def testInternalBias(self):
batch_size = 4
num_hidden = 6
num_dims = 8
test_inputs = tf.random_normal(shape=(batch_size, num_dims))
nade = Nade(num_dims, num_hidden, internal_bias=True)
log_prob, cond_probs = nade.log_prob(test_inputs)
sample, sample_prob = nade.sample(n=batch_size)
with self.test_session() as sess:
sess.run([tf.global_variables_initializer()])
self.assertEqual(log_prob.eval().shape, (batch_size, ))
self.assertEqual(cond_probs.eval().shape, (batch_size, num_dims))
self.assertEqual(sample.eval().shape, (batch_size, num_dims))
self.assertEqual(sample_prob.eval().shape, (batch_size, ))
def testInternalBias(self):
batch_size = 4
num_hidden = 6
num_dims = 8
test_inputs = tf.random_normal(shape=(batch_size, num_dims))
nade = Nade(num_dims, num_hidden, internal_bias=True)
log_prob, cond_probs = nade.log_prob(test_inputs)
sample, sample_prob = nade.sample(n=batch_size)
with self.test_session() as sess:
sess.run([tf.global_variables_initializer()])
self.assertEqual(log_prob.eval().shape, (batch_size,))
self.assertEqual(cond_probs.eval().shape, (batch_size, num_dims))
self.assertEqual(sample.eval().shape, (batch_size, num_dims))
self.assertEqual(sample_prob.eval().shape, (batch_size,))