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- wtm=Input((4,4,1))
- image = Input((28, 28, 1))
- conv1 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl1e')(image)
- conv2 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl2e')(conv1)
- conv3 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl3e')(conv2)
- BN=BatchNormalization()(conv3)
- encoded = Conv2D(1, (5, 5), activation='relu', padding='same',name='encoded_I')(BN)
- rep=Kr.layers.Lambda(lambda x:Kr.backend.repeat(x,28))
- a=rep(Kr.layers.Lambda(lambda x:x[1,1])(wtm))
- add_const = Kr.layers.Lambda(lambda x: x[0] + x[1])
- encoded_merged = add_const([encoded,a])
- #-----------------------decoder------------------------------------------------
- #------------------------------------------------------------------------------
- deconv1 = Conv2D(64, (5, 5), activation='elu', padding='same', name='convl1d')(encoded_merged)
- deconv2 = Conv2D(64, (5, 5), activation='elu', padding='same', name='convl2d')(deconv1)
- deconv3 = Conv2D(64, (5, 5), activation='elu',padding='same', name='convl3d')(deconv2)
- deconv4 = Conv2D(64, (5, 5), activation='elu',padding='same', name='convl4d')(deconv3)
- BNd=BatchNormalization()(deconv4)
- #DrO2=Dropout(0.25,name='DrO2')(BNd)
- decoded = Conv2D(1, (5, 5), activation='sigmoid', padding='same', name='decoder_output')(BNd)
- #model=Model(inputs=image,outputs=decoded)
- model=Model(inputs=[image,wtm],outputs=decoded)
- decoded_noise = GaussianNoise(0.5)(decoded)
- #----------------------w extraction------------------------------------
- convw1 = Conv2D(64, (5,5), activation='relu', name='conl1w')(decoded_noise)#24
- convw2 = Conv2D(64, (5,5), activation='relu', name='convl2w')(convw1)#20
- #Avw1=AveragePooling2D(pool_size=(2,2))(convw2)
- convw3 = Conv2D(64, (5,5), activation='relu' ,name='conl3w')(convw2)#16
- convw4 = Conv2D(64, (5,5), activation='relu' ,name='conl4w')(convw3)#12
- #Avw2=AveragePooling2D(pool_size=(2,2))(convw4)
- convw5 = Conv2D(64, (5,5), activation='relu', name='conl5w')(convw4)#8
- convw6 = Conv2D(64, (5,5), activation='relu', name='conl6w')(convw5)#4
- convw7 = Conv2D(64, (5,5), activation='relu',padding='same', name='conl7w',dilation_rate=(2,2))(convw6)#4
- convw8 = Conv2D(64, (5,5), activation='relu', padding='same',name='conl8w',dilation_rate=(2,2))(convw7)#4
- convw9 = Conv2D(64, (5,5), activation='relu',padding='same', name='conl9w',dilation_rate=(2,2))(convw8)#4
- convw10 = Conv2D(64, (5,5), activation='relu',padding='same', name='conl10w',dilation_rate=(2,2))(convw9)#4
- BNed=BatchNormalization()(convw10)
- pred_w = Conv2D(1, (1, 1), activation='sigmoid', padding='same', name='reconstructed_W',dilation_rate=(2,2))(BNed)
- w_extraction=Model(inputs=[image,wtm],outputs=[decoded,pred_w])
- w_extraction.summary()
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