def testDiscreteBottleneckVQCond(self):
hidden_size = 60
z_size = 4
x = tf.zeros(shape=[100, 1, hidden_size], dtype=tf.float32)
with tf.variable_scope("test2", reuse=tf.AUTO_REUSE):
means = tf.get_variable("means",
shape=[1, 1, 2**z_size, hidden_size],
initializer=tf.constant_initializer(0.),
dtype=tf.float32)
ema_count = []
ema_count_i = tf.get_variable(
"ema_count",
[1, 2**z_size],
initializer=tf.constant_initializer(0),
trainable=False)
ema_count.append(ema_count_i)
ema_means = []
with tf.colocate_with(means):
ema_means_i = tf.get_variable("ema_means",
initializer=means.initialized_value()[0],
trainable=False)
ema_means.append(ema_means_i)
cond = tf.cast(0.0, tf.bool)
x_means_dense, x_means_hot, _, _, _ = discretization.discrete_bottleneck(
x, hidden_size, z_size, 32, means=means, num_blocks=1, cond=cond,
ema_means=ema_means, ema_count=ema_count, name="test2")
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
x_means_dense_eval, x_means_hot_eval = sess.run(
[x_means_dense, x_means_hot])
means_eval = sess.run(means)
self.assertEqual(x_means_dense_eval.shape, (100, 1, hidden_size))
self.assertEqual(x_means_hot_eval.shape, (100, 1))
self.assertAllClose(means_eval, np.zeros((1, 1, 2**z_size,
hidden_size)))
def testDiscreteBottleneckVQCond(self):
hidden_size = 60
z_size = 4
x = tf.zeros(shape=[100, 1, hidden_size], dtype=tf.float32)
with tf.variable_scope("test2", reuse=tf.AUTO_REUSE):
means = tf.get_variable("means",
shape=[1, 1, 2**z_size, hidden_size],
initializer=tf.constant_initializer(0.),
dtype=tf.float32)
ema_count = []
ema_count_i = tf.get_variable(
"ema_count",
[1, 2**z_size],
initializer=tf.constant_initializer(0),
trainable=False)
ema_count.append(ema_count_i)
ema_means = []
with tf.colocate_with(means):
ema_means_i = tf.get_variable("ema_means",
initializer=means.initialized_value()[0],
trainable=False)
ema_means.append(ema_means_i)
cond = tf.cast(0.0, tf.bool)
x_means_dense, x_means_hot, _, _, _ = discretization.discrete_bottleneck(
x, hidden_size, z_size, 32, means=means, num_blocks=1, cond=cond,
ema_means=ema_means, ema_count=ema_count, name="test2")
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
x_means_dense_eval, x_means_hot_eval = sess.run(
[x_means_dense, x_means_hot])
means_eval = sess.run(means)
self.assertEqual(x_means_dense_eval.shape, (100, 1, hidden_size))
self.assertEqual(x_means_hot_eval.shape, (100, 1))
self.assertAllClose(means_eval, np.zeros((1, 1, 2**z_size,
hidden_size)))
