23-07-06

dev container 실행 코드 추가

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
 
@@ -26,58 +26,73 @@ 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),
-              activation='relu', input_shape=(28, 28, 1)))
+    model.add(
+        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(10, activation='softmax'))
+    model.add(Dense(128, activation="relu"))
+    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:
         pso_mnist = Optimizer(
             model,
-            loss=loss[0], 
+            loss=loss[0],
             n_particles=100,
-            c0=0.35, 
-            c1=0.8, 
+            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,
             y_test,
             epochs=200,
             save=True,
-            save_path="./result/mnist", 
-            renewal="acc", 
+            save_path="./result/mnist",
+            renewal="acc",
             empirical_balance=False,
-            Dispersion=False, 
-            check_point=25
-            )
+            Dispersion=False,
+            check_point=25,
+        )
     except Exception as e:
         print(e)
     finally:
-        gc.collect()
+        gc.collect()