mirror of
https://github.com/jung-geun/PSO.git
synced 2025-12-20 04:50:45 +09:00
88 lines
2.5 KiB
Python
88 lines
2.5 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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from pso import Optimizer
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# from optimizer import Optimizer
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import numpy as np
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from datetime import date
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from tqdm import tqdm
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import gc
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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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# model.summary()
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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 = 'binary_crossentropy'
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# loss = 'categorical_crossentropy'
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# loss = 'sparse_categorical_crossentropy'
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# loss = 'kullback_leibler_divergence'
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# loss = 'poisson'
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# loss = 'cosine_similarity'
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# loss = 'log_cosh'
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loss = 'huber_loss'
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# loss = 'mean_absolute_error'
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# loss = 'mean_absolute_percentage_error'
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# loss = 'mean_squared_error'
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pso_mnist = Optimizer(model, loss=loss, n_particles=50, c0=0.4, c1=0.8, w_min=0.4, w_max=0.95, negative_swarm=0.3)
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weight, score = pso_mnist.fit(
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x_test, y_test, epochs=500, save=True, save_path="./result/mnist", renewal="acc", empirical_balance=True, Dispersion=False, check_point=10)
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# pso_mnist.model_save("./result/mnist")
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# pso_mnist.save_info("./result/mnist")
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gc.collect() |