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Unet.py
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Unet.py
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#! /usr/bin/env python
# coding=utf-8
#================================================================
# Copyright (C) 2019 * Ltd. All rights reserved.
#
# Editor : VIM
# File name : Unet.py
# Author : YunYang1994
# Created date: 2019-09-20 16:49:08
# Description :
#
#================================================================
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.layers import Conv2D, Input, MaxPooling2D, Dropout, concatenate, UpSampling2D
def Unet(num_class, image_size):
inputs = Input(shape=[image_size, image_size, 1])
conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same')(inputs)
conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same')(pool1)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same')(pool2)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same')(pool3)
conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same')(conv4)
drop4 = Dropout(0.5)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same')(pool4)
conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same')(conv5)
drop5 = Dropout(0.5)(conv5)
up6 = Conv2D(512, 2, activation = 'relu', padding = 'same')(UpSampling2D(size = (2,2))(drop5))
merge6 = concatenate([drop4,up6], axis = 3)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same')(merge6)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same')(conv6)
up7 = Conv2D(256, 2, activation = 'relu', padding = 'same')(UpSampling2D(size = (2,2))(conv6))
merge7 = concatenate([conv3,up7], axis = 3)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same')(merge7)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same')(conv7)
up8 = Conv2D(128, 2, activation = 'relu', padding = 'same')(UpSampling2D(size = (2,2))(conv7))
merge8 = concatenate([conv2,up8], axis = 3)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same')(merge8)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same')(conv8)
up9 = Conv2D(64, 2, activation = 'relu', padding = 'same')(UpSampling2D(size = (2,2))(conv8))
merge9 = concatenate([conv1,up9], axis = 3)
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same')(merge9)
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same')(conv9)
conv9 = Conv2D(2, 3, activation = 'relu', padding = 'same')(conv9)
conv10 = Conv2D(num_class, 1, activation = 'sigmoid')(conv9)
model = Model(inputs = inputs, outputs = conv10)
model.compile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics = ['accuracy'])
return model