#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.
from paddle.trainer_config_helpers import *

settings(learning_rate=1e-4, batch_size=1000)

a = data_layer(name='feature_a', size=200)
b = data_layer(name='feature_b', size=200)

fc_param = ParamAttr(name='fc_param', initial_max=1.0, initial_min=-1.0)
bias_param = ParamAttr(name='bias_param', initial_mean=0.0, initial_std=0.0)

softmax_param = ParamAttr(
    name='softmax_param', initial_max=1.0, initial_min=-1.0)

hidden_a = fc_layer(
    input=a, size=200, param_attr=fc_param, bias_attr=bias_param)
hidden_b = fc_layer(
    input=b, size=200, param_attr=fc_param, bias_attr=bias_param)

predict = fc_layer(
    input=[hidden_a, hidden_b],
    param_attr=[softmax_param, softmax_param],
    bias_attr=False,
    size=10,
    act=SoftmaxActivation())

outputs(
    classification_cost(
        input=predict, label=data_layer(
            name='label', size=10)))