def test_softmax_mf_sample_consistent():
# A test of the Softmax class
# Verifies that the mean field update is consistent with
# the sampling function
# Since a Softmax layer contains only one random variable
# (with n_classes possible values) the mean field assumption
# does not impose any restriction so mf_update simply gives
# the true expected value of h given v.
# We can thus use mf_update to compute the expected value
# of a sample of y conditioned on v, and check that samples
# drawn using the layer's sample method convert to that
# value.
rng = np.random.RandomState([2012, 11, 1, 1154])
theano_rng = MRG_RandomStreams(2012 + 11 + 1 + 1154)
num_samples = 1000
tol = .042
# Make DBM
num_vis = rng.randint(1, 11)
n_classes = rng.randint(1, 11)
v = BinaryVector(num_vis)
v.set_biases(rng.uniform(-1., 1., (num_vis, )).astype(config.floatX))
y = Softmax(n_classes=n_classes, layer_name='y', irange=1.)
y.set_biases(rng.uniform(-1., 1., (n_classes, )).astype(config.floatX))
dbm = DBM(visible_layer=v, hidden_layers=[y], batch_size=1, niter=50)
# Randomly pick a v to condition on
# (Random numbers are generated via dbm.rng)
layer_to_state = dbm.make_layer_to_state(1)
v_state = layer_to_state[v]
y_state = layer_to_state[y]
# Infer P(y | v) using mean field
expected_y = y.mf_update(state_below=v.upward_state(v_state))
expected_y = expected_y[0, :]
expected_y = expected_y.eval()
# copy all the states out into a batch size of num_samples
cause_copy = sharedX(np.zeros((num_samples, ))).dimshuffle(0, 'x')
v_state = v_state[0, :] + cause_copy
y_state = y_state[0, :] + cause_copy
y_samples = y.sample(state_below=v.upward_state(v_state),
theano_rng=theano_rng)
y_samples = function([], y_samples)()
check_multinomial_samples(y_samples, (num_samples, n_classes), expected_y,
tol)
def test_softmax_mf_sample_consistent():
# A test of the Softmax class
# Verifies that the mean field update is consistent with
# the sampling function
# Since a Softmax layer contains only one random variable
# (with n_classes possible values) the mean field assumption
# does not impose any restriction so mf_update simply gives
# the true expected value of h given v.
# We can thus use mf_update to compute the expected value
# of a sample of y conditioned on v, and check that samples
# drawn using the layer's sample method convert to that
# value.
rng = np.random.RandomState([2012,11,1,1154])
theano_rng = MRG_RandomStreams(2012+11+1+1154)
num_samples = 1000
tol = .042
# Make DBM
num_vis = rng.randint(1,11)
n_classes = rng.randint(1, 11)
v = BinaryVector(num_vis)
v.set_biases(rng.uniform(-1., 1., (num_vis,)).astype(config.floatX))
y = Softmax(
n_classes = n_classes,
layer_name = 'y',
irange = 1.)
y.set_biases(rng.uniform(-1., 1., (n_classes,)).astype(config.floatX))
dbm = DBM(visible_layer = v,
hidden_layers = [y],
batch_size = 1,
niter = 50)
# Randomly pick a v to condition on
# (Random numbers are generated via dbm.rng)
layer_to_state = dbm.make_layer_to_state(1)
v_state = layer_to_state[v]
y_state = layer_to_state[y]
# Infer P(y | v) using mean field
expected_y = y.mf_update(
state_below = v.upward_state(v_state))
expected_y = expected_y[0, :]
expected_y = expected_y.eval()
# copy all the states out into a batch size of num_samples
cause_copy = sharedX(np.zeros((num_samples,))).dimshuffle(0,'x')
v_state = v_state[0,:] + cause_copy
y_state = y_state[0,:] + cause_copy
y_samples = y.sample(state_below = v.upward_state(v_state), theano_rng=theano_rng)
y_samples = function([], y_samples)()
check_multinomial_samples(y_samples, (num_samples, n_classes), expected_y, tol)