def sgd_da(corruption_level):
# Initialize RNGs
rng = numpy.random.RandomState(123)
theano_rng = RandomStreams(rng.randint(2 ** 30))
# Build the logistic regression class
# Images in MNIST are 28*28, there are 10 output classes
da = AutoEncoder(
numpy_rng=rng,
theano_rng=theano_rng,
input=x,
n_visible=28 * 28,
n_hidden=500
)
# Cost to minimize
cost = da.loss(corruption_level=corruption_level)
# Stochastic Gradient descent
updates = simple_sgd(cost, da.params, learning_rate)
train_da = theano.function(
inputs=[index],
outputs=cost,
updates=updates,
givens=[
(x, train_set_x[index * batch_size: (index + 1) * batch_size]),
]
)
################
# TRAIN MODEL #
################
start_time = timeit.default_timer()
print("... Training the model")
for epoch in range(n_epochs):
c = []
for batch_index in range(n_train_batches):
c.append(train_da(batch_index))
print('Training epoch {0}, cost {1}'.format(epoch, numpy.mean(c)))
end_time = timeit.default_timer()
training_time = (end_time - start_time)
print(('The {0} corruption code for file ' +
os.path.split(__file__)[1] +
' ran for {1:.2f}m').format(corruption_level * 100,
(training_time) / 60.))
image = Image.fromarray(
tile_raster_images(X=da.W.get_value(borrow=True).T,
img_shape=(28, 28), tile_shape=(10, 10),
tile_spacing=(1, 1))
)
image.save('filters_corrpution_' + str(int(corruption_level * 100))
+ '.png')
def sgd_da(corruption_level):
# Initialize RNGs
rng = numpy.random.RandomState(123)
theano_rng = RandomStreams(rng.randint(2**30))
# Build the logistic regression class
# Images in MNIST are 28*28, there are 10 output classes
da = AutoEncoder(numpy_rng=rng,
theano_rng=theano_rng,
input=x,
n_visible=28 * 28,
n_hidden=500)
# Cost to minimize
cost = da.loss(corruption_level=corruption_level)
# Stochastic Gradient descent
updates = simple_sgd(cost, da.params, learning_rate)
train_da = theano.function(
inputs=[index],
outputs=cost,
updates=updates,
givens=[
(x, train_set_x[index * batch_size:(index + 1) * batch_size]),
])
################
# TRAIN MODEL #
################
start_time = timeit.default_timer()
print("... Training the model")
for epoch in range(n_epochs):
c = []
for batch_index in range(n_train_batches):
c.append(train_da(batch_index))
print('Training epoch {0}, cost {1}'.format(epoch, numpy.mean(c)))
end_time = timeit.default_timer()
training_time = (end_time - start_time)
print(
('The {0} corruption code for file ' + os.path.split(__file__)[1] +
' ran for {1:.2f}m').format(corruption_level * 100,
(training_time) / 60.))
image = Image.fromarray(
tile_raster_images(X=da.W.get_value(borrow=True).T,
img_shape=(28, 28),
tile_shape=(10, 10),
tile_spacing=(1, 1)))
image.save('filters_corrpution_' + str(int(corruption_level * 100)) +
'.png')
