Plotting History
The Keras fit() method returns an R object containing
the training history, including the value of metrics at the end of each
epoch . You can plot the training metrics by epoch using the
plot() method.
For example, here we compile and fit a model with the “accuracy”
metric:
model %>% compile(
loss = 'categorical_crossentropy',
optimizer = optimizer_rmsprop(),
metrics = c('accuracy')
)
history <- model %>% fit(
x_train, y_train,
epochs = 30, batch_size = 128,
validation_split = 0.2
)
We can then plot the training history as follows:
The history will be plotted using ggplot2 if available (if not
then base graphics will be used), include all specified metrics as well
as the loss, and draw a smoothing line if there are 10 or more epochs.
You can customize all of this behavior via various options of the plot
method.
If you want to create a custom visualization you can call the
as.data.frame() method on the history to obtain a data
frame with factors for each metric as well as training
vs. validation:
history_df <- as.data.frame(history)
str(history_df)
'data.frame': 120 obs. of 4 variables:
$ epoch : int 1 2 3 4 5 6 7 8 9 10 ...
$ value : num 0.87 0.941 0.954 0.962 0.965 ...
$ metric: Factor w/ 2 levels "acc","loss": 1 1 1 1 1 1 1 1 1 1 ...
$ data : Factor w/ 2 levels "training","validation": 1 1 1 1 1 1 1 1 1 1 ...
RStudio IDE
If you are training your model within the RStudio IDE then real time
metrics are available within the Viewer pane:
The view_metrics argument of the fit()
method controls whether real time metrics are displayed. By default
metrics are automatically displayed if one or more metrics are specified
in the call to compile() and there is more than one
training epoch.
You can explicitly control whether metrics are displayed by
specifying the view_metrics argument. You can also set a
global session default using the keras.view_metrics
option:
# don't show metrics during this run
history <- model %>% fit(
x_train, y_train,
epochs = 30, batch_size = 128,
view_metrics = FALSE,
validation_split = 0.2
)
# set global default to never show metrics
options(keras.view_metrics = FALSE)
Note that when view_metrics is TRUE metrics
will be displayed even when not running within RStudio (in that case
metrics will be displayed in an external web browser).
TensorBoard
TensorBoard is a visualization tool included with TensorFlow that
enables you to visualize dynamic graphs of your Keras training and test
metrics, as well as activation histograms for the different layers in
your model.
For example, here’s a TensorBoard display for Keras accuracy and loss
metrics:
Recording Data
To record data that can be visualized with TensorBoard, you add a
TensorBoard callback to the fit() function. For
example:
history <- model %>% fit(
x_train, y_train,
batch_size = batch_size,
epochs = epochs,
verbose = 1,
callbacks = callback_tensorboard("logs/run_a"),
validation_split = 0.2
)
See the documentation on the callback_tensorboard()
function for the various available options. The most important option is
the log_dir, which determines which directory logs are
written to for a given training run.
You should either use a distinct log directory for each training run
or remove the log directory between runs.
Viewing Data
To view TensorBoard data for a given set of runs you use the
tensorboard() function, pointing it to the previously
specified log_dir:
tensorboard("logs/run_a")
It’s often useful to run TensorBoard while you are training a model.
To do this, simply launch tensorboard within the training directory
right before you begin training:
# launch TensorBoard (data won't show up until after the first epoch)
tensorboard("logs/run_a")
# fit the model with the TensorBoard callback
history <- model %>% fit(
x_train, y_train,
batch_size = batch_size,
epochs = epochs,
verbose = 1,
callbacks = callback_tensorboard("logs/run_a"),
validation_split = 0.2
)
Keras writes TensorBoard data at the end of each epoch so you won’t
see any data in TensorBoard until 10-20 seconds after the end of the
first epoch (TensorBoard automatically refreshes it’s display every 30
seconds during training).
Comparing Runs
TensorBoard will automatically include all runs logged within the
sub-directories of the specified log_dir, for example, if
you logged another run using:
callback_tensorboard(log_dir = "logs/run_b")
Then called tensorboard as follows:
The TensorBoard visualization would look like this:
You can also pass multiple log directories. For example:
tensorboard(c("logs/run_a", "logs/run_b"))
Customization
Metrics
In the above examples TensorBoard metrics are logged for loss and
accuracy. The TensorBoard callback will log data for any metrics which
are specified in the metrics parameter of the
compile() function. For example, in the following code:
model %>% compile(
loss = 'mean_squared_error',
optimizer = 'sgd',
metrics= c('mae', 'acc')
)
TensorBoard data series will be created for the loss (mean squared
error) as well as for the mean absolute error and accuracy metrics.
Options
The callback_tensorboard() function includes a number of
other options that control logging during training:
callback_tensorboard(log_dir = "logs", histogram_freq = 0,
write_graph = TRUE, write_images = FALSE, embeddings_freq = 0,
embeddings_layer_names = NULL, embeddings_metadata = NULL)
log_dir |
Path of the directory to save the log files to be parsed by
Tensorboard. |
histogram_freq |
Frequency (in epochs) at which to compute activation histograms for
the layers of the model. If set to 0 (the default), histograms won’t be
computed. |
write_graph |
Whether to visualize the graph in Tensorboard. The log file can
become quite large when write_graph is set to TRUE |
write_images |
Whether to write model weights to visualize as image in
Tensorboard. |
embeddings_freq |
Frequency (in epochs) at which selected embedding layers will be
saved. |
embeddings_layer_names |
A list of names of layers to keep eye on. If NULL or
empty list all the embedding layers will be watched. |
embeddings_metadata |
A named list which maps layer name to a file name in which metadata
for this embedding layer is saved. See the details
about the metadata file format. In case if the same metadata file is
used for all embedding layers, string can be passed. |
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
