def TextGeneration(conn,
model_table='text_generator',
neurons=10,
max_output_length=15,
n_blocks=3,
rnn_type='gru'):
'''
Generates a text generation model.
Parameters
----------
conn : CAS
Specifies the CAS connection object.
model_table : string, optional
Specifies the name of CAS table to store the model.
neurons : int, optional
Specifies the number of neurons to be in each layer.
Default: 10
n_blocks : int, optional
Specifies the number of bidirectional blocks to be added to the model.
Default: 3
max_output_length : int, optional
Specifies the maximum number of tokens to generate
Default: 15
rnn_type : string, optional
Specifies the type of the rnn layer.
Default: GRU
Valid Values: RNN, LSTM, GRU
Returns
-------
:class:`Sequential`
'''
conn.retrieve('loadactionset',
_messagelevel='error',
actionset='deeplearn')
if n_blocks >= 3:
model = Sequential(conn=conn, model_table=model_table)
b = Bidirectional(n=neurons,
name='bi_' + rnn_type + '_layer_',
n_blocks=n_blocks - 2,
rnn_type=rnn_type)
model.add(b)
b2 = Bidirectional(n=neurons,
output_type='encoding',
src_layers=b.get_last_layers(),
rnn_type=rnn_type,
name='bi_' + rnn_type + '_lastlayer')
model.add(b2)
model.add(
Recurrent(n=neurons,
output_type='arbitrarylength',
src_layers=b2.get_last_layers(),
rnn_type=rnn_type,
max_output_length=max_output_length))
model.add(OutputLayer())
elif n_blocks >= 2:
model = Sequential(conn=conn, model_table=model_table)
b2 = Bidirectional(n=neurons,
output_type='encoding',
rnn_type=rnn_type,
name='bi_' + rnn_type + '_layer_')
model.add(b2)
model.add(
Recurrent(n=neurons,
output_type='arbitrarylength',
src_layers=b2.get_last_layers(),
rnn_type=rnn_type,
max_output_length=max_output_length))
model.add(OutputLayer())
else:
raise DLPyError(
'The number of blocks for a text generation model should be at least 2.'
)
return model
def TextClassification(conn,
model_table='text_classifier',
neurons=10,
n_blocks=3,
rnn_type='gru'):
'''
Generates a text classification model
Parameters
----------
conn : CAS
Specifies the CAS connection object.
model_table : string, optional
Specifies the name of CAS table to store the model.
neurons : int, optional
Specifies the number of neurons to be in each layer.
Default: 10
n_blocks : int, optional
Specifies the number of bidirectional blocks to be added to the model.
Default: 3
rnn_type : string, optional
Specifies the type of the rnn layer.
Default: GRU
Valid Values: RNN, LSTM, GRU
Returns
-------
:class:`Sequential`
'''
conn.retrieve('loadactionset',
_messagelevel='error',
actionset='deeplearn')
if n_blocks >= 2:
model = Sequential(conn=conn, model_table=model_table)
b = Bidirectional(n=neurons,
name='bi_' + rnn_type + '_layer_',
n_blocks=n_blocks - 1,
rnn_type=rnn_type)
model.add(b)
model.add(
Bidirectional(
n=neurons,
output_type='encoding',
src_layers=b.get_last_layers(),
rnn_type=rnn_type,
name='bi_' + rnn_type + '_lastlayer_',
))
model.add(OutputLayer())
elif n_blocks == 1:
model = Sequential(conn=conn, model_table=model_table)
model.add(
Bidirectional(n=neurons, output_type='encoding',
rnn_type=rnn_type))
model.add(OutputLayer())
else:
raise DLPyError(
'The number of blocks for a text classification model should be at least 1.'
)
return model