sample_trainer_config_opt_a.conf

# Copyright (c) 2016 Baidu, Inc. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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# See the License for the specific language governing permissions and
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#Todo(luotao02) This config is only used for unitest. It is out of date now, and will be updated later.

################################### Data Configuration ###################################
TrainData(ProtoData(files = "train.list"))
################################### Algorithm Configuration ###################################
Settings(
    learning_rate_decay_a = 0.0,
    learning_rate_decay_b = 0.0,
    learning_rate = 1e-03,
    batch_size = 1000,
    algorithm = 'sgd',
    num_batches_per_send_parameter = 1,
    num_batches_per_get_parameter = 1,
    learning_method='sparse_momentum',
)
default_momentum(0.5)
################################### Network Configuration ###################################
Layer(type = "data", name = "input", size = 784)
Layer(inputs = [Input("input", parameter_name = "_layer1.w")], name = "layer1", bias = Bias(parameter_name = "_layer1.bias"), active_type = "sigmoid", type = "fc", size = 800)
Layer(inputs = [Input("layer1", parameter_name = "_layer2.w")], name = "layer2", bias = Bias(parameter_name = "_layer2.bias"), active_type = "sigmoid", type = "fc", size = 800)
#Layer(inputs = [Input("layer2", parameter_name = "_layer_output.w", decay_rate = 0.02)], name = "output", bias = Bias(parameter_name = "_layer_output.bias"), active_type = "margin", type = "fc", size = 10)
#Layer(inputs = [Input("layer2", parameter_name = "_layer_output.w", decay_rate = 0.02)], name = "output", bias = Bias(parameter_name = "_layer_output.bias"), type = "fc", size = 10)
Layer(inputs = [Input("layer2", parameter_name = "_layer_output.w")], name = "output", bias = Bias(parameter_name = "_layer_output.bias"), active_type = "softmax", type = "fc", size = 10)
Layer(type = "data", name = "label", size = 1)
Layer(inputs = [Input("output"), Input("label")], type = "multi-class-cross-entropy", name = "cost")
#Layer(inputs = [Input("output"), Input("label")], type = "huber", name = "cost")
Evaluator(inputs=["output", "label"], type = "classification_error", name = "classification_error")
Inputs("input", "label")
Outputs("cost")