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150 lines
3.2 KiB
150 lines
3.2 KiB
#edit-mode: -*- python -*-
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# Copyright (c) 2016 Baidu, Inc. 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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#Todo(luotao02) This config is only used for unitest. It is out of date now, and will be updated later.
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TrainData(
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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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)
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)
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TestData(
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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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)
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)
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Settings(
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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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batch_size = 100,
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learning_rate = 0.001,
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learning_rate_decay_a = 1e-5,
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learning_rate_decay_b = 0.5,
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)
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default_initial_std(0.2)
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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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model_type("nn")
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# Input Layer does not need to specify the device number.
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Layer(
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name = "input",
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type = "data",
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size = 3,
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)
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# Calculate in the CPU.
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Layer(
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name = "layer1_1",
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type = "fc",
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size = 5,
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active_type = "sigmoid",
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device = -1,
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inputs = "input",
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)
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# Calculate in the GPU 0.
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Layer(
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name = "layer2_1",
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type = "fc",
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size = 10,
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active_type = "sigmoid",
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device = 0,
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inputs = "layer1_1",
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)
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# Calculate in the GPU 1.
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Layer(
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name = "layer2_2",
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type = "fc",
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size = 10,
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active_type = "sigmoid",
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device = 1,
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inputs = "layer1_1",
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)
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# Calculate in the GPU 0.
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Layer(
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name = "layer3_1",
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type = "fc",
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size = 10,
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device = 0,
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active_type = "sigmoid",
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inputs = ["layer2_1", "layer2_2"],
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)
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# Calculate in the GPU 1.
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Layer(
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name = "layer3_2",
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type = "fc",
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size = 10,
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device = 1,
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active_type = "sigmoid",
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inputs = ["layer2_1", "layer2_2"],
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)
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Layer(
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name = "output",
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type = "fc",
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size = 10,
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device = output_device,
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active_type = "sigmoid",
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inputs = ["layer3_1", "layer3_2"],
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)
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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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Layer(
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name = "label",
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type = "data",
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device = output_device,
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size = 1,
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)
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Layer(
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name = "cost",
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type = "multi-class-cross-entropy",
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device = output_device,
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inputs = ["output", "label"],
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)
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Evaluator(
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name = "error",
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type = "classification_error",
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inputs = ["output", "label"])
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Inputs("input", "label")
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Outputs("cost")
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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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Inputs("input")
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Outputs("output")
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