local/lib/python3.10/dist-packages (from ipython->pso2keras==0.1.5) (4.8.0)\n",
"Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->pso2keras==0.1.5) (2.8.2)\n",
"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->pso2keras==0.1.5) (2022.7.1)\n",
"Requirement already satisfied: parso<0.9.0,>=0.8.0 in /usr/local/lib/python3.10/dist-packages (from jedi>=0.16->ipython->pso2keras==0.1.5) (0.8.3)\n",
"Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.10/dist-packages (from pexpect>4.3->ipython->pso2keras==0.1.5) (0.7.0)\n",
"Requirement already satisfied: wcwidth in /usr/local/lib/python3.10/dist-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython->pso2keras==0.1.5) (0.2.6)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->pso2keras==0.1.5) (1.16.0)\n",
"Downloading pso2keras-0.1.5-py3-none-any.whl (11 kB)\n",
"Installing collected packages: jedi, pso2keras\n",
" Attempting uninstall: pso2keras\n",
" Found existing installation: pso2keras 0.1.0\n",
" Uninstalling pso2keras-0.1.0:\n",
" Successfully uninstalled pso2keras-0.1.0\n",
"Successfully installed jedi-0.18.2 pso2keras-0.1.5\n",
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. 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<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "52249d81446e4ae29b376ade96d15636"
}
},
"metadata": {}
},
{
"output_type": "error",
"ename": "KeyboardInterrupt",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-5-3fcd5b26d5e8>\u001b[0m in \u001b[0;36m<cell line: 73>\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",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/util/dispatch.py\u001b[0m in \u001b[0;36mop_dispatch_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 1174\u001b[0m \u001b[0;31m# Fallback dispatch system (dispatch v1):\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1175\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-> 1176\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mdispatch_target\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 1177\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mValueError\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[1;32m 1178\u001b[0m \u001b[0;31m# Note: convert_to_eager_tensor currently raises a ValueError, not a\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/backend.py\u001b[0m in \u001b[0;36mbatch_set_value\u001b[0;34m(tuples)\u001b[0m\n\u001b[1;32m 4310\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtuples\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4311\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\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-> 4312\u001b[0;31m \u001b[0m_assign_value_to_variable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\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 4313\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4314\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mget_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_default\u001b[0m\u001b[0;34m(\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/backend.py\u001b[0m in \u001b[0;36m_assign_value_to_variable\u001b[0;34m(variable, value)\u001b[0m\n\u001b[1;32m 4358\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4359\u001b[0m \u001b[0;31m# For the normal tf.Variable assign\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4360\u001b[0;31m \u001b[0mvariable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massign\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\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 4361\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4362\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/ops/resource_variable_ops.py\u001b[0m in \u001b[0;36massign\u001b[0;34m(self, value, use_locking, name, read_value)\u001b[0m\n\u001b[1;32m 972\u001b[0m \u001b[0;31m# initialize the variable.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 973\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0m_handle_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandle\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--> 974\u001b[0;31m \u001b[0mvalue_tensor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_to_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\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 975\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_shape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_compatible_with\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue_tensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\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[1;32m 976\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\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;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mconvert_to_tensor\u001b[0;34m(value, dtype, name, as_ref, preferred_dtype, dtype_hint, ctx, accepted_result_types)\u001b[0m\n\u001b[1;32m 1640\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1641\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mret\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1642\u001b[0;31m \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconversion_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mas_ref\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mas_ref\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 1643\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1644\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mret\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNotImplemented\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;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/constant_op.py\u001b[0m in \u001b[0;36mconstant\u001b[0;34m(value, dtype, shape, name)\u001b[0m\n\u001b[1;32m 266\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcalled\u001b[0m \u001b[0mon\u001b[0m \u001b[0ma\u001b[0m \u001b[0msymbolic\u001b[0m \u001b[0mtensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 267\u001b[0m \"\"\"\n\u001b[0;32m--> 268\u001b[0;31m return _constant_impl(value, dtype, shape, name, verify_shape=False,\n\u001b[0m\u001b[1;32m 269\u001b[0m allow_broadcast=True)\n\u001b[1;32m 270\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/constant_op.py\u001b[0m in \u001b[0;36mconvert_to_eager_tensor\u001b[0;34m(value, ctx, dtype)\u001b[0m\n\u001b[1;32m 101\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_datatype_enum\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 102\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\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[0;32mreturn\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEagerTensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\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[0;34m\u001b[0m\u001b[0m\n",
"\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"
]
}
]
}