Unet.py

#This is the main architecture of U-Net and it can not be run directly.
#The Full code can be find https://github.com/zhixuhao/unet
inputs = Input((512, 512,1))
conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)#
conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)#
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)#

#128个滤波器，每个滤波器大小为3*3
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)#
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)#250*250*128
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)#

#256个滤波器，每个滤波器大小为3*3
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)#123*123*256
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)#121*121*256
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)#

#512个滤波器，每个滤波器大小为3*3
conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)#58*58*512
conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4)#56*56*512
drop4 = Dropout(0.5)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)#

#1024个滤波器，每个滤波器大小为3*3
conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)#26*26*1024
conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)#24*24*1024
drop5 = Dropout(0.5)(conv5)#

#UpSampling2D中的size参数是指将数据的行和列分别重复size[0]和size[1]次，本例为2，即输入为H*W，输出为2H*2W
up6 = Conv2D(512, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(drop5))#47*47*512
merge6 = merge([drop4,up6], mode = 'concat', concat_axis = 3)#按照第三个轴进行融合 #47*47*512
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge6)#45*45*512
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)#47*47*512

up7 = Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv6))
merge7 = merge([conv3,up7], mode = 'concat', concat_axis = 3)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge7)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv7)

up8 = Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv7))
merge8 = merge([conv2,up8], mode = 'concat', concat_axis = 3)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge8)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv8)

up9 = Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv8))
merge9 = merge([conv1,up9], mode = 'concat', concat_axis = 3)
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge9)
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
conv9 = Conv2D(2, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)

conv10 = Conv2D(1, 1, activation = 'sigmoid')(conv9)

model = Model(input = inputs, output = conv10)
model.summary()

def focal_loss(gamma=2, alpha=0.75):
    def focal_loss_fixed(y_true, y_pred):
        pt_1 = tf.where(tf.equal(y_true, 1), y_pred, tf.ones_like(y_pred))
        pt_0 = tf.where(tf.equal(y_true, 0), y_pred, tf.zeros_like(y_pred))
        return -K.sum(alpha * K.pow(1. - pt_1, gamma) * K.log(pt_1))-K.sum((1-alpha) * K.pow( pt_0, gamma) * K.log(1. - pt_0))
    return focal_loss_fixed    

model.compile(optimizer = Adam(lr = 1e-4), loss =[focal_loss(gamma=2,alpha=0.6)], metrics = ['accuracy'])