Evaluate a Keras Model

This functions returns the loss value and metrics values for the model in test mode. Computation is done in batches (see the batch_size arg.)

Usage

# S3 method for keras.src.models.model.Model
evaluate(
  object,
  x = NULL,
  y = NULL,
  ...,
  batch_size = NULL,
  verbose = getOption("keras.verbose", default = "auto"),
  sample_weight = NULL,
  steps = NULL,
  callbacks = NULL
)

Arguments

object

Keras model object

x

Input data. It can be:

An R array (or array-like), or a list of arrays (in case the model has multiple inputs).
A backend-native tensor, or a list of tensors (in case the model has multiple inputs).
A named list mapping input names to the corresponding array/tensors, if the model has named inputs.
A tf.data.Dataset. Should return a tuple of either (inputs, targets) or (inputs, targets, sample_weights).
A generator returning (inputs, targets) or (inputs, targets, sample_weights).

y

Target data. Like the input data x, it could be either R array(s) or backend-native tensor(s). If x is a tf.data.Dataset or generator function, y should not be specified (since targets will be obtained from the iterator/dataset).

...

For forward/backward compatability.

batch_size

Integer or NULL. Number of samples per batch of computation. If unspecified, batch_size will default to 32. Do not specify the batch_size if your data is in the form of a a tf dataset or generator (since they generate batches).

verbose

"auto", 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = single line. "auto" becomes 1 for most cases, 2 if in a knitr render or running on a distributed training server. Note that the progress bar is not particularly useful when logged to a file, so verbose=2 is recommended when not running interactively (e.g. in a production environment). Defaults to "auto".

sample_weight

Optional array or tensor of weights for the training samples, used for weighting the loss function (during training only). You can either pass a flat (1D) array or tensor with the same length as the input samples (1:1 mapping between weights and samples), or in the case of temporal data, you can pass a 2D array or tensor with shape (samples, sequence_length) to apply a different weight to every timestep of every sample. This argument is not supported when x is a tf.data.Dataset, or Python generator function. Instead, provide sample_weights as the third element of x. Note that sample weighting does not apply to metrics specified via the metrics argument in compile(). To apply sample weighting to your metrics, you can specify them via the weighted_metrics in compile() instead.

steps

Integer or NULL. Total number of steps (batches of samples) before declaring the evaluation round finished. Ignored with the default value of NULL. If x is a tf.data.Dataset and steps is NULL, evaluation will run until the dataset is exhausted. In the case of an infinitely repeating dataset, it will run indefinitely.

callbacks

List of Callback instances. List of callbacks to apply during evaluation.

Value

Scalar test loss (if the model has a single output and no metrics) or list of scalars (if the model has multiple outputs and/or metrics). The attribute model$metrics_names will give you the display labels for the scalar outputs.

Usage

Arguments

Value

See also