Note that you can change how long the model trains for by tweaking either
num_epochs or the
max_iterations parameters during training,
turicreate.drawing_classifier.create. Here are a few optional
parameters that you can pass to
.create that can change how you train models:
batch sizeis the number of training examples in one forward/backward pass. The larger the batch size, the more memory you would need during training.
num_epochsis the number of forward/backward passes of all the training examples.
max_iterationsis the number of passes, each pass using a
batch_sizenumber of examples.
If you specify both
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.
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.