CNN.py

# -*- coding: utf-8 -*-


from theano.tensor.nnet import conv
import theano.tensor as T
import numpy, theano
import Image

rng = numpy.random.RandomState(23455)


input = T.tensor4(name = 'input')


w_shape = (2,3,9,9) 
w_bound = numpy.sqrt(3*9*9)
W = theano.shared(numpy.asarray(rng.uniform(low = -1.0/w_bound, high = 1.0/w_bound,size = w_shape),
                                dtype = input.dtype),name = 'W')

b_shape = (2,)
b = theano.shared(numpy.asarray(rng.uniform(low = -.5, high = .5, size = b_shape),
                                dtype = input.dtype),name = 'b')
                                
conv_out = conv.conv2d(input,W)

output = T.nnet.sigmoid(conv_out + b.dimshuffle('x',0,'x','x'))
f = theano.function([input],output)





# demo
import pylab
from PIL import Image


img1 = Image.open('C://Users//Terry//Desktop//img2.jpg')
width1,height1 = img1.size
img1 = numpy.asarray(img1, dtype = 'float32')/256. # (height, width, 3)


img1_rgb = img1.swapaxes(0,2).swapaxes(1,2).reshape(1,3,height1,width1) #(3,height,width)


img2 = Image.open('C://Users//Terry//Desktop//Lena.jpg')
width2,height2 = img2.size
img2 = numpy.asarray(img2,dtype = 'float32')/256.
img2_rgb = img2.swapaxes(0,2).swapaxes(1,2).reshape(1,3,height2,width2) #(3,height,width)




minibatch_img = numpy.concatenate((img1_rgb,img2_rgb),axis = 0)
filtered_img = f(minibatch_img)



pylab.subplot(2,3,1);pylab.axis('off');
pylab.imshow(img1)

pylab.subplot(2,3,4);pylab.axis('off');
pylab.imshow(img2)

pylab.gray()
pylab.subplot(2,3,2); pylab.axis("off")
pylab.imshow(filtered_img[0,0,:,:]) 

pylab.subplot(2,3,3); pylab.axis("off")
pylab.imshow(filtered_img[0,1,:,:]) 

pylab.subplot(2,3,5); pylab.axis("off")
pylab.imshow(filtered_img[1,0,:,:]) 

pylab.subplot(2,3,6); pylab.axis("off")
pylab.imshow(filtered_img[1,1,:,:]) 
pylab.show()