text: from paddle.trainer_config_helpers import *
  
  settings(learning_rate=1e-4, batch_size=1000)
  
  data_1 = data_layer(name='data_a', size=100)
  data_2 = data_layer(name='data_b', size=100)
  
  mixed_param = ParamAttr(name='mixed_param')
  
  with mixed_layer(size=400, bias_attr=False) as m1:
      m1 += full_matrix_projection(input=data_1, param_attr=mixed_param)
  
  with mixed_layer(size=400, bias_attr=False) as m2:
      m2 += full_matrix_projection(input=data_2, param_attr=mixed_param)
  
  lstm_param = ParamAttr(name='lstm_param')
  lstm_bias = ParamAttr(name='lstm_bias', initial_mean=0., initial_std=0.)
  
  lstm1 = lstmemory_group(
      input=m1,
      param_attr=lstm_param,
      lstm_bias_attr=lstm_bias,
      input_proj_bias_attr=False)
  
  lstm2 = lstmemory_group(
      input=m2,
      param_attr=lstm_param,
      lstm_bias_attr=lstm_bias,
      input_proj_bias_attr=False)
  
  softmax_param = ParamAttr(name='softmax_param')
  
  predict = fc_layer(
      input=[last_seq(input=lstm1), last_seq(input=lstm2)],
      size=10,
      param_attr=[softmax_param, softmax_param],
      bias_attr=False,
      act=SoftmaxActivation())
  outputs(
      classification_cost(
          input=predict, label=data_layer(
              name='label', size=10)))
  