sample_trainer_config.conf

#edit-mode: -*- python -*-
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#
# 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
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from paddle.trainer_config_helpers import *

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

TestData(SimpleData(
           files = "legacy/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=5,
               bias_attr=False,
               act=SigmoidActivation())

fc2 = fc_layer(input=data, size=9,
               bias_attr=False,
               act=LinearActivation())

fc3 = fc_layer(input=data, size=3,
               bias_attr=False,
               act=TanhActivation())

fc4 = fc_layer(input=data, size=5,
               bias_attr=False,
               act=LinearActivation(),
               param_attr=ParamAttr(name='sharew'))

fc5 = fc_layer(input=data, size=5,
               bias_attr=False,
               act=BReluActivation())

fc6 = fc_layer(input=data, size=5,
               bias_attr=False,
               act=SoftReluActivation())

fc7 = fc_layer(input=data, size=3,
               bias_attr=False,
               act=SquareActivation())

fc8 = fc_layer(input=data, size=5,
               bias_attr=True,
               act=SquareActivation())

with mixed_layer(size=3, act=SoftmaxActivation()) as layer9:
    layer9 += full_matrix_projection(input=fc1)
    layer9 += full_matrix_projection(input=fc2)
    layer9 += full_matrix_projection(input=fc3)
    layer9 += trans_full_matrix_projection(input=fc4,
                                           param_attr=ParamAttr(name='sharew'))
    layer9 += full_matrix_projection(input=fc5)
    layer9 += full_matrix_projection(input=fc6)
    layer9 += full_matrix_projection(input=fc7)
    layer9 += full_matrix_projection(input=fc8)

if get_config_arg('with_cost', bool, True):
    # 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(classification_cost(input=layer9, label=lbl))
else:    
    # This is for prediction where we don't have label
    # and don't need to calculate cost
    outputs(layer9)