evaluate.keras.engine.training.Model
Evaluate a Keras model
Description
Evaluate a Keras model
Usage
## S3 method for class 'keras.engine.training.Model'
evaluate(
object,
x = NULL,
y = NULL,
batch_size = NULL,
verbose = "auto",
sample_weight = NULL,
steps = NULL,
callbacks = NULL,
...
) Arguments
| Arguments | Description |
|---|---|
| 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()