#edit-mode: -*- python -*-
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# 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 *

TrainData(SimpleData(
    files = "trainer/tests/sample_filelist.txt",
    feat_dim = 3,
    context_len = 0,
    buffer_capacity = 1000000,
))

settings(batch_size = 100)

data = data_layer(name='input', size=3)

fc1 = fc_layer(input=data, size=12,
               bias_attr=False,
               act=SigmoidActivation())

fc2 = fc_layer(input=data, size=19,
               bias_attr=False,
               act=LinearActivation())

fc3 = fc_layer(input=data, size=5,
               bias_attr=False,
               act=TanhActivation())

fc4 = fc_layer(input=data, size=5,
               bias_attr=False,
               act=LinearActivation())

# This is for training the neural network.
# We need to have another data layer for label
# and a layer for calculating cost
lbl = data_layer(name='label', size=1)

outputs(hsigmoid(input=[fc1, fc2, fc3, fc4],
                 label=lbl,
                 num_classes=3))