23-07-06

dev container 실행 코드 추가

.devcontainer/Dockerfile

@@ -1,16 +0,0 @@
-FROM mcr.microsoft.com/devcontainers/miniconda:0-3
-
-# Copy environment.yml (if found) to a temp location so we update the environment. Also
-# copy "noop.txt" so the COPY instruction does not fail if no environment.yml exists.
-COPY environment.yml* .devcontainer/noop.txt /tmp/conda-tmp/
-RUN if [ -f "/tmp/conda-tmp/environment.yml" ]; then umask 0002 && /opt/conda/bin/conda env update -n base -f /tmp/conda-tmp/environment.yml; fi \
-    && rm -rf /tmp/conda-tmp
-
-# [Optional] Uncomment to install a different version of Python than the default
-# RUN conda install -y python=3.6 \
-#     && pip install --no-cache-dir pipx \
-#     && pipx reinstall-all
-
-# [Optional] Uncomment this section to install additional OS packages.
-# RUN apt-get update && export DEBIAN_FRONTEND=noninteractive \
-#     && apt-get -y install --no-install-recommends <your-package-list-here>

.devcontainer/devcontainer.json

@@ -20,12 +20,9 @@
 		"ghcr.io/devcontainers/features/python:1": {
 			"installTools": true,
 			"version": "3.9"
-		},
-		"ghcr.io/rocker-org/devcontainer-features/miniforge:1": {
-			"version": "latest",
-			"variant": "Miniforge-pypy3"
 		}
-	}
+	},
+	"postCreateCommand": "conda env create --file environment.yaml --name pso"
 	// Features to add to the dev container. More info: https://containers.dev/features.
 	// "features": {},
 	// Use 'forwardPorts' to make a list of ports inside the container available locally.

.devcontainer/noop.txt

@@ -1,3 +0,0 @@
-This file is copied into the container along with environment.yml* from the
-parent folder. This is done to prevent the Dockerfile COPY instruction from 
-failing if no environment.yml is found.

.github/workflows/python-package-conda.yml

@@ -20,5 +20,5 @@ jobs:
           echo $CONDA/bin >> $GITHUB_PATH
       - name: Install dependencies
         run: |
-          conda env update --file environment.yaml --name pso
+          conda env create --file environment.yaml --name pso
           conda activate pso

.vscode/settings.json

@@ -0,0 +1,6 @@
+{
+    "[python]": {
+        "editor.defaultFormatter": "ms-python.black-formatter"
+    },
+    "python.formatting.provider": "none"
+}

iris.py

@@ -1,6 +1,6 @@
 import os
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
 
 import gc
 
@@ -16,12 +16,13 @@ from tensorflow.keras.models import Sequential
 
 def make_model():
     model = Sequential()
-    model.add(layers.Dense(10, activation='relu', input_shape=(4,)))
+    model.add(layers.Dense(10, activation="relu", input_shape=(4,)))
-    model.add(layers.Dense(10, activation='relu'))
+    model.add(layers.Dense(10, activation="relu"))
-    model.add(layers.Dense(3, activation='softmax'))
+    model.add(layers.Dense(3, activation="softmax"))
 
     return model
 
+
 def load_data():
     iris = load_iris()
     x = iris.data
@@ -29,14 +30,17 @@ def load_data():
 
     y = keras.utils.to_categorical(y, 3)
 
-    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, shuffle=True, stratify=y)
+    x_train, x_test, y_train, y_test = train_test_split(
+        x, y, test_size=0.2, shuffle=True, stratify=y
+    )
 
     return x_train, x_test, y_train, y_test
 
+
 model = make_model()
 x_train, x_test, y_train, y_test = load_data()
 
-loss = ['categorical_crossentropy']
+loss = ["categorical_crossentropy"]
 
 pso_iris = Optimizer(
     model,
@@ -48,7 +52,7 @@ pso_iris = Optimizer(
     w_max=1.0,
     negative_swarm=0.1,
     mutation_swarm=0.2,
-    )
+)
 
 best_score = pso_iris.fit(
     x_train,
@@ -59,7 +63,7 @@ best_score = pso_iris.fit(
     renewal="acc",
     empirical_balance=False,
     Dispersion=False,
-    check_point=25
+    check_point=25,
-    )
+)
 
 gc.collect()

mnist.py

@@ -1,7 +1,7 @@
 # %%
 import os
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
 
 import gc
 
@@ -26,31 +26,46 @@ def get_data():
     print(f"x_test : {x_test[0].shape} | y_test : {y_test[0].shape}")
     return x_train, y_train, x_test, y_test
 
+
 def get_data_test():
     (x_train, y_train), (x_test, y_test) = mnist.load_data()
     x_test = x_test.reshape((10000, 28, 28, 1))
 
     return x_test, y_test
 
+
 def make_model():
     model = Sequential()
-    model.add(Conv2D(32, kernel_size=(5, 5),
+    model.add(
-              activation='relu', input_shape=(28, 28, 1)))
+        Conv2D(32, kernel_size=(5, 5), activation="relu", input_shape=(28, 28, 1))
+    )
     model.add(MaxPooling2D(pool_size=(3, 3)))
-    model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
+    model.add(Conv2D(64, kernel_size=(3, 3), activation="relu"))
     model.add(MaxPooling2D(pool_size=(2, 2)))
     model.add(Dropout(0.25))
     model.add(Flatten())
-    model.add(Dense(128, activation='relu'))
+    model.add(Dense(128, activation="relu"))
-    model.add(Dense(10, activation='softmax'))
+    model.add(Dense(10, activation="softmax"))
 
     return model
 
+
 # %%
 model = make_model()
 x_test, y_test = get_data_test()
 
-loss = ['mse', 'categorical_crossentropy', 'binary_crossentropy', 'kullback_leibler_divergence', 'poisson', 'cosine_similarity', 'log_cosh', 'huber_loss', 'mean_absolute_error', 'mean_absolute_percentage_error']
+loss = [
+    "mse",
+    "categorical_crossentropy",
+    "binary_crossentropy",
+    "kullback_leibler_divergence",
+    "poisson",
+    "cosine_similarity",
+    "log_cosh",
+    "huber_loss",
+    "mean_absolute_error",
+    "mean_absolute_percentage_error",
+]
 
 if __name__ == "__main__":
     try:
@@ -61,10 +76,10 @@ if __name__ == "__main__":
             c0=0.35,
             c1=0.8,
             w_min=0.7,
-            w_max=1.0,
+            w_max=1.1,
             negative_swarm=0.2,
-            mutation_swarm=0.2,
+            mutation_swarm=0.1,
-            )
+        )
 
         best_score = pso_mnist.fit(
             x_test,
@@ -75,8 +90,8 @@ if __name__ == "__main__":
             renewal="acc",
             empirical_balance=False,
             Dispersion=False,
-            check_point=25
+            check_point=25,
-            )
+        )
     except Exception as e:
         print(e)
     finally:

pso/optimizer.py

@@ -15,10 +15,13 @@ gpus = tf.config.experimental.list_physical_devices("GPU")
 if gpus:
     try:
         # tf.config.experimental.set_visible_devices(gpus[0], "GPU")
+        print(tf.config.experimental.get_visible_devices("GPU"))
         tf.config.experimental.set_memory_growth(gpus[0], True)
+        print("set memory growth")
     except RuntimeError as e:
         print(e)
 
+
 class Optimizer:
     """
     particle swarm optimization
@@ -66,10 +69,11 @@ class Optimizer:
         self.w_max = w_max  # 최대 관성 수치
         self.negative_swarm = negative_swarm  # 최적해와 반대로 이동할 파티클 비율 - 0 ~ 1 사이의 값
         self.mutation_swarm = mutation_swarm  # 관성을 추가로 사용할 파티클 비율 - 0 ~ 1 사이의 값
-        self.g_best_score = [0 , np.inf]  # 최고 점수 - 시작은 0으로 초기화
+        self.g_best_score = [0, np.inf]  # 최고 점수 - 시작은 0으로 초기화
         self.g_best = None  # 최고 점수를 받은 가중치
         self.g_best_ = None  # 최고 점수를 받은 가중치 - 값의 분산을 위한 변수
         self.avg_score = 0  # 평균 점수
+        self.save_path = None  # 저장 위치
 
         negative_count = 0
 
@@ -85,12 +89,14 @@ class Optimizer:
                 loss,
                 negative=True if i < negative_swarm * self.n_particles else False,
                 mutation=mutation_swarm,
-                )
+            )
             if i < negative_swarm * self.n_particles:
                 negative_count += 1
 
         print(f"negative swarm : {negative_count} / {self.n_particles}")
-        print(f"mutation swarm : {mutation_swarm * self.n_particles} / {self.n_particles}")
+        print(
+            f"mutation swarm : {mutation_swarm * self.n_particles} / {self.n_particles}"
+        )
 
         gc.collect()
 
@@ -110,7 +116,6 @@ class Optimizer:
         del self.avg_score
         gc.collect()
 
-
     def _encode(self, weights):
         """
         가중치를 1차원으로 풀어서 반환
@@ -135,7 +140,6 @@ class Optimizer:
 
         return w_gpu, shape, length
 
-
     def _decode(self, weight, shape, length):
         """
         _encode 로 인코딩된 가중치를 원본 shape으로 복원
@@ -183,7 +187,6 @@ class Optimizer:
         else:
             return 1 + np.abs(score)
 
-
     def fit(
         self,
         x,
@@ -263,10 +266,17 @@ class Optimizer:
             del local_score
             gc.collect()
 
-        print(f"initial g_best_score : {self.g_best_score[0] if self.renewal == 'acc' else self.g_best_score[1]}")
+        print(
+            f"initial g_best_score : {self.g_best_score[0] if self.renewal == 'acc' else self.g_best_score[1]}"
+        )
 
         try:
-            epochs_pbar = tqdm(range(epochs), desc=f"best {self.g_best_score[0]:.4f}|{self.g_best_score[1]:.4f}", ascii=True, leave=True)
+            epochs_pbar = tqdm(
+                range(epochs),
+                desc=f"best {self.g_best_score[0]:.4f}|{self.g_best_score[1]:.4f}",
+                ascii=True,
+                leave=True,
+            )
             for epoch in epochs_pbar:
                 acc = 0
                 loss = 0
@@ -277,9 +287,16 @@ class Optimizer:
 
                 ts = self.c0 + np.random.rand() * (self.c1 - self.c0)
 
-                part_pbar = tqdm(range(len(self.particles)), desc=f"acc : {max_score:.4f} loss : {min_loss:.4f}", ascii=True, leave=False)
+                part_pbar = tqdm(
+                    range(len(self.particles)),
+                    desc=f"acc : {max_score:.4f} loss : {min_loss:.4f}",
+                    ascii=True,
+                    leave=False,
+                )
                 for i in part_pbar:
-                    part_pbar.set_description(f"acc : {max_score:.4f} loss : {min_loss:.4f}")
+                    part_pbar.set_description(
+                        f"acc : {max_score:.4f} loss : {min_loss:.4f}"
+                    )
                     w = self.w_max - (self.w_max - self.w_min) * epoch / epochs
 
                     g_, g_sh, g_len = self._encode(self.g_best)
@@ -339,7 +356,9 @@ class Optimizer:
                                 if score[0] < self.g_best_score[1]:
                                     self.g_best_score[1] = score[0]
                                     self.g_best = self.particles[i].get_best_weights()
-                            epochs_pbar.set_description(f"best {self.g_best_score[0]:.4f} | {self.g_best_score[1]:.4f}")
+                            epochs_pbar.set_description(
+                                f"best {self.g_best_score[0]:.4f} | {self.g_best_score[1]:.4f}"
+                            )
                     elif renewal == "loss":
                         if score[0] <= self.g_best_score[1]:
                             if score[0] < self.g_best_score[1]:
@@ -349,7 +368,9 @@ class Optimizer:
                                 if score[1] > self.g_best_score[0]:
                                     self.g_best_score[0] = score[1]
                                     self.g_best = self.particles[i].get_best_weights()
-                            epochs_pbar.set_description(f"best {self.g_best_score[0]:.4f} | {self.g_best_score[1]:.4f}")
+                            epochs_pbar.set_description(
+                                f"best {self.g_best_score[0]:.4f} | {self.g_best_score[1]:.4f}"
+                            )
 
                     if score[0] == None:
                         score[0] = np.inf

pso/particle.py

@@ -15,7 +15,10 @@ class Particle:
     4. 가중치 업데이트
     5. 2번으로 돌아가서 반복
     """
-    def __init__(self, model: keras.models, loss, negative: bool = False, mutation: float = 0):
+
+    def __init__(
+        self, model: keras.models, loss, negative: bool = False, mutation: float = 0
+    ):
         """
         Args:
             model (keras.models): 학습 및 검증을 위한 모델
@@ -68,7 +71,6 @@ class Particle:
 
         return w_gpu, shape, length
 
-
     def _decode(self, weight: list, shape, length):
         """
         _encode 로 인코딩된 가중치를 원본 shape으로 복원
@@ -114,7 +116,7 @@ class Particle:
                 self.best_score = score[1]
                 self.best_weights = self.model.get_weights()
         elif renewal == "loss":
-            if score[0] == 'nan':
+            if score[0] == "nan":
                 score[0] = np.inf
             if score[0] < self.best_score:
                 self.best_score = score[0]
@@ -198,7 +200,7 @@ class Particle:
 
         if np.random.rand() < self.mutation:
             m_v = np.random.uniform(-0.1, 0.1, len(encode_v))
-            new_v  = m_v
+            new_v = m_v
 
         self.velocities = self._decode(new_v, w_sh, w_len)
 

xor.py

@@ -1,10 +1,11 @@
 # %%
 import os
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
 
 import numpy as np
 import tensorflow as tf
+
 # from pso_tf import PSO
 from pso import Optimizer
 from tensorflow import keras
@@ -14,31 +15,64 @@ from tensorflow.keras.models import Sequential
 print(tf.__version__)
 print(tf.config.list_physical_devices())
 
+
 def get_data():
     x = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
     y = np.array([[0], [1], [1], [0]])
     return x, y
 
+
 def make_model():
     leyer = []
-    leyer.append(layers.Dense(2, activation='sigmoid', input_shape=(2,)))
+    leyer.append(layers.Dense(2, activation="sigmoid", input_shape=(2,)))
     # leyer.append(layers.Dense(2, activation='sigmoid'))
-    leyer.append(layers.Dense(1, activation='sigmoid'))
+    leyer.append(layers.Dense(1, activation="sigmoid"))
 
     model = Sequential(leyer)
 
     return model
 
+
 # %%
 model = make_model()
 x_test, y_test = get_data()
 
-loss = ['mean_squared_error', 'mean_squared_logarithmic_error', 'binary_crossentropy', 'categorical_crossentropy', 'sparse_categorical_crossentropy', 'kullback_leibler_divergence', 'poisson', 'cosine_similarity', 'log_cosh', 'huber_loss', 'mean_absolute_error', 'mean_absolute_percentage_error']
+loss = [
+    "mean_squared_error",
+    "mean_squared_logarithmic_error",
+    "binary_crossentropy",
+    "categorical_crossentropy",
+    "sparse_categorical_crossentropy",
+    "kullback_leibler_divergence",
+    "poisson",
+    "cosine_similarity",
+    "log_cosh",
+    "huber_loss",
+    "mean_absolute_error",
+    "mean_absolute_percentage_error",
+]
 
-pso_xor = Optimizer(model,
+pso_xor = Optimizer(
-    loss=loss[0], n_particles=75, c0=0.35, c1=0.8, w_min=0.6, w_max=1.2, negative_swarm=0.25, mutation_swarm=0.25)
+    model,
+    loss=loss[0],
+    n_particles=75,
+    c0=0.35,
+    c1=0.8,
+    w_min=0.6,
+    w_max=1.2,
+    negative_swarm=0.25,
+    mutation_swarm=0.25,
+)
 best_score = pso_xor.fit(
-    x_test, y_test, epochs=200, save=True, save_path="./result/xor", renewal="acc", empirical_balance=False, Dispersion=False, check_point=25)
+    x_test,
+    y_test,
+    epochs=200,
+    save=True,
+    save_path="./result/xor",
+    renewal="acc",
+    empirical_balance=False,
+    Dispersion=False,
+    check_point=25,
+)
 
 # %%
-