# Advanced Usage

### Advanced Parameters during Training

Note that you can change how long the model trains for by tweaking either
the `num_epochs`

or the `max_iterations`

parameters during training,
passed into `turicreate.drawing_classifier.create`

. Here are a few optional
parameters that you can pass to `.create`

that can change how you train models:

`batch size`

is the number of training examples in one forward/backward pass. The larger the batch size, the more memory you would need during training.`num_epochs`

is the number of forward/backward passes of*all*the training examples.`max_iterations`

is the number of passes, each pass using a`batch_size`

number of examples.

If you specify both `max_iterations`

and `num_epochs`

, `max_iterations`

would
take precedence and the model would train for that number of iterations and the
provided value of `num_epochs`

would be ignored.

When training a model it may be useful to look at the progress table in the output to see if the model has converged. This will help determine whether `max_iterations`

needs to be increased to allow model to converge to an optimal solution.

### Warm Start

To boost the accuracy of the Drawing Classifier models you train, and to help those models converge faster, we provide the option of loading in a pretrained model for a warm start.

A cold start to training would be when the weights are randomly initialized. A warm start is when the weights in the neural network are loaded from those of an already trained model. This improves the initial values of the weights in the network to something better than random, thereby improving accuracy and also helping the model converge faster.

We have published a pre-trained model that automatically gets downloaded when you pass in
`auto`

to the `warm_start`

parameter. This model is trained on millions of drawings from
the “Quick,Draw!” dataset.