/
unet_definity.py
71 lines (63 loc) · 3.44 KB
/
unet_definity.py
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def unet(inputs):
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)
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)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(256, 3, activation='relu', padding='same',
kernel_initializer='he_normal')(pool2)
conv3 = Conv2D(256, 3, activation='relu', padding='same',
kernel_initializer='he_normal')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(512, 3, activation='relu', padding='same',
kernel_initializer='he_normal')(pool3)
conv4 = Conv2D(512, 3, activation='relu', padding='same',
kernel_initializer='he_normal')(conv4)
drop4 = Dropout(0.5)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
conv5 = Conv2D(1024, 3, activation='relu', padding='same',
kernel_initializer='he_normal')(pool4)
conv5 = Conv2D(1024, 3, activation='relu', padding='same',
kernel_initializer='he_normal')(conv5)
drop5 = Dropout(0.5)(conv5)
up6 = Conv2D(512, 2, activation='relu', padding='same',
kernel_initializer='he_normal'
)(UpSampling2D(size=(2, 2))(drop5))
merge6 = Concatenate(axis=3)([drop4, up6]) # usr add
conv6 = Conv2D(512, 3, activation='relu', padding='same',
kernel_initializer='he_normal')(merge6)
conv6 = Conv2D(512, 3, activation='relu', padding='same',
kernel_initializer='he_normal')(conv6)
up7 = Conv2D(256, 2, activation='relu', padding='same',
kernel_initializer='he_normal'
)(UpSampling2D(size=(2, 2))(conv6))
merge7 = Concatenate(axis=3)([conv3, up7]) # usr add
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 = Concatenate(axis=3)([conv2, up8]) # usr add
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 = Concatenate(axis=3)([conv1, up9]) # usr add
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)
return conv10