# Denoise autoencoder not training properly [closed]

I'm trying to make a denoise autoencoder wherein the encoder part is vgg16 and decoder is opposite of vgg16(encoder) network. My dataset consists of 5K images in grayscale.

Now while training, the loss and accuracy doesn't changes. I can think of reducing filters in the initial decoder layers but i fear that's going to affect the autoencoder. Here, i'm really clueless about what approach to follow.

VGG16 is not trained to be used as an encoder for image reconstruction, it is trained to extract features from an image using which we can do classification task on the image.
This is why, you cannot use VGG16 as the encoder part of your denoise autoencoder.
However, if you want, you can use that architecture of VGG16 as the encoder part of your autoencoder, by just retraining those layers of VGG16,

vggmodel = keras.applications.vgg16.VGG16()
model_encoder = Sequential()
num = 0
for i, layer in enumerate(vggmodel.layers):
if i<19:

model_encoder.summary()
for layer in model_encoder.layers:
layer.trainable=True # Set encoder to trainable, and your autoencoder should work.


Again, there is a problem with your current architecture of autoencoder -it downsamples too much, leading to a significant loss of data. A smaller architecture would work better. For example:

model = Sequential()