def test_embedding_block():
input_shape = (32, )
block = block_module.EmbeddingBlock()
hp = kerastuner.HyperParameters()
block.build(hp, ak.Input(shape=input_shape).build())
assert name_in_hps('pretraining', hp)
assert name_in_hps('embedding_dim', hp)
def test_embedding_block():
input_shape = (32,)
block = block_module.EmbeddingBlock()
block.max_features = 100
block.set_state(block.get_state())
hp = kerastuner.HyperParameters()
block.build(hp, ak.Input(shape=input_shape).build())
assert common.name_in_hps('pretraining', hp)
assert common.name_in_hps('embedding_dim', hp)
def assemble(self, input_node):
output_node = input_node
ratio = self.sw_ratio()
if not isinstance(input_node, node.TextNode):
raise ValueError('The input_node should be a TextNode.')
if ratio < 1500:
output_node = processor.TextToNgramVector()(output_node)
output_node = block.DenseBlock()(output_node)
else:
output_node = processor.TextToIntSequence()(output_node)
output_node = block.EmbeddingBlock(
pretrained=(ratio < 15000))(output_node)
output_node = block.ConvBlock(separable=True)(output_node)
return output_node
def build(self, hp, inputs=None):
input_node = nest.flatten(inputs)[0]
output_node = input_node
vectorizer = self.vectorizer or hp.Choice(
'vectorizer', ['sequence', 'ngram'], default='sequence')
if not isinstance(input_node, node.TextNode):
raise ValueError('The input_node should be a TextNode.')
if vectorizer == 'ngram':
output_node = preprocessor.TextToNgramVector()(output_node)
output_node = block.DenseBlock()(output_node)
else:
output_node = preprocessor.TextToIntSequence()(output_node)
output_node = block.EmbeddingBlock(
pretraining=self.pretraining)(output_node)
output_node = block.ConvBlock(separable=True)(output_node)
return output_node
def test_embedding_block_with_pretraining(get_file, load_embedding_index):
load_embedding_index.return_value = {'test': np.ones((100, ))}
get_file.return_value = ''
input_shape = (32, )
block = block_module.EmbeddingBlock(pretraining='glove')
block.max_features = 2
block.word_index = {'test': 1}
block.set_state(block.get_state())
hp = kerastuner.HyperParameters()
block.build(hp, ak.Input(shape=input_shape).build())
embedding_matrix = block._build_embedding_matrix('glove')
assert np.array_equal(embedding_matrix[1], np.ones((100, )))
assert not common.name_in_hps('pretraining', hp)
assert not common.name_in_hps('embedding_dim', hp)
