matchzoo.models.cdssm¶
An implementation of CDSSM (CLSM) model.
Module Contents¶
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class
matchzoo.models.cdssm.CDSSM¶ Bases:
matchzoo.engine.base_model.BaseModelCDSSM 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)¶ Returns: model default parameters.
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classmethod
get_default_preprocessor(cls)¶ Returns: Default preprocessor.
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classmethod
get_default_padding_callback(cls)¶ Returns: Default padding callback.
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_create_base_network(self)¶ 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.Moduleof CDSSM network, tensor in tensor out.
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build(self)¶ Build model structure.
CDSSM use Siamese architecture.
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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