23-05-29

EBPSO 알고리즘 구현 - 선택지로 추가
random 으로 분산시키는 방법 구현 - 선택지로 추가
iris 기준 98퍼센트로 나오나 정확한 결과를 지켜봐야 할것으로 보임

mnist.py

@@ -3,7 +3,7 @@ import os
 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
 
 import tensorflow as tf
-# tf.random.set_seed(777)  # for reproducibility
+tf.random.set_seed(777)  # for reproducibility
 
 from tensorflow import keras
 from keras.datasets import mnist
@@ -12,32 +12,43 @@ from keras.layers import Dense, Dropout, Flatten
 from keras.layers import Conv2D, MaxPooling2D
 from keras import backend as K
 
-from pso_tf import PSO
+# from pso_tf import PSO
+from pso import Optimizer
+# from optimizer import Optimizer
 
 import numpy as np
-import matplotlib.pyplot as plt
 
 from datetime import date
 from tqdm import tqdm
-import json
+
+import gc
 
 print(tf.__version__)
 print(tf.config.list_physical_devices())
+print(f"Num GPUs Available: {len(tf.config.list_physical_devices('GPU'))}")
+
 
 def get_data():
     (x_train, y_train), (x_test, y_test) = mnist.load_data()
 
     x_train, x_test = x_train / 255.0, x_test / 255.0
-    x_train = x_train.reshape((60000, 28 ,28, 1))
-    x_test = x_test.reshape((10000, 28 ,28, 1))
+    x_train = x_train.reshape((60000, 28, 28, 1))
+    x_test = x_test.reshape((10000, 28, 28, 1))
 
     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}")
     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(MaxPooling2D(pool_size=(2, 2)))
@@ -50,87 +61,28 @@ def make_model():
 
     return model
 
-# %%
-'''
-optimizer parameter
-'''
-lr = 0.1
-momentun = 0.8
-decay = 1e-04
-nestrov = True
-
-'''
-pso parameter
-'''
-n_particles = 30
-maxiter = 50
-# epochs = 1
-w = 0.8
-c0 = 0.6
-c1 = 1.6
-
-def auto_tuning(n_particles=n_particles, maxiter=maxiter, c0=c0, c1=c1, w=w):
-    x_train, y_train, x_test, y_test = get_data()
-    model = make_model()
-
-    loss = keras.losses.MeanSquaredError()
-    optimizer = keras.optimizers.SGD(lr=lr, momentum=momentun, decay=decay, nesterov=nestrov)
-
-
-    pso_m = PSO(model=model, loss_method=loss, n_particles=n_particles)
-    # c0 : 지역 최적값 중요도
-    # c1 : 전역 최적값 중요도
-    # w : 관성 (현재 속도를 유지하는 정도)
-    best_weights, score = pso_m.optimize(x_train, y_train, x_test, y_test, maxiter=maxiter, c0=c0, c1=c1, w=w)
-    model.set_weights(best_weights)
-
-    score_ = model.evaluate(x_test, y_test, verbose=2)
-    print(f" Test loss: {score_}")
-    score = round(score_[1]*100, 2)
-
-    day = date.today().strftime("%Y-%m-%d")
-
-    os.makedirs(f'./model', exist_ok=True)
-    model.save(f'./model/{day}_{score}_mnist.h5')
-    json_save = {
-        "name" : f"{day}_{score}_mnist.h5",
-        "score" : score_,
-        "maxiter" : maxiter,
-        "c0" : c0,
-        "c1" : c1,
-        "w" : w    
-    }
-    with open(f'./model/{day}_{score}_pso_mnist.json', 'a') as f:
-        json.dump(json_save, f)
-        f.write(',\n')
-                    
-    return model
-
-# auto_tuning(n_particles=30, maxiter=1000, c0=0.5, c1=1.5, w=0.75)
-
-
-# %%
-# print(f"정답 > {y_test}")
-def get_score(model):
-    x_train, y_train, x_test, y_test = get_data()
-    
-    predicted_result = model.predict(x_test)
-    predicted_labels = np.argmax(predicted_result, axis=1)
-    not_correct = []
-    for i in tqdm(range(len(y_test)), desc="진행도"):
-        if predicted_labels[i] != y_test[i]:
-            not_correct.append(i)
-            # print(f"추론 > {predicted_labels[i]} | 정답 > {y_test[i]}")
-            
-    print(f"틀린 갯수 > {len(not_correct)}/{len(y_test)}")
-
-    # for i in range(3):
-        # plt.imshow(x_test[not_correct[i]].reshape(28,28), cmap='Greys')
-    # plt.show() 
-    
-get_score(auto_tuning(n_particles=30, maxiter=50, c0=0.5, c1=1.5, w=0.75))
 
 # %%
 
+model = make_model()
+x_test, y_test = get_data_test()
+# loss = 'binary_crossentropy'
+# loss = 'categorical_crossentropy'
+# loss = 'sparse_categorical_crossentropy'
+# loss = 'kullback_leibler_divergence'
+# loss = 'poisson'
+# loss = 'cosine_similarity'
+# loss = 'log_cosh'
+# loss = 'huber_loss'
+# loss = 'mean_absolute_error'
+# loss = 'mean_absolute_percentage_error'
+loss = 'mean_squared_error'
 
 
+pso_mnist = Optimizer(model, loss=loss, n_particles=50, c0=0.4, c1=0.8, w_min=0.75, w_max=1.4)
+weight, score = pso_mnist.fit(
+    x_test, y_test, epochs=1000, save=True, save_path="./result/mnist", renewal="acc", empirical_balance=False, Dispersion=True)
+pso_mnist.model_save("./result/mnist")
+pso_mnist.save_info("./result/mnist")
+
+gc.collect()