def test_temperature_update(self):
"""Test if temperature updates affect gradients."""
flow = flows.DiscreteFlow(self.N, self.K,
temperature=10., layers=[("M",[256, 256]), ("F", None), ("M",[])])
with tf.GradientTape() as tape:
loss = -tf.reduce_sum(forward(flow, self.base_samples))
g1 = tape.gradient(loss, flow.trainable_variables)
with tf.GradientTape() as tape:
flow.temperature = 1.0
loss = -tf.reduce_sum(forward(flow, self.base_samples))
g2 = tape.gradient(loss, flow.trainable_variables)
with tf.GradientTape() as tape:
flow.temperature = 0.1
loss = -tf.reduce_sum(forward(flow, self.base_samples))
g3 = tape.gradient(loss, flow.trainable_variables)
difference_g1g2 = sum( np.sum(abs(e1-e2)) for e1, e2 in zip(g1,g2) )
difference_g1g3 = sum( np.sum(abs(e1-e2)) for e1, e2 in zip(g1,g3) )
logger.debug("Total gradient difference for temperature 10 vs 1: %s" % difference_g1g2)
logger.debug("Total gradient difference for temperature 10 vs 0.1: %s" % difference_g1g3)
self.assertNotEqual(difference_g1g2, 0.0)
self.assertNotEqual(difference_g1g3, 0.0)
self.assertNotEqual(difference_g1g3, difference_g1g2)
def test_discrete_flows_different_inputs(self):
"""Tests various flows by passing two different inputs and checking if outputs differ."""
N = self.N
for i, layers in enumerate(self.layers_specs):
logger.debug("#"*30+" [%i/%i] testing repeated samples (reverse) %s" % \
(i+1, len(self.layers_specs), layers))
flow = flows.DiscreteFlow(self.N, self.K, temperature=5.0, layers=copy.deepcopy(layers))
out = flow(self.equal_samples[0:1])
out2 = flow(self.equal_samples2[0:1])
self.assertTrue(out.shape==out2.shape)
self.assertTrue( sum(bool(tf.reduce_any(out[0,n]!=out2[0,n])) for n in range(self.N))>0, \
"at least one dim in outputs must differ" )
def test_discrete_flows_deterministic_reverse(self):
"""Tests various flows by passing set of exactly the same samples and checking if result is the same."""
N = self.N
for i, layers in enumerate(self.layers_specs):
logger.debug("#"*30+" [%i/%i] testing repeated samples (reverse) %s" % \
(i+1, len(self.layers_specs), layers))
flow = flows.DiscreteFlow(self.N, self.K, temperature=5.0, layers=copy.deepcopy(layers))
out = flow.reverse(self.equal_samples)
self.assertTrue( sum([bool(tf.reduce_all(out[n] == out[n+1])) for n in range(N-1)])==N-1,
"all samples the same")
out = flow.reverse(self.equal_samples2)
self.assertTrue( sum([bool(tf.reduce_all(out[n] == out[n+1])) for n in range(N-1)])==N-1,
"all samples the same")
def test_discrete_flows_invertability(self):
"""Tests various flows by passing samples both directions and checking if input is recovered."""
for i, layers in enumerate(self.layers_specs):
logger.debug("#"*30+" [%i/%i] testing invertability of %s" % (i+1, len(self.layers_specs), layers))
flow = flows.DiscreteFlow(self.N, self.K, temperature=5.0, layers=copy.deepcopy(layers))
self.assert_forward_reverse(flow, str(layers))