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12 Miscellaneous


Plotting Samples and Filters


The code for this section is available for download here.

To plot a sample, what we need to do is to take the visible units, which are a flattened image (there is no 2D structure to the visible units, just a 1D string of unit activations) and reshape it into a 2D image. The order in which the points from the 1D array go into the 2D image is given by the order in which the inital MNIST images where converted into a 1D array. Lucky for us this is just a call of the numpy.reshape function.

Plotting the weights is a bit more tricky. We have n_hidden hidden units, each of them corresponding to a column of the weight matrix. A column has the same shape as the visible, where the weight corresponding to the connection with visible unit j is at position j. Therefore, if we reshape every such column, using numpy.reshape, we get a filter image that tells us how this hidden unit is influenced by the input image.

We need a utility function that takes a minibatch, or the weight matrix, and converts each row ( for the weight matrix we do a transpose ) into a 2D image and then tile these images together. Once we converted the minibatch or the weights in this image of tiles, we can use PIL to plot and save. PIL is a standard python libarary to deal with images.

Tiling minibatches together is done for us by the tile_raster_image function which we provide here.

def scale_to_unit_interval(ndar, eps=1e-8):
  """ Scales all values in the ndarray ndar to be between 0 and 1 """
  ndar = ndar.copy()
  ndar -= ndar.min()
  ndar *= 1.0 / (ndar.max() + eps)
  return ndar

def tile_raster_images(X, img_shape, tile_shape, tile_spacing=(0, 0),
  Transform an array with one flattened image per row, into an array in
  which images are reshaped and layed out like tiles on a floor.

  This function is useful for visualizing datasets whose rows are images,
  and also columns of matrices for transforming those rows
  (such as the first layer of a neural net).

  :type X: a 2-D ndarray or a tuple of 4 channels, elements of which can
  be 2-D ndarrays or None;
  :param X: a 2-D array in which every row is a flattened image.

  :type img_shape: tuple; (height, width)
  :param img_shape: the original shape of each image

  :type tile_shape: tuple; (rows, cols)
  :param tile_shape: the number of images to tile (rows, cols)

  :param output_pixel_vals: if output should be pixel values (i.e. int8
  values) or floats

  :param scale_rows_to_unit_interval: if the values need to be scaled before
  being plotted to [0,1] or not

  :returns: array suitable for viewing as an image.
  :rtype: a 2-d array with same dtype as X.


  assert len(img_shape) == 2
  assert len(tile_shape) == 2
  assert len(tile_spacing) == 2

  # The expression below can be re-written in a more C style as
  # follows :
  # out_shape = [0,0]
  # out_shape[0] = (img_shape[0] + tile_spacing[0]) * tile_shape[0] -
  #                tile_spacing[0]
  # out_shape[1] = (img_shape[1] + tile_spacing[1]) * tile_shape[1] -
  #                tile_spacing[1]
  out_shape = [(ishp + tsp) * tshp - tsp for ishp, tshp, tsp
                      in zip(img_shape, tile_shape, tile_spacing)]

  if isinstance(X, tuple):
      assert len(X) == 4
      # Create an output numpy ndarray to store the image
      if output_pixel_vals:
          out_array = numpy.zeros((out_shape[0], out_shape[1], 4), dtype='uint8')
          out_array = numpy.zeros((out_shape[0], out_shape[1], 4), dtype=X.dtype)

      #colors default to 0, alpha defaults to 1 (opaque)
      if output_pixel_vals:
          channel_defaults = [0, 0, 0, 255]
          channel_defaults = [0., 0., 0., 1.]

      for i in xrange(4):
          if X[i] is None:
              # if channel is None, fill it with zeros of the correct
              # dtype
              out_array[:, :, i] = numpy.zeros(out_shape,
                      dtype='uint8' if output_pixel_vals else out_array.dtype
                      ) + channel_defaults[i]
              # use a recurrent call to compute the channel and store it
              # in the output
              out_array[:, :, i] = tile_raster_images(X[i], img_shape, tile_shape, tile_spacing, scale_rows_to_unit_interval, output_pixel_vals)
      return out_array

      # if we are dealing with only one channel
      H, W = img_shape
      Hs, Ws = tile_spacing

      # generate a matrix to store the output
      out_array = numpy.zeros(out_shape, dtype='uint8' if output_pixel_vals else X.dtype)

      for tile_row in xrange(tile_shape[0]):
          for tile_col in xrange(tile_shape[1]):
              if tile_row * tile_shape[1] + tile_col < X.shape[0]:
                  if scale_rows_to_unit_interval:
                      # if we should scale values to be between 0 and 1
                      # do this by calling the `scale_to_unit_interval`
                      # function
                      this_img = scale_to_unit_interval(X[tile_row * tile_shape[1] + tile_col].reshape(img_shape))
                      this_img = X[tile_row * tile_shape[1] + tile_col].reshape(img_shape)
                  # add the slice to the corresponding position in the
                  # output array
                      tile_row * (H+Hs): tile_row * (H + Hs) + H,
                      tile_col * (W+Ws): tile_col * (W + Ws) + W
                      ] \
                      = this_img * (255 if output_pixel_vals else 1)
      return out_array