mirror of
https://github.com/jung-geun/PSO.git
synced 2025-12-20 04:50:45 +09:00
96 lines
2.6 KiB
Python
96 lines
2.6 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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import numpy as np
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np.random.seed(777)
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import gc
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from datetime import date
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from keras import backend as K
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from keras.datasets import mnist
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from keras.layers import Conv2D, Dense, Dropout, Flatten, MaxPooling2D
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from keras.models import Sequential
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# from pso_tf import PSO
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from pso import Optimizer
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from tensorflow import keras
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from tqdm import tqdm
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# print(tf.__version__)
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# print(tf.config.list_physical_devices())
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# print(f"Num GPUs Available: {len(tf.config.list_physical_devices('GPU'))}")
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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 get_data_test():
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(x_train, y_train), (x_test, y_test) = mnist.load_data()
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x_test = x_test.reshape((10000, 28, 28, 1))
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return 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),
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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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return model
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# %%
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model = make_model()
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x_test, y_test = get_data_test()
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loss = ['mse', 'categorical_crossentropy', 'binary_crossentropy', 'kullback_leibler_divergence', 'poisson', 'cosine_similarity', 'log_cosh', 'huber_loss', 'mean_absolute_error', 'mean_absolute_percentage_error']
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if __name__ == "__main__":
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try:
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pso_mnist = Optimizer(
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model,
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loss=loss[0],
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n_particles=75,
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c0=0.3,
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c1=0.7,
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w_min=0.6,
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w_max=0.9,
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negative_swarm=0.25,
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momentun_swarm=0,
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)
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best_score = pso_mnist.fit(
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x_test,
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y_test,
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epochs=200,
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save=True,
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save_path="./result/mnist",
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renewal="acc",
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empirical_balance=False,
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Dispersion=False,
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check_point=25
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)
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except Exception as e:
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print(e)
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finally:
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gc.collect() |