matchzoo.models
¶
Submodules¶
matchzoo.models.anmm
matchzoo.models.arci
matchzoo.models.arcii
matchzoo.models.bert
matchzoo.models.bimpm
matchzoo.models.cdssm
matchzoo.models.conv_knrm
matchzoo.models.dense_baseline
matchzoo.models.diin
matchzoo.models.drmm
matchzoo.models.drmmtks
matchzoo.models.dssm
matchzoo.models.duet
matchzoo.models.esim
matchzoo.models.hbmp
matchzoo.models.knrm
matchzoo.models.match_pyramid
matchzoo.models.match_srnn
matchzoo.models.matchlstm
matchzoo.models.mvlstm
matchzoo.models.parameter_readme_generator
Package Contents¶
Classes¶
A simple densely connected baseline model. |
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Deep structured semantic model. |
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CDSSM Model implementation. |
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DRMM Model. |
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DRMMTKS Model. |
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ESIM Model. |
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KNRM Model. |
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ConvKNRM Model. |
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BiMPM Model. |
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MatchLSTM Model. |
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ArcI Model. |
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ArcII Model. |
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Bert Model. |
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MVLSTM Model. |
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MatchPyramid Model. |
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aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model. |
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HBMP model. |
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Duet Model. |
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DIIN model. |
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Match-SRNN Model. |
Functions¶
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-
class
matchzoo.models.
DenseBaseline
(params: typing.Optional[ParamTable] = None)¶ Bases:
matchzoo.engine.base_model.BaseModel
A simple densely connected baseline model.
Examples
>>> model = DenseBaseline() >>> model.params['mlp_num_layers'] = 2 >>> model.params['mlp_num_units'] = 300 >>> model.params['mlp_num_fan_out'] = 128 >>> model.params['mlp_activation_func'] = 'relu' >>> model.guess_and_fill_missing_params(verbose=0) >>> model.build()
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classmethod
get_default_params
(cls) → ParamTable¶ - Returns
model default parameters.
-
build
(self)¶ Build.
-
forward
(self, inputs)¶ Forward.
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classmethod
-
class
matchzoo.models.
DSSM
(params: typing.Optional[ParamTable] = None)¶ Bases:
matchzoo.engine.base_model.BaseModel
Deep structured semantic model.
Examples
>>> model = DSSM() >>> model.params['mlp_num_layers'] = 3 >>> model.params['mlp_num_units'] = 300 >>> model.params['mlp_num_fan_out'] = 128 >>> model.params['mlp_activation_func'] = 'relu' >>> model.guess_and_fill_missing_params(verbose=0) >>> model.build()
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classmethod
get_default_params
(cls) → ParamTable¶ - Returns
model default parameters.
-
classmethod
get_default_preprocessor
(cls, truncated_mode: str = 'pre', truncated_length_left: typing.Optional[int] = None, truncated_length_right: typing.Optional[int] = None, filter_mode: str = 'df', filter_low_freq: float = 1, filter_high_freq: float = float('inf'), remove_stop_words: bool = False, ngram_size: typing.Optional[int] = 3) → BasePreprocessor¶ Model default preprocessor.
The preprocessor’s transform should produce a correctly shaped data pack that can be used for training.
- Returns
Default preprocessor.
-
classmethod
get_default_padding_callback
(cls)¶ - Returns
Default padding callback.
-
build
(self)¶ Build model structure.
DSSM use Siamese arthitecture.
-
forward
(self, inputs)¶ Forward.
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classmethod
-
class
matchzoo.models.
CDSSM
(params: typing.Optional[ParamTable] = None)¶ Bases:
matchzoo.engine.base_model.BaseModel
CDSSM Model implementation.
Learning Semantic Representations Using Convolutional Neural Networks for Web Search. (2014a) A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval. (2014b)
Examples
>>> import matchzoo as mz >>> model = CDSSM() >>> model.params['task'] = mz.tasks.Ranking() >>> model.params['vocab_size'] = 4 >>> model.params['filters'] = 32 >>> model.params['kernel_size'] = 3 >>> model.params['conv_activation_func'] = 'relu' >>> model.build()
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classmethod
get_default_params
(cls) → ParamTable¶ - Returns
model default parameters.
-
classmethod
get_default_preprocessor
(cls, truncated_mode: str = 'pre', truncated_length_left: typing.Optional[int] = None, truncated_length_right: typing.Optional[int] = None, filter_mode: str = 'df', filter_low_freq: float = 1, filter_high_freq: float = float('inf'), remove_stop_words: bool = False, ngram_size: typing.Optional[int] = 3) → BasePreprocessor¶ Model default preprocessor.
The preprocessor’s transform should produce a correctly shaped data pack that can be used for training.
- Returns
Default preprocessor.
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classmethod
get_default_padding_callback
(cls, fixed_length_left: int = None, fixed_length_right: int = None, 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') → BaseCallback¶ 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.
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_create_base_network
(self) → nn.Module¶ Apply conv and maxpooling operation towards to each letter-ngram.
The input shape is fixed_text_length`*`number of letter-ngram, as described in the paper, n is 3, number of letter-trigram is about 30,000 according to their observation.
- Returns
A
nn.Module
of CDSSM network, tensor in tensor out.
-
build
(self)¶ Build model structure.
CDSSM use Siamese architecture.
-
forward
(self, inputs)¶ Forward.
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guess_and_fill_missing_params
(self, verbose: int = 1)¶ Guess and fill missing parameters in
params
.Use this method to automatically fill-in hyper parameters. This involves some guessing so the parameter it fills could be wrong. For example, the default task is Ranking, and if we do not set it to Classification manually for data packs prepared for classification, then the shape of the model output and the data will mismatch.
- Parameters
verbose – Verbosity.
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classmethod
-
class
matchzoo.models.
DRMM
(params: typing.Optional[ParamTable] = None)¶ Bases:
matchzoo.engine.base_model.BaseModel
DRMM Model.
Examples
>>> model = DRMM() >>> model.params['mlp_num_layers'] = 1 >>> model.params['mlp_num_units'] = 5 >>> model.params['mlp_num_fan_out'] = 1 >>> model.params['mlp_activation_func'] = 'tanh' >>> model.guess_and_fill_missing_params(verbose=0) >>> model.build()
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classmethod
get_default_params
(cls) → ParamTable¶ - Returns
model default parameters.
-
classmethod
get_default_padding_callback
(cls, fixed_length_left: int = None, fixed_length_right: int = None, pad_value: typing.Union[int, str] = 0, pad_mode: str = 'pre')¶ - Returns
Default padding callback.
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build
(self)¶ Build model structure.
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forward
(self, inputs)¶ Forward.
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classmethod
-
class
matchzoo.models.
DRMMTKS
(params: typing.Optional[ParamTable] = None)¶ Bases:
matchzoo.engine.base_model.BaseModel
DRMMTKS Model.
Examples
>>> model = DRMMTKS() >>> model.params['top_k'] = 10 >>> model.params['mlp_num_layers'] = 1 >>> model.params['mlp_num_units'] = 5 >>> model.params['mlp_num_fan_out'] = 1 >>> model.params['mlp_activation_func'] = 'tanh' >>> model.guess_and_fill_missing_params(verbose=0) >>> model.build()
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classmethod
get_default_params
(cls) → ParamTable¶ - Returns
model default parameters.
-
classmethod
get_default_padding_callback
(cls, fixed_length_left: int = 10, fixed_length_right: int = 100, pad_word_value: typing.Union[int, str] = 0, pad_word_mode: str = 'pre', with_ngram: bool = False, fixed_ngram_length: int = None, pad_ngram_value: typing.Union[int, str] = 0, pad_ngram_mode: str = 'pre') → BaseCallback¶ 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.
-
build
(self)¶ Build model structure.
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forward
(self, inputs)¶ Forward.
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classmethod
-
class
matchzoo.models.
ESIM
(params: typing.Optional[ParamTable] = None)¶ Bases:
matchzoo.engine.base_model.BaseModel
ESIM Model.
Examples
>>> model = ESIM() >>> model.guess_and_fill_missing_params(verbose=0) >>> model.build()
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classmethod
get_default_params
(cls) → ParamTable¶ - Returns
model default parameters.
-
build
(self)¶ Instantiating layers.
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forward
(self, inputs)¶ Forward.
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classmethod
-
class
matchzoo.models.
KNRM
(params: typing.Optional[ParamTable] = None)¶ Bases:
matchzoo.engine.base_model.BaseModel
KNRM Model.
Examples
>>> model = KNRM() >>> model.params['kernel_num'] = 11 >>> model.params['sigma'] = 0.1 >>> model.params['exact_sigma'] = 0.001 >>> model.guess_and_fill_missing_params(verbose=0) >>> model.build()
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classmethod
get_default_params
(cls) → ParamTable¶ - Returns
model default parameters.
-
build
(self)¶ Build model structure.
-
forward
(self, inputs)¶ Forward.
-
classmethod
-
class
matchzoo.models.
ConvKNRM
(params: typing.Optional[ParamTable] = None)¶ Bases:
matchzoo.engine.base_model.BaseModel
ConvKNRM Model.
Examples
>>> model = ConvKNRM() >>> model.params['filters'] = 128 >>> model.params['conv_activation_func'] = 'tanh' >>> model.params['max_ngram'] = 3 >>> model.params['use_crossmatch'] = True >>> model.params['kernel_num'] = 11 >>> model.params['sigma'] = 0.1 >>> model.params['exact_sigma'] = 0.001 >>> model.guess_and_fill_missing_params(verbose=0) >>> model.build()
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classmethod
get_default_params
(cls) → ParamTable¶ - Returns
model default parameters.
-
build
(self)¶ Build model structure.
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forward
(self, inputs)¶ Forward.
-
classmethod
-
class
matchzoo.models.
BiMPM
(params: typing.Optional[ParamTable] = None)¶ Bases:
matchzoo.engine.base_model.BaseModel
BiMPM Model.
Reference: - https://github.com/galsang/BIMPM-pytorch/blob/master/model/BIMPM.py
Examples
>>> model = BiMPM() >>> model.params['num_perspective'] = 4 >>> model.guess_and_fill_missing_params(verbose=0) >>> model.build()
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classmethod
get_default_params
(cls) → ParamTable¶ - Returns
model default parameters.
-
build
(self)¶ Make function layers.
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forward
(self, inputs)¶ Forward.
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reset_parameters
(self)¶ Init Parameters.
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dropout
(self, v)¶ Dropout Layer.
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classmethod
-
class
matchzoo.models.
MatchLSTM
(params: typing.Optional[ParamTable] = None)¶ Bases:
matchzoo.engine.base_model.BaseModel
MatchLSTM Model.
https://github.com/shuohangwang/mprc/blob/master/qa/rankerReader.lua.
Examples
>>> model = MatchLSTM() >>> model.params['dropout'] = 0.2 >>> model.params['hidden_size'] = 200 >>> model.guess_and_fill_missing_params(verbose=0) >>> model.build()
-
classmethod
get_default_params
(cls) → ParamTable¶ - Returns
model default parameters.
-
build
(self)¶ Instantiating layers.
-
forward
(self, inputs)¶ Forward.
-
classmethod
-
class
matchzoo.models.
ArcI
(params: typing.Optional[ParamTable] = None)¶ Bases:
matchzoo.engine.base_model.BaseModel
ArcI Model.
Examples
>>> model = ArcI() >>> model.params['left_filters'] = [32] >>> model.params['right_filters'] = [32] >>> model.params['left_kernel_sizes'] = [3] >>> model.params['right_kernel_sizes'] = [3] >>> model.params['left_pool_sizes'] = [2] >>> model.params['right_pool_sizes'] = [4] >>> model.params['conv_activation_func'] = 'relu' >>> model.params['mlp_num_layers'] = 1 >>> model.params['mlp_num_units'] = 64 >>> model.params['mlp_num_fan_out'] = 32 >>> model.params['mlp_activation_func'] = 'relu' >>> model.params['dropout_rate'] = 0.5 >>> model.guess_and_fill_missing_params(verbose=0) >>> model.build()
-
classmethod
get_default_params
(cls) → ParamTable¶ - Returns
model default parameters.
-
classmethod
get_default_padding_callback
(cls, fixed_length_left: int = 10, fixed_length_right: int = 100, pad_word_value: typing.Union[int, str] = 0, pad_word_mode: str = 'pre', with_ngram: bool = False, fixed_ngram_length: int = None, pad_ngram_value: typing.Union[int, str] = 0, pad_ngram_mode: str = 'pre') → BaseCallback¶ 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.
-
build
(self)¶ Build model structure.
ArcI use Siamese arthitecture.
-
forward
(self, inputs)¶ Forward.
-
classmethod
_make_conv_pool_block
(cls, in_channels: int, out_channels: int, kernel_size: int, activation: nn.Module, pool_size: int) → nn.Module¶ Make conv pool block.
-
classmethod
-
class
matchzoo.models.
ArcII
(params: typing.Optional[ParamTable] = None)¶ Bases:
matchzoo.engine.base_model.BaseModel
ArcII Model.
Examples
>>> model = ArcII() >>> model.params['embedding_output_dim'] = 300 >>> model.params['kernel_1d_count'] = 32 >>> model.params['kernel_1d_size'] = 3 >>> model.params['kernel_2d_count'] = [16, 32] >>> model.params['kernel_2d_size'] = [[3, 3], [3, 3]] >>> model.params['pool_2d_size'] = [[2, 2], [2, 2]] >>> model.guess_and_fill_missing_params(verbose=0) >>> model.build()
-
classmethod
get_default_params
(cls) → ParamTable¶ - Returns
model default parameters.
-
classmethod
get_default_padding_callback
(cls, fixed_length_left: int = 10, fixed_length_right: int = 100, pad_word_value: typing.Union[int, str] = 0, pad_word_mode: str = 'pre', with_ngram: bool = False, fixed_ngram_length: int = None, pad_ngram_value: typing.Union[int, str] = 0, pad_ngram_mode: str = 'pre') → BaseCallback¶ 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.
-
build
(self)¶ Build model structure.
ArcII has the desirable property of letting two sentences meet before their own high-level representations mature.
-
forward
(self, inputs)¶ Forward.
-
classmethod
_make_conv_pool_block
(cls, in_channels: int, out_channels: int, kernel_size: tuple, activation: nn.Module, pool_size: tuple) → nn.Module¶ Make conv pool block.
-
classmethod
-
class
matchzoo.models.
Bert
(params: typing.Optional[ParamTable] = None)¶ Bases:
matchzoo.engine.base_model.BaseModel
Bert Model.
-
classmethod
get_default_params
(cls) → ParamTable¶ - Returns
model default parameters.
-
classmethod
get_default_preprocessor
(cls, mode: str = 'bert-base-uncased') → BasePreprocessor¶ - Returns
Default preprocessor.
-
classmethod
get_default_padding_callback
(cls, fixed_length_left: int = None, fixed_length_right: int = None, pad_value: typing.Union[int, str] = 0, pad_mode: str = 'pre')¶ - Returns
Default padding callback.
-
build
(self)¶ Build model structure.
-
forward
(self, inputs)¶ Forward.
-
classmethod
-
class
matchzoo.models.
MVLSTM
(params: typing.Optional[ParamTable] = None)¶ Bases:
matchzoo.engine.base_model.BaseModel
MVLSTM Model.
Examples
>>> model = MVLSTM() >>> model.params['hidden_size'] = 32 >>> model.params['top_k'] = 50 >>> model.params['mlp_num_layers'] = 2 >>> model.params['mlp_num_units'] = 20 >>> model.params['mlp_num_fan_out'] = 10 >>> model.params['mlp_activation_func'] = 'relu' >>> model.params['dropout_rate'] = 0.0 >>> model.guess_and_fill_missing_params(verbose=0) >>> model.build()
-
classmethod
get_default_params
(cls) → ParamTable¶ - Returns
model default parameters.
-
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 = False, fixed_ngram_length: int = None, pad_ngram_value: typing.Union[int, str] = 0, pad_ngram_mode: str = 'pre') → BaseCallback¶ 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.
-
build
(self)¶ Build model structure.
-
forward
(self, inputs)¶ Forward.
-
classmethod
-
class
matchzoo.models.
MatchPyramid
(params: typing.Optional[ParamTable] = None)¶ Bases:
matchzoo.engine.base_model.BaseModel
MatchPyramid Model.
Examples
>>> model = MatchPyramid() >>> model.params['embedding_output_dim'] = 300 >>> model.params['kernel_count'] = [16, 32] >>> model.params['kernel_size'] = [[3, 3], [3, 3]] >>> model.params['dpool_size'] = [3, 10] >>> model.guess_and_fill_missing_params(verbose=0) >>> model.build()
-
classmethod
get_default_params
(cls) → ParamTable¶ - Returns
model default parameters.
-
build
(self)¶ Build model structure.
MatchPyramid text matching as image recognition.
-
forward
(self, inputs)¶ Forward.
-
classmethod
_make_conv_pool_block
(cls, in_channels: int, out_channels: int, kernel_size: tuple, activation: nn.Module) → nn.Module¶ Make conv pool block.
-
classmethod
-
class
matchzoo.models.
aNMM
(params: typing.Optional[ParamTable] = None)¶ Bases:
matchzoo.engine.base_model.BaseModel
aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model.
Examples
>>> model = aNMM() >>> model.params['embedding_output_dim'] = 300 >>> model.guess_and_fill_missing_params(verbose=0) >>> model.build()
-
classmethod
get_default_params
(cls) → ParamTable¶ - Returns
model default parameters.
-
build
(self)¶ Build model structure.
aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model.
-
forward
(self, inputs)¶ Forward.
-
classmethod
-
class
matchzoo.models.
HBMP
(params: typing.Optional[ParamTable] = None)¶ Bases:
matchzoo.engine.base_model.BaseModel
HBMP model.
Examples
>>> model = HBMP() >>> model.params['embedding_input_dim'] = 200 >>> model.params['embedding_output_dim'] = 100 >>> model.params['mlp_num_layers'] = 1 >>> model.params['mlp_num_units'] = 10 >>> model.params['mlp_num_fan_out'] = 10 >>> model.params['mlp_activation_func'] = nn.LeakyReLU(0.1) >>> model.params['lstm_hidden_size'] = 5 >>> model.params['lstm_num'] = 3 >>> model.params['num_layers'] = 3 >>> model.params['dropout_rate'] = 0.1 >>> model.guess_and_fill_missing_params(verbose=0) >>> model.build()
-
classmethod
get_default_params
(cls) → ParamTable¶ - Returns
model default parameters.
-
build
(self)¶ Build model structure.
HBMP use Siamese arthitecture.
-
forward
(self, inputs)¶ Forward.
-
classmethod
-
class
matchzoo.models.
DUET
(params: typing.Optional[ParamTable] = None)¶ 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) → ParamTable¶ - 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') → BaseCallback¶ 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.
-
classmethod
-
class
matchzoo.models.
DIIN
(params: typing.Optional[ParamTable] = None)¶ Bases:
matchzoo.engine.base_model.BaseModel
DIIN model.
Examples
>>> model = DIIN() >>> model.params['embedding_input_dim'] = 10000 >>> model.params['embedding_output_dim'] = 300 >>> model.params['mask_value'] = 0 >>> model.params['char_embedding_input_dim'] = 100 >>> model.params['char_embedding_output_dim'] = 8 >>> model.params['char_conv_filters'] = 100 >>> model.params['char_conv_kernel_size'] = 5 >>> model.params['first_scale_down_ratio'] = 0.3 >>> model.params['nb_dense_blocks'] = 3 >>> model.params['layers_per_dense_block'] = 8 >>> model.params['growth_rate'] = 20 >>> model.params['transition_scale_down_ratio'] = 0.5 >>> model.params['conv_kernel_size'] = (3, 3) >>> model.params['pool_kernel_size'] = (2, 2) >>> model.params['dropout_rate'] = 0.2 >>> model.guess_and_fill_missing_params(verbose=0) >>> model.build()
-
classmethod
get_default_params
(cls) → ParamTable¶ - Returns
model default parameters.
-
classmethod
get_default_preprocessor
(cls, truncated_mode: str = 'pre', truncated_length_left: typing.Optional[int] = None, truncated_length_right: typing.Optional[int] = None, filter_mode: str = 'df', filter_low_freq: float = 1, filter_high_freq: float = float('inf'), remove_stop_words: bool = False, ngram_size: typing.Optional[int] = 1) → BasePreprocessor¶ Model default preprocessor.
The preprocessor’s transform should produce a correctly shaped data pack that can be used for training.
- Returns
Default preprocessor.
-
classmethod
get_default_padding_callback
(cls, fixed_length_left: int = 10, fixed_length_right: int = 30, 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') → BaseCallback¶ 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.
-
build
(self)¶ Build model structure.
-
forward
(self, inputs)¶ Forward.
-
classmethod
-
class
matchzoo.models.
MatchSRNN
(params: typing.Optional[ParamTable] = None)¶ Bases:
matchzoo.engine.base_model.BaseModel
Match-SRNN Model.
Examples
>>> model = MatchSRNN() >>> model.params['channels'] = 4 >>> model.params['units'] = 10 >>> model.params['dropout'] = 0.2 >>> model.params['direction'] = 'lt' >>> model.guess_and_fill_missing_params(verbose=0) >>> model.build()
-
classmethod
get_default_params
(cls) → ParamTable¶ - Returns
model default parameters.
-
build
(self)¶ Build model structure.
-
forward
(self, inputs)¶ Forward.
-
classmethod
-
matchzoo.models.
list_available
() → list¶