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Convolutional Neural Network,CNN

CNN을 tensorflow로 구현한 것을 정리합니다.

CNN이란

　CNN은 주로 이미지 인식이나 이미지 분류 등의 작업에 사용되는 신경망을 말합니다. 회귀층과 풀링층, 전결합층이라는 층을 가지고 있는 것이 특징입니다. 　Convolutional Neural Network의 약자로, 우리말로는 컨볼루션 신경망이라고 한다.

CNN 구현

tensorflow에 Conv2D가 있으므로 이를 사용한다.

import tensorflow as tf

model = tf.keras.models.Sequential([
  tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(28,28,1)),
  tf.keras.layers.MaxPooling2D((2,2)),
  tf.keras.layers.Flatten(),
  tf.keras.layers.Dense(10, activation='softmax')
])

Alex net

AlexNet은 2012년 인공지능 이미지 인식 대회에서 우승한 모델입니다. 4번째 AI의 봄, 딥러닝의 시작인 모델입니다. 현재 AI 붐/딥러닝 붐의 시발점이 된 모델이다. [AlexNet-CNN[(https://www.researchgate.net/figure/An-illustration-of-the-architecture-of-AlexNet-CNN-14_fig4_312188377)

def alexet(input_shape=(227, 227, 3), num_classes=1000):
    model = models.Sequential()

    model.add(layers.Conv2D(96, (11, 11), strides=(4, 4), activation='relu', input_shape=input_shape, padding="valid"))
    model.add(layers.BatchNormalization())
    model.add(layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding="valid"))

    model.add(layers.Conv2D(256, (5, 5), strides=(1, 1), activation='relu', padding="same"))
    model.add(layers.BatchNormalization())
    model.add(layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding="valid"))

    model.add(layers.Conv2D(384, (3, 3), strides=(1, 1), activation='relu', padding="same"))

    model.add(layers.Conv2D(384, (3, 3), strides=(1, 1), activation='relu', padding="same"))

    model.add(layers.Conv2D(256, (3, 3), strides=(1, 1), activation='relu', padding="same"))
    model.add(layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding="valid"))

    model.add(layers.Flatten())

    model.add(layers.Dense(4096, activation='relu'))
    model.add(layers.Dropout(0.5))

    model.add(layers.Dense(4096, activation='relu'))
    model.add(layers.Dropout(0.5))

    model.add(layers.Dense(num_classes, activation='softmax'))

    return model

VGG16

VGG16은 16층으로 구성된 모델로 ILSVRC2014에서 준우승한 모델이다.

def vgg16(input_shape=(224, 224, 3), num_classes=1000):
    model = models.Sequential()

    model.add(layers.Conv2D(64, (3, 3), activation='relu', padding='same', input_shape=input_shape))
    model.add(layers.Conv2D(64, (3, 3), activation='relu', padding='same'))
    model.add(layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))


    model.add(layers.Conv2D(128, (3, 3), activation='relu', padding='same'))
    model.add(layers.Conv2D(128, (3, 3), activation='relu', padding='same'))
    model.add(layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))


    model.add(layers.Conv2D(256, (3, 3), activation='relu', padding='same'))
    model.add(layers.Conv2D(256, (3, 3), activation='relu', padding='same'))
    model.add(layers.Conv2D(256, (3, 3), activation='relu', padding='same'))
    model.add(layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))


    model.add(layers.Conv2D(512, (3, 3), activation='relu', padding='same'))
    model.add(layers.Conv2D(512, (3, 3), activation='relu', padding='same'))
    model.add(layers.Conv2D(512, (3, 3), activation='relu', padding='same'))
    model.add(layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))


    model.add(layers.Conv2D(512, (3, 3), activation='relu', padding='same'))
    model.add(layers.Conv2D(512, (3, 3), activation='relu', padding='same'))
    model.add(layers.Conv2D(512, (3, 3), activation='relu', padding='same'))
    model.add(layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))


    model.add(layers.Flatten())
    model.add(layers.Dense(4096, activation='relu'))
    model.add(layers.Dropout(0.5))
    model.add(layers.Dense(4096, activation='relu'))
    model.add(layers.Dropout(0.5))

    model.add(layers.Dense(num_classes, activation='softmax'))

    return model

ResNet

ILSVRC2015 우승 일반 컨볼루션 네트워크에 바로가기 연결을 더한 구조!

import tensorflow as tf
from tensorflow.keras.applications.resnet50 import ResNet50 import ResNet50

# 학습된 가중치를 가진 ResNet50 모델을 생성합니다.
model = ResNet50(weights='imagenet')

model.summary()

def identity_block(input_tensor, kernel_size, filters):
    filters1, filters2, filters3 = filters

    x = layers.Conv2D(filters1, (1, 1))(input_tensor)
    x = layers.BatchNormalization()(x)
    x = layers.Activation('relu')(x)

    x = layers.Conv2D(filters2, kernel_size, padding='same')(x)
    x = layers.BatchNormalization()(x)
    x = layers.Activation('relu')(x)

    x = layers.Conv2D(filters3, (1, 1))(x)
    x = layers.BatchNormalization()(x)

    # shotcut
    x = layers.add([x, input_tensor])
    x = layers.Activation('relu')(x)

    return x

EfficientNet

구글이 개발한 CNN으ㄹ 모델 최적화를 통해 계산 효율과 높은 정확도를 동시에 구현했다.

import tensorflow as tf
from tensorflow.keras.applications import EfficientNetB0

model = EfficientNetB0(weights='imagenet')

model.summary()