4주차 과제

import os
os.environ['KAGGLE_USERNAME'] = 'spartasoommni' # username
os.environ['KAGGLE_KEY'] = 'cf783e19878a7a9e8284d2ea28c7517c' # key

!kaggle datasets download -d puneet6060/intel-image-classification
!unzip -q intel-image-classification.zip

from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Dense, Conv2D, MaxPooling2D, Flatten, Dropout
from tensorflow.keras.optimizers import Adam, SGD
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import OneHotEncoder

train_datagen = ImageDataGenerator(
  rescale=1./255, # 일반화
  rotation_range=10, # 랜덤하게 이미지를 회전 (단위: 도, 0-180)
  zoom_range=0.1, # 랜덤하게 이미지 확대 (%)
  width_shift_range=0.1, # 랜덤하게 이미지를 수평으로 이동 (%)
  height_shift_range=0.1, # 랜덤하게 이미지를 수직으로 이동 (%)
  horizontal_flip=True # 랜덤하게 이미지를 수평으로 뒤집기
)

test_datagen = ImageDataGenerator(
  rescale=1./255 # 일반화
)

train_gen = train_datagen.flow_from_directory(
  'seg_train/seg_train',
  target_size=(224, 224), # (height, width)
  batch_size=32,
  seed=2021,
  class_mode='categorical',
  shuffle=True
)

test_gen = test_datagen.flow_from_directory(
  'seg_test/seg_test',
  target_size=(224, 224), # (height, width)
  batch_size=32,
  seed=2021,
  class_mode='categorical',
  shuffle=False
)

preview_batch = train_gen.__getitem__(0)
preview_imgs, preview_labels = preview_batch
plt.title(str(preview_labels[0]))
plt.imshow(preview_imgs[0])

from tensorflow.keras.applications import ResNet50

input = Input(shape=(224, 224, 3))
base_model = ResNet50(weights='imagenet', include_top=False, input_tensor=input, pooling='max')
x = base_model.output
x = Dropout(rate=0.25)(x)
x = Dense(256, activation='relu')(x)
output = Dense(6, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=output)
model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0.001), metrics=['acc'])

from tensorflow.keras.callbacks import ModelCheckpoint
history = model.fit(
  train_gen,
  validation_data=test_gen, # 검증 데이터를 넣어주면 한 epoch이 끝날때마다 자동으로 검증
  epochs=20, # epochs 복수형으로 쓰기!
  callbacks=[ModelCheckpoint('model.h5', monitor='val_acc', verbose=1, save_best_only=True)]
)

from tensorflow.keras.models import load_model
model = load_model('model.h5')
print('Model loaded!')

test_imgs, test_labels = test_gen.__getitem__(1)
y_pred = model.predict(test_imgs)
classes = dict((v, k) for k, v in test_gen.class_indices.items())
fig, axes = plt.subplots(4, 8, figsize=(20, 12))
for img, test_label, pred_label, ax in zip(test_imgs, test_labels, y_pred, axes.flatten()):
  test_label = classes[np.argmax(test_label)]
  pred_label = classes[np.argmax(pred_label)]
  
  ax.set_title('GT:%s\nPR:%s' % (test_label, pred_label))
  ax.imshow(img)