# Path: utils.py
# 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:`PIL.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
#                 dt = out_array.dtype
#                 if output_pixel_vals:
#                     dt = 'uint8'
#                 out_array[:, :, i] = numpy.zeros(out_shape,
#                         dtype=dt) + 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
#         dt = X.dtype
#         if output_pixel_vals:
#             dt = 'uint8'
#         out_array = numpy.zeros(out_shape, dtype=dt)
# 
#         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]:
#                     this_x = X[tile_row * tile_shape[1] + tile_col]
#                     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(
#                             this_x.reshape(img_shape))
#                     else:
#                         this_img = this_x.reshape(img_shape)
#                     # add the slice to the corresponding position in the
#                     # output array
#                     c = 1
#                     if output_pixel_vals:
#                         c = 255
#                     out_array[
#                         tile_row * (H + Hs): tile_row * (H + Hs) + H,
#                         tile_col * (W + Ws): tile_col * (W + Ws) + W
#                         ] = this_img * c
#         return out_array


import cPickle
import gzip
import os
import sys
import time
import numpy
import theano
import theano.tensor as T
import PIL.Image
from theano.tensor.shared_randomstreams import RandomStreams
from logistic_sgd import load_data
from utils import tile_raster_images
    cost, updates = da.get_cost_updates(corruption_level=0.3, learning_rate=learning_rate)
    #½«ÊäÈëÑù±¾Ã¿¸öÏñËØµãÒÔ30%µÄ¸ÅÂÊ±»Çå0

    train_da = theano.function([index], cost, updates=updates,
         givens={x: train_set_x[index * batch_size:
                                  (index + 1) * batch_size]})

    start_time = time.clock()

    ############
    # TRAINING #
    ############

    # go through training epochs
    for epoch in xrange(training_epochs):
        # go through trainng set
        c = []
        for batch_index in xrange(n_train_batches):
            c.append(train_da(batch_index))

        print 'Training epoch %d, cost ' % epoch, numpy.mean(c)

    end_time = time.clock()

    training_time = (end_time - start_time)

    print >> sys.stderr, ('The 30% corruption code for file ' +
                          os.path.split(__file__)[1] +
                          ' ran for %.2fm' % (training_time / 60.))


# Path: utils.py
# 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:`PIL.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
#                 dt = out_array.dtype
#                 if output_pixel_vals:
#                     dt = 'uint8'
#                 out_array[:, :, i] = numpy.zeros(out_shape,
#                         dtype=dt) + 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
#         dt = X.dtype
#         if output_pixel_vals:
#             dt = 'uint8'
#         out_array = numpy.zeros(out_shape, dtype=dt)
# 
#         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]:
#                     this_x = X[tile_row * tile_shape[1] + tile_col]
#                     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(
#                             this_x.reshape(img_shape))
#                     else:
#                         this_img = this_x.reshape(img_shape)
#                     # add the slice to the corresponding position in the
#                     # output array
#                     c = 1
#                     if output_pixel_vals:
#                         c = 255
#                     out_array[
#                         tile_row * (H + Hs): tile_row * (H + Hs) + H,
#                         tile_col * (W + Ws): tile_col * (W + Ws) + W
#                         ] = this_img * c
#         return out_array


# Path: TheanoDL/dA.py
import cPickle
import gzip
import os
import sys
import time
import numpy
import theano
import theano.tensor as T
import PIL.Image
from theano.tensor.shared_randomstreams import RandomStreams
from logistic_sgd import load_data
from utils import tile_raster_images

    cost, updates = da.get_cost_updates(corruption_level=0.3, learning_rate=learning_rate)
    #½«ÊäÈëÑù±¾Ã¿¸öÏñËØµãÒÔ30%µÄ¸ÅÂÊ±»Çå0

    train_da = theano.function([index], cost, updates=updates,
         givens={x: train_set_x[index * batch_size:
                                  (index + 1) * batch_size]})

    start_time = time.clock()

    ############
    # TRAINING #
    ############

    # go through training epochs
    for epoch in xrange(training_epochs):
        # go through trainng set
        c = []
        for batch_index in xrange(n_train_batches):
            c.append(train_da(batch_index))

        print 'Training epoch %d, cost ' % epoch, numpy.mean(c)

    end_time = time.clock()

    training_time = (end_time - start_time)

    print >> sys.stderr, ('The 30% corruption code for file ' +
                          os.path.split(__file__)[1] +
                          ' ran for %.2fm' % (training_time / 60.))


    image = PIL.Image.fromarray(tile_raster_images(