[Survey] MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
이러한 문제의 해결 방법의 하나로서 계산 시간이나 메모리와 성능의 트레이드 오프를 취할 수 있는 네트워크 MobileNet 1 를 Google이 만든 것 같기 때문에 조사해 보았다고 하는 이야기입니다.
MobileNet이란?
특징
메커니즘
MobileNets 1
네트워크 구조
CIFAR10에서 $\alpha=0.5$로 한 경우의 구조.
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) (None, 32, 32, 3) 0
_________________________________________________________________
conv1 (Conv2D) (None, 16, 16, 16) 432
_________________________________________________________________
conv1_bn (BatchNormalization (None, 16, 16, 16) 64
_________________________________________________________________
conv1_relu (Activation) (None, 16, 16, 16) 0
_________________________________________________________________
conv_dw_1 (DepthwiseConv2D) (None, 16, 16, 16) 144
_________________________________________________________________
conv_dw_1_bn (BatchNormaliza (None, 16, 16, 16) 64
_________________________________________________________________
conv_dw_1_relu (Activation) (None, 16, 16, 16) 0
_________________________________________________________________
conv_pw_1 (Conv2D) (None, 16, 16, 32) 512
_________________________________________________________________
conv_pw_1_bn (BatchNormaliza (None, 16, 16, 32) 128
_________________________________________________________________
conv_pw_1_relu (Activation) (None, 16, 16, 32) 0
_________________________________________________________________
conv_dw_2 (DepthwiseConv2D) (None, 8, 8, 32) 288
_________________________________________________________________
conv_dw_2_bn (BatchNormaliza (None, 8, 8, 32) 128
_________________________________________________________________
conv_dw_2_relu (Activation) (None, 8, 8, 32) 0
_________________________________________________________________
conv_pw_2 (Conv2D) (None, 8, 8, 64) 2048
_________________________________________________________________
conv_pw_2_bn (BatchNormaliza (None, 8, 8, 64) 256
_________________________________________________________________
conv_pw_2_relu (Activation) (None, 8, 8, 64) 0
_________________________________________________________________
...
_________________________________________________________________
conv_dw_13 (DepthwiseConv2D) (None, 1, 1, 512) 4608
_________________________________________________________________
conv_dw_13_bn (BatchNormaliz (None, 1, 1, 512) 2048
_________________________________________________________________
conv_dw_13_relu (Activation) (None, 1, 1, 512) 0
_________________________________________________________________
conv_pw_13 (Conv2D) (None, 1, 1, 512) 262144
_________________________________________________________________
conv_pw_13_bn (BatchNormaliz (None, 1, 1, 512) 2048
_________________________________________________________________
conv_pw_13_relu (Activation) (None, 1, 1, 512) 0
_________________________________________________________________
global_average_pooling2d_1 ( (None, 512) 0
_________________________________________________________________
reshape_1 (Reshape) (None, 1, 1, 512) 0
_________________________________________________________________
dropout (Dropout) (None, 1, 1, 512) 0
_________________________________________________________________
conv_preds (Conv2D) (None, 1, 1, 10) 5130
_________________________________________________________________
act_softmax (Activation) (None, 1, 1, 10) 0
_________________________________________________________________
reshape_2 (Reshape) (None, 10) 0
=================================================================
Total params: 834,666
Trainable params: 823,722
Non-trainable params: 10,944
_________________________________________________________________
검증 환경
샘플 코드
그런 곳이었습니다.
import keras
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.applications import MobileNet
batch_size = 32
classes = 10
epochs = 200
(X_train, y_train), (X_test, y_test) = cifar10.load_data()
Y_train = keras.utils.to_categorical(y_train, classes)
Y_test = keras.utils.to_categorical(y_test, classes)
img_input = keras.layers.Input(shape=(32, 32, 3))
model = MobileNet(input_tensor=img_input, alpha=0.5, weights=None, classes=classes)
model.compile(loss='categorical_crossentropy', optimizer="nadam", metrics=['accuracy'])
X_train = X_train.astype('float32') / 255
X_test = X_test.astype('float32') / 255
datagen = ImageDataGenerator(
featurewise_center=False, # set input mean to 0 over the dataset
samplewise_center=False, # set each sample mean to 0
featurewise_std_normalization=False, # divide inputs by std of the dataset
samplewise_std_normalization=False, # divide each input by its std
zca_whitening=False, # apply ZCA whitening
rotation_range=0, # randomly rotate images in the range (degrees, 0 to 180)
width_shift_range=0.1, # randomly shift images horizontally (fraction of total width)
height_shift_range=0.1, # randomly shift images vertically (fraction of total height)
horizontal_flip=True, # randomly flip images
vertical_flip=False) # randomly flip images
datagen.fit(X_train)
model.fit_generator(datagen.flow(X_train, Y_train, batch_size=batch_size),
steps_per_epoch=X_train.shape[0] // batch_size,
epochs=epochs,
validation_data=(X_test, Y_test))
주의사항
ImageNet의 실험 결과
----------------------------------------------------------------------------
Width Multiplier (alpha) | ImageNet Acc | Multiply-Adds (M) | Params (M)
----------------------------------------------------------------------------
| 1.0 MobileNet-224 | 70.6 % | 529 | 4.2 |
| 0.75 MobileNet-224 | 68.4 % | 325 | 2.6 |
| 0.50 MobileNet-224 | 63.7 % | 149 | 1.3 |
| 0.25 MobileNet-224 | 50.6 % | 41 | 0.5 |
----------------------------------------------------------------------------
------------------------------------------------------------------------
Resolution | ImageNet Acc | Multiply-Adds (M) | Params (M)
------------------------------------------------------------------------
| 1.0 MobileNet-224 | 70.6 % | 529 | 4.2 |
| 1.0 MobileNet-192 | 69.1 % | 529 | 4.2 |
| 1.0 MobileNet-160 | 67.2 % | 529 | 4.2 |
| 1.0 MobileNet-128 | 64.4 % | 529 | 4.2 |
------------------------------------------------------------------------
요약
References
Howard et al., MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017. ↩
Chollet, Keras , 2015. ↩
Reference
이 문제에 관하여([Survey] MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications), 우리는 이곳에서 더 많은 자료를 발견하고 링크를 클릭하여 보았다 https://qiita.com/namakemono/items/4fbc7758ffba4791e581텍스트를 자유롭게 공유하거나 복사할 수 있습니다.하지만 이 문서의 URL은 참조 URL로 남겨 두십시오.
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