Model performance metrics

metric_binary_accuracy(y_true, y_pred)

metric_binary_crossentropy(y_true, y_pred)

metric_categorical_accuracy(y_true, y_pred)

metric_categorical_crossentropy(y_true, y_pred)

metric_cosine_proximity(y_true, y_pred)

metric_hinge(y_true, y_pred)

metric_kullback_leibler_divergence(y_true, y_pred)

metric_mean_absolute_error(y_true, y_pred)

metric_mean_absolute_percentage_error(y_true, y_pred)

metric_mean_squared_error(y_true, y_pred)

metric_mean_squared_logarithmic_error(y_true, y_pred)

metric_poisson(y_true, y_pred)

metric_sparse_categorical_crossentropy(y_true, y_pred)

metric_squared_hinge(y_true, y_pred)

metric_top_k_categorical_accuracy(y_true, y_pred, k = 5)

metric_sparse_top_k_categorical_accuracy(y_true, y_pred, k = 5)

Arguments

y_true	True labels (tensor)
y_pred	Predictions (tensor of the same shape as y_true).
k	An integer, number of top elements to consider.

Note

Metric functions are to be supplied in the metrics parameter of the compile() function.

Custom Metrics

You can provide an arbitrary R function as a custom metric. Note that the y_true and y_pred parameters are tensors, so computations on them should use backend tensor functions. See below for an example.

Note that a name ('mean_pred') is provided for the custom metric function. This name is used within training progress output.

If you want to save and load a model with custom metrics, you should also specify the metric in the call the load_model_hdf5(). For example: load_model_hdf5("my_model.h5", c('mean_pred' = metric_mean_pred)).

Alternatively, you can wrap all of your code in a call to with_custom_object_scope() which will allow you to refer to the metric by name just like you do with built in keras metrics.

Documentation on the available backend tensor functions can be found at https://keras.rstudio.com/articles/backend.html#backend-functions.

Metrics with Parameters

To use metrics with parameters (e.g. metric_top_k_categorical_accurary()) you should create a custom metric that wraps the call with the parameter. See below for an example.

Examples

# NOT RUN {
# create metric using backend tensor functions
metric_mean_pred <- function(y_true, y_pred) {
  k_mean(y_pred)
}

model %>% compile(
  optimizer = optimizer_rmsprop(),
  loss = loss_binary_crossentropy,
  metrics = c('accuracy',
              'mean_pred' = metric_mean_pred)
)

# create custom metric to wrap metric with parameter
metric_top_3_categorical_accuracy <- function(y_true, y_pred) {
  metric_top_k_categorical_accuracy(y_true, y_pred, k = 3)
}

model %>% compile(
  loss = 'categorical_crossentropy',
  optimizer = optimizer_rmsprop(),
  metrics = c(top_3_categorical_accuracy = metric_top_3_categorical_accuracy)
)
# }