def get_default_params(cls) -> ParamTable: """:return: model default parameters.""" params = super().get_default_params(with_embedding=True) params.add( Param(name='kernel_count', value=[32], desc="The kernel count of the 2D convolution " "of each block.")) params.add( Param(name='kernel_size', value=[[3, 3]], desc="The kernel size of the 2D convolution " "of each block.")) params.add( Param(name='activation', value='relu', desc="The activation function.")) params.add( Param(name='dpool_size', value=[3, 10], desc="The max-pooling size of each block.")) params.add( Param('dropout_rate', 0.0, hyper_space=hyper_spaces.quniform(low=0.0, high=0.8, q=0.01), desc="The dropout rate.")) return params
def get_default_params(cls) -> ParamTable: """:return: model default parameters.""" # set :attr:`with_multi_layer_perceptron` to False to support # user-defined variable dense layer units params = super().get_default_params(with_multi_layer_perceptron=True) params.add( Param(name='vocab_size', value=419, desc="Size of vocabulary.")) params.add( Param(name='filters', value=3, desc="Number of filters in the 1D convolution " "layer.")) params.add( Param(name='kernel_size', value=3, desc="Number of kernel size in the 1D " "convolution layer.")) params.add( Param(name='conv_activation_func', value='relu', desc="Activation function in the convolution" " layer.")) params.add( Param(name='dropout_rate', value=0.3, desc="The dropout rate.")) return params
def get_default_params(cls) -> ParamTable: """:return: model default parameters.""" params = super().get_default_params(with_embedding=True) params.add( Param(name='mode', value='bert-base-uncased', desc="Pretrained Bert model.")) params.add( Param('dropout_rate', 0.0, hyper_space=hyper_spaces.quniform(low=0.0, high=0.8, q=0.01), desc="The dropout rate.")) params.add( Param(name='kernel_num', value=11, hyper_space=hyper_spaces.quniform(low=5, high=20), desc="The number of RBF kernels.")) params.add( Param(name='sigma', value=0.1, hyper_space=hyper_spaces.quniform(low=0.01, high=0.2, q=0.01), desc="The `sigma` defines the kernel width.")) params.add( Param(name='exact_sigma', value=0.001, desc="The `exact_sigma` denotes the `sigma` " "for exact match.")) return params
def get_default_params(cls) -> ParamTable: """:return: model default parameters.""" params = super().get_default_params( with_embedding=False, with_multi_layer_perceptron=True ) params.add(Param(name='mask_value', value=0, desc="The value to be masked from inputs.")) params.add(Param(name='left_length', value=10, desc='Length of left input.')) params.add(Param(name='right_length', value=40, desc='Length of right input.')) params.add(Param(name='lm_filters', value=300, desc="Filter size of 1D convolution layer in " "the local model.")) params.add(Param(name='vocab_size', value=419, desc="Vocabulary size of the tri-letters used in " "the distributed model.")) params.add(Param(name='dm_filters', value=300, desc="Filter size of 1D convolution layer in " "the distributed model.")) params.add(Param(name='dm_kernel_size', value=3, desc="Kernel size of 1D convolution layer in " "the distributed model.")) params.add(Param(name='dm_conv_activation_func', value='relu', desc="Activation functions of the convolution layer " "in the distributed model.")) params.add(Param(name='dm_right_pool_size', value=8, desc="Kernel size of 1D convolution layer in " "the distributed model.")) params.add(Param( name='dropout_rate', value=0.5, hyper_space=hyper_spaces.quniform(low=0.0, high=0.8, q=0.02), desc="The dropout rate.")) return params
def get_default_params(cls) -> ParamTable: """:return: model default parameters.""" params = super().get_default_params(with_embedding=True) params['optimizer'] = 'adam' params.add( Param(name='alpha', value=0.1, desc="Negative slope coefficient of LeakyReLU " "function.")) params.add( Param(name='mlp_num_layers', value=3, desc="The number of layers of mlp.")) params.add( Param(name='mlp_num_units', value=[10, 10], desc="The hidden size of the FC layers, but not " "include the final layer.")) params.add( Param(name='lstm_num_units', value=5, desc="The hidden size of the LSTM layer.")) params.add( Param(name='dropout_rate', value=0.1, hyper_space=hyper_spaces.quniform(low=0.0, high=0.8, q=0.01), desc="The dropout rate.")) return params
def get_default_params(cls) -> ParamTable: """:return: model default parameters.""" params = super().get_default_params(with_embedding=True) params['optimizer'] = 'adam' # params.add(Param('dim_word_embedding', 50)) # TODO(tjf): remove unused params in the final version # params.add(Param('dim_char_embedding', 50)) # params.add(Param('word_embedding_mat')) # params.add(Param('char_embedding_mat')) # params.add(Param('embedding_random_scale', 0.2)) # params.add(Param('activation_embedding', 'softmax')) # BiMPM Setting params.add( Param( 'perspective', { 'full': True, 'max-pooling': True, 'attentive': True, 'max-attentive': True })) params.add(Param('mp_dim', 3)) params.add(Param('att_dim', 3)) params.add(Param('hidden_size', 4)) params.add(Param('dropout_rate', 0.0)) params.add(Param('w_initializer', 'glorot_uniform')) params.add(Param('b_initializer', 'zeros')) params.add(Param('activation_hidden', 'linear')) params.add(Param('with_match_highway', False)) params.add(Param('with_aggregation_highway', False)) return params
def get_default_params(cls) -> ParamTable: """:return: model default parameters.""" params = super().get_default_params(with_embedding=True, with_multi_layer_perceptron=True) params['optimizer'] = 'adam' params.add( Param(name='num_blocks', value=1, desc="Number of convolution blocks.")) params.add( Param(name='left_filters', value=[32], desc="The filter size of each convolution " "blocks for the left input.")) params.add( Param(name='left_kernel_sizes', value=[3], desc="The kernel size of each convolution " "blocks for the left input.")) params.add( Param(name='right_filters', value=[32], desc="The filter size of each convolution " "blocks for the right input.")) params.add( Param(name='right_kernel_sizes', value=[3], desc="The kernel size of each convolution " "blocks for the right input.")) params.add( Param(name='conv_activation_func', value='relu', desc="The activation function in the " "convolution layer.")) params.add( Param(name='left_pool_sizes', value=[2], desc="The pooling size of each convolution " "blocks for the left input.")) params.add( Param(name='right_pool_sizes', value=[2], desc="The pooling size of each convolution " "blocks for the right input.")) params.add( Param(name='padding', value='same', hyper_space=hyper_spaces.choice(['same', 'valid', 'causal']), desc= "The padding mode in the convolution layer. It should be one" "of `same`, `valid`, and `causal`.")) params.add( Param('dropout_rate', 0.0, hyper_space=hyper_spaces.quniform(low=0.0, high=0.8, q=0.01), desc="The dropout rate.")) return params
def get_default_params(cls): """Get default parameters.""" params = super().get_default_params(with_embedding=True) params.add( Param(name='lm_filters', value=32, desc="Filter size of 1D convolution layer in " "the local model.")) params.add( Param(name='lm_hidden_sizes', value=[32], desc="A list of hidden size of the MLP layer " "in the local model.")) params.add( Param(name='dm_filters', value=32, desc="Filter size of 1D convolution layer in " "the distributed model.")) params.add( Param(name='dm_kernel_size', value=3, desc="Kernel size of 1D convolution layer in " "the distributed model.")) params.add( Param(name='dm_q_hidden_size', value=32, desc="Hidden size of the MLP layer for the " "left text in the distributed model.")) params.add( Param(name='dm_d_mpool', value=3, desc="Max pooling size for the right text in " "the distributed model.")) params.add( Param(name='dm_hidden_sizes', value=[32], desc="A list of hidden size of the MLP layer " "in the distributed model.")) params.add( Param(name='padding', value='same', desc="The padding mode in the convolution " "layer. It should be one of `same`, " "`valid`, " "and `causal`.")) params.add( Param(name='activation_func', value='relu', desc="Activation function in the convolution" " layer.")) params.add( Param(name='dropout_rate', value=0.5, hyper_space=hyper_spaces.quniform(low=0.0, high=0.8, q=0.02), desc="The dropout rate.")) return params
def get_default_params(cls) -> ParamTable: """:return: model default parameters.""" params = super().get_default_params( with_embedding=True, with_multi_layer_perceptron=True ) params.add(Param(name='mask_value', value=0, desc="The value to be masked from inputs.")) params.add(Param(name='hist_bin_size', value=30, desc="The number of bin size of the histogram.")) params['mlp_num_fan_out'] = 1 return params
def get_default_params(cls) -> ParamTable: """:return: model default parameters.""" params = super().get_default_params() params.add( Param(name='mode', value='bert-base-uncased', desc="Pretrained Bert model.")) params.add( Param('dropout_rate', 0.2, hyper_space=hyper_spaces.quniform(low=0.0, high=0.8, q=0.01), desc="The dropout rate.")) return params
def get_default_params(cls) -> ParamTable: """:return: model default parameters.""" params = super().get_default_params(with_embedding=True, with_multi_layer_perceptron=True) params.add( Param(name='left_length', value=10, desc='Length of left input.')) params.add( Param(name='right_length', value=100, desc='Length of right input.')) params.add( Param(name='conv_activation_func', value='relu', desc="The activation function in the " "convolution layer.")) params.add( Param(name='left_filters', value=[32], desc="The filter size of each convolution " "blocks for the left input.")) params.add( Param(name='left_kernel_sizes', value=[3], desc="The kernel size of each convolution " "blocks for the left input.")) params.add( Param(name='left_pool_sizes', value=[2], desc="The pooling size of each convolution " "blocks for the left input.")) params.add( Param(name='right_filters', value=[32], desc="The filter size of each convolution " "blocks for the right input.")) params.add( Param(name='right_kernel_sizes', value=[3], desc="The kernel size of each convolution " "blocks for the right input.")) params.add( Param(name='right_pool_sizes', value=[2], desc="The pooling size of each convolution " "blocks for the right input.")) params.add( Param('dropout_rate', 0.0, hyper_space=hyper_spaces.quniform(low=0.0, high=0.8, q=0.01), desc="The dropout rate.")) return params
def get_default_params(cls) -> ParamTable: """:return: model default parameters.""" params = super().get_default_params( with_embedding=True, with_multi_layer_perceptron=True ) params.add(Param(name='mask_value', value=-1, desc="The value to be masked from inputs.")) params['input_shapes'] = [(5,), (300,)] params.add(Param( 'top_k', value=10, hyper_space=hyper_spaces.quniform(low=2, high=100), desc="Size of top-k pooling layer." )) return params
def get_default_params(cls): """Get default parameters.""" params = super().get_default_params() params.add(Param(name='filters', value=128, desc="The filter size in the convolution" " layer.")) params.add(Param(name='conv_activation_func', value='relu', desc="The activation function in the " "convolution layer.")) params.add(Param(name='max_ngram', value=3, desc="The maximum length of n-grams for the " "convolution layer.")) params.add(Param(name='use_crossmatch', value=True, desc="Whether to match left n-grams and right " "n-grams of different lengths")) return params
def test_hyper_space(param_table): new_param = Param( name='my_param', value=1, hyper_space=quniform(low=1, high=5) ) param_table.add(new_param) hyper_space = param_table.hyper_space assert hyper_space
def get_default_params(cls) -> ParamTable: """:return: model default parameters.""" params = super().get_default_params( with_embedding=True, with_multi_layer_perceptron=True) params.add(Param(name='lstm_hidden_size', value=5, desc="Integer, the hidden size of the " "bi-directional LSTM layer.")) params.add(Param(name='lstm_num', value=3, desc="Integer, number of LSTM units")) params.add(Param(name='num_layers', value=1, desc="Integer, number of LSTM layers.")) params.add(Param( name='dropout_rate', value=0.0, hyper_space=hyper_spaces.quniform( low=0.0, high=0.8, q=0.01), desc="The dropout rate." )) return params
def get_default_params(cls): """Get default parameters.""" params = super().get_default_params(with_embedding=True) params.add( Param('lstm_num_units', 256, hyper_space=hyper_spaces.quniform(low=128, high=384, q=32), desc="The hidden size in the LSTM layer.")) params.add( Param('fc_num_units', 200, hyper_space=hyper_spaces.quniform(low=100, high=300, q=20), desc="The hidden size in the full connection layer.")) params.add( Param('dropout_rate', 0.0, hyper_space=hyper_spaces.quniform(low=0.0, high=0.9, q=0.01), desc="The dropout rate.")) return params
def get_default_params(cls) -> ParamTable: """:return: model default parameters.""" params = super().get_default_params(with_embedding=True) params.add(Param( name='dropout_rate', value=0.1, desc="The dropout rate.", hyper_space=hyper_spaces.quniform(0, 1, 0.05) )) params.add(Param( name='num_layers', value=2, desc="Number of hidden layers in the MLP " "layer." )) params.add(Param( name='hidden_sizes', value=[30, 30], desc="Number of hidden size for each hidden" " layer" )) return params
def get_default_params(cls) -> ParamTable: """:return: model default parameters.""" params = super().get_default_params(with_embedding=True) params.add( Param(name='channels', value=4, desc="Number of word interaction tensor channels")) params.add( Param(name='units', value=10, desc="Number of SpatialGRU units")) params.add( Param(name='direction', value='lt', desc="Direction of SpatialGRU scanning")) params.add( Param(name='dropout_rate', value=0.0, hyper_space=hyper_spaces.quniform(low=0.0, high=0.8, q=0.01), desc="The dropout rate.")) return params
def get_default_params(cls) -> ParamTable: """:return: model default parameters.""" params = super().get_default_params(with_embedding=True) params.add(Param(name='mask_value', value=0, desc="The value to be masked from inputs.")) params.add(Param(name='num_bins', value=200, desc="Integer, number of bins.")) params.add(Param(name='hidden_sizes', value=[100], desc="Number of hidden size for each hidden layer")) params.add(Param(name='activation', value='relu', desc="The activation function.")) params.add(Param( 'dropout_rate', 0.0, hyper_space=hyper_spaces.quniform( low=0.0, high=0.8, q=0.01), desc="The dropout rate." )) return params
def get_default_params(cls) -> ParamTable: """:return: model default parameters.""" params = super().get_default_params(with_embedding=True, with_multi_layer_perceptron=True) params.add( Param(name='lstm_units', value=32, desc="Integer, the hidden size in the " "bi-directional LSTM layer.")) params.add( Param(name='dropout_rate', value=0.0, desc="Float, the dropout rate.")) params.add( Param('top_k', value=10, hyper_space=hyper_spaces.quniform(low=2, high=100), desc="Integer, the size of top-k pooling layer.")) params['optimizer'] = 'adam' return params
def get_default_params(cls): """Get default parameters.""" params = super().get_default_params(with_embedding=True) params.add( Param(name='kernel_num', value=11, hyper_space=hyper_spaces.quniform(low=5, high=20), desc="The number of RBF kernels.")) params.add( Param(name='sigma', value=0.1, hyper_space=hyper_spaces.quniform(low=0.01, high=0.2, q=0.01), desc="The `sigma` defines the kernel width.")) params.add( Param(name='exact_sigma', value=0.001, desc="The `exact_sigma` denotes the `sigma` " "for exact match.")) return params
def get_default_params(cls) -> ParamTable: """Get default parameters.""" params = super().get_default_params(with_embedding=True, with_multi_layer_perceptron=True) params.add( Param(name='dropout_rate', value=0.5, desc="The dropout rate for all fully-connected layer")) params.add( Param(name='lstm_dim', value=8, desc="The dimension of LSTM layer.")) params.add( Param(name='mask_value', value=0, desc="The value would be regarded as pad")) return params
def get_default_params(cls) -> ParamTable: """:return: model default parameters.""" params = super().get_default_params(with_embedding=True, with_multi_layer_perceptron=True) params.add( Param(name='mask_value', value=-1, desc="The value to be masked from inputs.")) params['optimizer'] = 'adam' params['input_shapes'] = [(5, ), ( 5, 30, )] return params
def get_default_params(cls) -> ParamTable: """:return: model default parameters.""" params = super().get_default_params( with_embedding=True, with_multi_layer_perceptron=True ) params.add(Param(name='hidden_size', value=32, desc="Integer, the hidden size in the " "bi-directional LSTM layer.")) params.add(Param(name='num_layers', value=1, desc="Integer, number of recurrent layers.")) params.add(Param( 'top_k', value=10, hyper_space=hyper_spaces.quniform(low=2, high=100), desc="Size of top-k pooling layer." )) params.add(Param( 'dropout_rate', 0.0, hyper_space=hyper_spaces.quniform( low=0.0, high=0.8, q=0.01), desc="Float, the dropout rate." )) return params
def get_default_params(cls) -> ParamTable: """:return: model default parameters.""" params = super().get_default_params(with_embedding=True, with_multi_layer_perceptron=False) params.add( Param(name='mask_value', value=0, desc="The value to be masked from inputs.")) params.add(Param(name='dropout', value=0.2, desc="Dropout rate.")) params.add( Param(name='hidden_size', value=100, hyper_space=hyper_spaces.quniform(low=100, high=300, q=100), desc="Hidden size.")) # BiMPM parameters params.add( Param(name='num_perspective', value=20, hyper_space=hyper_spaces.quniform(low=20, high=100, q=20), desc='num_perspective')) return params
def get_default_params(cls) -> ParamTable: """:return: model default parameters.""" params = super().get_default_params(with_embedding=True) params.add( Param(name='left_length', value=10, desc='Length of left input.')) params.add( Param(name='right_length', value=100, desc='Length of right input.')) params.add( Param(name='kernel_1d_count', value=32, desc="Kernel count of 1D convolution layer.")) params.add( Param(name='kernel_1d_size', value=3, desc="Kernel size of 1D convolution layer.")) params.add( Param(name='kernel_2d_count', value=[32], desc="Kernel count of 2D convolution layer in" "each block")) params.add( Param(name='kernel_2d_size', value=[(3, 3)], desc="Kernel size of 2D convolution layer in" " each block.")) params.add( Param(name='activation', value='relu', desc="Activation function.")) params.add( Param(name='pool_2d_size', value=[(2, 2)], desc="Size of pooling layer in each block.")) params.add( Param('dropout_rate', 0.0, hyper_space=hyper_spaces.quniform(low=0.0, high=0.8, q=0.01), desc="The dropout rate.")) return params
def get_default_params(cls) -> ParamTable: """:return: model default parameters.""" params = super().get_default_params(with_embedding=True) params.add( Param(name='filters', value=128, desc="The filter size in the convolution layer.")) params.add( Param(name='conv_activation_func', value='relu', desc="The activation function in the convolution layer.")) params.add( Param(name='max_ngram', value=3, desc="The maximum length of n-grams for the convolution " "layer.")) params.add( Param(name='use_crossmatch', value=True, desc="Whether to match left n-grams and right n-grams of " "different lengths")) params.add( Param(name='kernel_num', value=11, hyper_space=hyper_spaces.quniform(low=5, high=20), desc="The number of RBF kernels.")) params.add( Param(name='sigma', value=0.1, hyper_space=hyper_spaces.quniform(low=0.01, high=0.2, q=0.01), desc="The `sigma` defines the kernel width.")) params.add( Param(name='exact_sigma', value=0.001, desc="The `exact_sigma` denotes the `sigma` " "for exact match.")) params.add( Param('dropout_rate', 0, hyper_space=hyper_spaces.quniform(low=0.0, high=0.8, q=0.01), desc="The dropout rate.")) return params
def get_default_params(cls) -> ParamTable: """:return: model default parameters.""" params = super().get_default_params(with_embedding=True) params['optimizer'] = 'adam' opt_space = hyper_spaces.choice(['adam', 'rmsprop', 'adagrad']) params.get('optimizer').hyper_space = opt_space params.add(Param(name='num_blocks', value=1, desc="Number of 2D convolution blocks.")) params.add(Param(name='kernel_1d_count', value=32, desc="Kernel count of 1D convolution layer.")) params.add(Param(name='kernel_1d_size', value=3, desc="Kernel size of 1D convolution layer.")) params.add(Param(name='kernel_2d_count', value=[32], desc="Kernel count of 2D convolution layer in" "each block")) params.add(Param(name='kernel_2d_size', value=[[3, 3]], desc="Kernel size of 2D convolution layer in" " each block.")) params.add(Param(name='activation', value='relu', desc="Activation function.")) params.add(Param(name='pool_2d_size', value=[[2, 2]], desc="Size of pooling layer in each block.")) params.add(Param( name='padding', value='same', hyper_space=hyper_spaces.choice( ['same', 'valid']), desc="The padding mode in the convolution layer. It should be one" "of `same`, `valid`." )) params.add(Param( name='dropout_rate', value=0.0, hyper_space=hyper_spaces.quniform(low=0.0, high=0.8, q=0.01), desc="The dropout rate." )) return params
def get_default_params(cls) -> ParamTable: """:return: model default parameters.""" # set :attr:`with_multi_layer_perceptron` to False to support # user-defined variable dense layer units params = super().get_default_params(with_multi_layer_perceptron=True) params.add( Param(name='filters', value=32, desc="Number of filters in the 1D convolution " "layer.")) params.add( Param(name='kernel_size', value=3, desc="Number of kernel size in the 1D " "convolution layer.")) params.add( Param(name='strides', value=1, desc="Strides in the 1D convolution layer.")) params.add( Param(name='padding', value='same', desc="The padding mode in the convolution " "layer. It should be one of `same`, " "`valid`, " "and `causal`.")) params.add( Param(name='conv_activation_func', value='relu', desc="Activation function in the convolution" " layer.")) params.add(Param(name='w_initializer', value='glorot_normal')) params.add(Param(name='b_initializer', value='zeros')) params.add( Param(name='dropout_rate', value=0.3, desc="The dropout rate.")) return params
def get_default_params(cls) -> ParamTable: """:return: model default parameters.""" params = super().get_default_params( with_embedding=True, with_multi_layer_perceptron=False ) params.add(Param(name='mask_value', value=0, desc="The value to be masked from inputs.")) params.add(Param(name='dropout', value=0.2, desc="Dropout rate.")) params.add(Param(name='hidden_size', value=200, desc="Hidden size.")) params.add(Param(name='lstm_layer', value=1, desc="Number of LSTM layers")) params.add(Param(name='drop_lstm', value=False, desc="Whether dropout LSTM.")) params.add(Param(name='concat_lstm', value=True, desc="Whether concat intermediate outputs.")) params.add(Param(name='rnn_type', value='lstm', desc="Choose rnn type, lstm or gru.")) return params