Training

In the following sections we’ll describe the various capabilities of tfruns. Our example training script (mnist_mlp.R) trains a Keras model to recognize MNIST digits.

To train a model with tfruns, just use the training_run() function in place of the source() function to execute your R script. For example:

library(tfruns)
training_run("mnist_mlp.R")

When training is completed, a summary of the run will automatically be displayed if you are within an interactive R session:

The metrics and output of each run are automatically captured within a run directory which is unique for each run that you initiate. Note that for Keras and TF Estimator models this data is captured automatically (no changes to your source code are required).

You can call the latest_run() function to view the results of the last run (including the path to the run directory which stores all of the run’s output):

latest_run()
$ run_dir           : chr "runs/2017-10-02T14-23-38Z"
$ eval_loss         : num 0.0956
$ eval_acc          : num 0.98
$ metric_loss       : num 0.0624
$ metric_acc        : num 0.984
$ metric_val_loss   : num 0.0962
$ metric_val_acc    : num 0.98
$ flag_dropout1     : num 0.4
$ flag_dropout2     : num 0.3
$ samples           : int 48000
$ validation_samples: int 12000
$ batch_size        : int 128
$ epochs            : int 20
$ epochs_completed  : int 20
$ metrics           : chr "(metrics data frame)"
$ model             : chr "(model summary)"
$ loss_function     : chr "categorical_crossentropy"
$ optimizer         : chr "RMSprop"
$ learning_rate     : num 0.001
$ script            : chr "mnist_mlp.R"
$ start             : POSIXct[1:1], format: "2017-10-02 14:23:38"
$ end               : POSIXct[1:1], format: "2017-10-02 14:24:24"
$ completed         : logi TRUE
$ output            : chr "(script ouptut)"
$ source_code       : chr "(source archive)"
$ context           : chr "local"
$ type              : chr "training"

The run directory used in the example above is “runs/2017-10-02T14-23-38Z”. Run directories are by default generated within the “runs” subdirectory of the current working directory, and use a timestamp as the name of the run directory. You can view the report for any given run using the view_run() function:

view_run("runs/2017-10-02T14-23-38Z")

Comparing Runs

Let’s make a couple of changes to our training script to see if we can improve model performance. We’ll change the number of units in our first dense layer to 128, change the learning_rate from 0.001 to 0.003 and run 30 rather than 20 epochs. After making these changes to the source code we re-run the script using training_run() as before:

training_run("mnist_mlp.R")

This will also show us a report summarizing the results of the run, but what we are really interested in is a comparison between this run and the previous one. We can view a comparison via the compare_runs() function:

compare_runs()

The comparison report shows the model attributes and metrics side-by-side, as well as differences in the source code and output of the training script.

Note that compare_runs() will by default compare the last two runs, however you can pass any two run directories you like to be compared.

Using Flags

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 you may want to vary. In the example script you can see that we have done this for the dropout layers:

FLAGS <- flags(
  flag_numeric("dropout1", 0.4),
  flag_numeric("dropout2", 0.3)
)

These flags are then used in the definition of our 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 = c(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 = c(dropout1 = dropout1))

Flag values are automatically included in run data with a “flag_” prefix (e.g. flag_dropout1, flag_dropout2).

See the article on training flags for additional documentation on using flags.

Analyzing Runs

We’ve demonstrated visualizing and comparing one or two runs, however as you accumulate more runs you’ll generally want to analyze and compare runs many runs. You can use the ls_runs() function to yield a data frame with summary information on all of the runs you’ve conducted within a given directory:

ls_runs()
# 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/2017-10-02T14-56-57Z    0.1263   0.9784      0.0773     0.9807          0.1283
2 runs/2017-10-02T14-56-04Z    0.1323   0.9783      0.0545     0.9860          0.1414
3 runs/2017-10-02T14-55-11Z    0.1407   0.9804      0.0348     0.9914          0.1542
4 runs/2017-10-02T14-51-44Z    0.1164   0.9801      0.0448     0.9882          0.1396
5 runs/2017-10-02T14-37-00Z    0.1338   0.9750      0.1097     0.9732          0.1328
6 runs/2017-10-02T14-23-38Z    0.0956   0.9796      0.0624     0.9835          0.0962
# ... 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>

Since ls_runs() returns a data frame you can also render a sortable, filterable version of it within RStudio using the View() function:

View(ls_runs())

The ls_runs() function also supports subset and order arguments. For example, the following will yield all runs with an eval accuracy better than 0.98:

ls_runs(eval_acc > 0.98, order = eval_acc)

You can pass the results of ls_runs() to compare runs (which will always compare the first two runs passed). For example, this will compare the two runs that performed best in terms of evaluation accuracy:

compare_runs(ls_runs(eval_acc > 0.98, order = eval_acc))

RStudio IDE

If you use RStudio with tfruns, it’s strongly recommended that you update to the current Preview Release of RStudio v1.1, as there are are a number of points of integration with the IDE that require this newer release.

Managing Runs

There are a variety of tools available for managing training run output, including:

1. Exporting run artifacts (e.g. saved models).

2. Copying and purging run directories.

3. Using a custom run directory for an experiment or other set of related runs.

The Managing Runs article provides additional details on using these features.