if phase == 'train':
X = mnist_dataset[0][0]
#labels = mnist_dataset[0][1]
elif phase == 'valid':
X = mnist_dataset[1][0]
#labels = mnist_dataset[1][1]
elif phase == 'test':
X = mnist_dataset[2][0]
#labels = mnist_dataset[2][1]
H0 = encode_x_to_h0(X)
H1 = encode_h0_to_h1(H0)
rH0 = decode_h1_to_h0(H1)
rX = decode_h0_to_x(rH0)
quadratic_losses = ((X - rX)**2).sum(axis=1)
cross_entropy_losses = (X * np.log(rX) +
(1 - X) * np.log(1 - rX)).sum(axis=1)
mean_losses[phase] = {}
mean_losses[phase]['quadratic_losses'] = quadratic_losses.mean()
mean_losses[phase]['cross_entropy_losses'] = cross_entropy_losses.mean()
print "phase %s" % (phase, )
print " mean %s : %f" % ('quadratic loss',
mean_losses[phase]['quadratic_losses'])
print " mean %s : %f" % ('cross-entropy loss',
mean_losses[phase]['cross_entropy_losses'])
def main(argv):
"""
"""
import getopt
import cPickle
try:
#opts, args = getopt.getopt(sys.argv[1:], "hv", ["input_x=", "input_h0=", "input_h1=", "input_rh0=", "output_h0=", "output_h1=", "output_rh0=", "output_rx="])
opts, args = getopt.getopt(sys.argv[1:], "hv", ["input_h1=", "output_rx=", "output_digits_png="])
except getopt.GetoptError as err:
# print help information and exit:
print str(err) # will print something like "option -a not recognized"
usage()
sys.exit(2)
verbose = False
input_h1 = None
output_rx = None
output_digits_png = None
for o, a in opts:
if o == "-v":
verbose = True
elif o in ("-h", "--help"):
usage()
sys.exit()
elif o in ("--input_h1"):
input_h1 = a
elif o in ("--output_rx"):
output_rx = a
elif o in ("--output_digits_png"):
output_digits_png = a
assert os.path.exists(input_h1)
assert output_rx is not None
##### Performing the conversion #####
H1 = cPickle.load(open(input_h1, "r"))
print "Read %s" % (input_h1,)
rH0 = decode_h1_to_h0(H1)
rX = decode_h0_to_x(rH0)
cPickle.dump(rX, open(output_rx, "w"))
print "Wrote %s" % (output_rx,)
##### Plotting the digits if desired #####
if output_digits_png is not None:
import refactor_gp
import refactor_gp.yann_dauphin_utils
from refactor_gp.yann_dauphin_utils import tile_raster_images
I = tile_raster_images(rX, (28,28), (int(rX.shape[0]/20) + 1, 20))
import matplotlib
import pylab
import matplotlib.pyplot as plt
from PIL import Image
im = Image.fromarray(I)
im.save(output_digits_png)
#im.save("/u/alaingui/umontreal/denoising_autoencoder/refactor_gp/junk/mnist.png")
print "Wrote %s" % (output_digits_png,)
if phase == 'train':
X = mnist_dataset[0][0]
#labels = mnist_dataset[0][1]
elif phase == 'valid':
X = mnist_dataset[1][0]
#labels = mnist_dataset[1][1]
elif phase == 'test':
X = mnist_dataset[2][0]
#labels = mnist_dataset[2][1]
H0 = encode_x_to_h0( X )
H1 = encode_h0_to_h1( H0 )
rH0 = decode_h1_to_h0( H1 )
rX = decode_h0_to_x( rH0 )
quadratic_losses = ((X - rX)**2).sum(axis=1)
cross_entropy_losses = (X * np.log(rX) + (1-X) * np.log(1-rX)).sum(axis=1)
mean_losses[phase] = {}
mean_losses[phase]['quadratic_losses'] = quadratic_losses.mean()
mean_losses[phase]['cross_entropy_losses'] = cross_entropy_losses.mean()
print "phase %s" % (phase,)
print " mean %s : %f" % ('quadratic loss', mean_losses[phase]['quadratic_losses'])
print " mean %s : %f" % ('cross-entropy loss', mean_losses[phase]['cross_entropy_losses'])
cPickle.dump(X, open(os.path.join(output_dir, "yann_%s_X.pkl" % (phase,)), "w"))
cPickle.dump(H0, open(os.path.join(output_dir, "yann_%s_H0.pkl" % (phase,)), "w"))
cPickle.dump(H1, open(os.path.join(output_dir, "yann_%s_H1.pkl" % (phase,)), "w"))
def main(argv):
"""
"""
import getopt
import cPickle
try:
# opts, args = getopt.getopt(sys.argv[1:], "hv", ["input_x=", "input_h0=", "input_h1=", "input_rh0=", "output_h0=", "output_h1=", "output_rh0=", "output_rx="])
opts, args = getopt.getopt(sys.argv[1:], "hv", ["input_h1=", "output_rx=", "output_digits_png="])
except getopt.GetoptError as err:
# print help information and exit:
print str(err) # will print something like "option -a not recognized"
usage()
sys.exit(2)
verbose = False
input_h1 = None
output_rx = None
output_digits_png = None
for o, a in opts:
if o == "-v":
verbose = True
elif o in ("-h", "--help"):
usage()
sys.exit()
elif o in ("--input_h1"):
input_h1 = a
elif o in ("--output_rx"):
output_rx = a
elif o in ("--output_digits_png"):
output_digits_png = a
assert os.path.exists(input_h1)
assert output_rx is not None
##### Performing the conversion #####
H1 = cPickle.load(open(input_h1, "r"))
print "Read %s" % (input_h1,)
rH0 = decode_h1_to_h0(H1)
rX = decode_h0_to_x(rH0)
cPickle.dump(rX, open(output_rx, "w"))
print "Wrote %s" % (output_rx,)
##### Plotting the digits if desired #####
if output_digits_png is not None:
import refactor_gp
import refactor_gp.yann_dauphin_utils
from refactor_gp.yann_dauphin_utils import tile_raster_images
I = tile_raster_images(rX, (28, 28), (int(rX.shape[0] / 20) + 1, 20))
import matplotlib
import pylab
import matplotlib.pyplot as plt
from PIL import Image
im = Image.fromarray(I)
im.save(output_digits_png)
# im.save("/u/alaingui/umontreal/denoising_autoencoder/refactor_gp/junk/mnist.png")
print "Wrote %s" % (output_digits_png,)
