Evaluate a Keras model
# 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,
...
)Model object to evaluate
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).
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).
Integer or NULL. Number of samples per gradient update.
If unspecified, batch_size will default to 32.
Verbosity mode (0 = silent, 1 = progress bar, 2 = one 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().
Total number of steps (batches of samples) before declaring the
evaluation round finished. Ignored with the default value of NULL.
List of callbacks to apply during evaluation.
Unused
Named list of model test loss (or losses for models with multiple outputs) and model metrics.
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()