Tuning a model often requires exploring the impact of changes to many hyperparameters. The best way to approach this is generally not by changing the source code of the training script as we did above, but instead by defining flags for key parameters then training over the combinations of those flags to determine which combination of flags yields the best model.
Here’s a declaration of 2 flags that control dropout rate within a model:
FLAGS <- flags( flag_numeric("dropout1", 0.4), flag_numeric("dropout2", 0.3) )
These flags are then used in the definition of the model here:
model <- keras_model_sequential() model %>% layer_dense(units = 128, activation = 'relu', input_shape = c(784)) %>% layer_dropout(rate = FLAGS$dropout1) %>% layer_dense(units = 128, activation = 'relu') %>% layer_dropout(rate = FLAGS$dropout2) %>% layer_dense(units = 10, activation = 'softmax')
Once we’ve defined flags, we can pass alternate flag values to
training_run() as follows:
training_run('mnist_mlp.R', flags = list(dropout1 = 0.2, dropout2 = 0.2))
You aren’t required to specify all of the flags (any flags excluded will simply use their default value).
Flags make it very straightforward to systematically explore the impact of changes to hyperparameters on model performance, for example:
for (dropout1 in c(0.1, 0.2, 0.3)) training_run('mnist_mlp.R', flags = list(dropout1 = dropout1))
Flag values are automatically included in run data with a “flag_” prefix (e.g.
See the article on training flags for additional documentation on using flags.
Above we demonstrated writing a loop to call
training_run() with various different flag values. A better way to accomplish this is the
tuning_run() function, which allows you to specify multiple values for each flag, and executes training runs for all combinations of the specified flags. For example:
# run various combinations of dropout1 and dropout2 runs <- tuning_run("mnist_mlp.R", flags = list( dropout1 = c(0.2, 0.3, 0.4), dropout2 = c(0.2, 0.3, 0.4) )) # find the best evaluation accuracy runs[order(runs$eval_acc, decreasing = TRUE), ]
# A tibble: 6 x 27 run_dir eval_loss eval_acc metric_loss metric_acc metric_val_loss <chr> <dbl> <dbl> <dbl> <dbl> <dbl> 1 runs/2017-10-02T14-55-11Z 0.1407 0.9804 0.0348 0.9914 0.1542 2 runs/2017-10-02T14-51-44Z 0.1164 0.9801 0.0448 0.9882 0.1396 3 runs/2017-10-02T14-23-38Z 0.0956 0.9796 0.0624 0.9835 0.0962 4 runs/2017-10-02T14-56-57Z 0.1263 0.9784 0.0773 0.9807 0.1283 5 runs/2017-10-02T14-56-04Z 0.1323 0.9783 0.0545 0.9860 0.1414 6 runs/2017-10-02T14-37-00Z 0.1338 0.9750 0.1097 0.9732 0.1328 # ... with 21 more variables: metric_val_acc <dbl>, flag_dropout1 <dbl>, # flag_dropout2 <dbl>, samples <int>, validation_samples <int>, batch_size <int>, # epochs <int>, epochs_completed <int>, metrics <chr>, model <chr>, loss_function <chr>, # optimizer <chr>, learning_rate <dbl>, script <chr>, start <dttm>, end <dttm>, # completed <lgl>, output <chr>, source_code <chr>, context <chr>, type <chr>
As you can see above, the
tuning_run() function returns a data frame containing a summary of all of the executed training runs.
You can also specify that all of the runs go into a dedicated runs directory, for example:
# run various combinations of dropout1 and dropout2 tuning_run("mnist_mlp.R", runs_dir = "dropout_tuning", flags = list( dropout1 = c(0.2, 0.3, 0.4), dropout2 = c(0.2, 0.3, 0.4) )) # list runs witin the specified runs_dir ls_runs(order = eval_acc, runs_dir = "dropout_tuning")
If the number of flag combinations is very large, you can also specify that only a random sample of combinations should be tried using the
sample parmaeter. For example:
# run random sample (0.3) of dropout1 and dropout2 combinations runs <- tuning_run("mnist_mlp.R", sample = 0.3, flags = list( dropout1 = c(0.2, 0.3, 0.4), dropout2 = c(0.2, 0.3, 0.4) ))