## Miscellaneous## Plotting Samples and FiltersNote 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 Plotting the weights is a bit more tricky. We have 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
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), scale_rows_to_unit_interval=True, output_pixel_vals=True): """ 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. (See:`Image.fromarray`.) :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') else: 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] else: 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] else: # 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 else: # 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)) else: this_img = X[tile_row * tile_shape[1] + tile_col].reshape(img_shape) # add the slice to the corresponding position in the # output array out_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 |

Artificial Intelligence + NLP + deep learning > AI > Machine Learning > Neural Networks > Deep Learning > python > MNIST (Theano) >