MatchZoo Model Reference

Naive

Model Documentation

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.

Model Hyper Parameters

	Name	Description	Default Value	Default Hyper-Space
0	model_class	Model class. Used internally for save/load. Changing this may cause unexpected behaviors.	<class ‘matchzoo.models.naive.Naive’>
1	input_shapes	Dependent on the model and data. Should be set manually.
2	task	Decides model output shape, loss, and metrics.
3	optimizer		adam	choice in [‘adam’, ‘adagrad’, ‘rmsprop’]

DSSM

Model Documentation

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()

Model Hyper Parameters

	Name	Description	Default Value	Default Hyper-Space
0	model_class	Model class. Used internally for save/load. Changing this may cause unexpected behaviors.	<class ‘matchzoo.models.dssm.DSSM’>
1	input_shapes	Dependent on the model and data. Should be set manually.
2	task	Decides model output shape, loss, and metrics.
3	optimizer		adam
4	with_multi_layer_perceptron	A flag of whether a multiple layer perceptron is used. Shouldn’t be changed.	True
5	mlp_num_units	Number of units in first mlp_num_layers layers.	128	quantitative uniform distribution in [8, 256), with a step size of 8
6	mlp_num_layers	Number of layers of the multiple layer percetron.	3	quantitative uniform distribution in [1, 6), with a step size of 1
7	mlp_num_fan_out	Number of units of the layer that connects the multiple layer percetron and the output.	64	quantitative uniform distribution in [4, 128), with a step size of 4
8	mlp_activation_func	Activation function used in the multiple layer perceptron.	relu

CDSSM

Model Documentation

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()

Model Hyper Parameters

	Name	Description	Default Value	Default Hyper-Space
0	model_class	Model class. Used internally for save/load. Changing this may cause unexpected behaviors.	<class ‘matchzoo.models.cdssm.CDSSM’>
1	input_shapes	Dependent on the model and data. Should be set manually.
2	task	Decides model output shape, loss, and metrics.
3	optimizer		adam
4	with_multi_layer_perceptron	A flag of whether a multiple layer perceptron is used. Shouldn’t be changed.	True
5	mlp_num_units	Number of units in first mlp_num_layers layers.	128	quantitative uniform distribution in [8, 256), with a step size of 8
6	mlp_num_layers	Number of layers of the multiple layer percetron.	3	quantitative uniform distribution in [1, 6), with a step size of 1
7	mlp_num_fan_out	Number of units of the layer that connects the multiple layer percetron and the output.	64	quantitative uniform distribution in [4, 128), with a step size of 4
8	mlp_activation_func	Activation function used in the multiple layer perceptron.	relu
9	filters	Number of filters in the 1D convolution layer.	32
10	kernel_size	Number of kernel size in the 1D convolution layer.	3
11	strides	Strides in the 1D convolution layer.	1
12	padding	The padding mode in the convolution layer. It should be one of same, valid, and causal.	same
13	conv_activation_func	Activation function in the convolution layer.	relu
14	w_initializer		glorot_normal
15	b_initializer		zeros
16	dropout_rate	The dropout rate.	0.3

DenseBaseline

Model Documentation

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()

Model Hyper Parameters

	Name	Description	Default Value	Default Hyper-Space
0	model_class	Model class. Used internally for save/load. Changing this may cause unexpected behaviors.	<class ‘matchzoo.models.dense_baseline.DenseBaseline’>
1	input_shapes	Dependent on the model and data. Should be set manually.
2	task	Decides model output shape, loss, and metrics.
3	optimizer		adam
4	with_multi_layer_perceptron	A flag of whether a multiple layer perceptron is used. Shouldn’t be changed.	True
5	mlp_num_units	Number of units in first mlp_num_layers layers.	256	quantitative uniform distribution in [16, 512), with a step size of 1
6	mlp_num_layers	Number of layers of the multiple layer percetron.	3	quantitative uniform distribution in [1, 5), with a step size of 1
7	mlp_num_fan_out	Number of units of the layer that connects the multiple layer percetron and the output.	64	quantitative uniform distribution in [4, 128), with a step size of 4
8	mlp_activation_func	Activation function used in the multiple layer perceptron.	relu

ArcI

Model Documentation

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()

Model Hyper Parameters

	Name	Description	Default Value	Default Hyper-Space
0	model_class	Model class. Used internally for save/load. Changing this may cause unexpected behaviors.	<class ‘matchzoo.models.arci.ArcI’>
1	input_shapes	Dependent on the model and data. Should be set manually.
2	task	Decides model output shape, loss, and metrics.
3	optimizer		adam
4	with_embedding	A flag used help auto module. Shouldn’t be changed.	True
5	embedding_input_dim	Usually equals vocab size + 1. Should be set manually.
6	embedding_output_dim	Should be set manually.
7	embedding_trainable	True to enable embedding layer training, False to freeze embedding parameters.	True
8	with_multi_layer_perceptron	A flag of whether a multiple layer perceptron is used. Shouldn’t be changed.	True
9	mlp_num_units	Number of units in first mlp_num_layers layers.	128	quantitative uniform distribution in [8, 256), with a step size of 8
10	mlp_num_layers	Number of layers of the multiple layer percetron.	3	quantitative uniform distribution in [1, 6), with a step size of 1
11	mlp_num_fan_out	Number of units of the layer that connects the multiple layer percetron and the output.	64	quantitative uniform distribution in [4, 128), with a step size of 4
12	mlp_activation_func	Activation function used in the multiple layer perceptron.	relu
13	num_blocks	Number of convolution blocks.	1
14	left_filters	The filter size of each convolution blocks for the left input.	[32]
15	left_kernel_sizes	The kernel size of each convolution blocks for the left input.	[3]
16	right_filters	The filter size of each convolution blocks for the right input.	[32]
17	right_kernel_sizes	The kernel size of each convolution blocks for the right input.	[3]
18	conv_activation_func	The activation function in the convolution layer.	relu
19	left_pool_sizes	The pooling size of each convolution blocks for the left input.	[2]
20	right_pool_sizes	The pooling size of each convolution blocks for the right input.	[2]
21	padding	The padding mode in the convolution layer. It should be oneof same, valid, and causal.	same	choice in [‘same’, ‘valid’, ‘causal’]
22	dropout_rate	The dropout rate.	0.0	quantitative uniform distribution in [0.0, 0.8), with a step size of 0.01

ArcII

Model Documentation

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()

Model Hyper Parameters

	Name	Description	Default Value	Default Hyper-Space
0	model_class	Model class. Used internally for save/load. Changing this may cause unexpected behaviors.	<class ‘matchzoo.models.arcii.ArcII’>
1	input_shapes	Dependent on the model and data. Should be set manually.
2	task	Decides model output shape, loss, and metrics.
3	optimizer		adam	choice in [‘adam’, ‘rmsprop’, ‘adagrad’]
4	with_embedding	A flag used help auto module. Shouldn’t be changed.	True
5	embedding_input_dim	Usually equals vocab size + 1. Should be set manually.
6	embedding_output_dim	Should be set manually.
7	embedding_trainable	True to enable embedding layer training, False to freeze embedding parameters.	True
8	num_blocks	Number of 2D convolution blocks.	1
9	kernel_1d_count	Kernel count of 1D convolution layer.	32
10	kernel_1d_size	Kernel size of 1D convolution layer.	3
11	kernel_2d_count	Kernel count of 2D convolution layer ineach block	[32]
12	kernel_2d_size	Kernel size of 2D convolution layer in each block.	[[3, 3]]
13	activation	Activation function.	relu
14	pool_2d_size	Size of pooling layer in each block.	[[2, 2]]
15	padding	The padding mode in the convolution layer. It should be oneof same, valid.	same	choice in [‘same’, ‘valid’]
16	dropout_rate	The dropout rate.	0.0	quantitative uniform distribution in [0.0, 0.8), with a step size of 0.01

MatchPyramid

Model Documentation

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()

Model Hyper Parameters

	Name	Description	Default Value	Default Hyper-Space
0	model_class	Model class. Used internally for save/load. Changing this may cause unexpected behaviors.	<class ‘matchzoo.models.match_pyramid.MatchPyramid’>
1	input_shapes	Dependent on the model and data. Should be set manually.
2	task	Decides model output shape, loss, and metrics.
3	optimizer		adam
4	with_embedding	A flag used help auto module. Shouldn’t be changed.	True
5	embedding_input_dim	Usually equals vocab size + 1. Should be set manually.
6	embedding_output_dim	Should be set manually.
7	embedding_trainable	True to enable embedding layer training, False to freeze embedding parameters.	True
8	num_blocks	Number of convolution blocks.	1
9	kernel_count	The kernel count of the 2D convolution of each block.	[32]
10	kernel_size	The kernel size of the 2D convolution of each block.	[[3, 3]]
11	activation	The activation function.	relu
12	dpool_size	The max-pooling size of each block.	[3, 10]
13	padding	The padding mode in the convolution layer.	same
14	dropout_rate	The dropout rate.	0.0	quantitative uniform distribution in [0.0, 0.8), with a step size of 0.01

KNRM

Model Documentation

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()

Model Hyper Parameters

	Name	Description	Default Value	Default Hyper-Space
0	model_class	Model class. Used internally for save/load. Changing this may cause unexpected behaviors.	<class ‘matchzoo.models.knrm.KNRM’>
1	input_shapes	Dependent on the model and data. Should be set manually.
2	task	Decides model output shape, loss, and metrics.
3	optimizer		adam
4	with_embedding	A flag used help auto module. Shouldn’t be changed.	True
5	embedding_input_dim	Usually equals vocab size + 1. Should be set manually.
6	embedding_output_dim	Should be set manually.
7	embedding_trainable	True to enable embedding layer training, False to freeze embedding parameters.	True
8	kernel_num	The number of RBF kernels.	11	quantitative uniform distribution in [5, 20), with a step size of 1
9	sigma	The sigma defines the kernel width.	0.1	quantitative uniform distribution in [0.01, 0.2), with a step size of 0.01
10	exact_sigma	The exact_sigma denotes the sigma for exact match.	0.001

DUET

Model Documentation

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()

Model Hyper Parameters

	Name	Description	Default Value	Default Hyper-Space
0	model_class	Model class. Used internally for save/load. Changing this may cause unexpected behaviors.	<class ‘matchzoo.models.duet.DUET’>
1	input_shapes	Dependent on the model and data. Should be set manually.
2	task	Decides model output shape, loss, and metrics.
3	optimizer		adam
4	with_embedding	A flag used help auto module. Shouldn’t be changed.	True
5	embedding_input_dim	Usually equals vocab size + 1. Should be set manually.
6	embedding_output_dim	Should be set manually.
7	embedding_trainable	True to enable embedding layer training, False to freeze embedding parameters.	True
8	lm_filters	Filter size of 1D convolution layer in the local model.	32
9	lm_hidden_sizes	A list of hidden size of the MLP layer in the local model.	[32]
10	dm_filters	Filter size of 1D convolution layer in the distributed model.	32
11	dm_kernel_size	Kernel size of 1D convolution layer in the distributed model.	3
12	dm_q_hidden_size	Hidden size of the MLP layer for the left text in the distributed model.	32
13	dm_d_mpool	Max pooling size for the right text in the distributed model.	3
14	dm_hidden_sizes	A list of hidden size of the MLP layer in the distributed model.	[32]
15	padding	The padding mode in the convolution layer. It should be one of same, valid, and causal.	same
16	activation_func	Activation function in the convolution layer.	relu
17	dropout_rate	The dropout rate.	0.5	quantitative uniform distribution in [0.0, 0.8), with a step size of 0.02

DRMMTKS

Model Documentation

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()

Model Hyper Parameters

	Name	Description	Default Value	Default Hyper-Space
0	model_class	Model class. Used internally for save/load. Changing this may cause unexpected behaviors.	<class ‘matchzoo.models.drmmtks.DRMMTKS’>
1	input_shapes	Dependent on the model and data. Should be set manually.	[(5,), (300,)]
2	task	Decides model output shape, loss, and metrics.
3	optimizer		adam
4	with_embedding	A flag used help auto module. Shouldn’t be changed.	True
5	embedding_input_dim	Usually equals vocab size + 1. Should be set manually.
6	embedding_output_dim	Should be set manually.
7	embedding_trainable	True to enable embedding layer training, False to freeze embedding parameters.	True
8	with_multi_layer_perceptron	A flag of whether a multiple layer perceptron is used. Shouldn’t be changed.	True
9	mlp_num_units	Number of units in first mlp_num_layers layers.	128	quantitative uniform distribution in [8, 256), with a step size of 8
10	mlp_num_layers	Number of layers of the multiple layer percetron.	3	quantitative uniform distribution in [1, 6), with a step size of 1
11	mlp_num_fan_out	Number of units of the layer that connects the multiple layer percetron and the output.	64	quantitative uniform distribution in [4, 128), with a step size of 4
12	mlp_activation_func	Activation function used in the multiple layer perceptron.	relu
13	mask_value	The value to be masked from inputs.	-1
14	top_k	Size of top-k pooling layer.	10	quantitative uniform distribution in [2, 100), with a step size of 1

DRMM

Model Documentation

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()

Model Hyper Parameters

	Name	Description	Default Value	Default Hyper-Space
0	model_class	Model class. Used internally for save/load. Changing this may cause unexpected behaviors.	<class ‘matchzoo.models.drmm.DRMM’>
1	input_shapes	Dependent on the model and data. Should be set manually.	[(5,), (5, 30)]
2	task	Decides model output shape, loss, and metrics.
3	optimizer		adam
4	with_embedding	A flag used help auto module. Shouldn’t be changed.	True
5	embedding_input_dim	Usually equals vocab size + 1. Should be set manually.
6	embedding_output_dim	Should be set manually.
7	embedding_trainable	True to enable embedding layer training, False to freeze embedding parameters.	True
8	with_multi_layer_perceptron	A flag of whether a multiple layer perceptron is used. Shouldn’t be changed.	True
9	mlp_num_units	Number of units in first mlp_num_layers layers.	128	quantitative uniform distribution in [8, 256), with a step size of 8
10	mlp_num_layers	Number of layers of the multiple layer percetron.	3	quantitative uniform distribution in [1, 6), with a step size of 1
11	mlp_num_fan_out	Number of units of the layer that connects the multiple layer percetron and the output.	64	quantitative uniform distribution in [4, 128), with a step size of 4
12	mlp_activation_func	Activation function used in the multiple layer perceptron.	relu
13	mask_value	The value to be masked from inputs.	-1

ANMM

Model Documentation

ANMM Model.

Examples:

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

Model Hyper Parameters

	Name	Description	Default Value	Default Hyper-Space
0	model_class	Model class. Used internally for save/load. Changing this may cause unexpected behaviors.	<class ‘matchzoo.models.anmm.ANMM’>
1	input_shapes	Dependent on the model and data. Should be set manually.
2	task	Decides model output shape, loss, and metrics.
3	optimizer		adam
4	with_embedding	A flag used help auto module. Shouldn’t be changed.	True
5	embedding_input_dim	Usually equals vocab size + 1. Should be set manually.
6	embedding_output_dim	Should be set manually.
7	embedding_trainable	True to enable embedding layer training, False to freeze embedding parameters.	True
8	dropout_rate	The dropout rate.	0.1	quantitative uniform distribution in [0, 1), with a step size of 0.05
9	num_layers	Number of hidden layers in the MLP layer.	2
10	hidden_sizes	Number of hidden size for each hidden layer	[30, 30]

MVLSTM

Model Documentation

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()

Model Hyper Parameters

	Name	Description	Default Value	Default Hyper-Space
0	model_class	Model class. Used internally for save/load. Changing this may cause unexpected behaviors.	<class ‘matchzoo.models.mvlstm.MVLSTM’>
1	input_shapes	Dependent on the model and data. Should be set manually.
2	task	Decides model output shape, loss, and metrics.
3	optimizer		adam
4	with_embedding	A flag used help auto module. Shouldn’t be changed.	True
5	embedding_input_dim	Usually equals vocab size + 1. Should be set manually.
6	embedding_output_dim	Should be set manually.
7	embedding_trainable	True to enable embedding layer training, False to freeze embedding parameters.	True
8	with_multi_layer_perceptron	A flag of whether a multiple layer perceptron is used. Shouldn’t be changed.	True
9	mlp_num_units	Number of units in first mlp_num_layers layers.	128	quantitative uniform distribution in [8, 256), with a step size of 8
10	mlp_num_layers	Number of layers of the multiple layer percetron.	3	quantitative uniform distribution in [1, 6), with a step size of 1
11	mlp_num_fan_out	Number of units of the layer that connects the multiple layer percetron and the output.	64	quantitative uniform distribution in [4, 128), with a step size of 4
12	mlp_activation_func	Activation function used in the multiple layer perceptron.	relu
13	lstm_units	Integer, the hidden size in the bi-directional LSTM layer.	32
14	dropout_rate	Float, the dropout rate.	0.0
15	top_k	Integer, the size of top-k pooling layer.	10	quantitative uniform distribution in [2, 100), with a step size of 1

MatchLSTM

Model Documentation

Match LSTM model.

Examples:

>>> model = MatchLSTM()
>>> model.guess_and_fill_missing_params(verbose=0)
>>> model.params['embedding_input_dim'] = 10000
>>> model.params['embedding_output_dim'] = 100
>>> model.params['embedding_trainable'] = True
>>> model.params['fc_num_units'] = 200
>>> model.params['lstm_num_units'] = 256
>>> model.params['dropout_rate'] = 0.5
>>> model.build()

Model Hyper Parameters

	Name	Description	Default Value	Default Hyper-Space
0	model_class	Model class. Used internally for save/load. Changing this may cause unexpected behaviors.	<class ‘matchzoo.contrib.models.match_lstm.MatchLSTM’>
1	input_shapes	Dependent on the model and data. Should be set manually.
2	task	Decides model output shape, loss, and metrics.
3	optimizer		adam
4	with_embedding	A flag used help auto module. Shouldn’t be changed.	True
5	embedding_input_dim	Usually equals vocab size + 1. Should be set manually.
6	embedding_output_dim	Should be set manually.
7	embedding_trainable	True to enable embedding layer training, False to freeze embedding parameters.	True
8	lstm_num_units	The hidden size in the LSTM layer.	256	quantitative uniform distribution in [128, 384), with a step size of 32
9	fc_num_units	The hidden size in the full connection layer.	200	quantitative uniform distribution in [100, 300), with a step size of 20
10	dropout_rate	The dropout rate.	0.0	quantitative uniform distribution in [0.0, 0.9), with a step size of 0.01

ConvKNRM

Model Documentation

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()

Model Hyper Parameters

	Name	Description	Default Value	Default Hyper-Space
0	model_class	Model class. Used internally for save/load. Changing this may cause unexpected behaviors.	<class ‘matchzoo.models.conv_knrm.ConvKNRM’>
1	input_shapes	Dependent on the model and data. Should be set manually.
2	task	Decides model output shape, loss, and metrics.
3	optimizer		adam
4	with_embedding	A flag used help auto module. Shouldn’t be changed.	True
5	embedding_input_dim	Usually equals vocab size + 1. Should be set manually.
6	embedding_output_dim	Should be set manually.
7	embedding_trainable	True to enable embedding layer training, False to freeze embedding parameters.	True
8	kernel_num	The number of RBF kernels.	11	quantitative uniform distribution in [5, 20), with a step size of 1
9	sigma	The sigma defines the kernel width.	0.1	quantitative uniform distribution in [0.01, 0.2), with a step size of 0.01
10	exact_sigma	The exact_sigma denotes the sigma for exact match.	0.001
11	filters	The filter size in the convolution layer.	128
12	conv_activation_func	The activation function in the convolution layer.	relu
13	max_ngram	The maximum length of n-grams for the convolution layer.	3
14	use_crossmatch	Whether to match left n-grams and right n-grams of different lengths	True