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#5 - 메모리의 점진적인 누수 파티클의 갯수 4000개 epochs 100회 기준 최종적으로 150GB 의 메모리 사용량을 보인다 하지만 초기의 메모리는 26GB 로 점진적으로 메모리의 사용량이 증가하는것으로 볼 수 있다
136 lines
3.2 KiB
Python
136 lines
3.2 KiB
Python
# %%
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import json
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import os
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import sys
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os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
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import numpy as np
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import tensorflow as tf
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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 tensorflow import keras
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from pso import optimizer
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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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y_train, y_test = tf.one_hot(y_train, 10), tf.one_hot(y_test, 10)
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x_train, x_test = tf.convert_to_tensor(x_train), tf.convert_to_tensor(x_test)
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y_train, y_test = tf.convert_to_tensor(y_train), tf.convert_to_tensor(y_test)
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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 / 255.0
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x_test = x_test.reshape((10000, 28, 28, 1))
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y_train, y_test = tf.one_hot(y_train, 10), tf.one_hot(y_test, 10)
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x_train, x_test = tf.convert_to_tensor(x_train), tf.convert_to_tensor(x_test)
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y_train, y_test = tf.convert_to_tensor(y_train), tf.convert_to_tensor(y_test)
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print(f"x_test : {x_test[0].shape} | y_test : {y_test[0].shape}")
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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(
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Conv2D(32, kernel_size=(5, 5), activation="sigmoid", input_shape=(28, 28, 1))
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)
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model.add(MaxPooling2D(pool_size=(3, 3)))
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model.add(Conv2D(64, kernel_size=(3, 3), activation="sigmoid"))
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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="sigmoid"))
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model.add(Dense(10, activation="softmax"))
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return model
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def random_state():
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with open(
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"result/mnist/20230723-061626/mean_squared_error_[0.6384999752044678, 0.0723000094294548].json",
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"r",
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) as f:
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json_ = json.load(f)
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rs = (
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json_["random_state_0"],
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np.array(json_["random_state_1"]),
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json_["random_state_2"],
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json_["random_state_3"],
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json_["random_state_4"],
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)
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return rs
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# %%
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model = make_model()
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x_train, y_train = get_data_test()
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loss = [
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"mean_squared_error",
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"categorical_crossentropy",
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"sparse_categorical_crossentropy",
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"binary_crossentropy",
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"kullback_leibler_divergence",
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"poisson",
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"cosine_similarity",
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"log_cosh",
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"huber_loss",
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"mean_absolute_error",
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"mean_absolute_percentage_error",
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]
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# rs = random_state()
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pso_mnist = optimizer(
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model,
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loss="mean_squared_error",
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n_particles=2000,
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c0=0.2,
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c1=0.4,
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w_min=0.3,
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w_max=0.7,
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negative_swarm=0.1,
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mutation_swarm=0.3,
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particle_min=-4,
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particle_max=4,
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)
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best_score = pso_mnist.fit(
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x_train,
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y_train,
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epochs=300,
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save_info=True,
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log=1,
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log_name="mnist",
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save_path="./logs/mnist",
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renewal="acc",
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check_point=25,
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empirical_balance=False,
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dispersion=False,
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)
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print("Done!")
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sys.exit(0)
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