This page provides detailed reference information about arguments you submit to AI Platform Training when running a training job using the built-in image classification algorithm.
Versioning
The built-in image classification algorithm uses TensorFlow 1.14.
Data format arguments
| Arguments | Details |
|---|---|
| training_data_path | Path to a TFRecord path pattern used for training. It can
be in glob pattern, e.g.,
Type: String |
| validation_data_path | Path to a TFRecord path pattern used for validation. It
can be in glob pattern, e.g.,
Type: String |
| job-dir | Path where model, checkpoints and other training artifacts will reside. The
following directories will be created here:
Required Type: String |
Hyperparameters
The built-in image classification algorithm has the following hyperparameters:
| Hyperparameter | Details |
|---|---|
| BASIC PARAMETERS | |
| num_classes | The number of classes in the training/validation data. This must match the
classes in training/validation dataset. For example, if
num_classes=5, then the image/class/label field of
each input tf.Example must be in the range [1, 5].Required Type: Integer |
| max_steps | The number of steps that the training job will run. After
max_steps, the training job will finish automatically.Required Type: Integer |
| train_batch_size | The number of images used in one training step. If this number is too big,
the job may fail with out-of-memory (OOM). Default: 32 Type: Integer |
| num_eval_images | The number of total images used for evaluation. Its value needs to be equal or less than the total images in the `validation_data_path`. Default: 0 Type: Integer |
| pretrained_checkpoint_path | The path to pretrained checkpoints. A good pre-trained checkpoint would be
helpful to increase the model convergence speed or achieve better model
quality. See `gs://builtin-algorithm-data-public/pretrained_checkpoints/classification/` for some pretrained checkpoints. |
| Learning Rate Parameters | |
| learning_rate_decay_type | The method by which the learning rate decays during training. Default: ‘cosine' Type: String Options: one of {cosine, stepwise} |
| warmup_learning_rate | The initial learning rate during warm-up phase. Default: 0 Type: Float |
| warmup_steps | The number of steps to run during the warm-up phase, or the length of the
warm-up phase in steps.
The training job uses warmup_learning_rate during the warm-up
phase. When the warm-up phase is over, the training job uses
initial_learning_rate.Default: 0 Type: Integer |
| initial_learning_rate | The initial learning rate after warmup period. Default: 0.0001 Type: Float |
| stepwise_learning_rate_steps | The steps to decay/change learning rates for stepwise learning
rate decay type. For example, 100,200 means the learning rate
will change (with respect to stepwise_learning_rate_levels) at step 100
and step 200. Note that it will be respected only when
learning_rate_decay_type is set to stepwise.Default: 100,200 Type: String |
| stepwise_learning_rate_levels | The learning rate value of each step for stepwise learning
rate decay type. Note that it will be respected only when
learning_rate_decay_type is set to stepwise.Default: 0.008,0.0008 Type: String |
| Optimizer Parameters | |
| optimizer_type | The optimizer used for training. Default: ‘momentum' Type: String Options: one of {momentum, adam, rmsprop} |
| optimizer_arguments | The arguments for optimizer. It is a comma separated list of "name=value"
pairs. It needs to be compatible with optimizer_type.For example,
Type: String |
| Model parameters | |
| image_size | The image size (width and height) used for training. Note that the training
job may be OOM if its value is too big. Default: 224 Type: Integer |
| model_type | That model architecture type used to train models. Type: String Options: one of {resnet-18, resnet-34, resnet-50, resnet-101, resnet-152, resnet-200, efficientnet-b0, efficientnet-b1, efficientnet-b2,, efficientnet-b3, efficientnet-b4, efficientnet-b5, efficientnet-b6, efficientnet-b7} |
| label_smoothing | Label smoothing parameter used in the softmax_cross_entropy. Default: 0 Type: Float Options: [0,1) |
| weight_decay | Weight decay co-efficiant for l2 regularization, e.g., loss =
cross_entropy + params['weight_decay']*l2_loss.Default: 0.0001 Type: Float Options: [0,+∞) |
Hyperparameter tuning
Hyperparameter tuning tests different hyperparameter configurations when training your model. It finds hyperparameter values that are optimal for the selected goal metric. For each tunable argument, you can specify a range of values to restrict and focus the possibilities AI Platform Training can try.
Learn more about hyperparameter tuning on AI Platform Training.
Goal metrics
For the image classification algorithm, the only option is to maximize accuracy:
| Objective Metric | Direction | Details |
|---|---|---|
| top_1_accuracy | MAXIMIZE | The highest prediction accuracy. |
Tunable hyperparameters
When training with the built-in image classification algorithm, you can tune the following hyperparameters. Start by tuning parameters with "high tunable value." These have the greatest impact on your goal metric.
| Hyperparameters | Type | Range/Values |
|---|---|---|
| PARAMETERS WITH HIGH TUNABLE VALUE | ||
| initial_learning_rate | DOUBLE |
[0, inf] |
| learning_rate_decay_type | CategoricalValues(STRING) |
one of {cosine, stepwise} |
| max_steps | INTEGER |
[0, inf] |
| optimizer_type | CategoricalValues(STRING) |
one of {momentum, adam, rmsprop} |
| OTHER PARAMETERS | ||
| optimizer_arguments | STRING |
See hyperparameter section for more details. |
| image_size | INTEGER |
|
| model_type | CategoricalValues(STRING) |
See hyperparameter section for more details. |
| label_smoothing | DOUBLE |
[0, 1) |
| weight_decay | DOUBLE |
[0, inf] |