20241022_01

from keras import Input

from keras.layers import Conv2D

from keras.layers import MaxPooling2D

from keras.layers import Dropout

from keras.models import Model

from keras.layers import concatenate

from tensorflow.keras.optimizers import Adam

from keras.layers import Conv2DTranspose

def Enhancednet(pretrained_weights=None):

input_shape = (None, None, 1)

inputs = Input(shape=input_shape, name='input_img')

conv1 = Conv2D(16, 5, activation='relu', padding='same')(inputs)

drop1 = Dropout(0.6)(conv1)

pool1 = MaxPooling2D(pool_size=(2, 2))(drop1)

conv2 = Conv2D(24, 5, activation='relu', padding='same')(pool1)

drop2 = Dropout(0.6)(conv2)

pool2 = MaxPooling2D(pool_size=(2, 2))(drop2)

conv3 = Conv2D(32, 5, activation='relu', padding='same')(pool2)

drop3 = Dropout(0.6)(conv3)

pool3 = MaxPooling2D(pool_size=(2, 2))(drop3)

conv4 = Conv2D(40, 5, activation='relu', padding='same')(pool3)

drop4 = Dropout(0.6)(conv4)

up5 = Conv2D(32, 3, activation='relu', padding='same')(

Conv2DTranspose(32, 5, activation='relu', padding="same", strides=2)(drop4))

merge5 = concatenate([drop3, up5], axis=3)

conv5 = Conv2D(32, 5, activation='relu', padding='same')(merge5)

drop5 = Dropout(0.6)(conv5)

up6 = Conv2D(24, 3, activation='relu', padding='same')(

Conv2DTranspose(24, 5, activation='relu', padding="same", strides=2)(drop5))

merge6 = concatenate([drop2, up6], axis=3)

conv6 = Conv2D(24, 5, activation='relu', padding='same')(merge6)

drop6 = Dropout(0.6)(conv6)

up7 = Conv2D(16, 3, activation='relu', padding='same')(

Conv2DTranspose(16, 5, activation='relu', padding="same", strides=2)(drop6))

merge7 = concatenate([drop1, up7], axis=3)

conv7 = Conv2D(16, 5, activation='relu', padding='same')(merge7)

drop7 = Dropout(0.6)(conv7)

conv8 = Conv2D(1, 1, activation='relu')(drop7)

model = Model(inputs=inputs, outputs=conv8)

opt = Adam()

model.compile(optimizer=opt, loss='mse', metrics=['accuracy'])

if pretrained_weights:

model.load_weights(pretrained_weights)

return model

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