text: """
  The Unet Model
  Author: Gerald M
  
  Adapted from paper [U-Net: Convolutional Networks for Biomedical Image
  Segmentation](arXiv:1505.04597)
  
  Modified version of unetmodelv2
  """
  
  from tensorflow.keras import Input, Model
  from tensorflow.keras.layers import concatenate, Conv2D, Conv2DTranspose, Dropout, ReLU, UpSampling2D, MaxPooling2D, BatchNormalization
  from tensorflow.keras.optimizers import Adam, SGD
  import tensorflow.keras.backend as K
  import losses
  
  def unet(optfn, lossfn, actfn, inputsize=(None, None, 1)):
      inputs = Input(inputsize)
  
      conv1 = Conv2D(32, 3, activation = actfn, padding = 'same', kernel_initializer = 'he_normal')(inputs)
      conv1 = Conv2D(32, 3, activation = actfn, padding = 'same', kernel_initializer = 'he_normal')(conv1)
      drop1 = BatchNormalization()(conv1)
      pool1 = MaxPooling2D(pool_size=(2, 2))(drop1)
  
      conv2 = Conv2D(64, 3, activation = actfn, padding = 'same', kernel_initializer = 'he_normal')(pool1)
      conv2 = Conv2D(64, 3, activation = actfn, padding = 'same', kernel_initializer = 'he_normal')(conv2)
      drop2 = BatchNormalization()(conv2)
      pool2 = MaxPooling2D(pool_size=(2, 2))(drop2)
  
      conv3 = Conv2D(128, 3, activation = actfn, padding = 'same', kernel_initializer = 'he_normal')(pool2)
      conv3 = Conv2D(128, 3, activation = actfn, padding = 'same', kernel_initializer = 'he_normal')(conv3)
      drop3 = BatchNormalization()(conv3)
      pool3 = MaxPooling2D(pool_size=(2, 2))(drop3)
  
      conv4 = Conv2D(256, 3, activation = actfn, padding = 'same', kernel_initializer = 'he_normal')(pool3)
      conv4 = Conv2D(256, 3, activation = actfn, padding = 'same', kernel_initializer = 'he_normal')(conv4)
      drop4 = BatchNormalization()(conv4)
      pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
  
      conv5 = Conv2D(512, 3, activation = actfn, padding = 'same', kernel_initializer = 'he_normal')(pool4)
      conv5 = Conv2D(512, 3, activation = actfn, padding = 'same', kernel_initializer = 'he_normal')(conv5)
      drop5 = BatchNormalization()(conv5)
  
      up6 = Conv2D(256, 2, activation = actfn, padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(drop5))
      merge6 = concatenate([drop4,up6], axis = 3)
      conv6 = Conv2D(256, 3, activation = actfn, padding = 'same', kernel_initializer = 'he_normal')(merge6)
      conv6 = Conv2D(256, 3, activation = actfn, padding = 'same', kernel_initializer = 'he_normal')(conv6)
      drop6 = BatchNormalization()(conv6)
  
      up7 = Conv2D(128, 2, activation = actfn, padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(drop6))
      merge7 = concatenate([conv3,up7], axis = 3)
      conv7 = Conv2D(128, 3, activation = actfn, padding = 'same', kernel_initializer = 'he_normal')(merge7)
      conv7 = Conv2D(128, 3, activation = actfn, padding = 'same', kernel_initializer = 'he_normal')(conv7)
      drop7 = BatchNormalization()(conv7)
  
      up8 = Conv2D(64, 2, activation = actfn, padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(drop7))
      merge8 = concatenate([conv2,up8], axis = 3)
      conv8 = Conv2D(64, 3, activation = actfn, padding = 'same', kernel_initializer = 'he_normal')(merge8)
      conv8 = Conv2D(64, 3, activation = actfn, padding = 'same', kernel_initializer = 'he_normal')(conv8)
      drop8 = BatchNormalization()(conv8)
  
      up9 = Conv2D(32, 2, activation = actfn, padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(drop8))
      merge9 = concatenate([conv1,up9], axis = 3)
      conv9 = Conv2D(32, 3, activation = actfn, padding = 'same', kernel_initializer = 'he_normal')(merge9)
      conv9 = Conv2D(32, 3, activation = actfn, padding = 'same', kernel_initializer = 'he_normal')(conv9)
      conv9 = Conv2D(2, 3, activation = actfn, padding = 'same', kernel_initializer = 'he_normal')(conv9)
      drop9 = BatchNormalization()(conv9)
  
      conv10 = Conv2D(1, 1, activation = 'sigmoid')(drop9)
  
      model = Model(inputs, conv10)
  
      # model.compile(optimizer=optfn, loss=[lossfn], metrics=[losses.dice_loss, lossfn])
  
      return model
  