a b/autoencoder.py 

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"""Autoencoder.ipynb





  
  

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**





  
  

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 * This file is part of Hybrid CNN-LSTM for COVID-19 Severity Score Prediction paper.





  
  

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 *





  
  

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 * Written by Ankan Ghosh Dastider and Farhan Sadik.





  
  

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 *





  
  

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 * Copyright (c) by the authors under Apache-2.0 License. Some rights reserved, see LICENSE.





  
  

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 */





  
  

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"""





  
  

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#denoising with autoencoder + classification





  
  

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from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D,concatenate,SeparableConv2D





  
  

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from keras.models import Model





  
  

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import tensorflow as tf





  
  

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IMG_WIDTH = 128





  
  

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IMG_HEIGHT = 128





  
  

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IMG_CHANNELS = 3





  
  

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densenet = DenseNet201(weights='imagenet', include_top=False)





  
  

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#Build the model





  
  

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inputs = Input((IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS))





  
  

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#s = Lambda(lambda x: x / 255)(inputs)





  
  

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s=inputs





  
  

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############





  
  

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# Encoding #





  
  

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############





  
  

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# Conv1 #





  
  

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x_128 = Conv2D(filters = 16, kernel_size = (3, 3), activation='relu', padding='same')(inputs) #128,128,16





  
  

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x = MaxPooling2D(pool_size = (2, 2), padding='same')(x_128)#64,64,16





  
  

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# Conv2 #





  
  

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x_64 = Conv2D(filters = 8, kernel_size = (3, 3), activation='relu', padding='same')(x)#64,64,8





  
  

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x_32 = MaxPooling2D(pool_size = (2, 2), padding='same')(x_64)#32,32,8 





  
  

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# Conv 3 #





  
  

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x_32_1 = Conv2D( 8, (3, 3), activation='relu', padding='same')(x_32) #32,32,8





  
  

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#x = MaxPooling2D(pool_size = (2, 2), padding='same')(x)





  
  

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# Conv 4 #





  
  

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#x = Conv2D( 8, (3, 3), activation='relu', padding='same')(x) #16





  
  

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#x = MaxPooling2D(pool_size = (2, 2), padding='same')(x)





  
  

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# Conv 5 #





  
  

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#x = Conv2D( 8, (3, 3), activation='relu', padding='same')(x) #8





  
  

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encoded_16 = MaxPooling2D(pool_size = (2, 2), padding='same')(x_32_1) #16,16,8





  
  

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#conv 6





  
  

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#x = Conv2D( 8, (3, 3), activation='relu', padding='same')(x) 





  
  

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#encoded = MaxPooling2D(pool_size = (2, 2), padding='same')(x)





  
  

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############





  
  

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# Decoding #





  
  

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############





  
  

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# DeConv1





  
  

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y_16 = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded_16)#16,16,8





  
  

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y_32 = UpSampling2D((2, 2))(y_16)#32,32,8





  
  

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y_32=concatenate([x_32,y_32])#32,32,8





  
  

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#temp_32=MaxPooling2D(pool_size = (2, 2), padding='same')(x_64) 





  
  

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#y_32= 





  
  

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# DeConv2





  
  

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y_32= Conv2D(8, (3, 3), activation='relu', padding='same')(y_32)#32,32,8





  
  

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y_64= UpSampling2D((2, 2))(y_32) #64,64,8





  
  

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y_64=concatenate([x_64,y_64]) #64,64,8





  
  

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# DeConv2





  
  

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y_64 = Conv2D(16, (3, 3), activation='relu', padding='same')(y_64)#64,64,16





  
  

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y_128= UpSampling2D((2, 2))(y_64)#128,128,16





  
  

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y_128=concatenate([y_128,x_128])





  
  

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# DeConv2





  
  

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#x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)





  
  

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#x = UpSampling2D((2, 2))(x)





  
  

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# DeConv2





  
  

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#x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)





  
  

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#x = UpSampling2D((2, 2))(x)





  
  

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# Deconv3





  
  

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#x = Conv2D(16, (3, 3), activation='relu')(x)





  
  

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#x = UpSampling2D((2, 2))(x)





  
  

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decoded = Conv2D(3, (3, 3), activation='sigmoid', padding='same')(y_128)#128,128,3





  
  

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decoded=concatenate([decoded,inputs ])





  
  

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decoded = Conv2D(3, (3, 3), activation='sigmoid', padding='same')(decoded)





  
  

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x=Conv2D(3, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(decoded)