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Unet3D.py
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from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.layers import Conv3D, Input, MaxPooling3D, Dropout, concatenate, UpSampling3D
import tensorflow as tf
def Unet3D(inputs,num_classes):
x=inputs
conv1 = Conv3D(8, 3, activation = 'relu', padding = 'same',data_format="channels_last")(x)
conv1 = Conv3D(8, 3, activation = 'relu', padding = 'same')(conv1)
pool1 = MaxPooling3D(pool_size=(2, 2, 2))(conv1)
conv2 = Conv3D(16, 3, activation = 'relu', padding = 'same')(pool1)
conv2 = Conv3D(16, 3, activation = 'relu', padding = 'same')(conv2)
pool2 = MaxPooling3D(pool_size=(2, 2, 2))(conv2)
conv3 = Conv3D(32, 3, activation = 'relu', padding = 'same')(pool2)
conv3 = Conv3D(32, 3, activation = 'relu', padding = 'same')(conv3)
pool3 = MaxPooling3D(pool_size=(2, 2, 2))(conv3)
conv4 = Conv3D(64, 3, activation = 'relu', padding = 'same')(pool3)
conv4 = Conv3D(64, 3, activation = 'relu', padding = 'same')(conv4)
drop4 = Dropout(0.5)(conv4)
pool4 = MaxPooling3D(pool_size=(2, 2, 2))(drop4)
conv5 = Conv3D(128, 3, activation = 'relu', padding = 'same')(pool4)
conv5 = Conv3D(128, 3, activation = 'relu', padding = 'same')(conv5)
drop5 = Dropout(0.5)(conv5)
up6 = Conv3D(64, 2, activation = 'relu', padding = 'same')(UpSampling3D(size = (2,2,2))(drop5))
merge6 = concatenate([drop4,up6],axis=-1)
conv6 = Conv3D(64, 3, activation = 'relu', padding = 'same')(merge6)
conv6 = Conv3D(64, 3, activation = 'relu', padding = 'same')(conv6)
up7 = Conv3D(32, 2, activation = 'relu', padding = 'same')(UpSampling3D(size = (2,2,2))(conv6))
merge7 = concatenate([conv3,up7],axis=-1)
conv7 = Conv3D(32, 3, activation = 'relu', padding = 'same')(merge7)
conv7 = Conv3D(32, 3, activation = 'relu', padding = 'same')(conv7)
up8 = Conv3D(16, 2, activation = 'relu', padding = 'same')(UpSampling3D(size = (2,2,2))(conv7))
merge8 = concatenate([conv2,up8],axis=-1)
conv8 = Conv3D(16, 3, activation = 'relu', padding = 'same')(merge8)
conv8 = Conv3D(16, 3, activation = 'relu', padding = 'same')(conv8)
up9 = Conv3D(8, 2, activation = 'relu', padding = 'same')(UpSampling3D(size = (2,2,2))(conv8))
merge9 = concatenate([conv1,up9],axis=-1)
conv9 = Conv3D(8, 3, activation = 'relu', padding = 'same')(merge9)
conv9 = Conv3D(8, 3, activation = 'relu', padding = 'same')(conv9)
conv10 = Conv3D(1, 1, activation = 'sigmoid')(conv9)
model = Model(inputs=inputs, outputs = conv10)
#model.compile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics = ['accuracy'])
return model