mirror of
https://github.com/jung-geun/PSO.git
synced 2025-12-19 20:44:39 +09:00
108 lines
2.5 KiB
Python
108 lines
2.5 KiB
Python
# %%
|
|
from pso import optimizer
|
|
from tensorflow import keras
|
|
from keras.models import Sequential
|
|
from keras.layers import Conv2D, Dense, Dropout, Flatten, MaxPooling2D
|
|
from keras.datasets import mnist
|
|
import tensorflow as tf
|
|
import numpy as np
|
|
import json
|
|
import os
|
|
import sys
|
|
|
|
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
|
|
|
|
|
|
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))
|
|
|
|
y_train, y_test = tf.one_hot(y_train, 10), tf.one_hot(y_test, 10)
|
|
|
|
x_train, x_test = tf.convert_to_tensor(x_train), tf.convert_to_tensor(x_test)
|
|
y_train, y_test = tf.convert_to_tensor(y_train), tf.convert_to_tensor(y_test)
|
|
|
|
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 make_model():
|
|
model = Sequential()
|
|
model.add(
|
|
Conv2D(32, kernel_size=(5, 5), activation="relu", input_shape=(28, 28, 1))
|
|
)
|
|
model.add(MaxPooling2D(pool_size=(2, 2)))
|
|
model.add(Dropout(0.5))
|
|
model.add(Conv2D(64, kernel_size=(3, 3), activation="relu"))
|
|
model.add(MaxPooling2D(pool_size=(2, 2)))
|
|
model.add(Flatten())
|
|
model.add(Dropout(0.5))
|
|
model.add(Dense(256, activation="relu"))
|
|
model.add(Dense(128, activation="relu"))
|
|
model.add(Dense(10, activation="softmax"))
|
|
|
|
return model
|
|
|
|
|
|
# %%
|
|
model = make_model()
|
|
x_train, y_train, x_test, y_test = get_data()
|
|
|
|
loss = [
|
|
"mean_squared_error",
|
|
"categorical_crossentropy",
|
|
"sparse_categorical_crossentropy",
|
|
"binary_crossentropy",
|
|
"kullback_leibler_divergence",
|
|
"poisson",
|
|
"cosine_similarity",
|
|
"log_cosh",
|
|
"huber_loss",
|
|
"mean_absolute_error",
|
|
"mean_absolute_percentage_error",
|
|
|
|
]
|
|
|
|
# rs = random_state()
|
|
|
|
pso_mnist = optimizer(
|
|
model,
|
|
loss="categorical_crossentropy",
|
|
n_particles=500,
|
|
c0=0.5,
|
|
c1=0.3,
|
|
w_min=0.1,
|
|
w_max=0.9,
|
|
negative_swarm=0.0,
|
|
mutation_swarm=0.1,
|
|
convergence_reset=True,
|
|
convergence_reset_patience=10,
|
|
convergence_reset_monitor="loss",
|
|
convergence_reset_min_delta=0.005,
|
|
)
|
|
|
|
best_score = pso_mnist.fit(
|
|
x_train,
|
|
y_train,
|
|
epochs=500,
|
|
save_info=True,
|
|
log=2,
|
|
log_name="mnist",
|
|
renewal="loss",
|
|
check_point=25,
|
|
empirical_balance=False,
|
|
dispersion=False,
|
|
batch_size=10000,
|
|
back_propagation=False,
|
|
validate_data=(x_test, y_test),
|
|
)
|
|
|
|
print("Done!")
|
|
|
|
sys.exit(0)
|