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76 lines
2.7 KiB
76 lines
2.7 KiB
# 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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################################### Data Configuration ###################
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TrainData(PyData(type="py",
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files = "./gserver/tests/pyDataProvider/pyDataProviderList",
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load_data_module="pyDataProvider",
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load_data_object="SimpleDataProvider"))
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################################### Algorithm Configuration #############
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Settings(
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learning_rate_decay_a = 1e-05,
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learning_rate_decay_b = 1e-06,
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learning_rate = 0.001,
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batch_size = 1,
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algorithm = 'sgd',
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num_batches_per_send_parameter = 1,
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num_batches_per_get_parameter = 1,
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)
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################################### Network Configuration ###############
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Layer(type = "data", name = "input1", size = 3)
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Layer(type = "data", name = "input2", size = 7)
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Layer(inputs = [Input("input1",
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decay_rate = 0.12,
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initial_std = 0.02,
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parameter_name = "_layer1_1.w"),
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Input("input2",
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decay_rate = 0.12,
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initial_std = 0.02,
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parameter_name = "_layer1_2.w"),
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],
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name = "layer1",
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bias = Bias(parameter_name = "_layer1.bias"),
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active_type = "sigmoid",
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type = "fc",
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size = 100)
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Layer(inputs = [Input("layer1",
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decay_rate = 0.06,
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initial_std = 0.02,
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parameter_name = "_layer2.w")],
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name = "layer2",
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bias = Bias(parameter_name = "_layer2.bias"),
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active_type = "sigmoid",
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type = "fc",
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size = 100)
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Layer(inputs = [Input("layer2",
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decay_rate = 0.02,
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initial_std = 0.02,
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parameter_name = "_layer_output.w")],
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name = "output",
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bias = Bias(parameter_name = "_layer_output.bias"),
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active_type = "softmax",
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type = "fc",
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size = 10)
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Layer(type = "data", name = "label", size = 1)
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Layer(inputs = [Input("output"), Input("label")],
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type = "multi-class-cross-entropy",
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name = "cost")
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Inputs("input1", "input2", "label")
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Outputs("cost")
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