코드 변경 내용: digits.py, iris.py, mnist.py, bean.py

Keras 모듈을 사용하여 코드를 업데이트했습니다.

fashion_mnist.py

@@ -1,15 +1,16 @@
 # %%
-from pso import optimizer
-from tensorflow import keras
-from keras.models import Sequential
-from keras.layers import Conv2D, Dense, Dropout, Flatten, MaxPooling2D
-from keras.datasets import mnist, fashion_mnist
-import tensorflow as tf
-import numpy as np
 import json
 import os
 import sys
 
+import numpy as np
+import tensorflow as tf
+from keras.datasets import fashion_mnist
+from keras.layers import Conv2D, Dense, Dropout, Flatten, MaxPooling2D
+from keras.models import Sequential
+
+from pso import optimizer
+
 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
 
 
@@ -22,10 +23,8 @@ def get_data():
 
     y_train, y_test = tf.one_hot(y_train, 10), tf.one_hot(y_test, 10)
 
-    x_train, x_test = tf.convert_to_tensor(
-        x_train), tf.convert_to_tensor(x_test)
-    y_train, y_test = tf.convert_to_tensor(
-        y_train), tf.convert_to_tensor(y_test)
+    x_train, x_test = tf.convert_to_tensor(x_train), tf.convert_to_tensor(x_test)
+    y_train, y_test = tf.convert_to_tensor(y_train), tf.convert_to_tensor(y_test)
 
     print(f"x_train : {x_train[0].shape} | y_train : {y_train[0].shape}")
     print(f"x_test : {x_test[0].shape} | y_test : {y_test[0].shape}")
@@ -36,8 +35,7 @@ def get_data():
 def make_model():
     model = Sequential()
     model.add(
-        Conv2D(32, kernel_size=(5, 5), 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=(2, 2)))
     model.add(Conv2D(64, kernel_size=(3, 3), activation="relu"))
@@ -51,72 +49,39 @@ def make_model():
     return model
 
 
-def random_state():
-    with open(
-        "result/mnist/20230723-061626/mean_squared_error_[0.6384999752044678, 0.0723000094294548].json",
-        "r",
-    ) as f:
-        json_ = json.load(f)
-        rs = (
-            json_["random_state_0"],
-            np.array(json_["random_state_1"]),
-            json_["random_state_2"],
-            json_["random_state_3"],
-            json_["random_state_4"],
-        )
-
-    return rs
-
-
 # %%
 model = make_model()
 x_train, y_train, x_test, y_test = get_data()
 
-loss = [
-    "mean_squared_error",
-    "categorical_crossentropy",
-    "sparse_categorical_crossentropy",
-    "binary_crossentropy",
-    "kullback_leibler_divergence",
-    "poisson",
-    "cosine_similarity",
-    "log_cosh",
-    "huber_loss",
-    "mean_absolute_error",
-    "mean_absolute_percentage_error",
-]
-
-# rs = random_state()
 
 pso_mnist = optimizer(
     model,
     loss="categorical_crossentropy",
-    n_particles=500,
-    c0=0.5,
-    c1=1.0,
-    w_min=0.7,
-    w_max=1.2,
-    negative_swarm=0.05,
-    mutation_swarm=0.3,
+    n_particles=200,
+    c0=0.7,
+    c1=0.5,
+    w_min=0.1,
+    w_max=0.8,
+    negative_swarm=0.0,
+    mutation_swarm=0.05,
     convergence_reset=True,
     convergence_reset_patience=10,
+    convergence_reset_monitor="loss",
 )
 
 best_score = pso_mnist.fit(
     x_train,
     y_train,
-    epochs=200,
+    epochs=1000,
     save_info=True,
     log=2,
     log_name="fashion_mnist",
-    renewal="acc",
+    renewal="loss",
     check_point=25,
-    empirical_balance=False,
-    dispersion=False,
     batch_size=5000,
-    back_propagation=True,
 )
 
 print("Done!")
 
 sys.exit(0)
+