def test_transformer_base():
reset_session()
# Construct the layer
from rinokeras.core.v1x.models.transformer import Transformer
layer = Transformer(discrete=True,
n_symbols_in=16,
n_symbols_out=16,
n_layers=6,
n_heads=4,
d_model=32,
d_filter=16)
# Make sure that the layer is not None
assert layer is not None
# Encoded values
source_tensor, _ = random_sequence_tensor(2, 32, 16)
target_tensor, _ = random_sequence_tensor(2, 32, 16)
source_mask, _ = random_mask_tensor(2, 32)
target_mask, _ = random_mask_tensor(2, 32)
# Get the output of the layer
value = layer((source_tensor, target_tensor),
mask=(source_mask, target_mask))
# Create a named temporary file for save/restore testing
with tempfile.TemporaryFile() as weights_file:
# Construct the session
output = run_simple_session_save_weights(inputs=[value],
feed={},
weights=[layer],
weights_file=weights_file)
assert_not_none(output)
assert_expected_shapes(output, [(2, 32, 16)])
# Check loading and restoring
load_restore_test(output=output,
inputs=[value],
feed={},
weights=[layer],
weights_file=weights_file)
# Do regression testing
check_regression('transformer_base',
output,
__file__,
'regression_outputs/test_transformer_outputs.json',
debug=_RK_REBUILD_REGRESSION)
# Do a config test
from_config_test(Transformer, layer)
def test_qanet_base():
reset_session()
# Construct the layer
from rinokeras.core.v1x.models.qanet import QANet
layer = QANet(n_chars=32, n_symbols=32)
# Make sure that the layer is not None
assert layer is not None
# Encoded values
context_tensor, _ = random_sequence_tensor(2, 8, 32)
query_tensor, _ = random_sequence_tensor(2, 8, 32)
context_char_tensor = tf.convert_to_tensor(
np.random.randint(0, 32, (2, 8, 16)))
query_char_tensor = tf.convert_to_tensor(
np.random.randint(0, 32, (2, 8, 16)))
# Get the output of the layer
value = layer(
(context_tensor, query_tensor, context_char_tensor, query_char_tensor))
# Create a named temporary file for save/restore testing
with tempfile.TemporaryFile() as weights_file:
# Construct the session
output = run_simple_session_save_weights(inputs=[value],
feed={},
weights=[layer],
weights_file=weights_file)
assert_not_none(output)
assert_expected_shapes(output, [(2, 8, 128)])
# Check loading and restoring
load_restore_test(output=output,
inputs=[value],
feed={},
weights=[layer],
weights_file=weights_file)
# Do regression testing
check_regression('qanet_base_expected_output',
output,
__file__,
'regression_outputs/test_qanet_outputs.json',
debug=_RK_REBUILD_REGRESSION)
# Do a config test
from_config_test(QANet, layer)
def test_convert_to_attention_mask_2():
reset_session()
# Construct the layer
from rinokeras.core.v1x.utils import convert_to_attention_mask
# Encoded values
input_tensor, _ = random_sequence_tensor(2, 32, 128)
input_mask, _ = random_mask_tensor(2, 32)
# Get the output of the layer
value = convert_to_attention_mask(input_tensor, input_mask)
# Construct the session
output = run_simple_session(inputs=[value], feed={})
assert_not_none(output)
assert_expected_shapes(output, [(2, 32, 32)])
# Do regression testing
check_regression('convert_to_attention_mask_2',
output,
__file__,
'regression_outputs/test_utils_outputs.json',
debug=_RK_REBUILD_REGRESSION)