Tune an Image Classification - TensorFlow model
Automatic model tuning, also known as hyperparameter tuning, finds the best version of a model by running many jobs that test a range of hyperparameters on your dataset. You choose the tunable hyperparameters, a range of values for each, and an objective metric. You choose the objective metric from the metrics that the algorithm computes. Automatic model tuning searches the hyperparameters chosen to find the combination of values that result in the model that optimizes the objective metric.
For more information about model tuning, see Automatic model tuning with SageMaker AI.
Metrics computed by the Image Classification - TensorFlow algorithm
The image classification algorithm is a supervised algorithm. It reports an accuracy metric that is computed during training. When tuning the model, choose this metric as the objective metric.
Metric Name | Description | Optimization Direction |
---|---|---|
validation:accuracy |
The ratio of the number of correct predictions to the total number of predictions made. |
Maximize |
Tunable Image Classification - TensorFlow hyperparameters
Tune an image classification model with the following hyperparameters. The
hyperparameters that have the greatest impact on image classification objective
metrics are: batch_size
, learning_rate
, and
optimizer
. Tune the optimizer-related hyperparameters, such as
momentum
, regularizers_l2
, beta_1
,
beta_2
, and eps
based on the
selected optimizer
. For example, use beta_1
and
beta_2
only when adam
is the
optimizer
.
For more information about which hyperparameters are used for each optimizer
, see
Image Classification - TensorFlow
Hyperparameters.
Parameter Name | Parameter Type | Recommended Ranges |
---|---|---|
batch_size |
IntegerParameterRanges |
MinValue: 8, MaxValue: 512 |
beta_1 |
ContinuousParameterRanges |
MinValue: 1e-6, MaxValue: 0.999 |
beta_2 |
ContinuousParameterRanges |
MinValue: 1e-6, MaxValue: 0.999 |
eps |
ContinuousParameterRanges |
MinValue: 1e-8, MaxValue: 1.0 |
learning_rate |
ContinuousParameterRanges |
MinValue: 1e-6, MaxValue: 0.5 |
momentum |
ContinuousParameterRanges |
MinValue: 0.0, MaxValue: 0.999 |
optimizer |
CategoricalParameterRanges |
['sgd', ‘adam’, ‘rmsprop’, 'nesterov', 'adagrad', 'adadelta'] |
regularizers_l2 |
ContinuousParameterRanges |
MinValue: 0.0, MaxValue: 0.999 |
train_only_top_layer |
ContinuousParameterRanges |
['True', 'False'] |