How does LRP works when used in Autoencoders?

Hello everyone!

I decided to use this amazing toolbox in my Autoencoder model but I'm having doubts about how it works in this type of models...

How will relevance scores be calculated in this case? Is it done in my encoder or when input is reconstructed?

Can anyone help me in this question?

My model is the following:
`

def ConnectomeCNNAutoencoder(input_shape, keep_pr=0.65, n_filter=32, n_dense1=64, n_classes=2, # keep_pr=0.65, n_filter=32, n_dense1=64, mode="autoencoder", sign="neg"):
input_1 = Input(shape=input_shape)
# Convolutional Encoder
bias_init = tf.constant_initializer(value=0.001)
conv1 = Conv2D(filters=n_filter , kernel_size=(1,input_shape[1]), strides=(1, 1),
padding= "valid", activation="selu", # "selu"
kernel_initializer="glorot_uniform",
bias_initializer=bias_init, name="conv1")(input_1)

# dropout1 = Dropout(keep_pr, name="dropout1")(conv1)
dropout1 = SpatialDropout2D(keep_pr, name="dropout1")(conv1)
conv2 = Conv2D(filters=n_filter*2 , kernel_size=(input_shape[1],1), strides=(1, 1),
padding= "valid", activation="selu", # "selu"
kernel_initializer="glorot_uniform",
bias_initializer=bias_init, name="conv2")(dropout1)
encoded = Dropout(keep_pr, name="dropout2")(conv2)

# Classification
reshape = Reshape((n_filter*2,), name="reshape1")(encoded)
dense1 = Dense(n_dense1, activation="selu", name="dense1", kernel_regularizer=keras.regularizers.l1_l2())(reshape)
# dense1 = Dense(n_dense1, activation="selu", name="dense1", kernel_regularizer=keras.regularizers.l1_l2(l1=0.001,l2=0.001))(reshape)
# dropout2 = Dropout(0.5, name="dropout2")

if n_classes == 1:
activation = "sigmoid"
else:
activation = "softmax"
output = Dense(n_classes, activation=activation, name="output")(dense1)


# Decoder
dim_reconstruct = tuple(encoded.get_shape().as_list()) # say, (1, 1, 60)
n_dense2 = np.product(dim_reconstruct[1:]) # will be 60
dense2 = Dense(n_dense2, activation="selu", name="dense2")(output)
reshape2 = Reshape(dim_reconstruct[1:], name="reshape2")(dense2)

# dropout3 = Dropout(keep_pr, name="dropout3")(reshape2)

conv3 = Conv2DTranspose(filters=n_filter*2 , kernel_size=(1,1), strides=(1, 1),
padding= "valid", activation="selu", # "selu"
kernel_initializer="glorot_uniform",
bias_initializer=bias_init, name="conv3")(reshape2)
# dropout3 = Dropout(keep_pr, name="dropout3")(conv3)
conv4 = Conv2DTranspose(filters=n_filter , kernel_size=(input_shape[1],1), strides=(1, 1),
padding= "valid", activation="selu", # "selu"
kernel_initializer="glorot_uniform",
bias_initializer=bias_init, name="conv4")(conv3)

if sign == "pos":
reconstructed_activation = "sigmoid"
elif sign == "neg":
reconstructed_activation = "tanh"

reconstructed_input = Conv2DTranspose(filters=input_shape[-1], kernel_size=(1,input_shape[1]), strides=(1, 1),
padding= "valid", activation=reconstructed_activation,
kernel_initializer="glorot_uniform",
bias_initializer=bias_init, name='autoencoder')(conv4)

if mode == "autoencoder":
model = keras.models.Model(inputs=input_1, outputs=[output, reconstructed_input])
elif mode =="encoder":
# Work as a feature extractor for others models
model = keras.models.Model(inputs=input_1, outputs=encoded)
elif mode == "decoder":
model = keras.models.Model(inputs=input_1, outputs=reconstructed_input)
return model

`