Global Setting

Introduction

This page will introduce global setting in cxxnet, including:

• Device Selection
• Printing Control
• Training Round
• Saving Model and Continue Training
• Prediction

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Global setting

Global setting is to set parameters which are used globally. Global setting parameters are in any sections outside netconfig and iter. To understand better, you may find compare to the MNIST.conf can be helpful.

Indeed you can set any parameter in the global area. Local setting has higher privilege and is able to override the global setting if they are in same name.

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Set Global Setting via Command Line

• Besides setting parameters via config file, cxxnet also support set parameters via command line. The syntax is like:

cxxnet_learner config.conf dev=gpu

Then the program will run in gpu mode. Note in command line mode, we must ensure dev=gpu contains no space so that they are passed in as single argument. The settings in command line will override settings in config file; this allows easy change of parameters via command line if you want to quick try different kinds of options.

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Set working hardware

• To use CPU, set the field

dev = cpu

• To use GPU, set the field

dev = gpu

We can also set specific device (say device 1) by using

dev = gpu:1

In default, it is dev=gpu

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Print information

• To print training error evaluation, just set this field to 1

eval_train = 1

• in default this field is 0, which means cxxnet won't print anything about training error.
• To turn off all information while training, set this field to 1

silent = 1

• In default this field is 0
• To control print frequent, change this field

print_step = 100

• In default it will print every 100 batch

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Set round of training

There are two field handle training round together: num_round and max_round

• num_round is used for number of round to train
• max_round is used for maximum number of round to train from now on

num_round = 15
max_round = 15

This configuration will make cxxnet train for 15 rounds on the training data.

More examples,

num_round = 50
max_round = 2

If we have a model trained 40 rounds, then use this configuration continue to train, cxxnet will stop at the 42 round.

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Saving model and continue training

• To save model while training round, set this field to saving frequent(a number)

save_model = 2
model_dir = path_of_dir_to_save_model

• In default, this field is 1, means cxxnet will save a model in every round
• To continue a training process, you need to set model_in as the input snapshot you want to continue from

model_in = path of model file

• Alternatively, if you save model every round (save_model=1), then you can use option continue, cxxnet will automatically search the latest model and start from that model

continue = 1

In default, if neither of the two values is set, cxxnet will start training from start.

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Prediction

• In default, cxxnet treats the configuration file as a training configuration. To make it predict, you need to add extra data iterator and specify the task to be pred and model you want to use to do prediction. For example

# Data Iterator Setting
pred = pred.txt
iter = mnist
iterator_optition_1 = ..
iterator_optition_1 = ...
iter = end
# Global Setting
task = pred
model_in = ./models/0014.model

• In which the mode_in is the path to the model which we need to use for prediction. The pred field is the file we will save the result. The iterator configuration is same to traditional iterator.