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UNet.py
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UNet.py
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from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, Dropout, concatenate
from tensorflow.keras.models import Model
def build_unet(input_shape=(224, 224, 3)):
fil = 16
inputs = Input(input_shape)
conv1 = Conv2D(fil, 3, strides = (1, 1), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)
conv1 = Conv2D(fil, 3, strides = (1, 1), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2), strides = (2, 2))(conv1)
conv2 = Conv2D(2*fil, 3, strides = (1, 1), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)
conv2 = Conv2D(2*fil, 3, strides = (1, 1), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2), strides = (2, 2))(conv2)
conv3 = Conv2D(4*fil, 3, strides = (1, 1), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)
conv3 = Conv2D(4*fil, 3, strides = (1, 1), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2), strides = (2, 2))(conv3)
conv4 = Conv2D(8*fil, 3, strides = (1, 1), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)
conv4 = Conv2D(8*fil, 3, strides = (1, 1), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4)
drop4 = Dropout(0.5)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2), strides = (2, 2))(drop4)
#bottleneck
conv5 = Conv2D(16*fil, 3, strides = (1, 1), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)
conv5 = Conv2D(16*fil, 3, strides = (1, 1), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)
drop5 = Dropout(0.5)(conv5)
up6 = Conv2D(8*fil, 2, strides = (1, 1), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(drop5))
merge6 = concatenate([drop4,up6], axis = 3)
conv6 = Conv2D(8*fil, 3, strides = (1, 1), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge6)
conv6 = Conv2D(8*fil, 3, strides = (1, 1), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)
up7 = Conv2D(4*fil, 2, strides = (1, 1), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv6))
merge7 = concatenate([conv3,up7], axis = 3)
conv7 = Conv2D(4*fil, 3, strides = (1, 1), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge7)
conv7 = Conv2D(4*fil, 3, strides = (1, 1), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv7)
up8 = Conv2D(2*fil, 2, strides = (1, 1), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv7))
merge8 = concatenate([conv2,up8], axis = 3)
conv8 = Conv2D(2*fil, 3, strides = (1, 1), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge8)
conv8 = Conv2D(2*fil, 3, strides = (1, 1), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv8)
up9 = Conv2D(fil, 2, strides = (1, 1), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv8))
merge9 = concatenate([conv1,up9], axis = 3)
conv9 = Conv2D(fil, 3, strides = (1, 1), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge9)
conv9 = Conv2D(fil, 3, strides = (1, 1), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
conv9 = Conv2D(2, 3, strides = (1, 1), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
outputs = Conv2D(1, 1, padding="same", activation="sigmoid")(conv9)
model = Model(inputs, outputs, name = "U-Net")
return model