evaluate

FullyConnected.evaluate(x=None, y=None, batch_size=None, verbose='auto', sample_weight=None, steps=None, callbacks=None, return_dict=False, **kwargs)

Returns the loss value & metrics values for the model in test mode.

Computation is done in batches (see the batch_size arg.)

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 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.

  • A tf.data.Dataset. Should return a tuple

    of either (inputs, targets) or (inputs, targets, sample_weights).

  • A generator or keras.utils.PyDataset returning

    (inputs, targets) or (inputs, targets, sample_weights).

• y – Target data. Like the input data x, it could be either NumPy array(s) or backend-native tensor(s). If x is a tf.data.Dataset or keras.utils.PyDataset instance, y should not be specified (since targets will be obtained from the iterator/dataset).

• batch_size – Integer or None. Number of samples per batch of computation. If unspecified, batch_size will default to 32. Do not specify the batch_size if your data is in the form of a dataset, generators, or keras.utils.PyDataset instances (since they generate batches).

• verbose – “auto”, 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = single line. “auto” becomes 1 for most cases. Note that the progress bar is not particularly useful when logged to a file, so verbose=2 is recommended when not running interactively (e.g. in a production environment). Defaults to “auto”.

• 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. This argument is not supported when x is a dataset, instead pass sample weights as the third element of x.

• steps – Integer or None. Total number of steps (batches of samples) before declaring the evaluation round finished. Ignored with the default value of None. If x is a tf.data.Dataset and steps is None, evaluation will run until the dataset is exhausted.

• callbacks – List of keras.callbacks.Callback instances. List of callbacks to apply during evaluation.

• 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.