text: 
  from __future__ import print_function
  
  __docformat__ = 'restructedtext en'
  
  dataset = "/Users/Aleksei/Desktop/testing"
  
  
  import numpy
  
  import theano
  import theano.tensor as T
  
  
  # start-snippet-1
  class HiddenLayer(object):
      def __init__(self, rng, input, n_in, n_out, W=None, b=None,
                   activation=T.tanh):
  
          self.input = input
          if W is None:
              W_values = numpy.asarray(
                  rng.uniform(
                      low=-numpy.sqrt(6. / (n_in + n_out)),
                      high=numpy.sqrt(6. / (n_in + n_out)),
                      size=(n_in, n_out)
                  ),
                  dtype=theano.config.floatX
              )
              if activation == theano.tensor.nnet.sigmoid:
                  W_values *= 4
  
              W = theano.shared(value=W_values, name='W', borrow=True)
  
          if b is None:
              b_values = numpy.zeros((n_out,), dtype=theano.config.floatX)
              b = theano.shared(value=b_values, name='b', borrow=True)
  
          self.W = W
          self.b = b
  
          lin_output = T.dot(input, self.W) + self.b
          self.output = (
              lin_output if activation is None
              else activation(lin_output)
          )
          # parameters of the model
          self.params = [self.W, self.b]
  