VGGNET 16 weight
# import library
import tensorflow as tf
from tensorflow.keras import Sequential, layers
# build vgg16 model
def vgg16():
model = Sequential()
model.add(layers.Conv2D(64, kernel_size = (3, 3), padding = 'same', activation = 'relu', input_shape=(224, 224, 3)))
model.add(layers.Conv2D(64, kernel_size = (3, 3), padding = 'same', activation = 'relu'))
model.add(layers.MaxPooling2D(2))
model.add(layers.Conv2D(128, kernel_size = (3, 3), padding = 'same', activation = 'relu'))
model.add(layers.Conv2D(128, kernel_size = (3, 3), padding = 'same', activation = 'relu'))
model.add(layers.MaxPooling2D(2))
model.add(layers.Conv2D(256, kernel_size = (3, 3), padding = 'same', activation = 'relu'))
model.add(layers.Conv2D(256, kernel_size = (3, 3), padding = 'same', activation = 'relu'))
model.add(layers.Conv2D(256, kernel_size = (3, 3), padding = 'same', activation = 'relu'))
model.add(layers.MaxPooling2D(2))
model.add(layers.Conv2D(512, kernel_size = (3, 3), padding = 'same', activation = 'relu'))
model.add(layers.Conv2D(512, kernel_size = (3, 3), padding = 'same', activation = 'relu'))
model.add(layers.Conv2D(512, kernel_size = (3, 3), padding = 'same', activation = 'relu'))
model.add(layers.MaxPooling2D(2))
model.add(layers.Conv2D(512, kernel_size = (3, 3), padding = 'same', activation = 'relu'))
model.add(layers.Conv2D(512, kernel_size = (3, 3), padding = 'same', activation = 'relu'))
model.add(layers.Conv2D(512, kernel_size = (3, 3), padding = 'same', activation = 'relu'))
model.add(layers.MaxPooling2D(2))
# Fully Connected Layer
model.add(layers.Flatten())
model.add(layers.Dense(4096, activation="relu"))
model.add(layers.Dense(4096, activation="relu"))
model.add(layers.Dense(1000, activation="softmax"))
return model
def main():
model = vgg16()
model.summary()
if __name__ == "__main__":
main()
관련 논문: Very Deep Convolutional Networks For Large-Scale Image Recognition
Very Deep Convolutional Networks For Large-Scale Image Recognition
목차0. Abstract1. Introduction2. ConvNet Configuration2.1 Architecture2.2 Configuration2.3 Discussion3. Classification Framework3.1 Training3.2 Testing3.3 Implementation Details4 Classification Experiments4.1 Single Scale Evaluation4.2 Multi-Scale Eval
ornni.tistory.com
링크
https://github.com/ornni/DL_algorithm/tree/main/VGGNet
DL_algorithm/VGGNet at main · ornni/DL_algorithm
deep learning algorithms. Contribute to ornni/DL_algorithm development by creating an account on GitHub.
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