results = model.evaluate(test_data, test_label, batch_size=32, return_dict=True, verbose=2)
时间: 2024-06-01 19:14:00 浏览: 15
As an AI language model, I cannot determine the exact outputs of the `model.evaluate()` function without information about the specific model, test data, and test labels used. However, I can provide some information about the function parameters and expected outputs:
- `test_data` and `test_label` are the input test data and labels, respectively, that the model will be evaluated on.
- `batch_size` determines the number of samples per evaluation batch. A larger batch size can lead to faster evaluation time, but may require more memory.
- `return_dict=True` specifies that the function should return the evaluation results as a dictionary with metric names as keys and metric values as values. If `return_dict=False`, the function will return a list of metric values in the order specified in the model's `metrics` attribute.
- `verbose=2` specifies the level of verbosity for the evaluation output. `verbose=0` will not display any output, `verbose=1` will display progress bars, and `verbose=2` will display one line per evaluation batch.
The expected output of `model.evaluate()` is a dictionary (if `return_dict=True`) or list (if `return_dict=False`) of evaluation metrics and their values on the test data. The specific metrics and their values will depend on the model and the evaluation criteria chosen.
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