--- a
+++ b/autoencoder.py
@@ -0,0 +1,93 @@
+"""Autoencoder.ipynb
+
+**
+ * This file is part of Hybrid CNN-LSTM for COVID-19 Severity Score Prediction paper.
+ *
+ * Written by Ankan Ghosh Dastider and Farhan Sadik.
+ *
+ * Copyright (c) by the authors under Apache-2.0 License. Some rights reserved, see LICENSE.
+ */
+ 
+"""
+#denoising with autoencoder + classification
+from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D,concatenate,SeparableConv2D
+from keras.models import Model
+import tensorflow as tf
+
+IMG_WIDTH = 128
+IMG_HEIGHT = 128
+IMG_CHANNELS = 3
+
+densenet = DenseNet201(weights='imagenet', include_top=False)
+#Build the model
+inputs = Input((IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS))
+#s = Lambda(lambda x: x / 255)(inputs)
+s=inputs
+
+############
+# Encoding #
+############
+
+# Conv1 #
+x_128 = Conv2D(filters = 16, kernel_size = (3, 3), activation='relu', padding='same')(inputs) #128,128,16
+x = MaxPooling2D(pool_size = (2, 2), padding='same')(x_128)#64,64,16
+
+# Conv2 #
+x_64 = Conv2D(filters = 8, kernel_size = (3, 3), activation='relu', padding='same')(x)#64,64,8
+x_32 = MaxPooling2D(pool_size = (2, 2), padding='same')(x_64)#32,32,8 
+
+# Conv 3 #
+x_32_1 = Conv2D( 8, (3, 3), activation='relu', padding='same')(x_32) #32,32,8
+#x = MaxPooling2D(pool_size = (2, 2), padding='same')(x)
+
+# Conv 4 #
+#x = Conv2D( 8, (3, 3), activation='relu', padding='same')(x) #16
+#x = MaxPooling2D(pool_size = (2, 2), padding='same')(x)
+# Conv 5 #
+#x = Conv2D( 8, (3, 3), activation='relu', padding='same')(x) #8
+encoded_16 = MaxPooling2D(pool_size = (2, 2), padding='same')(x_32_1) #16,16,8
+#conv 6
+#x = Conv2D( 8, (3, 3), activation='relu', padding='same')(x) 
+#encoded = MaxPooling2D(pool_size = (2, 2), padding='same')(x)
+
+############
+# Decoding #
+############
+
+# DeConv1
+y_16 = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded_16)#16,16,8
+y_32 = UpSampling2D((2, 2))(y_16)#32,32,8
+
+y_32=concatenate([x_32,y_32])#32,32,8
+
+#temp_32=MaxPooling2D(pool_size = (2, 2), padding='same')(x_64) 
+#y_32= 
+
+# DeConv2
+y_32= Conv2D(8, (3, 3), activation='relu', padding='same')(y_32)#32,32,8
+y_64= UpSampling2D((2, 2))(y_32) #64,64,8
+y_64=concatenate([x_64,y_64]) #64,64,8
+
+# DeConv2
+y_64 = Conv2D(16, (3, 3), activation='relu', padding='same')(y_64)#64,64,16
+y_128= UpSampling2D((2, 2))(y_64)#128,128,16
+
+y_128=concatenate([y_128,x_128])
+
+# DeConv2
+#x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
+#x = UpSampling2D((2, 2))(x)
+
+# DeConv2
+#x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
+#x = UpSampling2D((2, 2))(x)
+
+# Deconv3
+#x = Conv2D(16, (3, 3), activation='relu')(x)
+#x = UpSampling2D((2, 2))(x)
+decoded = Conv2D(3, (3, 3), activation='sigmoid', padding='same')(y_128)#128,128,3
+
+decoded=concatenate([decoded,inputs ])
+decoded = Conv2D(3, (3, 3), activation='sigmoid', padding='same')(decoded)
+
+x=Conv2D(3, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(decoded)