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23-10-18

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batch size 적용 -> 속도 개선
역전파 1회 적용 -> 조기 수렴을 일부 방지
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jung-geun
2023-10-18 14:42:59 +09:00
parent f32433d935
commit dfc0df7d48
10 changed files with 157 additions and 63 deletions
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45
mnist.py
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@@ -1,19 +1,17 @@
# %%
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"
import numpy as np
import tensorflow as tf
from keras.datasets import mnist
from keras.layers import Conv2D, Dense, Dropout, Flatten, MaxPooling2D
from keras.models import Sequential
from tensorflow import keras
from pso import optimizer
def get_data():
(x_train, y_train), (x_test, y_test) = mnist.load_data()
@@ -24,8 +22,10 @@ def get_data():
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)
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}")
@@ -40,8 +40,10 @@ def get_data_test():
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)
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_test : {x_test[0].shape} | y_test : {y_test[0].shape}")
@@ -51,7 +53,8 @@ def get_data_test():
def make_model():
model = Sequential()
model.add(
Conv2D(32, kernel_size=(5, 5), activation="sigmoid", input_shape=(28, 28, 1))
Conv2D(32, kernel_size=(5, 5), activation="sigmoid",
input_shape=(28, 28, 1))
)
model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Conv2D(64, kernel_size=(3, 3), activation="sigmoid"))
@@ -83,7 +86,7 @@ def random_state():
# %%
model = make_model()
x_train, y_train = get_data_test()
x_train, y_train, x_test, y_test = get_data()
loss = [
"mean_squared_error",
@@ -104,12 +107,12 @@ loss = [
pso_mnist = optimizer(
model,
loss="mean_squared_error",
n_particles=2000,
n_particles=600,
c0=0.2,
c1=0.4,
w_min=0.3,
w_max=0.7,
negative_swarm=0.1,
w_max=0.5,
negative_swarm=0.05,
mutation_swarm=0.3,
particle_min=-4,
particle_max=4,
@@ -118,16 +121,16 @@ pso_mnist = optimizer(
best_score = pso_mnist.fit(
x_train,
y_train,
epochs=300,
epochs=200,
save_info=True,
log=1,
log=2,
log_name="mnist",
save_path="./logs/mnist",
renewal="acc",
check_point=25,
empirical_balance=False,
dispersion=False,
batch_size=32,
)
print("Done!")