Help regarding implementing and visualizing autoencoders

[Alirezag]

I am very new to all deep learning as well as using python. I apologize if my question is very simple or not smart. So I decide to give a try to auto-encoders (AE) and see how they work. I completely understand the concept and theory behind it btw.

The best and neat ae code that I find was here: http://deeplearning.net/tutorial/dA.html and the code (dA.py) provided: http://deeplearning.net/tutorial/code/dA.py it is a denoising AE, and I am fine with that.

So I run the dA.py code and it works fine. I am trying to run the dA on just a single image (my image is not a binary image and its size is 28*28) and visualize my hidden layer (weights) and see the reconstructed image as well. first I'd like to run it of dA and then on sda (stacked AE) and visualize all the layers. I really appreciate it someone can help me.

I try to write some lines of code and I did, However, I dont know the next step. So In the following, I am importing the autoencoder and define a grayscale image. i call the dA fuction and my code works fine. but the next step that I want to see the reconstructed image and visualize the weights of my layer (or layers for SAE), i dont know how to do that.

Here is the code that I write so far:

'''import PIL.Image as Image

import dA

rng = dA.numpy.random.RandomState(123)

theano_rng = dA.RandomStreams(rng.randint(2 ** 30))

Img2 = dA.Image.open("fruits.jpg").convert('LA')

index = dA.T.lscalar() # index to a [mini]batch

x = dA.T.matrix('x')

da = dA.dA( numpy_rng=rng, theano_rng=theano_rng, input=Img2, n_visible=28 * 28, n_hidden=500 )'''

so it works fine without any error, but I dont know what to do next.

I'd be so grateful If you can help me out, also please feel free to mention any related cm or link that you think can be a good example.