DataScience/TensorFlow[CNN]
딥러니 텐서플로우 CNN을 이용하여, CIFAR-10 이미지 분류하기, 데이트셋이 이미 넘파이라면?
Wave sea
2023. 1. 2. 12:10
CNN을 이용하여, CIFAR-10 이미지 분류하기¶
STEP 0: 데이터셋 설명¶
CIFAR-10 is a dataset that consists of several images divided into the following 10 classes:
- Airplanes
- Cars
- Birds
- Cats
- Deer
- Dogs
- Frogs
- Horses
- Ships
- Trucks
The dataset stands for the Canadian Institute For Advanced Research (CIFAR)
CIFAR-10 is widely used for machine learning and computer vision applications.
The dataset consists of 60,000 32x32 color images and 6,000 images of each class.
Images have low resolution (32x32).
Data Source: https://www.cs.toronto.edu/~kriz/cifar.html
STEP #1: 환결 설정 및 라이브러리 임포트¶
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라이브러리 임포트 하고, 데이터 셋 가져오기¶
In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn
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from keras.datasets import cifar10
(X_train, y_train) , (X_test, y_test) = cifar10.load_data()
Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz 170498071/170498071 [==============================] - 7s 0us/step
In [3]:
X_train.shape
Out[3]:
(50000, 32, 32, 3)
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y_train
Out[4]:
array([[6],
[9],
[9],
...,
[9],
[1],
[1]], dtype=uint8)
STEP #2: 이미지 Visualizing 해보자¶
In [6]:
X_train.shape
Out[6]:
(50000, 32, 32, 3)
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plt.imshow( X_train[1] )
plt.show()
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y_train[1]
Out[11]:
array([9], dtype=uint8)
STEP #3: DATA 준비¶
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X_train = X_train / 255.0
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X_test = X_test / 255.0
이 딥러닝의 결과는, 총 10개의 결과로 분류하는 문제이므로, y_train 과 y_test 의 값을 원핫인코딩으로 바꾸시오.¶
In [16]:
X_train.shape
Out[16]:
(50000, 32, 32, 3)
In [17]:
y_train
Out[17]:
array([[6],
[9],
[9],
...,
[9],
[1],
[1]], dtype=uint8)
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from keras.utils import to_categorical
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y_train = to_categorical(y_train, 10)
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y_test = to_categorical(y_test, 10)
피처스케일링 하시오.¶
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STEP #4: 모델링 및 학습시키기¶
Data Augmentation 을 통한, 모델 Improving¶
- 원래 주어진 이미지를 통해, 이를 변형하여서 학습을 시키면 정확도가 증가한다.
- 다음처럼 flipping, enlarging, rotating 을 통해서 원래의 이미지를 변형하여 학습시킨다.
- 또한 이미지를 이동시키거나 밝기 조정도 데이터 증강에 해당된다.
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from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, Dense, Flatten, Dropout
from tensorflow.keras.optimizers import Adam
In [28]:
def build_model() :
model = Sequential()
model.add(Conv2D(32, (3,3), activation='relu', input_shape=(32,32,3)))
model.add( MaxPooling2D( (2,2) ) )
model.add(Conv2D(64, (3,3), activation='relu'))
model.add( MaxPooling2D( (2,2) ) )
model.add( Flatten() )
model.add( Dropout(0.4) )
model.add( Dense(128, 'relu') )
model.add( Dense(10, 'softmax') )
model.compile('rmsprop', 'categorical_crossentropy', ['accuracy'])
return model
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model = build_model()
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model.summary()
Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_2 (Conv2D) (None, 30, 30, 32) 896
max_pooling2d_2 (MaxPooling (None, 15, 15, 32) 0
2D)
conv2d_3 (Conv2D) (None, 13, 13, 64) 18496
max_pooling2d_3 (MaxPooling (None, 6, 6, 64) 0
2D)
flatten_1 (Flatten) (None, 2304) 0
dropout_1 (Dropout) (None, 2304) 0
dense_2 (Dense) (None, 128) 295040
dense_3 (Dense) (None, 10) 1290
=================================================================
Total params: 315,722
Trainable params: 315,722
Non-trainable params: 0
_________________________________________________________________
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epoch_history = model.fit(X_train, y_train, epochs=10)
Epoch 1/10 1563/1563 [==============================] - 15s 5ms/step - loss: 1.4899 - accuracy: 0.4690 Epoch 2/10 1563/1563 [==============================] - 8s 5ms/step - loss: 1.1613 - accuracy: 0.5925 Epoch 3/10 1563/1563 [==============================] - 7s 4ms/step - loss: 1.0477 - accuracy: 0.6337 Epoch 4/10 1563/1563 [==============================] - 7s 4ms/step - loss: 0.9755 - accuracy: 0.6608 Epoch 5/10 1563/1563 [==============================] - 7s 4ms/step - loss: 0.9194 - accuracy: 0.6814 Epoch 6/10 1563/1563 [==============================] - 7s 4ms/step - loss: 0.8745 - accuracy: 0.6981 Epoch 7/10 1563/1563 [==============================] - 7s 4ms/step - loss: 0.8415 - accuracy: 0.7111 Epoch 8/10 1563/1563 [==============================] - 7s 5ms/step - loss: 0.8084 - accuracy: 0.7208 Epoch 9/10 1563/1563 [==============================] - 7s 5ms/step - loss: 0.7831 - accuracy: 0.7299 Epoch 10/10 1563/1563 [==============================] - 7s 4ms/step - loss: 0.7639 - accuracy: 0.7388
STEP #5: 모델 평가¶
테스트셋을 통해서, 정확도를 구해보자¶
In [32]:
## 원래있던 넘파이를 이미지 증강하는 예시
# 정확도를 올리는 2가지 이미지증강,트랜스퍼러닝(이게더 조음)
model.evaluate(X_test, y_test)
313/313 [==============================] - 1s 3ms/step - loss: 0.8323 - accuracy: 0.7274
Out[32]:
[0.8323127031326294, 0.727400004863739]
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from keras.preprocessing.image import ImageDataGenerator
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train_datagen = ImageDataGenerator( width_shift_range=0.2, height_shift_range=0.2,
zoom_range=0.2, horizontal_flip=True)
# 원핫을 이미 하였기때문에 resample 생략
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train_generator = train_datagen.flow(X_train, y_train) # 이미 넘파이일때의 차이 flow만하면됨
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model = build_model() # 위에서 학습한것을 초기화
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model.fit(train_generator , epochs = 10)
Epoch 1/10 1563/1563 [==============================] - 32s 20ms/step - loss: 1.7431 - accuracy: 0.3632 Epoch 2/10 1563/1563 [==============================] - 30s 19ms/step - loss: 1.4902 - accuracy: 0.4635 Epoch 3/10 1563/1563 [==============================] - 30s 19ms/step - loss: 1.3959 - accuracy: 0.5010 Epoch 4/10 1563/1563 [==============================] - 32s 20ms/step - loss: 1.3357 - accuracy: 0.5243 Epoch 5/10 1563/1563 [==============================] - 30s 19ms/step - loss: 1.3039 - accuracy: 0.5387 Epoch 6/10 1563/1563 [==============================] - 31s 20ms/step - loss: 1.2816 - accuracy: 0.5474 Epoch 7/10 1563/1563 [==============================] - 31s 20ms/step - loss: 1.2673 - accuracy: 0.5564 Epoch 8/10 1563/1563 [==============================] - 32s 20ms/step - loss: 1.2587 - accuracy: 0.5590 Epoch 9/10 1563/1563 [==============================] - 31s 20ms/step - loss: 1.2490 - accuracy: 0.5644 Epoch 10/10 1563/1563 [==============================] - 31s 20ms/step - loss: 1.2432 - accuracy: 0.5703
Out[41]:
<keras.callbacks.History at 0x7f359c13ac40>
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model.evaluate(X_test, y_test) # 학습하지 않은 데이터로 평가
313/313 [==============================] - 1s 3ms/step - loss: 0.9953 - accuracy: 0.6555
Out[42]:
[0.9952670931816101, 0.6554999947547913]
테스트셋으로 예측한 결과를 가져오시오. 그리고 첫번째 이미지에 대한 결과값을 확인하시오.¶
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y_pred = model.predict(X_test)
313/313 [==============================] - 1s 2ms/step
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y_pred.shape
Out[46]:
(10000, 10)
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y_pred[0].argmax()
Out[49]:
3
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plt.imshow(X_test[0])
plt.show()
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y_test[0].argmax()
Out[52]:
3
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y_pred 에서, 각각 가장 큰 값들의 인덱스만 가져오시오. 그러면 그 인덱스를 통해서, 10가지 중에 무엇인지 확인이 가능합니다.¶
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y_pred = y_pred.argmax(axis = 1)
실제 값들(ground truth) y_test 에서도, 가장 큰 값들의 인덱스만 가져오시오. 그러면 두개를 비교할 수 있습니다.¶
In [61]:
y_test = y_test.argmax(axis = 1)
컨퓨전 매트릭스를 확인하시오. 그리고 seaborn 의 히트맵으로 시각화 하시오.¶
In [63]:
from sklearn.metrics import confusion_matrix
import seaborn as sb
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cm = confusion_matrix(y_test, y_pred)
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cm
Out[65]:
array([[728, 26, 17, 30, 7, 24, 5, 33, 42, 88],
[ 6, 739, 1, 8, 2, 10, 5, 11, 4, 214],
[ 85, 13, 329, 111, 53, 177, 107, 95, 10, 20],
[ 14, 10, 14, 464, 16, 320, 64, 64, 7, 27],
[ 25, 2, 29, 83, 422, 87, 107, 223, 14, 8],
[ 9, 4, 14, 121, 16, 736, 19, 63, 1, 17],
[ 5, 5, 14, 83, 15, 63, 762, 34, 4, 15],
[ 10, 1, 2, 37, 10, 101, 10, 804, 2, 23],
[ 92, 47, 2, 28, 5, 19, 3, 15, 680, 109],
[ 15, 44, 1, 13, 1, 9, 1, 17, 8, 891]])
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sb.heatmap(cm , annot=True, fmt='.0f', cmap='RdPu')
plt.show()
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STEP #6: 모델 저장하기¶
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model.save('cifa10_model.h5') # 현재 경로에 저장함.
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# 원하는 경로에 저장하고 싶으면 os를 이용
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