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83 lines
2.6 KiB
83 lines
2.6 KiB
#edit-mode: -*- python -*-
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# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from paddle.trainer_config_helpers import *
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TrainData(ProtoData(
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files = "dummy_list",
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constant_slots = [1.0],
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async_load_data = True))
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TestData(SimpleData(
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files = "trainer/tests/sample_filelist.txt",
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feat_dim = 3,
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context_len = 0,
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buffer_capacity = 1000000,
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async_load_data = False))
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settings(batch_size = 100)
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data = data_layer(name='input', size=3)
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wt = data_layer(name='weight', size=1)
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fc1 = fc_layer(input=data, size=5,
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bias_attr=True,
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act=SigmoidActivation())
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fc2 = fc_layer(input=data, size=12,
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bias_attr=True,
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param_attr=ParamAttr(name='sharew'),
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act=LinearActivation())
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fc3 = fc_layer(input=data, size=3,
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bias_attr=True,
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act=TanhActivation())
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fc4 = fc_layer(input=data, size=5,
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bias_attr=True,
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layer_attr=ExtraAttr(drop_rate=0.5),
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act=SquareActivation())
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pool = img_pool_layer(input=fc2,
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pool_size=2,
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pool_size_y=3,
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num_channels=1,
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padding=1,
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padding_y=2,
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stride=2,
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stride_y=3,
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pool_type=CudnnAvgPooling())
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concat = concat_layer(input=[fc3, fc4])
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with mixed_layer(size=3, act=SoftmaxActivation()) as output:
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output += full_matrix_projection(input=fc1)
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output += trans_full_matrix_projection(input=fc2,
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param_attr=ParamAttr(name='sharew'))
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output += full_matrix_projection(input=concat)
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output += identity_projection(input=fc3)
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lbl = data_layer(name='label', size=1)
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cost = classification_cost(input=output, label=lbl, weight=wt,
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layer_attr=ExtraAttr(device=-1))
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nce = nce_layer(input=fc2, label=lbl, weight=wt,
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num_classes=3,
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neg_distribution=[0.1, 0.3, 0.6])
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outputs(cost, nce)
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