23-08-06

메모리 누수 다소 해결
Fixes #2
EBPSO 의 구현 부분의 문제가 있어 수정중

mnist.py

@@ -5,8 +5,6 @@ import sys
 
 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
 
-import gc
-
 import numpy as np
 import tensorflow as tf
 from keras.datasets import mnist
@@ -14,7 +12,7 @@ from keras.layers import Conv2D, Dense, Dropout, Flatten, MaxPooling2D
 from keras.models import Sequential
 from tensorflow import keras
 
-from pso import Optimizer
+from pso import optimizer
 
 
 def get_data():
@@ -40,10 +38,10 @@ def get_data_test():
     x_test = x_test / 255.0
     x_test = x_test.reshape((10000, 28, 28, 1))
 
-    y_test = tf.one_hot(y_test, 10)
+    y_train, y_test = tf.one_hot(y_train, 10), tf.one_hot(y_test, 10)
 
-    x_test = tf.convert_to_tensor(x_test)
-    y_test = 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_test : {x_test[0].shape} | y_test : {y_test[0].shape}")
 
@@ -53,14 +51,14 @@ def get_data_test():
 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="sigmoid", 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="sigmoid"))
     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="sigmoid"))
     model.add(Dense(10, activation="softmax"))
 
     return model
@@ -101,33 +99,34 @@ loss = [
     "mean_absolute_percentage_error",
 ]
 
-rs = random_state()
+# rs = random_state()
 
-pso_mnist = Optimizer(
+pso_mnist = optimizer(
     model,
-    loss=loss[0],
-    n_particles=500,
-    c0=0.3,
-    c1=0.5,
-    w_min=0.4,
-    w_max=0.7,
+    loss="mean_squared_error",
+    n_particles=990,
+    c0=0.2,
+    c1=0.4,
+    w_min=0.3,
+    w_max=0.6,
     negative_swarm=0.1,
     mutation_swarm=0.3,
     particle_min=-4,
     particle_max=4,
-    random_state=rs,
 )
 
 best_score = pso_mnist.fit(
     x_train,
     y_train,
-    epochs=250,
+    epochs=200,
     save_info=True,
     log=2,
     log_name="mnist",
     save_path="./result/mnist",
     renewal="acc",
     check_point=25,
+    empirical_balance=False,
+    dispersion=False,
 )
 
 print("Done!")