Evaluate a Keras model
evaluate(object, x, y, batch_size = 32, verbose = 1, sample_weight = NULL)Model object to evaluate
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.
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.
Number of samples per gradient update.
Verbosity mode (0 = silent, 1 = verbose, 2 = one log line per epoch).
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().
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).
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