def check(input_dim, expected_shape, broadcastable=None,
conserve_memory=True):
bn = BatchNormalization(input_dim=input_dim,
broadcastable=broadcastable,
conserve_memory=conserve_memory)
if broadcastable is None:
if not isinstance(input_dim, collections.Sequence):
b_input_dim = (input_dim,)
else:
b_input_dim = input_dim
input_broadcastable = tuple(False for _ in range(len(b_input_dim)))
else:
input_broadcastable = broadcastable
bn.allocate()
assert conserve_memory == bn.conserve_memory
assert input_dim == bn.input_dim
assert bn.broadcastable == broadcastable
assert bn.scale.broadcastable == input_broadcastable
assert bn.shift.broadcastable == input_broadcastable
assert bn.population_mean.broadcastable == input_broadcastable
assert bn.population_stdev.broadcastable == input_broadcastable
assert_allclose(bn.population_mean.get_value(borrow=True), 0.)
assert_allclose(bn.population_stdev.get_value(borrow=True), 1.)
assert_equal(bn.scale.get_value(borrow=True).shape, expected_shape)
assert_equal(bn.shift.get_value(borrow=True).shape, expected_shape)
assert_equal(bn.population_mean.get_value(borrow=True).shape,
expected_shape)
assert_equal(bn.population_stdev.get_value(borrow=True).shape,
expected_shape)
assert numpy.isnan(bn.shift.get_value(borrow=True)).all()
assert numpy.isnan(bn.scale.get_value(borrow=True)).all()
bn.initialize()
assert_allclose(bn.shift.get_value(borrow=True), 0.)
assert_allclose(bn.scale.get_value(borrow=True), 1.)
def join(small_image,
big_image,
n_filter_small,
n_filter_big,
big_img_size_in,
ordering=''):
# upsample small image
upsampled_small = tensor.repeat(small_image, 2, axis=2)
upsampled_small = tensor.repeat(upsampled_small, 2, axis=3)
img_size_small = (n_filter_small, big_img_size_in[0], big_img_size_in[1])
img_size_big = (n_filter_big, big_img_size_in[0], big_img_size_in[1])
bn_small = BatchNormalization(img_size_small,
name='bn_small%s' % (ordering, ))
bn_small.initialize()
bn_big = BatchNormalization(img_size_big, name='bn_big%s' % (ordering, ))
bn_big.initialize()
depth_concat = tensor.concatenate(
[bn_small.apply(upsampled_small),
bn_big.apply(big_image)], axis=1)
return depth_concat
def apply_setup(input_dim, broadcastable, conserve_memory):
"""Common setup code."""
bn = BatchNormalization(input_dim, broadcastable, conserve_memory,
epsilon=1e-4)
bn.initialize()
b_len = (len(input_dim) if isinstance(input_dim, collections.Sequence)
else 1)
x = tensor.TensorType(theano.config.floatX,
[False] * (b_len + 1))()
return bn, x
def test_batch_normalization_simple():
x = tensor.matrix()
eps = 1e-4
bn = BatchNormalization(input_dim=4, epsilon=eps)
bn.initialize()
with batch_normalization(bn):
y = bn.apply(x)
rng = numpy.random.RandomState((2016, 1, 18))
x_ = rng.uniform(size=(5, 4)).astype(theano.config.floatX)
y_ = y.eval({x: x_})
y_expected = (x_ - x_.mean(axis=0)) / numpy.sqrt(x_.var(axis=0) + eps)
assert_allclose(y_, y_expected, rtol=1e-4)
def test_batch_normalization_simple():
x = tensor.matrix()
eps = 1e-4
bn = BatchNormalization(input_dim=4, epsilon=eps)
bn.initialize()
with batch_normalization(bn):
y = bn.apply(x)
rng = numpy.random.RandomState((2016, 1, 18))
x_ = rng.uniform(size=(5, 4)).astype(theano.config.floatX)
y_ = y.eval({x: x_})
y_expected = (x_ - x_.mean(axis=0)) / numpy.sqrt(x_.var(axis=0) + eps)
assert_allclose(y_, y_expected, rtol=1e-4)
