Evaluate a Keras model

evaluate(object, x, y, batch_size = 32, verbose = 1, sample_weight = 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

Number of samples per gradient update.

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().

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).

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