keras (version 2.8.0)

evaluate.keras.engine.training.Model: Evaluate a Keras model

Description

Evaluate a Keras model

Usage

# S3 method for keras.engine.training.Model
evaluate(
  object,
  x = NULL,
  y = NULL,
  batch_size = NULL,
  verbose = 1,
  sample_weight = NULL,
  steps = NULL,
  callbacks = NULL,
  ...
)

Arguments

object

Model object to evaluate

x

Vector, matrix, or array of test data (or list if the model has multiple inputs). If all inputs in the model are named, you can also pass a list mapping input names to data. x can be NULL (default) if feeding from framework-native tensors (e.g. TensorFlow data tensors). You can also pass a tfdataset or a generator returning a list with (inputs, targets) or (inputs, targets, sample_weights).

y

Vector, matrix, or array of target (label) data (or list if the model has multiple outputs). If all outputs in the model are named, you can also pass a list mapping output names to data. y can be NULL (default) if feeding from framework-native tensors (e.g. TensorFlow data tensors).

batch_size

Integer or NULL. Number of samples per gradient update. If unspecified, batch_size will default to 32.

verbose

Verbosity mode (0 = silent, 1 = progress bar, 2 = one line per epoch).

sample_weight

Optional array of the same length as x, containing weights to apply to the model's loss for each sample. In the case of temporal data, you can pass a 2D array with shape (samples, sequence_length), to apply a different weight to every timestep of every sample. In this case you should make sure to specify sample_weight_mode="temporal" in compile().

steps

Total number of steps (batches of samples) before declaring the evaluation round finished. Ignored with the default value of NULL.

callbacks

List of callbacks to apply during evaluation.

...

Unused

Value

Named list of model test loss (or losses for models with multiple outputs) and model metrics.

See Also

Other model functions: compile.keras.engine.training.Model(), evaluate_generator(), fit.keras.engine.training.Model(), fit_generator(), get_config(), get_layer(), keras_model_sequential(), keras_model(), multi_gpu_model(), pop_layer(), predict.keras.engine.training.Model(), predict_generator(), predict_on_batch(), predict_proba(), summary.keras.engine.training.Model(), train_on_batch()