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Configuration of Optimization Algorithms and Hyperparameters

After a neural network model has been set up, it usually requires training before using for prediction or inference. The training process means to optimize parameters of the nerwork which are usually updated with the back propagation algorithm and a specified optimizer. In this article, we will introduce how to setup optimizers and hyperparameters in OneFlow to users.

Key point summary of this article:

  • Configuration examples of job functions for training and prediction.

  • The use of optimizer and learning strategies.

  • Common errors due to misconfiguration and corresponding solutions.

Users can directly use the training and inferencing configurations described in Example of configutraion section without knowing the design concept of OneFlow. For more detials please refer to optimizer api

Job Function Configuration

In [Recognizing MNIST Handwritten Digits] (... /quick_start/lenet_mnist.md#global_function), we have learned about the concept of the oneflow.global_function decorator and the job function. The configuration of this article base on that.

The job function can be configured by passing the function_config parameter to the decorator.

If you are not familiar with oneflow.global_function, please refer to Recognizing MNIST Handwritten Digits and Job Function Definitions and Calls.

Example of Configurations

Configuration for prediction/inference

Here we define a job function to evaluate the model: eval_job

We set up the configurations of eval_job() in get_eval_config fucntion and pass it to @flow.global_function. At the same time, we set the type parameter of the @flow.global_function to "predict" for evaluation task. This way, OneFlow does not propagate backwards in this job function.

def get_eval_config():
  config = flow.function_config()
  config.default_data_type(flow.float)
  return config

@flow.global_function(type="predict", get_eval_config())
def eval_job() -> tp.Numpy:
  # build up neural network here

Configuration for training

If you specify the type parameter of @flow.global_function to be train, you can get a job function for training.

In the following code, train_job is the job function used for training and it is configured with the default function_config (so there is no parameter passed to function_config).

The reason you need to specify the following settings like optimizer, learning rate and other hyperparameters in the job function is because OneFlow will back propagate for train functions.

@flow.global_function(type="train")
def train_job(
    images: tp.Numpy.Placeholder((BATCH_SIZE, 1, 28, 28), dtype=flow.float),
    labels: tp.Numpy.Placeholder((BATCH_SIZE,), dtype=flow.int32),
) -> tp.Numpy:
    with flow.scope.placement("gpu", "0:0"):
        logits = lenet(images, train=True)
        loss = flow.nn.sparse_softmax_cross_entropy_with_logits(
            labels, logits, name="softmax_loss"
        )

    lr_scheduler = flow.optimizer.PiecewiseConstantScheduler([], [0.1])
    flow.optimizer.SGD(lr_scheduler, momentum=0).minimize(loss)
    return loss
In above code:

  1. PiecewiseConstantScheduler` sets the learning rate (0.1) and the learning strategy (PiecewiseConstantScheduler, a segment scaling strategy). There are other learning strategies built inside OneFlow. Such as: CosineSchedulerCustomSchedulerInverseTimeScheduler and etc.

  2. In flow.optimizer.SGD(lr_scheduler, momentum=0).minimize(loss), set the optimizer to SGD and specify the optimization target as loss. OneFlow contains multiple optimizers such as: SGDAdamAdamWLazyAdamLARSRMSProp. More information please refer to API documentation.

FAQ

  • Error Check failed: job().job_conf().train_conf().has_model_update_conf()

If the type of the job function is "train", but optimizer and optimization target are not configured. OneFlow will report an error during back propagation because OneFlow does not know how to update the parameters. Solution: Configure optimizer for the job function and specify the optimization target.

  • Error Check failed: NeedBackwardOp

If the type of the job function is "predict" but optimizer is incorrectly configured. Then optimizer cannot get the reversed data because OneFlow does not generate a reversed map for the predict job function. Solution: Remove the optimizer statement from the predict function.