from tensorflow.keras.layers import Input
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Activation
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import UpSampling2D
from tensorflow.keras.layers import Reshape
from tensorflow.keras.models import Model

class AE():
    @staticmethod
    def create_model(IMG_W, IMG_H):
        input_layer = Input(shape=(IMG_W, IMG_H, 1))

        # encoder
        h = Conv2D(64, (3, 3), activation='relu', padding='same')(input_layer)
        h = MaxPooling2D((2, 2), padding='same')(h)

        # decoder
        h = Conv2D(64, (3, 3), activation='relu', padding='same')(h)
        h = UpSampling2D((2, 2))(h)
        output_layer = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(h)
        
        model = Model(input_layer, output_layer)
        model.compile(optimizer='adam', loss='mse')

        return model
        
f62719ba049c279651c749173fa2dfeb120a4d0c
<mask token>

<mask token>


class AE:
    <mask token>

<mask token>


class AE:

    @staticmethod
    def create_model(IMG_W, IMG_H):
        input_layer = Input(shape=(IMG_W, IMG_H, 1))
        h = Conv2D(64, (3, 3), activation='relu', padding='same')(input_layer)
        h = MaxPooling2D((2, 2), padding='same')(h)
        h = Conv2D(64, (3, 3), activation='relu', padding='same')(h)
        h = UpSampling2D((2, 2))(h)
        output_layer = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(
            h)
        model = Model(input_layer, output_layer)
        model.compile(optimizer='adam', loss='mse')
        return model

from tensorflow.keras.layers import Input
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Activation
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import UpSampling2D
from tensorflow.keras.layers import Reshape
from tensorflow.keras.models import Model


class AE:

    @staticmethod
    def create_model(IMG_W, IMG_H):
        input_layer = Input(shape=(IMG_W, IMG_H, 1))
        h = Conv2D(64, (3, 3), activation='relu', padding='same')(input_layer)
        h = MaxPooling2D((2, 2), padding='same')(h)
        h = Conv2D(64, (3, 3), activation='relu', padding='same')(h)
        h = UpSampling2D((2, 2))(h)
        output_layer = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(
            h)
        model = Model(input_layer, output_layer)
        model.compile(optimizer='adam', loss='mse')
        return model

from tensorflow.keras.layers import Input
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Activation
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import UpSampling2D
from tensorflow.keras.layers import Reshape
from tensorflow.keras.models import Model

class AE():
    @staticmethod
    def create_model(IMG_W, IMG_H):
        input_layer = Input(shape=(IMG_W, IMG_H, 1))

        # encoder
        h = Conv2D(64, (3, 3), activation='relu', padding='same')(input_layer)
        h = MaxPooling2D((2, 2), padding='same')(h)

        # decoder
        h = Conv2D(64, (3, 3), activation='relu', padding='same')(h)
        h = UpSampling2D((2, 2))(h)
        output_layer = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(h)
        
        model = Model(input_layer, output_layer)
        model.compile(optimizer='adam', loss='mse')

        return model
        