text: import numpy as np
  import theano
  import time
  
  from fTheanoNNclassCORE import *
  
  
  learnStep = 0.0001
  batchSize = 32
  CV_size = 1
  
  OPTIONS = OptionsStore(learnStep=learnStep,
                         rmsProp=0.9,
                         minibatch_size=batchSize,
                         CV_size=CV_size)
  
  L1 = LayerCNN(size_in=28224,
                size_out=25600,
                activation=FunctionModel.ReLU,
                weightDecay=False,
                sparsity=False,
                beta=2,
                dropout=False,
                kernel_shape=(16, 1, 8, 8),
                stride=4,
                pooling=False,
                pooling_shape=2,
                optimized=True)
  
  L2 = LayerCNN(size_in=25600,
                size_out=10368,
                activation=FunctionModel.ReLU,
                weightDecay=False,
                sparsity=False,
                beta=2,
                dropout=False,
                kernel_shape=(32, 16, 4, 4),
                stride=2,
                pooling=False,
                pooling_shape=2,
                optimized=True)
  
  L3 = LayerNN(size_in=10368,
               size_out=4,
               dropout=0.5,
               activation=FunctionModel.Sigmoid)
  
  NN = TheanoNNclass(OPTIONS, (L1, L2, L3))
  NN.trainCompile()
  NN.predictCompile()
  
  feed = np.zeros((28224, batchSize))
  
  res = NN.predictCalc(feed).out
  
  print res.shape