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87 lines
2.9 KiB
87 lines
2.9 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(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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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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settings(batch_size = 100)
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# Output layer, label layer, cost layer, preferably set to the same environment.
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output_device = 0
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# Input Layer does not need to specify the device number.
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data = data_layer(name='input', size=3)
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# Calculate in the CPU.
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fc1 = fc_layer(input=data, size=5,
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bias_attr=True,
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layer_attr=ExtraAttr(device=-1),
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act=SigmoidActivation())
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# Calculate in the GPU 0.
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fc2 = fc_layer(input=fc1, size=10,
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bias_attr=True,
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layer_attr=ExtraAttr(device=0),
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act=SigmoidActivation())
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# Calculate in the GPU 1.
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fc3 = fc_layer(input=fc1, size=10,
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bias_attr=True,
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layer_attr=ExtraAttr(device=1),
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act=SigmoidActivation())
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# Calculate in the GPU 0.
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fc4 = fc_layer(input=[fc2,fc3], size=10,
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bias_attr=True,
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layer_attr=ExtraAttr(device=0),
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act=SigmoidActivation())
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# Calculate in the GPU 1.
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fc5 = fc_layer(input=[fc2,fc3], size=10,
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bias_attr=True,
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layer_attr=ExtraAttr(device=1),
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act=SigmoidActivation())
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output = fc_layer(input=[fc4,fc5], size=10,
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bias_attr=True,
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layer_attr=ExtraAttr(device=output_device),
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act=SoftmaxActivation())
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if get_config_arg('with_cost', bool, True):
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# This is for training the neural network.
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# We need to have another data layer for label
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# and a layer for calculating cost
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lbl = data_layer(name='label', size=1,
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layer_attr=ExtraAttr(device=output_device))
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outputs(classification_cost(input=output,
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label=lbl,
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layer_attr=ExtraAttr(device=output_device)))
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else:
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# This is for prediction where we don't have label
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# and don't need to calculate cost
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outputs(output)
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