Evaluate a Keras model

evaluate(object, x, y, batch_size = NULL, verbose = 1,
  sample_weight = NULL, steps = NULL)

Arguments

object

Model object to evaluate

x

Vector, matrix, or array of training 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.

y

Vector, matrix, or array of target 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.

batch_size

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

verbose

Verbosity mode (0 = silent, 1 = verbose, 2 = one log 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.

Value

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

See also

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