Tensorflow: My loss function produces huge number

Question

I'm trying image inpainting using a NN with weights pretrained using denoising autoencoders. All according to https://papers.nips.cc/paper/4686-image-denoising-and-inpainting-with-deep-neural-networks.pdf

I have made the custom loss function they are using.

My set is a batch of overlapping patches (196x32x32) of an image. My input are the corrupted batches of the image, and the output should be the cleaned ones.

Part of my loss function is

dif_y = tf.subtract(y_xi,y_)

dif_norm = tf.norm(dif_y, ord = 'euclidean', axis = (1,2))

Where y_xi(196 x 1 x 3072) is the reconstructed clean image and y_ (196 x 1 x 3072) is the real clean image. So I actually I subtract all images from their corrupted version and I sum all those differences. I think it is normal to be a very big number.

train_step = tf.train.AdamOptimizer().minimize(loss)

The loss value begins at around 3*10^7 and is converging after 200 runs (I loop for 1000) at a close value. So my output image will be miles away from the original.

Edit: starts at 3.02391e+07 and converges to 3.02337e+07

Is there any way my loss value is correct? If so, how can I dramatically reduce it?

Thanks

Edit 2: My loss function

dif_y = tf.subtract(y,y_)
dif_norm = tf.norm(dif_y, ord = 'euclidean', axis = (1,2))
sqr_norm = tf.square(dif_norm)
prod = tf.multiply(sqr_norm,0.5)
sum_norm2 = tf.reduce_sum(prod,0)
error_1 = tf.divide(sum_norm2,196)

Answer 1

Just for the record if anyone else has a similar problem: Remember to normalize your data! I was actually subtracting values in range [0,1] from values in range [0,255]. Very noobish mistake, I learned it the hard way!

Input values / 255

Expected values / 255

Problem solved.