성능 향상 using (# of filters, depth)

1. 각종 모듈 호출

import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Conv2D, BatchNormalization, Activation, MaxPooling2D,\
                                                                     Flatten, Dropout, Dense
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping

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2. 각종 함수 정의

def make_zero_to_one(images, labels) :
    
    images = np.array(images/255., dtype = np.float32)
    labels = np.array(labels, dtype = np.float32)
    
    return images, labels


def ohe(labels) :
    
    labels = to_categorical(labels)
    
    return labels


def tr_val_test(train_images, train_labels, test_images, test_labels, val_rate) :
    
    tr_images, val_images, tr_labels, val_labels = \
                          train_test_split(train_images, train_labels, test_size = val_rate)
    
    return (tr_images, tr_labels), (val_images, val_labels), (test_images, test_labels)


def create_before_model(tr_images, verbose):
    
    input_size = tr_images.shape[1]

    input_tensor = Input(shape=(input_size, input_size, 3))

    x = Conv2D(filters=32, kernel_size=(3, 3), padding='same')(input_tensor)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)

    x = Conv2D(filters=32, kernel_size=(3, 3), padding='same')(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = MaxPooling2D(pool_size=(2, 2))(x)

    x = Conv2D(filters=64, kernel_size=3, padding='same')(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)

    x = Conv2D(filters=64, kernel_size=3, padding='same')(x)
    x = Activation('relu')(x)
    x = Activation('relu')(x)
    x = MaxPooling2D(pool_size=2)(x)

    x = Conv2D(filters=128, kernel_size=3, padding='same')(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)

    x = Conv2D(filters=128, kernel_size=3, padding='same')(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)

    x = Flatten(name='flatten')(x)
    x = Dropout(rate=0.5)(x)
    x = Dense(300, activation='relu', name='fc1')(x)
    x = Dropout(rate=0.3)(x)
    output = Dense(10, activation='softmax', name='output')(x)

    model = Model(inputs=input_tensor, outputs=output)
    
    return model

    if verbose == True :
        model.summary()
        
        
def create_after_model(tr_images, verbose):
    
    input_size = tr_images.shape[1]

    input_tensor = Input(shape=(input_size, input_size, 3))

    x = Conv2D(filters=64, kernel_size=(3, 3), padding='same')(input_tensor)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)

    x = Conv2D(filters=64, kernel_size=(3, 3), padding='same')(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = MaxPooling2D(pool_size=(2, 2))(x)

    x = Conv2D(filters=128, kernel_size=3, padding='same')(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)

    x = Conv2D(filters=128, kernel_size=3, padding='same')(x)
    x = Activation('relu')(x)
    x = Activation('relu')(x)
    x = MaxPooling2D(pool_size=2)(x)

    x = Conv2D(filters=256, kernel_size=3, padding='same')(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)

    x = Conv2D(filters=256, kernel_size=3, padding='same')(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    
    x = Conv2D(filters=512, kernel_size=3, strides=2, padding='same')(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)

    x = Flatten(name='flatten')(x)
    x = Dropout(rate=0.5)(x)
    x = Dense(300, activation='relu', name='fc1')(x)
    x = Dropout(rate=0.3)(x)
    output = Dense(10, activation='softmax', name='output')(x)

    model = Model(inputs=input_tensor, outputs=output)
    
    return model

    if verbose == True :
        model.summary
        
        
def lets_compare_two(before, after) :
    
    fig, axs = plt.subplots(nrows = 1, ncols = 2, figsize = (22, 6))
    
    axs[0].plot(before.history["val_accuracy"], label = "before")
    axs[0].plot(after.history["val_accuracy"], label = "after")
    axs[0].set_title("val_accuracy")
    axs[0].set_xlabel("epochs")
    axs[0].set_ylabel("val_acc")
    axs[0].legend()
    
    axs[1].plot(before.history["val_loss"], label = "before")
    axs[1].plot(after.history["val_loss"], label = "after")
    axs[1].set_title("val_loss")
    axs[1].set_xlabel("epochs")
    axs[1].set_ylabel("val_loss")
    axs[1].legend()
    
    plt.show()

---

3. 데이터 불러오기 및 전처리 과정

(train_images, train_labels), (test_images, test_labels) = cifar10.load_data()

train_images, train_labels = make_zero_to_one(train_images, train_labels)
test_images, test_labels   = make_zero_to_one(test_images, test_labels)

train_labels = ohe(train_labels)
test_labels  = ohe(test_labels)

(tr_images, tr_labels), (val_images, val_labels), (test_images, test_labels) = \
         tr_val_test(train_images, train_labels, test_images, test_labels, val_rate = 0.15)

---

4. 모델 생성(기존)및 편집

model_before = create_before_model(tr_images, verbose = True)

model_before.compile(optimizer = Adam(learning_rate = 0.001), loss = "categorical_crossentropy", metrics = ["accuracy"])

rlr = ReduceLROnPlateau(monitor = "val_loss", factor = 0.2, patience = 5, mode = "min", verbose = True)
ely = EarlyStopping(monitor = "val_loss", patience = 13, mode = "min", verbose = True)


result_before = model_before.fit(x = tr_images, y = tr_labels, batch_size = 32, epochs = 40, shuffle = True,
                                 validation_data = (val_images, val_labels), callbacks = [rlr, ely])

---

5. 모델 생성(수정)및 편집

model_after = create_after_model(tr_images, verbose = True)

model_after.compile(optimizer = Adam(learning_rate = 0.001), loss = "categorical_crossentropy", metrics = ["accuracy"])

rlr = ReduceLROnPlateau(monitor = "val_loss", factor = 0.2, patience = 5, mode = "min", verbose = True)
ely = EarlyStopping(monitor = "val_loss", patience = 13, mode = "min", verbose = True)


result_after = model_after.fit(x = tr_images, y = tr_labels, batch_size = 32, epochs = 40, shuffle = True,
                                 validation_data = (val_images, val_labels), callbacks = [rlr, ely])

---

6. 성능 비교

lets_compare_two(result_before, result_after)

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모델의 깊이와 filter의 개수를 늘리면서 성능이 개선되는 것을 확인해보려고 하였으나, 눈에 띄게 달라지는 점은 찾을 수 없었다.