def test_conv_encoder_highway_default_forward(hybridize, mask):
encoder = model.ConvolutionalEncoder()
encoder.initialize(init='One')
if hybridize:
encoder.hybridize()
print(encoder)
inputs = mx.nd.random.uniform(shape=(10, 20, 15))
if mask:
output = encoder(inputs, mx.nd.ones(inputs.shape[:-1]))
else:
output = encoder(inputs)
assert output.shape == (20, 525), output.shape
def test_conv_encoder_nohighway_forward_largeinputs(hybridize, mask):
encoder = model.ConvolutionalEncoder(embed_size=7,
num_filters=(1, 1, 2, 3),
ngram_filter_sizes=(1, 2, 3, 4),
output_size=30)
print(encoder)
encoder.initialize()
if hybridize:
encoder.hybridize()
inputs = mx.nd.random.uniform(shape=(4, 8, 7))
if mask:
output = encoder(inputs, mx.nd.ones(inputs.shape[:-1]))
else:
output = encoder(inputs)
assert output.shape == (8, 30), output.shape
def test_conv_encoder_highway_forward(hybridize, mask):
encoder = model.ConvolutionalEncoder(embed_size=2,
num_filters=(2, 1),
ngram_filter_sizes=(1, 2),
num_highway=2,
output_size=1)
print(encoder)
encoder.initialize()
if hybridize:
encoder.hybridize()
inputs = mx.nd.array([[[.7, .8], [.1, 1.5], [.7, .8]], [[.7, .8], [.1, 1.5], [.7, .8]]])
if mask:
output = encoder(inputs, mx.nd.ones(inputs.shape[:-1]))
else:
output = encoder(inputs)
print(output)
assert output.shape == (3, 1), output.shape
def test_conv_encoder_nonhighway_forward(hybridize, mask):
encoder = model.ConvolutionalEncoder(embed_size=2, num_filters=(1, 1),
ngram_filter_sizes=(1, 2))
print(encoder)
encoder.initialize(init='One')
if hybridize:
encoder.hybridize()
inputs = mx.nd.array([[[.7, .8], [.1, 1.5], [.2, .3]], [[.5, .6], [.2, 2.5], [.4, 4]]])
if mask:
output = encoder(inputs, mx.nd.ones(inputs.shape[:-1]))
else:
output = encoder(inputs)
assert output.shape == (3, 2), output.shape
assert_almost_equal(output.asnumpy(),
mx.nd.array([[1.37, 1.42],
[1.49, 1.49],
[1.5, 1.5]]).asnumpy(),
decimal=2)