tensosflow.keras.Sequential 的 evaluate() 函数总结

def evaluate(self,
x=None,
y=None,
batch_size=None,
verbose=1,
sample_weight=None,
steps=None,
callbacks=None,
max_queue_size=10,
workers=1,
use_multiprocessing=False):
“”"
返回损失值和度量值(metrics)
参数说明：
x: Input data. It could be:
- A Numpy array (or array-like), or a list of arrays
(in case the model has multiple inputs).
- A dict mapping input names to the corresponding
array/tensors, if the model has named inputs.
- A generator or keras.utils.Sequence returning
(inputs, targets) or (inputs, targets, sample weights).
- None (default) if feeding from framework-native
tensors (e.g. TensorFlow data tensors).
y: Target data. Like the input data x,
it could be either Numpy array(s), framework-native tensor(s),
list of Numpy arrays (if the model has multiple outputs) or
None (default) if feeding from framework-native tensors
(e.g. TensorFlow data tensors).
If output layers in the model are named, you can also pass a
dictionary mapping output names to Numpy arrays.
If x is a generator, or keras.utils.Sequence instance,
y should not be specified (since targets will be obtained
from x).
batch_size: Integer or None.
Number of samples per gradient update.
If unspecified, batch_size will default to 32.
Do not specify the batch_size if your data is in the
form of symbolic tensors, generators, or
keras.utils.Sequence instances (since they generate batches).
verbose: 0 or 1. Verbosity mode.
0 = silent, 1 = progress bar.
sample_weight: Optional Numpy array of weights for
the test samples, used for weighting the loss function.
You can either pass a flat (1D)
Numpy array with the same length as the input samples
(1:1 mapping between weights and samples),
or 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: Integer or None.
Total number of steps (batches of samples)
before declaring the evaluation round finished.
Ignored with the default value of None.
callbacks: List of keras.callbacks.Callback instances.
List of callbacks to apply during evaluation.
See callbacks.
max_queue_size: Integer. Used for generator or keras.utils.Sequence
input only. Maximum size for the generator queue.
If unspecified, max_queue_size will default to 10.
workers: Integer. Used for generator or keras.utils.Sequence input
only. Maximum number of processes to spin up when using
process-based threading. If unspecified, workers will default
to 1. If 0, will execute the generator on the main thread.
use_multiprocessing: Boolean. Used for generator or
keras.utils.Sequence input only. If True, use process-based
threading. If unspecified, use_multiprocessing will default to
False. Note that because this implementation relies on
multiprocessing, you should not pass non-picklable arguments to
the generator as they can’t be passed easily to children processes.

#Raises
ValueError: in case of invalid arguments.
#Returns
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). The attribute model.metrics_names will give you
the display labels for the scalar outputs.

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