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, mixed_bias_attr=False)
lstm2 = lstmemory_group(input=m2, param_attr=lstm_param, lstm_bias_attr=lstm_bias, mixed_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)))