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# -*- coding: utf-8 -*-
"""
Created on Sun May 10 21:42:46 2020
@author: serdarhelli
"""
#### MODEL ###
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
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