predict

FullyConnected.predict(x, batch_size=None, verbose='auto', steps=None, callbacks=None)

Generates output predictions for the input samples.

Computation is done in batches. This method is designed for batch processing of large numbers of inputs. It is not intended for use inside of loops that iterate over your data and process small numbers of inputs at a time.

For small numbers of inputs that fit in one batch, directly use __call__() for faster execution, e.g., model(x), or model(x, training=False) if you have layers such as BatchNormalization that behave differently during inference.

Note: See [this FAQ entry]( https://keras.io/getting_started/faq/#whats-the-difference-between-model-methods-predict-and-call) for more details about the difference between Model methods predict() and __call__().

Parameters:

  • x –

    Input samples. 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 tf.data.Dataset.

    • A keras.utils.PyDataset instance.

  • batch_size – Integer or None. Number of samples per batch. If unspecified, batch_size will default to 32. Do not specify the batch_size if your data is in the form of 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”.

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

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

Returns:

NumPy array(s) of predictions.