Image Classification fine tunning w ResNetV2

Image Classification fine tuning w ResNetV2

import os 
import keras
import numpy as np 
import pandas as pd
from glob import glob
import tensorflow as tf
from tensorflow.keras.utils import load_img, img_to_array
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential, load_model
from keras.layers import GlobalAvgPool2D, Dense, Dropout
from keras.callbacks import EarlyStopping, ModelCheckpoint
from tensorflow.keras.applications import ResNet50V2

# Data Visualization
import plotly.express as px
import matplotlib.pyplot as plt

from zipfile import ZipFile

z = ZipFile('/content/drive/MyDrive/lesson_data/animal_data.zip')
z.extractall()

# Class Names(폴더 이름이 클래스 네임(종류))
root_path = './Animal Classification/Animal Classification/Training Data/'
test_path = './Animal Classification/Animal Classification/Testing Data/'
valid_path = './Animal Classification/Animal Classification/Validation Data/'

class_names = sorted(os.listdir(root_path))
n_classes = len(class_names)


# 이미지 증식(이미지들은 rescale 1/255.를 해줘야함
train_gen = ImageDataGenerator(rescale=1/255., rotation_range=10, horizontal_flip=True)
valid_gen = ImageDataGenerator(rescale=1/255.)
test_gen = ImageDataGenerator(rescale=1/255)

# Load Data
train_ds = train_gen.flow_from_directory(root_path, class_mode='binary', target_size=(256,256), shuffle=True, batch_size=32) 
valid_ds = valid_gen.flow_from_directory(valid_path, class_mode='binary', target_size=(256,256), shuffle=True, batch_size=32) 
test_ds = test_gen.flow_from_directory(test_path, class_mode='binary', target_size=(256,256), shuffle=True, batch_size=32)

# 0번 GPU를 사용하여 학습
with tf.device("/GPU:0"):
    ## Pre-Trained Model 
    base_model = ResNet50V2(input_shape=(256,256,3), include_top=False)
    # include_top은 마지막 레이어 클래스를 예측하는 Dense Layer를 사용하지 않는다는 의미입니다.
    # 레이어와 이미 학습된 가중치만을 사용한다면 include_top을 False로 해야합니다.

    base_model.trainable = False # 기존 레이어의 가중치의 학습 안함

    # Model Architecture
    name = 'ResNet50V2'#저장할 체크포인트 파일 이름

    #ResNet50V2가 이미 깊게 쌓여진 모델이라 다른 레이어를 많이 만들 필요는 없음
    model = Sequential([
        base_model,
        GlobalAvgPool2D(),
        Dense(256, activation='relu', kernel_initializer='he_normal'), #가중치 초기화(kenel_initailizer에는 3종류가 있는데 여기서는 HE 초기화를 사용)
        Dropout(0.2),
        Dense(n_classes, activation='softmax')
    ], name=name)

    # Callbacks
    cbs = [EarlyStopping(patience=3, restore_best_weights=True), ModelCheckpoint(name + ".h5", save_best_only=True)]

    # Model 
    opt = tf.keras.optimizers.Adam(learning_rate=2e-3)
    model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])

    # Model Training
    history = model.fit(train_ds, validation_data=valid_ds, callbacks=cbs, epochs=15)

data = pd.DataFrame(history.history)
data

# loss	accuracy	val_loss	val_accuracy
# 0	0.224692	0.922000	0.142165	0.9544
# 1	0.164413	0.943733	0.137304	0.9576
# 2	0.126533	0.957733	0.126196	0.9596
# 3	0.101600	0.964933	0.119228	0.9636
# 4	0.090214	0.970933	0.113578	0.9668
# 5	0.078054	0.974133	0.110474	0.9672
# 6	0.071814	0.977467	0.105573	0.9668
# 7	0.065274	0.981067	0.104021	0.9680
# 8	0.065265	0.979867	0.101604	0.9684
# 9	0.055805	0.982000	0.098761	0.9704
# 10	0.047383	0.986267	0.098213	0.9692
# 11	0.048346	0.983867	0.097358	0.9700
# 12	0.040546	0.987733	0.097479	0.9700
# 13	0.043776	0.987467	0.096769	0.9696
# 14	0.034877	0.990000	0.095712	0.9704

model = load_model('./ResNet50V2.h5')
model.summary()
model.evaluate(test_ds)
#0.9559

#이미지 경로 불러오고, 이미지 보여주는 함수입니다.
def load_image(path):
    image = tf.cast(tf.image.resize(img_to_array(load_img(path))/255., (256,256)), tf.float32)
    return image
def show_image(image, title=None):
    plt.imshow(image)
    plt.axis('off')
    plt.title(title)

#경로 설정
path = './Animal Classification/Animal Classification/Interesting Images/'
interesting_images = [glob(path + name + "/*") for name in class_names]


# 예측 결과(시각화)
for name in class_names:
    plt.figure(figsize=(25, 8))
    cat_interesting = interesting_images[class_names.index(name)]
    for i, i_path in enumerate(cat_interesting):
        name = i_path.split("/")[-1].split(".")[0]
        image = load_image(i_path)
        plt.subplot(1,len(cat_interesting),i+1)
        
        # Model Prediction 
        org_class = name.title()
        preds = model.predict(image[np.newaxis,...])[0]
        pred_class = class_names[np.argmax(preds)]
        confidence_score = np.round(preds[np.argmax(preds)],2)
        
        
        # Configure Title
        title = f"Pred : {pred_class}\nConfidence : {confidence_score:.2}"
        
        show_image(image, title=title)
    plt.show()