MatchZoo Model Reference¶
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 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 | task | Decides model output shape, loss, and metrics. | ||
2 | out_activation_func | Activation function used in output layer. | ||
3 | with_embedding | A flag used help auto module. Shouldn’t be changed. | True | |
4 | embedding | FloatTensor containing weights for the Embedding. | ||
5 | embedding_input_dim | Usually equals vocab size + 1. Should be set manually. | ||
6 | embedding_output_dim | Should be set manually. | ||
7 | padding_idx | If given, pads the output with the embedding vector atpadding_idx (initialized to zeros) whenever it encountersthe index. | 0 | |
8 | embedding_freeze | True to freeze embedding layer training, False to enable embedding parameters. | False | |
9 | with_multi_layer_perceptron | A flag of whether a multiple layer perceptron is used. Shouldn’t be changed. | True | |
10 | 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 |
11 | mlp_num_layers | Number of layers of the multiple layer percetron. | 3 | quantitative uniform distribution in [1, 5), with a step size of 1 |
12 | 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 |
13 | mlp_activation_func | Activation function used in the multiple layer perceptron. | relu |
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 | task | Decides model output shape, loss, and metrics. | ||
2 | out_activation_func | Activation function used in output layer. | ||
3 | with_multi_layer_perceptron | A flag of whether a multiple layer perceptron is used. Shouldn’t be changed. | True | |
4 | 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 |
5 | mlp_num_layers | Number of layers of the multiple layer percetron. | 3 | quantitative uniform distribution in [1, 6), with a step size of 1 |
6 | 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 |
7 | mlp_activation_func | Activation function used in the multiple layer perceptron. | relu | |
8 | vocab_size | Size of vocabulary. | 419 |
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:
>>> 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()
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 | task | Decides model output shape, loss, and metrics. | ||
2 | out_activation_func | Activation function used in output layer. | ||
3 | with_multi_layer_perceptron | A flag of whether a multiple layer perceptron is used. Shouldn’t be changed. | True | |
4 | 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 |
5 | mlp_num_layers | Number of layers of the multiple layer percetron. | 3 | quantitative uniform distribution in [1, 6), with a step size of 1 |
6 | 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 |
7 | mlp_activation_func | Activation function used in the multiple layer perceptron. | relu | |
8 | vocab_size | Size of vocabulary. | 419 | |
9 | filters | Number of filters in the 1D convolution layer. | 3 | |
10 | kernel_size | Number of kernel size in the 1D convolution layer. | 3 | |
11 | conv_activation_func | Activation function in the convolution layer. | relu | |
12 | dropout_rate | The dropout rate. | 0.3 |
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 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 | task | Decides model output shape, loss, and metrics. | ||
2 | out_activation_func | Activation function used in output layer. | ||
3 | with_embedding | A flag used help auto module. Shouldn’t be changed. | True | |
4 | embedding | FloatTensor containing weights for the Embedding. | ||
5 | embedding_input_dim | Usually equals vocab size + 1. Should be set manually. | ||
6 | embedding_output_dim | Should be set manually. | ||
7 | padding_idx | If given, pads the output with the embedding vector atpadding_idx (initialized to zeros) whenever it encountersthe index. | 0 | |
8 | embedding_freeze | True to freeze embedding layer training, False to enable embedding parameters. | False | |
9 | with_multi_layer_perceptron | A flag of whether a multiple layer perceptron is used. Shouldn’t be changed. | True | |
10 | 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 |
11 | mlp_num_layers | Number of layers of the multiple layer percetron. | 3 | quantitative uniform distribution in [1, 6), with a step size of 1 |
12 | mlp_num_fan_out | Number of units of the layer that connects the multiple layer percetron and the output. | 1 | quantitative uniform distribution in [4, 128), with a step size of 4 |
13 | mlp_activation_func | Activation function used in the multiple layer perceptron. | relu | |
14 | mask_value | The value to be masked from inputs. | 0 | |
15 | hist_bin_size | The number of bin size of the histogram. | 30 |
DRMMTKS¶
Model Documentation¶
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()
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 | task | Decides model output shape, loss, and metrics. | ||
2 | out_activation_func | Activation function used in output layer. | ||
3 | with_embedding | A flag used help auto module. Shouldn’t be changed. | True | |
4 | embedding | FloatTensor containing weights for the Embedding. | ||
5 | embedding_input_dim | Usually equals vocab size + 1. Should be set manually. | ||
6 | embedding_output_dim | Should be set manually. | ||
7 | padding_idx | If given, pads the output with the embedding vector atpadding_idx (initialized to zeros) whenever it encountersthe index. | 0 | |
8 | embedding_freeze | True to freeze embedding layer training, False to enable embedding parameters. | False | |
9 | with_multi_layer_perceptron | A flag of whether a multiple layer perceptron is used. Shouldn’t be changed. | True | |
10 | 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 |
11 | mlp_num_layers | Number of layers of the multiple layer percetron. | 3 | quantitative uniform distribution in [1, 6), with a step size of 1 |
12 | mlp_num_fan_out | Number of units of the layer that connects the multiple layer percetron and the output. | 1 | quantitative uniform distribution in [4, 128), with a step size of 4 |
13 | mlp_activation_func | Activation function used in the multiple layer perceptron. | relu | |
14 | mask_value | The value to be masked from inputs. | 0 | |
15 | top_k | Size of top-k pooling layer. | 10 | quantitative uniform distribution in [2, 100), with a step size of 1 |
ESIM¶
Model Documentation¶
ESIM Model.
- Examples:
>>> model = ESIM() >>> 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.esim.ESIM’> | |
1 | task | Decides model output shape, loss, and metrics. | ||
2 | out_activation_func | Activation function used in output layer. | ||
3 | with_embedding | A flag used help auto module. Shouldn’t be changed. | True | |
4 | embedding | FloatTensor containing weights for the Embedding. | ||
5 | embedding_input_dim | Usually equals vocab size + 1. Should be set manually. | ||
6 | embedding_output_dim | Should be set manually. | ||
7 | padding_idx | If given, pads the output with the embedding vector atpadding_idx (initialized to zeros) whenever it encountersthe index. | 0 | |
8 | embedding_freeze | True to freeze embedding layer training, False to enable embedding parameters. | False | |
9 | mask_value | The value to be masked from inputs. | 0 | |
10 | dropout | Dropout rate. | 0.2 | |
11 | hidden_size | Hidden size. | 200 | |
12 | lstm_layer | Number of LSTM layers | 1 | |
13 | drop_lstm | Whether dropout LSTM. | False | |
14 | concat_lstm | Whether concat intermediate outputs. | True | |
15 | rnn_type | Choose rnn type, lstm or gru. | lstm |
KNRM¶
Model Documentation¶
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()
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 | task | Decides model output shape, loss, and metrics. | ||
2 | out_activation_func | Activation function used in output layer. | ||
3 | with_embedding | A flag used help auto module. Shouldn’t be changed. | True | |
4 | embedding | FloatTensor containing weights for the Embedding. | ||
5 | embedding_input_dim | Usually equals vocab size + 1. Should be set manually. | ||
6 | embedding_output_dim | Should be set manually. | ||
7 | padding_idx | If given, pads the output with the embedding vector atpadding_idx (initialized to zeros) whenever it encountersthe index. | 0 | |
8 | embedding_freeze | True to freeze embedding layer training, False to enable embedding parameters. | False | |
9 | kernel_num | The number of RBF kernels. | 11 | quantitative uniform distribution in [5, 20), with a step size of 1 |
10 | sigma | The sigma defines the kernel width. | 0.1 | quantitative uniform distribution in [0.01, 0.2), with a step size of 0.01 |
11 | exact_sigma | The exact_sigma denotes the sigma for exact match. | 0.001 |
ConvKNRM¶
Model Documentation¶
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()
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 | task | Decides model output shape, loss, and metrics. | ||
2 | out_activation_func | Activation function used in output layer. | ||
3 | with_embedding | A flag used help auto module. Shouldn’t be changed. | True | |
4 | embedding | FloatTensor containing weights for the Embedding. | ||
5 | embedding_input_dim | Usually equals vocab size + 1. Should be set manually. | ||
6 | embedding_output_dim | Should be set manually. | ||
7 | padding_idx | If given, pads the output with the embedding vector atpadding_idx (initialized to zeros) whenever it encountersthe index. | 0 | |
8 | embedding_freeze | True to freeze embedding layer training, False to enable embedding parameters. | False | |
9 | filters | The filter size in the convolution layer. | 128 | |
10 | conv_activation_func | The activation function in the convolution layer. | relu | |
11 | max_ngram | The maximum length of n-grams for the convolution layer. | 3 | |
12 | use_crossmatch | Whether to match left n-grams and right n-grams of different lengths | True | |
13 | kernel_num | The number of RBF kernels. | 11 | quantitative uniform distribution in [5, 20), with a step size of 1 |
14 | sigma | The sigma defines the kernel width. | 0.1 | quantitative uniform distribution in [0.01, 0.2), with a step size of 0.01 |
15 | exact_sigma | The exact_sigma denotes the sigma for exact match. | 0.001 |
BiMPM¶
Model Documentation¶
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()
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.bimpm.BiMPM’> | |
1 | task | Decides model output shape, loss, and metrics. | ||
2 | out_activation_func | Activation function used in output layer. | ||
3 | with_embedding | A flag used help auto module. Shouldn’t be changed. | True | |
4 | embedding | FloatTensor containing weights for the Embedding. | ||
5 | embedding_input_dim | Usually equals vocab size + 1. Should be set manually. | ||
6 | embedding_output_dim | Should be set manually. | ||
7 | padding_idx | If given, pads the output with the embedding vector atpadding_idx (initialized to zeros) whenever it encountersthe index. | 0 | |
8 | embedding_freeze | True to freeze embedding layer training, False to enable embedding parameters. | False | |
9 | mask_value | The value to be masked from inputs. | 0 | |
10 | dropout | Dropout rate. | 0.2 | |
11 | hidden_size | Hidden size. | 100 | quantitative uniform distribution in [100, 300), with a step size of 100 |
12 | num_perspective | num_perspective | 20 | quantitative uniform distribution in [20, 100), with a step size of 20 |
MatchLSTM¶
Model Documentation¶
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()
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.matchlstm.MatchLSTM’> | |
1 | task | Decides model output shape, loss, and metrics. | ||
2 | out_activation_func | Activation function used in output layer. | ||
3 | with_embedding | A flag used help auto module. Shouldn’t be changed. | True | |
4 | embedding | FloatTensor containing weights for the Embedding. | ||
5 | embedding_input_dim | Usually equals vocab size + 1. Should be set manually. | ||
6 | embedding_output_dim | Should be set manually. | ||
7 | padding_idx | If given, pads the output with the embedding vector atpadding_idx (initialized to zeros) whenever it encountersthe index. | 0 | |
8 | embedding_freeze | True to freeze embedding layer training, False to enable embedding parameters. | False | |
9 | mask_value | The value to be masked from inputs. | 0 | |
10 | dropout | Dropout rate. | 0.2 | |
11 | hidden_size | Hidden size. | 200 | |
12 | lstm_layer | Number of LSTM layers | 1 | |
13 | drop_lstm | Whether dropout LSTM. | False | |
14 | concat_lstm | Whether concat intermediate outputs. | True | |
15 | rnn_type | Choose rnn type, lstm or gru. | lstm |
ArcI¶
Model Documentation¶
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()
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 | task | Decides model output shape, loss, and metrics. | ||
2 | out_activation_func | Activation function used in output layer. | ||
3 | with_embedding | A flag used help auto module. Shouldn’t be changed. | True | |
4 | embedding | FloatTensor containing weights for the Embedding. | ||
5 | embedding_input_dim | Usually equals vocab size + 1. Should be set manually. | ||
6 | embedding_output_dim | Should be set manually. | ||
7 | padding_idx | If given, pads the output with the embedding vector atpadding_idx (initialized to zeros) whenever it encountersthe index. | 0 | |
8 | embedding_freeze | True to freeze embedding layer training, False to enable embedding parameters. | False | |
9 | with_multi_layer_perceptron | A flag of whether a multiple layer perceptron is used. Shouldn’t be changed. | True | |
10 | 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 |
11 | mlp_num_layers | Number of layers of the multiple layer percetron. | 3 | quantitative uniform distribution in [1, 6), with a step size of 1 |
12 | 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 |
13 | mlp_activation_func | Activation function used in the multiple layer perceptron. | relu | |
14 | left_length | Length of left input. | 10 | |
15 | right_length | Length of right input. | 100 | |
16 | conv_activation_func | The activation function in the convolution layer. | relu | |
17 | left_filters | The filter size of each convolution blocks for the left input. | [32] | |
18 | left_kernel_sizes | The kernel size of each convolution blocks for the left input. | [3] | |
19 | left_pool_sizes | The pooling size of each convolution blocks for the left input. | [2] | |
20 | right_filters | The filter size of each convolution blocks for the right input. | [32] | |
21 | right_kernel_sizes | The kernel size of each convolution blocks for the right input. | [3] | |
22 | right_pool_sizes | The pooling size of each convolution blocks for the right input. | [2] | |
23 | 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['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 | task | Decides model output shape, loss, and metrics. | ||
2 | out_activation_func | Activation function used in output layer. | ||
3 | with_embedding | A flag used help auto module. Shouldn’t be changed. | True | |
4 | embedding | FloatTensor containing weights for the Embedding. | ||
5 | embedding_input_dim | Usually equals vocab size + 1. Should be set manually. | ||
6 | embedding_output_dim | Should be set manually. | ||
7 | padding_idx | If given, pads the output with the embedding vector atpadding_idx (initialized to zeros) whenever it encountersthe index. | 0 | |
8 | embedding_freeze | True to freeze embedding layer training, False to enable embedding parameters. | False | |
9 | left_length | Length of left input. | 10 | |
10 | right_length | Length of right input. | 100 | |
11 | kernel_1d_count | Kernel count of 1D convolution layer. | 32 | |
12 | kernel_1d_size | Kernel size of 1D convolution layer. | 3 | |
13 | kernel_2d_count | Kernel count of 2D convolution layer ineach block | [32] | |
14 | kernel_2d_size | Kernel size of 2D convolution layer in each block. | [(3, 3)] | |
15 | activation | Activation function. | relu | |
16 | pool_2d_size | Size of pooling layer in each block. | [(2, 2)] | |
17 | dropout_rate | The dropout rate. | 0.0 | quantitative uniform distribution in [0.0, 0.8), with a step size of 0.01 |
Bert¶
Model Documentation¶
Bert Model.
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.bert.Bert’> | |
1 | task | Decides model output shape, loss, and metrics. | ||
2 | out_activation_func | Activation function used in output layer. | ||
3 | mode | Pretrained Bert model. | bert-base-uncased | |
4 | dropout_rate | The dropout rate. | 0.0 | quantitative uniform distribution in [0.0, 0.8), with a step size of 0.01 |
MVLSTM¶
Model Documentation¶
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()
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 | task | Decides model output shape, loss, and metrics. | ||
2 | out_activation_func | Activation function used in output layer. | ||
3 | with_embedding | A flag used help auto module. Shouldn’t be changed. | True | |
4 | embedding | FloatTensor containing weights for the Embedding. | ||
5 | embedding_input_dim | Usually equals vocab size + 1. Should be set manually. | ||
6 | embedding_output_dim | Should be set manually. | ||
7 | padding_idx | If given, pads the output with the embedding vector atpadding_idx (initialized to zeros) whenever it encountersthe index. | 0 | |
8 | embedding_freeze | True to freeze embedding layer training, False to enable embedding parameters. | False | |
9 | with_multi_layer_perceptron | A flag of whether a multiple layer perceptron is used. Shouldn’t be changed. | True | |
10 | 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 |
11 | mlp_num_layers | Number of layers of the multiple layer percetron. | 3 | quantitative uniform distribution in [1, 6), with a step size of 1 |
12 | 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 |
13 | mlp_activation_func | Activation function used in the multiple layer perceptron. | relu | |
14 | hidden_size | Integer, the hidden size in the bi-directional LSTM layer. | 32 | |
15 | num_layers | Integer, number of recurrent layers. | 1 | |
16 | top_k | Size of top-k pooling layer. | 10 | quantitative uniform distribution in [2, 100), with a step size of 1 |
17 | dropout_rate | Float, 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['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 | task | Decides model output shape, loss, and metrics. | ||
2 | out_activation_func | Activation function used in output layer. | ||
3 | with_embedding | A flag used help auto module. Shouldn’t be changed. | True | |
4 | embedding | FloatTensor containing weights for the Embedding. | ||
5 | embedding_input_dim | Usually equals vocab size + 1. Should be set manually. | ||
6 | embedding_output_dim | Should be set manually. | ||
7 | padding_idx | If given, pads the output with the embedding vector atpadding_idx (initialized to zeros) whenever it encountersthe index. | 0 | |
8 | embedding_freeze | True to freeze embedding layer training, False to enable embedding parameters. | False | |
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 | dropout_rate | The dropout rate. | 0.0 | quantitative uniform distribution in [0.0, 0.8), with a step size of 0.01 |
aNMM¶
Model Documentation¶
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()
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 | task | Decides model output shape, loss, and metrics. | ||
2 | out_activation_func | Activation function used in output layer. | ||
3 | with_embedding | A flag used help auto module. Shouldn’t be changed. | True | |
4 | embedding | FloatTensor containing weights for the Embedding. | ||
5 | embedding_input_dim | Usually equals vocab size + 1. Should be set manually. | ||
6 | embedding_output_dim | Should be set manually. | ||
7 | padding_idx | If given, pads the output with the embedding vector atpadding_idx (initialized to zeros) whenever it encountersthe index. | 0 | |
8 | embedding_freeze | True to freeze embedding layer training, False to enable embedding parameters. | False | |
9 | mask_value | The value to be masked from inputs. | 0 | |
10 | num_bins | Integer, number of bins. | 200 | |
11 | hidden_sizes | Number of hidden size for each hidden layer | [100] | |
12 | activation | The activation function. | relu | |
13 | dropout_rate | The dropout rate. | 0.0 | quantitative uniform distribution in [0.0, 0.8), with a step size of 0.01 |
HBMP¶
Model Documentation¶
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()
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.hbmp.HBMP’> | |
1 | task | Decides model output shape, loss, and metrics. | ||
2 | out_activation_func | Activation function used in output layer. | ||
3 | with_embedding | A flag used help auto module. Shouldn’t be changed. | True | |
4 | embedding | FloatTensor containing weights for the Embedding. | ||
5 | embedding_input_dim | Usually equals vocab size + 1. Should be set manually. | ||
6 | embedding_output_dim | Should be set manually. | ||
7 | padding_idx | If given, pads the output with the embedding vector atpadding_idx (initialized to zeros) whenever it encountersthe index. | 0 | |
8 | embedding_freeze | True to freeze embedding layer training, False to enable embedding parameters. | False | |
9 | with_multi_layer_perceptron | A flag of whether a multiple layer perceptron is used. Shouldn’t be changed. | True | |
10 | 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 |
11 | mlp_num_layers | Number of layers of the multiple layer percetron. | 3 | quantitative uniform distribution in [1, 6), with a step size of 1 |
12 | 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 |
13 | mlp_activation_func | Activation function used in the multiple layer perceptron. | relu | |
14 | lstm_hidden_size | Integer, the hidden size of the bi-directional LSTM layer. | 5 | |
15 | lstm_num | Integer, number of LSTM units | 3 | |
16 | num_layers | Integer, number of LSTM layers. | 1 | |
17 | dropout_rate | The dropout rate. | 0.0 | quantitative uniform distribution in [0.0, 0.8), with a step size of 0.01 |
DUET¶
Model Documentation¶
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()
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 | task | Decides model output shape, loss, and metrics. | ||
2 | out_activation_func | Activation function used in output layer. | ||
3 | with_multi_layer_perceptron | A flag of whether a multiple layer perceptron is used. Shouldn’t be changed. | True | |
4 | 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 |
5 | mlp_num_layers | Number of layers of the multiple layer percetron. | 3 | quantitative uniform distribution in [1, 6), with a step size of 1 |
6 | 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 |
7 | mlp_activation_func | Activation function used in the multiple layer perceptron. | relu | |
8 | mask_value | The value to be masked from inputs. | 0 | |
9 | left_length | Length of left input. | 10 | |
10 | right_length | Length of right input. | 40 | |
11 | lm_filters | Filter size of 1D convolution layer in the local model. | 300 | |
12 | vocab_size | Vocabulary size of the tri-letters used in the distributed model. | 419 | |
13 | dm_filters | Filter size of 1D convolution layer in the distributed model. | 300 | |
14 | dm_kernel_size | Kernel size of 1D convolution layer in the distributed model. | 3 | |
15 | dm_conv_activation_func | Activation functions of the convolution layer in the distributed model. | relu | |
16 | dm_right_pool_size | Kernel size of 1D convolution layer in the distributed model. | 8 | |
17 | dropout_rate | The dropout rate. | 0.5 | quantitative uniform distribution in [0.0, 0.8), with a step size of 0.02 |
DIIN¶
Model Documentation¶
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()
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.diin.DIIN’> | |
1 | task | Decides model output shape, loss, and metrics. | ||
2 | out_activation_func | Activation function used in output layer. | ||
3 | with_embedding | A flag used help auto module. Shouldn’t be changed. | True | |
4 | embedding | FloatTensor containing weights for the Embedding. | ||
5 | embedding_input_dim | Usually equals vocab size + 1. Should be set manually. | ||
6 | embedding_output_dim | Should be set manually. | ||
7 | padding_idx | If given, pads the output with the embedding vector atpadding_idx (initialized to zeros) whenever it encountersthe index. | 0 | |
8 | embedding_freeze | True to freeze embedding layer training, False to enable embedding parameters. | False | |
9 | mask_value | The value to be masked from inputs. | 0 | |
10 | char_embedding_input_dim | The input dimension of character embedding layer. | 100 | |
11 | char_embedding_output_dim | The output dimension of character embedding layer. | 8 | |
12 | char_conv_filters | The filter size of character convolution layer. | 100 | |
13 | char_conv_kernel_size | The kernel size of character convolution layer. | 5 | |
14 | first_scale_down_ratio | The channel scale down ratio of the convolution layer before densenet. | 0.3 | |
15 | nb_dense_blocks | The number of blocks in densenet. | 3 | |
16 | layers_per_dense_block | The number of convolution layers in dense block. | 8 | |
17 | growth_rate | The filter size of each convolution layer in dense block. | 20 | |
18 | transition_scale_down_ratio | The channel scale down ratio of the convolution layer in transition block. | 0.5 | |
19 | conv_kernel_size | The kernel size of convolution layer in dense block. | (3, 3) | |
20 | pool_kernel_size | The kernel size of pooling layer in transition block. | (2, 2) | |
21 | dropout_rate | The dropout rate. | 0.0 | quantitative uniform distribution in [0.0, 0.8), with a step size of 0.01 |
MatchSRNN¶
Model Documentation¶
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()
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_srnn.MatchSRNN’> | |
1 | task | Decides model output shape, loss, and metrics. | ||
2 | out_activation_func | Activation function used in output layer. | ||
3 | with_embedding | A flag used help auto module. Shouldn’t be changed. | True | |
4 | embedding | FloatTensor containing weights for the Embedding. | ||
5 | embedding_input_dim | Usually equals vocab size + 1. Should be set manually. | ||
6 | embedding_output_dim | Should be set manually. | ||
7 | padding_idx | If given, pads the output with the embedding vector atpadding_idx (initialized to zeros) whenever it encountersthe index. | 0 | |
8 | embedding_freeze | True to freeze embedding layer training, False to enable embedding parameters. | False | |
9 | channels | Number of word interaction tensor channels | 4 | |
10 | units | Number of SpatialGRU units | 10 | |
11 | direction | Direction of SpatialGRU scanning | lt | |
12 | dropout | The dropout rate. | 0.2 | quantitative uniform distribution in [0.0, 0.8), with a step size of 0.01 |