matchzoo.models package¶

Submodules¶

matchzoo.models.anmm module¶

An implementation of aNMM Model.

class matchzoo.models.anmm.ANMM(params=None, backend=None)¶

Bases: matchzoo.engine.base_model.BaseModel

ANMM Model.

Examples

>>> model = ANMM()
>>> model.guess_and_fill_missing_params(verbose=0)
>>> model.build()

build()¶

Build model structure.

aNMM model based on bin weighting and query term attentions

classmethod get_default_params()¶

Return type:	`ParamTable`
Returns:	model default parameters.

matchzoo.models.arci module¶

An implementation of ArcI Model.

class matchzoo.models.arci.ArcI(params=None, backend=None)¶

Bases: matchzoo.engine.base_model.BaseModel

ArcI Model.

Examples

>>> model = ArcI()
>>> model.params['num_blocks'] = 1
>>> 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()

build()¶

Build model structure.

ArcI use Siamese arthitecture.

classmethod get_default_params()¶

Return type:	`ParamTable`
Returns:	model default parameters.

matchzoo.models.arcii module¶

An implementation of ArcII Model.

class matchzoo.models.arcii.ArcII(params=None, backend=None)¶

Bases: matchzoo.engine.base_model.BaseModel

ArcII Model.

Examples

>>> model = ArcII()
>>> model.params['embedding_output_dim'] = 300
>>> model.params['num_blocks'] = 2
>>> 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()

build()¶

Build model structure.

ArcII has the desirable property of letting two sentences meet before their own high-level representations mature.

classmethod get_default_params()¶

Return type:	`ParamTable`
Returns:	model default parameters.

matchzoo.models.cdssm module¶

An implementation of CDSSM (CLSM) model.

class matchzoo.models.cdssm.CDSSM(params=None, backend=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

>>> model = CDSSM()
>>> model.params['optimizer'] = 'adam'
>>> model.params['filters'] =  32
>>> model.params['kernel_size'] = 3
>>> model.params['conv_activation_func'] = 'relu'
>>> model.guess_and_fill_missing_params(verbose=0)
>>> model.build()

build()¶

Build model structure.

CDSSM use Siamese architecture.

classmethod get_default_params()¶

Return type:	`ParamTable`
Returns:	model default parameters.

classmethod get_default_preprocessor()¶

Returns:	Default preprocessor.

guess_and_fill_missing_params(verbose=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 (`int`) – Verbosity.

matchzoo.models.conv_knrm module¶

ConvKNRM model.

class matchzoo.models.conv_knrm.ConvKNRM(params=None, backend=None)¶

Bases: matchzoo.models.knrm.KNRM

ConvKNRM model.

Examples

>>> model = ConvKNRM()
>>> model.params['embedding_input_dim'] = 10000
>>> model.params['embedding_output_dim'] = 300
>>> model.params['embedding_trainable'] = True
>>> 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()

build()¶: Build model.

get_default_params()¶: Get default parameters.

matchzoo.models.dense_baseline module¶

A simple densely connected baseline model.

class matchzoo.models.dense_baseline.DenseBaseline(params=None, backend=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()
>>> model.compile()

build()¶: Model structure.

classmethod get_default_params()¶

Return type:	`ParamTable`
Returns:	model default parameters.

matchzoo.models.drmm module¶

An implementation of DRMM Model.

class matchzoo.models.drmm.DRMM(params=None, backend=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()
>>> model.compile()

classmethod attention_layer(attention_input, attention_mask=None)¶

Performs attention on the input.

Parameters:	attention_input (`Any`) – The input tensor for attention layer. attention_mask (`Optional`[`Any`]) – A tensor to mask the invalid values.
Return type:	`Layer`
Returns:	The masked output tensor.

build()¶: Build model structure.

classmethod get_default_params()¶

Return type:	`ParamTable`
Returns:	model default parameters.

matchzoo.models.drmmtks module¶

An implementation of DRMMTKS Model.

class matchzoo.models.drmmtks.DRMMTKS(params=None, backend=None)¶

Bases: matchzoo.engine.base_model.BaseModel

DRMMTKS Model.

Examples

>>> model = DRMMTKS()
>>> model.params['embedding_input_dim'] = 10000
>>> model.params['embedding_output_dim'] = 100
>>> model.params['top_k'] = 20
>>> 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()

classmethod attention_layer(attention_input, attention_mask=None)¶

Performs attention on the input.

Parameters:	attention_input (`Any`) – The input tensor for attention layer. attention_mask (`Optional`[`Any`]) – A tensor to mask the invalid values.
Return type:	`Layer`
Returns:	The masked output tensor.

build()¶: Build model structure.

classmethod get_default_params()¶

Return type:	`ParamTable`
Returns:	model default parameters.

matchzoo.models.dssm module¶

An implementation of DSSM, Deep Structured Semantic Model.

class matchzoo.models.dssm.DSSM(params=None, backend=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()

build()¶

Build model structure.

DSSM use Siamese arthitecture.

classmethod get_default_params()¶

Return type:	`ParamTable`
Returns:	model default parameters.

classmethod get_default_preprocessor()¶

Returns:	Default preprocessor.

matchzoo.models.duet module¶

DUET Model.

class matchzoo.models.duet.DUET(params=None, backend=None)¶

Bases: matchzoo.engine.base_model.BaseModel

DUET Model.

Examples

>>> model = DUET()
>>> model.params['embedding_input_dim'] = 1000
>>> model.params['embedding_output_dim'] = 300
>>> model.params['lm_filters'] = 32
>>> model.params['lm_hidden_sizes'] = [64, 32]
>>> model.params['dropout_rate'] = 0.5
>>> model.params['dm_filters'] = 32
>>> model.params['dm_kernel_size'] = 3
>>> model.params['dm_d_mpool'] = 4
>>> model.params['dm_hidden_sizes'] = [64, 32]
>>> model.guess_and_fill_missing_params(verbose=0)
>>> model.build()

build()¶: Build model.

classmethod get_default_params()¶: Get default parameters.

matchzoo.models.knrm module¶

KNRM model.

class matchzoo.models.knrm.KNRM(params=None, backend=None)¶

Bases: matchzoo.engine.base_model.BaseModel

KNRM model.

Examples

>>> model = KNRM()
>>> model.params['embedding_input_dim'] =  10000
>>> model.params['embedding_output_dim'] =  10
>>> model.params['embedding_trainable'] = 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()

build()¶: Build model.

classmethod get_default_params()¶: Get default parameters.

matchzoo.models.match_pyramid module¶

An implementation of MatchPyramid Model.

class matchzoo.models.match_pyramid.MatchPyramid(params=None, backend=None)¶

Bases: matchzoo.engine.base_model.BaseModel

MatchPyramid Model.

Examples

>>> model = MatchPyramid()
>>> model.params['embedding_output_dim'] = 300
>>> model.params['num_blocks'] = 2
>>> 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()

build()¶

Build model structure.

MatchPyramid text matching as image recognition.

classmethod get_default_params()¶

Return type:	`ParamTable`
Returns:	model default parameters.

matchzoo.models.mvlstm module¶

An implementation of MVLSTM Model.

class matchzoo.models.mvlstm.MVLSTM(params=None, backend=None)¶

Bases: matchzoo.engine.base_model.BaseModel

MVLSTM Model.

Examples

>>> model = MVLSTM()
>>> model.params['lstm_units'] = 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'] = 0.5
>>> model.guess_and_fill_missing_params(verbose=0)
>>> model.build()

build()¶: Build model structure.

classmethod get_default_params()¶

Return type:	`ParamTable`
Returns:	model default parameters.

matchzoo.models.naive module¶

Naive model with a simplest structure for testing purposes.

class matchzoo.models.naive.Naive(params=None, backend=None)¶

Bases: matchzoo.engine.base_model.BaseModel

Naive model with a simplest structure for testing purposes.

Bare minimum functioning model. The best choice to get things rolling. The worst choice to fit and evaluate performance.

build()¶: Build.

classmethod get_default_params()¶: Default parameters.

matchzoo.models.parameter_readme_generator module¶

matchzoo/models/README.md generater.

Module contents¶

matchzoo.models.list_available()¶

Return type:	`list`