test_on_batch

FullyConnected.test_on_batch(x, y=None, sample_weight=None, reset_metrics=True, return_dict=False)

Test the model on a single batch of samples.

Parameters:

• 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 TensorFlow tensor, or a list of tensors (in case the model has

    multiple inputs).

  • A dict mapping input names to the corresponding array/tensors,

    if the model has named inputs.

• y – Target data. Like the input data x, it could be either Numpy array(s) or TensorFlow tensor(s). It should be consistent with x (you cannot have Numpy inputs and tensor targets, or inversely).

• 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.

• reset_metrics – If True, the metrics returned will be only for this batch. If False, the metrics will be statefully accumulated across batches.

• return_dict – If True, loss and metric results are returned as a dict, with each key being the name of the metric. If False, they are returned as a list.

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.

Raises:

RuntimeError – If model.test_on_batch is wrapped in a tf.function.