# 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. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ################################### Data Configuration ################### TrainData(PyData(type="py", files = "./gserver/tests/pyDataProvider/pyDataProviderList", load_data_module="pyDataProvider", load_data_object="SimpleDataProvider")) ################################### Algorithm Configuration ############# Settings( learning_rate_decay_a = 1e-05, learning_rate_decay_b = 1e-06, learning_rate = 0.001, batch_size = 1, algorithm = 'sgd', num_batches_per_send_parameter = 1, num_batches_per_get_parameter = 1, ) ################################### Network Configuration ############### Layer(type = "data", name = "input1", size = 3) Layer(type = "data", name = "input2", size = 7) Layer(inputs = [Input("input1", decay_rate = 0.12, initial_std = 0.02, parameter_name = "_layer1_1.w"), Input("input2", decay_rate = 0.12, initial_std = 0.02, parameter_name = "_layer1_2.w"), ], name = "layer1", bias = Bias(parameter_name = "_layer1.bias"), active_type = "sigmoid", type = "fc", size = 100) Layer(inputs = [Input("layer1", decay_rate = 0.06, initial_std = 0.02, parameter_name = "_layer2.w")], name = "layer2", bias = Bias(parameter_name = "_layer2.bias"), active_type = "sigmoid", type = "fc", size = 100) Layer(inputs = [Input("layer2", decay_rate = 0.02, initial_std = 0.02, 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") Inputs("input1", "input2", "label") Outputs("cost")