def test_gaussian_vis_layer_make_state():
"""
Verifies that GaussianVisLayer.make_state creates
a shared variable whose value passes check_gaussian_samples
In this case the layer lives in a VectorSpace
"""
n = 5
rows = None
cols = None
channels = None
num_samples = 1000
tol = .042 # tolerated variance
beta = 1/tol # precision parameter
layer = GaussianVisLayer(nvis = n, init_beta=beta)
rng = np.random.RandomState([2012,11,1])
mean = rng.uniform(1e-6, 1. - 1e-6, (n,))
z= mean
layer.set_biases(z.astype(config.floatX))
init_state = layer.make_state(num_examples=num_samples,
numpy_rng=rng)
value = init_state.get_value()
check_gaussian_samples(value, num_samples, n, rows, cols, channels, mean, tol)
def test_gaussian_vis_layer_make_state():
"""
Verifies that GaussianVisLayer.make_state creates
a shared variable whose value passes check_gaussian_samples
In this case the layer lives in a VectorSpace
"""
n = 5
rows = None
cols = None
channels = None
num_samples = 1000
tol = .042 # tolerated variance
beta = 1 / tol # precision parameter
layer = GaussianVisLayer(nvis=n, init_beta=beta)
rng = np.random.RandomState([2012, 11, 1])
mean = rng.uniform(1e-6, 1. - 1e-6, (n, ))
z = mean
layer.set_biases(z.astype(config.floatX))
init_state = layer.make_state(num_examples=num_samples, numpy_rng=rng)
value = init_state.get_value()
check_gaussian_samples(value, num_samples, n, rows, cols, channels, mean,
tol)
def test_gaussian_vis_layer_sample_conv():
"""
Verifies that GaussianVisLayer.sample returns an expression
whose value passes check_gaussian_samples.
In this case the layer lives in a Conv2DSpace
"""
assert hasattr(np, 'exp')
n = None
num_samples = 1000
tol = .042 # tolerated variance
beta = 1/tol # precision parameter
rows = 3
cols = 3
channels = 3
# axes for batch, rows, cols, channels, can be given in any order
axes = ['b', 0, 1, 'c']
random.shuffle(axes)
axes = tuple(axes)
print 'axes:', axes
class DummyLayer(object):
"""
A layer that we build for the test that just uses a state
as its downward message.
"""
def downward_state(self, state):
return state
def downward_message(self, state):
return state
vis = GaussianVisLayer(nvis=None,rows=rows, cols=cols, channels=channels, init_beta=beta, axes=axes)
hid = DummyLayer()
rng = np.random.RandomState([2012,11,1,259])
mean = rng.uniform(1e-6, 1. - 1e-6, (rows, cols, channels))
ofs = rng.randn(rows,cols,channels)
vis.set_biases(ofs.astype(config.floatX))
#z = inverse_sigmoid_numpy(mean) - ofs
z = mean -ofs
z_var = sharedX(np.zeros((num_samples, rows, cols, channels)) + z)
theano_rng = MRG_RandomStreams(2012+11+1)
sample = vis.sample(state_above=z_var, layer_above=hid,
theano_rng=theano_rng)
sample = sample.eval()
check_gaussian_samples(sample, num_samples, n, rows, cols, channels, mean, tol)
def test_gaussian_vis_layer_sample():
"""
Verifies that GaussianVisLayer.sample returns an expression
whose value passes check_gaussian_samples
In this case the layer lives in a VectorSpace
"""
assert hasattr(np, 'exp')
n = 5
num_samples = 1000
tol = .042 # tolerated variance
beta = 1 / tol # precision parameter
rows = None
cols = None
channels = None
class DummyLayer(object):
"""
A layer that we build for the test that just uses a state
as its downward message.
"""
def downward_state(self, state):
return state
def downward_message(self, state):
return state
vis = GaussianVisLayer(nvis=n, init_beta=beta)
hid = DummyLayer()
rng = np.random.RandomState([2012, 11, 1, 259])
mean = rng.uniform(1e-6, 1. - 1e-6, (n, ))
ofs = rng.randn(n)
vis.set_biases(ofs.astype(config.floatX))
#z = inverse_sigmoid_numpy(mean) - ofs
z = mean - ofs # linear activation function
z_var = sharedX(np.zeros((num_samples, n)) + z)
# mean will be z_var + mu
theano_rng = MRG_RandomStreams(2012 + 11 + 1)
sample = vis.sample(state_above=z_var,
layer_above=hid,
theano_rng=theano_rng)
sample = sample.eval()
check_gaussian_samples(sample, num_samples, n, rows, cols, channels, mean,
tol)
def test_gaussian_vis_layer_sample():
"""
Verifies that GaussianVisLayer.sample returns an expression
whose value passes check_gaussian_samples
In this case the layer lives in a VectorSpace
"""
assert hasattr(np, 'exp')
n = 5
num_samples = 1000
tol = .042 # tolerated variance
beta = 1/tol # precision parameter
rows = None
cols = None
channels = None
class DummyLayer(object):
"""
A layer that we build for the test that just uses a state
as its downward message.
"""
def downward_state(self, state):
return state
def downward_message(self, state):
return state
vis = GaussianVisLayer(nvis=n, init_beta=beta)
hid = DummyLayer()
rng = np.random.RandomState([2012,11,1,259])
mean = rng.uniform(1e-6, 1. - 1e-6, (n,))
ofs = rng.randn(n)
vis.set_biases(ofs.astype(config.floatX))
#z = inverse_sigmoid_numpy(mean) - ofs
z=mean -ofs # linear activation function
z_var = sharedX(np.zeros((num_samples, n)) + z)
# mean will be z_var + mu
theano_rng = MRG_RandomStreams(2012+11+1)
sample = vis.sample(state_above=z_var, layer_above=hid,
theano_rng=theano_rng)
sample = sample.eval()
check_gaussian_samples(sample, num_samples, n, rows, cols, channels, mean, tol)
def test_gaussian_vis_layer_make_state_conv():
"""
Verifies that GaussianVisLayer.make_state creates
a shared variable whose value passes check_gaussian_samples
In this case the layer lives in a Conv2DSpace
"""
n = None
rows = 3
cols = 3
channels = 3
num_samples = 1000
tol = .042 # tolerated variance
beta = 1 / tol # precision parameter
# axes for batch, rows, cols, channels, can be given in any order
axes = ['b', 0, 1, 'c']
random.shuffle(axes)
axes = tuple(axes)
print 'axes:', axes
layer = GaussianVisLayer(rows=rows,
cols=cols,
channels=channels,
init_beta=beta,
axes=axes)
# rng = np.random.RandomState([2012,11,1])
rng = np.random.RandomState()
mean = rng.uniform(1e-6, 1. - 1e-6, (rows, cols, channels))
#z = inverse_sigmoid_numpy(mean)
z = mean
layer.set_biases(z.astype(config.floatX))
init_state = layer.make_state(num_examples=num_samples, numpy_rng=rng)
value = init_state.get_value()
check_gaussian_samples(value, num_samples, n, rows, cols, channels, mean,
tol)
def test_gaussian_vis_layer_make_state_conv():
"""
Verifies that GaussianVisLayer.make_state creates
a shared variable whose value passes check_gaussian_samples
In this case the layer lives in a Conv2DSpace
"""
n = None
rows = 3
cols = 3
channels = 3
num_samples = 1000
tol = .042 # tolerated variance
beta = 1/tol # precision parameter
# axes for batch, rows, cols, channels, can be given in any order
axes = ['b', 0, 1, 'c']
random.shuffle(axes)
axes = tuple(axes)
print 'axes:', axes
layer = GaussianVisLayer(rows=rows, cols=cols, channels=channels, init_beta=beta, axes=axes)
# rng = np.random.RandomState([2012,11,1])
rng = np.random.RandomState()
mean = rng.uniform(1e-6, 1. - 1e-6, (rows, cols, channels))
#z = inverse_sigmoid_numpy(mean)
z= mean
layer.set_biases(z.astype(config.floatX))
init_state = layer.make_state(num_examples=num_samples,
numpy_rng=rng)
value = init_state.get_value()
check_gaussian_samples(value, num_samples, n, rows, cols, channels, mean, tol)
def test_gaussian_vis_layer_sample_conv():
"""
Verifies that GaussianVisLayer.sample returns an expression
whose value passes check_gaussian_samples.
In this case the layer lives in a Conv2DSpace
"""
assert hasattr(np, 'exp')
n = None
num_samples = 1000
tol = .042 # tolerated variance
beta = 1 / tol # precision parameter
rows = 3
cols = 3
channels = 3
# axes for batch, rows, cols, channels, can be given in any order
axes = ['b', 0, 1, 'c']
random.shuffle(axes)
axes = tuple(axes)
print 'axes:', axes
class DummyLayer(object):
"""
A layer that we build for the test that just uses a state
as its downward message.
"""
def downward_state(self, state):
return state
def downward_message(self, state):
return state
vis = GaussianVisLayer(nvis=None,
rows=rows,
cols=cols,
channels=channels,
init_beta=beta,
axes=axes)
hid = DummyLayer()
rng = np.random.RandomState([2012, 11, 1, 259])
mean = rng.uniform(1e-6, 1. - 1e-6, (rows, cols, channels))
ofs = rng.randn(rows, cols, channels)
vis.set_biases(ofs.astype(config.floatX))
#z = inverse_sigmoid_numpy(mean) - ofs
z = mean - ofs
z_var = sharedX(np.zeros((num_samples, rows, cols, channels)) + z)
theano_rng = MRG_RandomStreams(2012 + 11 + 1)
sample = vis.sample(state_above=z_var,
layer_above=hid,
theano_rng=theano_rng)
sample = sample.eval()
check_gaussian_samples(sample, num_samples, n, rows, cols, channels, mean,
tol)
