def reconstruct(model, nrows, ncols):
dataset = BinarizedMNIST('valid')
originals, = dataset.get_data(request=range(nrows * ncols))
figure, axes = pyplot.subplots(nrows=nrows, ncols=ncols)
for n, (i, j) in enumerate(itertools.product(xrange(nrows),
xrange(ncols))):
ax = axes[i][j]
ax.axis('off')
ax.imshow(originals[n].reshape((28, 28)),
cmap=cm.Greys_r,
interpolation='nearest')
draw = model.top_bricks[0]
x = tensor.matrix('x')
x_hat = draw.reconstruct(x)
computation_graph = ComputationGraph([x_hat])
f = theano.function([x], x_hat, updates=computation_graph.updates)
reconstructions = f(originals)
figure, axes = pyplot.subplots(nrows=nrows, ncols=ncols)
for n, (i, j) in enumerate(itertools.product(xrange(nrows),
xrange(ncols))):
ax = axes[i][j]
ax.axis('off')
ax.imshow(reconstructions[n].reshape((28, 28)),
cmap=cm.Greys_r,
interpolation='nearest')
pyplot.show()
def reconstruct(model, nrows, ncols):
dataset = BinarizedMNIST('valid')
originals, = dataset.get_data(request=range(nrows * ncols))
figure, axes = pyplot.subplots(nrows=nrows, ncols=ncols)
for n, (i, j) in enumerate(itertools.product(xrange(nrows),
xrange(ncols))):
ax = axes[i][j]
ax.axis('off')
ax.imshow(originals[n].reshape((28, 28)), cmap=cm.Greys_r,
interpolation='nearest')
draw = model.top_bricks[0]
x = tensor.matrix('x')
x_hat = draw.reconstruct(x)
computation_graph = ComputationGraph([x_hat])
f = theano.function([x], x_hat, updates=computation_graph.updates)
reconstructions = f(originals)
figure, axes = pyplot.subplots(nrows=nrows, ncols=ncols)
for n, (i, j) in enumerate(itertools.product(xrange(nrows),
xrange(ncols))):
ax = axes[i][j]
ax.axis('off')
ax.imshow(reconstructions[n].reshape((28, 28)), cmap=cm.Greys_r,
interpolation='nearest')
pyplot.show()
def test_binarized_mnist_no_split():
skip_if_not_available(datasets=['binarized_mnist'])
dataset = BinarizedMNIST()
handle = dataset.open()
data = dataset.get_data(handle, slice(0, 70000))[0]
assert data.shape == (70000, 1, 28, 28)
assert dataset.num_examples == 70000
dataset.close(handle)
def test_binarized_mnist_test():
skip_if_not_available(datasets=['binarized_mnist'])
mnist_test = BinarizedMNIST('test')
handle = mnist_test.open()
data = mnist_test.get_data(handle, slice(0, 10000))[0]
assert data.shape == (10000, 1, 28, 28)
assert mnist_test.num_examples == 10000
mnist_test.close(handle)
def test_binarized_mnist_test():
skip_if_not_available(datasets=['binarized_mnist.hdf5'])
dataset = BinarizedMNIST('test', load_in_memory=False)
handle = dataset.open()
data, = dataset.get_data(handle, slice(0, 10))
assert data.dtype == 'uint8'
assert data.shape == (10, 1, 28, 28)
assert hashlib.md5(data).hexdigest() == '0fa539ed8cb008880a61be77f744f06a'
assert dataset.num_examples == 10000
dataset.close(handle)
def test_binarized_mnist_valid():
skip_if_not_available(datasets=['binarized_mnist.hdf5'])
dataset = BinarizedMNIST('valid', load_in_memory=False)
handle = dataset.open()
data, = dataset.get_data(handle, slice(0, 10))
assert data.dtype == 'uint8'
assert data.shape == (10, 1, 28, 28)
assert hashlib.md5(data).hexdigest() == '65e8099613162b3110a7618037011617'
assert dataset.num_examples == 10000
dataset.close(handle)
def test_binarized_mnist_train():
skip_if_not_available(datasets=['binarized_mnist.hdf5'])
dataset = BinarizedMNIST('train', load_in_memory=False)
handle = dataset.open()
data, = dataset.get_data(handle, slice(0, 10))
assert data.dtype == 'uint8'
assert data.shape == (10, 1, 28, 28)
assert hashlib.md5(data).hexdigest() == '0922fefc9a9d097e3b086b89107fafce'
assert dataset.num_examples == 50000
dataset.close(handle)
def test_mnist():
skip_if_not_available(datasets=['binarized_mnist'])
mnist_train = BinarizedMNIST('train')
assert len(mnist_train.features) == 50000
assert mnist_train.num_examples == 50000
mnist_valid = BinarizedMNIST('valid')
assert len(mnist_valid.features) == 10000
assert mnist_valid.num_examples == 10000
mnist_test = BinarizedMNIST('test')
assert len(mnist_test.features) == 10000
assert mnist_test.num_examples == 10000
first_feature, = mnist_train.get_data(request=[0])
assert first_feature.shape == (1, 784)
assert first_feature.dtype.kind == 'f'
assert_raises(ValueError, BinarizedMNIST, 'dummy')
mnist_test = cPickle.loads(cPickle.dumps(mnist_test))
assert len(mnist_test.features) == 10000
mnist_test_unflattened = BinarizedMNIST('test', flatten=False)
assert mnist_test_unflattened.features.shape == (10000, 28, 28)