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""" |
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Created on Sun May 10 21:42:46 2020 |
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@author: serdarhelli |
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""" |
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from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, BatchNormalization,concatenate,Conv2DTranspose,Dropout |
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from tensorflow.keras.models import Model |
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def UNET (input_shape=(512,512,1),last_activation='sigmoid'): |
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inputs=Input(shape=input_shape) |
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conv1 = Conv2D(32,(3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs) |
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d1=Dropout(0.1)(conv1) |
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conv2 = Conv2D(32,(3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(d1) |
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b=BatchNormalization()(conv2) |
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pool1 = MaxPooling2D(pool_size=(2, 2))(b) |
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conv3 = Conv2D(64,(3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1) |
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d2=Dropout(0.2)(conv3) |
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conv4 = Conv2D(64,(3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(d2) |
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b1=BatchNormalization()(conv4) |
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pool2 = MaxPooling2D(pool_size=(2, 2))(b1) |
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conv5 = Conv2D(128,(3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2) |
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d3=Dropout(0.3)(conv5) |
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conv6 = Conv2D(128,(3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(d3) |
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b2=BatchNormalization()(conv6) |
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pool3 = MaxPooling2D(pool_size=(2, 2))(b2) |
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conv7 = Conv2D(256,(3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3) |
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d4=Dropout(0.4)(conv7) |
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conv8 = Conv2D(256,(3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(d4) |
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b3=BatchNormalization()(conv8) |
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pool4 = MaxPooling2D(pool_size=(2, 2))(b3) |
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conv9 = Conv2D(512,(3,3),activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4) |
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d5=Dropout(0.5)(conv9) |
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conv10 = Conv2D(512,(3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(d5) |
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b4=BatchNormalization()(conv10) |
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conv11 = Conv2DTranspose(512,(4,4), activation = 'relu', padding = 'same', strides=(2,2),kernel_initializer = 'he_normal')(b4) |
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x= concatenate([conv11,conv8]) |
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conv12 = Conv2D(256,(3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(x) |
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d6=Dropout(0.4)(conv12) |
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conv13 = Conv2D(256,(3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(d6) |
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b5=BatchNormalization()(conv13) |
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conv14 = Conv2DTranspose(256,(4,4), activation = 'relu', padding = 'same', strides=(2,2),kernel_initializer = 'he_normal')(b5) |
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x1=concatenate([conv14,conv6]) |
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conv15 = Conv2D(128,3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(x1) |
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d7=Dropout(0.3)(conv15) |
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conv16 = Conv2D(128,3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(d7) |
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b6=BatchNormalization()(conv16) |
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conv17 = Conv2DTranspose(128,(4,4), activation = 'relu', padding = 'same',strides=(2,2), kernel_initializer = 'he_normal')(b6) |
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x2=concatenate([conv17,conv4]) |
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conv18 = Conv2D(64,(3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(x2) |
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d8=Dropout(0.2)(conv18) |
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conv19 = Conv2D(64,(3,3) ,activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(d8) |
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b7=BatchNormalization()(conv19) |
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conv20 = Conv2DTranspose(64,(4,4), activation = 'relu', padding = 'same',strides=(2,2), kernel_initializer = 'he_normal')(b7) |
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x3=concatenate([conv20,conv2]) |
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conv21 = Conv2D(32,(3,3) ,activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(x3) |
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d9=Dropout(0.1)(conv21) |
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conv22 = Conv2D(32,(3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(d9) |
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outputs = Conv2D(1,(1,1), activation = last_activation, padding = 'same', kernel_initializer = 'he_normal')(conv22) |
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model2 = Model( inputs = inputs, outputs = outputs) |
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return model2 |
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