LeNetConvPoolLayer.py

import numpy
import theano
import theano.tensor as T
from theano.tensor.signal import downsample
from theano.tensor.nnet import conv
from ReLU import ReLU

random_seed = 1234
drop_p = 0.1
from theano.tensor.shared_randomstreams import RandomStreams
rng = numpy.random.RandomState(random_seed)

def _dropout(rng, ivec, p):
  srng = theano.tensor.shared_randomstreams.RandomStreams(rng.randint(999999))
  mask = srng.binomial(n=1, p=1-p, size=ivec.shape)
  output = ivec * T.cast(mask, theano.config.floatX)
  return output

class LeNetConvPoolLayer(object):
   """"
    Convolutional neural network (convolutional and pooling layer)
   """  
  def __init__(self, input, filter_shape, image_shape,W=None, b=None, poolsize=(3, 1)):
    assert image_shape[1] == filter_shape[1]
    self.W = W
    self.b = b 
    tmp = numpy.ones((filter_shape[0],), dtype=theano.config.floatX)
    tmp = -tmp*10000
    self.test = theano.shared(value=tmp, borrow=True)
    conv_out = conv.conv2d(input=input, filters=self.W, filter_shape=filter_shape, image_shape=image_shape)  # The convolutiona operator
    conv_out2 = T.where(T.neq(conv_out,0), conv_out, self.test.dimshuffle('x', 0, 'x', 'x'))
    pooled_out = downsample.max_pool_2d(conv_out2,ds=poolsize, ignore_border=True)                           # The max-pooling operator
    pooled_out2 = T.where(T.neq(pooled_out, -10000) ,pooled_out, -self.b.dimshuffle('x', 0, 'x', 'x')) 
    self.output = ReLU(pooled_out2 + self.b.dimshuffle('x', 0, 'x', 'x'))
    self.params = [self.W, self.b]