gistfile1.txt

def rbmEmotions(big=False, reconstructRandom=False):
  #data, labels = readMultiPIE(big, equalize=args.equalize)
  data, labels = readother.read()
  print "data.shape"
  print data.shape
  data = data / 255.0
  labels = labels / 255.0
  if args.relu:
    activationFunction = Rectified()
    data = scale(data)
  else:
    activationFunction = Sigmoid()

  #trainData = data[0:-1, :]
  Data = np.concatenate((data, labels), axis=1)
  trainData = Data[0:-1, :]
  print "trainData",trainData.shape
  # Train the network
  if args.train:
    # The number of hidden units is taken from a deep learning tutorial
    # The data are the values of the images have to be normalized before being
    # presented to the network
    nrVisible = len(data[0])
    nrHidden = 800
    # use 1 dropout to test the rbm for now
    net = rbm.RBM(nrVisible, nrHidden, 1.2, 1, 1,
                  visibleActivationFunction=activationFunction,
                  hiddenActivationFunction=activationFunction,
                  rmsprop=args.rbmrmsprop,
                  nesterov=args.rbmnesterov,
                  sparsityConstraint=args.sparsity,
                  sparsityRegularization=0.5,
                  trainingEpochs=args.maxEpochs,
                  sparsityTraget=0.01)
    net.train(trainData)
    print net.weights.T.shape
    t = visualizeWeights(net.weights.T, SMALL_SIZE, (10,10))
  else:
    # Take the saved network and use that for reconstructions
    f = open(args.netFile, "rb")
    t = pickle.load(f)
    net = pickle.load(f)
    f.close()

  # get a random image and see it looks like
  # if reconstructRandom:
  #   test = np.random.random_sample(test.shape)

  # Show the initial image first
  test = Data[-1, :]
  print "test.shape"
  print test.shape

  plt.imshow(vectorToImage(test, SMALL_SIZE), cmap=plt.cm.gray)
  plt.axis('off')
  plt.savefig('initialface.png', transparent=True)

  recon = net.reconstruct(test.reshape(1, test.shape[0]))
  print recon.shape

  plt.imshow(vectorToImage(recon, SMALL_SIZE), cmap=plt.cm.gray)
  plt.axis('off')
  plt.savefig('reconstructface.png', transparent=True)


  # Show the weights and their form in a tile fashion
  # Plot the weights
  plt.imshow(t, cmap=plt.cm.gray)
  plt.axis('off')
  if args.rbmrmsprop:
    st='rmsprop'
  else:
    st = 'simple'
  plt.savefig('weights' + st + '.png', transparent=True)


  # let's make some sparsity checks
  hidden = net.hiddenRepresentation(test.reshape(1, test.shape[0]))
  print hidden.sum()
  print "done"


  if args.save:
    f = open(args.netFile, "wb")
    pickle.dump(t, f)
    pickle.dump(net, f)