Actually, fine tuning of RBM based DBN is not only using the labels to retrain the model by error back-propagation.
If you have numpy and scipy available (and if you are manipulating large arrays in Python, I would recommend them), then the scipy.misc.pilutil.toimage function is very handy. A simple example:
import numpy as np
import scipy.misc.pilutil as smp
# Create a 1024x1024x3 array of 8 bit unsigned integers
data = np.zeros( (1024,1024,3), dtype=np.uint8 )
data[512,512] = [254,0,0] # Makes the middle pixel red
data[512,513] = [0,0,255] # Makes the next pixel blue
img = smp.toimage( data ) # Create a PIL image
img.show() # View in default viewer
The nice thing is toimage copes with diferent data types very well, so a 2d array of floating point numbers gets sensibly converted to greyscale etc.
to save the image to file, the function smp.imsave(filename, data) could be directly used.
Also there are other kind of methods: http://stackoverflow.com/questions/434583/what-is-the-fastest-way-to-draw-an-image-from-discrete-pixel-values-in-python
An example image:
