def testAttention1D(self):
batch = 5
target_length = 7
source_length = 13
source_depth = 9
target_depth = 11
attention_size = 21
output_size = 15
num_heads = 7
source = np.random.rand(batch, source_length, source_depth)
target = np.random.rand(batch, target_length, target_depth)
mask = np.random.rand(batch, target_length, source_length)
with self.test_session() as session:
a = common_layers.attention_1d_v0(
tf.constant(source, dtype=tf.float32),
tf.constant(target, dtype=tf.float32), attention_size,
output_size, num_heads, tf.constant(mask, dtype=tf.float32))
session.run(tf.global_variables_initializer())
res = session.run(a)
self.assertEqual(res.shape, (batch, target_length, output_size))
def testAttention1D(self):
batch = 5
target_length = 7
source_length = 13
source_depth = 9
target_depth = 11
attention_size = 21
output_size = 15
num_heads = 7
source = np.random.rand(batch, source_length, source_depth)
target = np.random.rand(batch, target_length, target_depth)
mask = np.random.rand(batch, target_length, source_length)
with self.test_session() as session:
a = common_layers.attention_1d_v0(
tf.constant(source, dtype=tf.float32),
tf.constant(target, dtype=tf.float32), attention_size, output_size,
num_heads, tf.constant(mask, dtype=tf.float32))
session.run(tf.global_variables_initializer())
res = session.run(a)
self.assertEqual(res.shape, (batch, target_length, output_size))