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