mirror of
https://github.com/jung-geun/PSO.git
synced 2025-12-20 04:50:45 +09:00
137 lines
3.6 KiB
Python
137 lines
3.6 KiB
Python
# %%
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import os
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
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import tensorflow as tf
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# tf.random.set_seed(777) # for reproducibility
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from tensorflow import keras
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from keras.datasets import mnist
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from keras.models import Sequential
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from keras.layers import Dense, Dropout, Flatten
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from keras.layers import Conv2D, MaxPooling2D
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from keras import backend as K
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from pso_tf import PSO
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import numpy as np
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import matplotlib.pyplot as plt
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from datetime import date
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from tqdm import tqdm
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import json
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print(tf.__version__)
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print(tf.config.list_physical_devices())
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def get_data():
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(x_train, y_train), (x_test, y_test) = mnist.load_data()
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x_train, x_test = x_train / 255.0, x_test / 255.0
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x_train = x_train.reshape((60000, 28 ,28, 1))
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x_test = x_test.reshape((10000, 28 ,28, 1))
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print(f"x_train : {x_train[0].shape} | y_train : {y_train[0].shape}")
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print(f"x_test : {x_test[0].shape} | y_test : {y_test[0].shape}")
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return x_train, y_train, x_test, y_test
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def make_model():
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model = Sequential()
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model.add(Conv2D(32, kernel_size=(5, 5), activation='relu', input_shape=(28,28,1)))
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model.add(MaxPooling2D(pool_size=(3, 3)))
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model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
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model.add(MaxPooling2D(pool_size=(2, 2)))
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model.add(Dropout(0.25))
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model.add(Flatten())
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model.add(Dense(128, activation='relu'))
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model.add(Dense(10, activation='softmax'))
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# model.summary()
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return model
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# %%
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'''
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optimizer parameter
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'''
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lr = 0.1
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momentun = 0.8
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decay = 1e-04
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nestrov = True
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'''
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pso parameter
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'''
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n_particles = 30
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maxiter = 50
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# epochs = 1
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w = 0.8
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c0 = 0.6
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c1 = 1.6
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def auto_tuning(n_particles=n_particles, maxiter=maxiter, c0=c0, c1=c1, w=w):
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x_train, y_train, x_test, y_test = get_data()
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model = make_model()
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loss = keras.losses.MeanSquaredError()
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optimizer = keras.optimizers.SGD(lr=lr, momentum=momentun, decay=decay, nesterov=nestrov)
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pso_m = PSO(model=model, loss_method=loss, n_particles=n_particles)
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# c0 : 지역 최적값 중요도
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# c1 : 전역 최적값 중요도
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# w : 관성 (현재 속도를 유지하는 정도)
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best_weights, score = pso_m.optimize(x_train, y_train, x_test, y_test, maxiter=maxiter, c0=c0, c1=c1, w=w)
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model.set_weights(best_weights)
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score_ = model.evaluate(x_test, y_test, verbose=2)
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print(f" Test loss: {score_}")
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score = round(score_[1]*100, 2)
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day = date.today().strftime("%Y-%m-%d")
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os.makedirs(f'./model', exist_ok=True)
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model.save(f'./model/{day}_{score}_mnist.h5')
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json_save = {
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"name" : f"{day}_{score}_mnist.h5",
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"score" : score_,
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"maxiter" : maxiter,
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"c0" : c0,
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"c1" : c1,
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"w" : w
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}
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with open(f'./model/{day}_{score}_pso_mnist.json', 'a') as f:
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json.dump(json_save, f)
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f.write(',\n')
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return model
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# auto_tuning(n_particles=30, maxiter=1000, c0=0.5, c1=1.5, w=0.75)
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# %%
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# print(f"정답 > {y_test}")
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def get_score(model):
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x_train, y_train, x_test, y_test = get_data()
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predicted_result = model.predict(x_test)
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predicted_labels = np.argmax(predicted_result, axis=1)
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not_correct = []
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for i in tqdm(range(len(y_test)), desc="진행도"):
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if predicted_labels[i] != y_test[i]:
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not_correct.append(i)
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# print(f"추론 > {predicted_labels[i]} | 정답 > {y_test[i]}")
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print(f"틀린 갯수 > {len(not_correct)}/{len(y_test)}")
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# for i in range(3):
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# plt.imshow(x_test[not_correct[i]].reshape(28,28), cmap='Greys')
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# plt.show()
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get_score(auto_tuning(n_particles=30, maxiter=50, c0=0.5, c1=1.5, w=0.75))
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# %%
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