matchzoo.models.duet

An implementation of DUET Model.

Module Contents

class matchzoo.models.duet.DUET

Bases: matchzoo.engine.base_model.BaseModel

Duet Model.

Examples

>>> model = DUET()
>>> model.params['left_length'] = 10
>>> model.params['right_length'] = 40
>>> model.params['lm_filters'] = 300
>>> model.params['mlp_num_layers'] = 2
>>> model.params['mlp_num_units'] = 300
>>> model.params['mlp_num_fan_out'] = 300
>>> model.params['mlp_activation_func'] = 'relu'
>>> model.params['vocab_size'] = 2000
>>> model.params['dm_filters'] = 300
>>> model.params['dm_conv_activation_func'] = 'relu'
>>> model.params['dm_kernel_size'] = 3
>>> model.params['dm_right_pool_size'] = 8
>>> model.params['dropout_rate'] = 0.5
>>> model.guess_and_fill_missing_params(verbose=0)
>>> model.build()

classmethod get_default_params(cls)

Returns:model default parameters.

classmethod get_default_preprocessor(cls, truncated_mode: str = 'pre', truncated_length_left: int = 10, truncated_length_right: int = 40, filter_mode: str = 'df', filter_low_freq: float = 1, filter_high_freq: float = float('inf'), remove_stop_words: bool = False, ngram_size: int = 3)

Returns:Default preprocessor.

classmethod get_default_padding_callback(cls, fixed_length_left: int = 10, fixed_length_right: int = 40, pad_word_value: typing.Union[int, str] = 0, pad_word_mode: str = 'pre', with_ngram: bool = True, fixed_ngram_length: int = None, pad_ngram_value: typing.Union[int, str] = 0, pad_ngram_mode: str = 'pre')

Model default padding callback.

The padding callback’s on_batch_unpacked would pad a batch of data to a fixed length.

Returns:Default padding callback.

classmethod _xor_match(cls, x, y)

Xor match of two inputs.

build(self)

Build model structure.

forward(self, inputs)

Forward.