Function fit

tensorlayer/utils.py:26–167

Training a given non time-series network by the given cost function, training data, batch_size, n_epoch etc. - MNIST example click `here `_. - In order to control the training details, the authors HIGHL

(
    network, train_op, cost, X_train, y_train, acc=None, batch_size=100, n_epoch=100, print_freq=5, X_val=None,
    y_val=None, eval_train=True, tensorboard_dir=None, tensorboard_epoch_freq=5, tensorboard_weight_histograms=True,
    tensorboard_graph_vis=True
)

def fit(
    network, train_op, cost, X_train, y_train, acc=None, batch_size=100, n_epoch=100, print_freq=5, X_val=None,
    y_val=None, eval_train=True, tensorboard_dir=None, tensorboard_epoch_freq=5, tensorboard_weight_histograms=True,
    tensorboard_graph_vis=True
):
    """Training a given non time-series network by the given cost function, training data, batch_size, n_epoch etc.

    - MNIST example click `here <https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_mnist_simple.py>`_.
    - In order to control the training details, the authors HIGHLY recommend ``tl.iterate`` see two MNIST examples `1 <https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_mlp_dropout1.py>`_, `2 <https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_mlp_dropout1.py>`_.

    Parameters
    ----------
    network : TensorLayer Model
        the network to be trained.
    train_op : TensorFlow optimizer
        The optimizer for training e.g. tf.optimizers.Adam().
    cost : TensorLayer or TensorFlow loss function
        Metric for loss function, e.g tl.cost.cross_entropy.
    X_train : numpy.array
        The input of training data
    y_train : numpy.array
        The target of training data
    acc : TensorFlow/numpy expression or None
        Metric for accuracy or others. If None, would not print the information.
    batch_size : int
        The batch size for training and evaluating.
    n_epoch : int
        The number of training epochs.
    print_freq : int
        Print the training information every ``print_freq`` epochs.
    X_val : numpy.array or None
        The input of validation data. If None, would not perform validation.
    y_val : numpy.array or None
        The target of validation data. If None, would not perform validation.
    eval_train : boolean
        Whether to evaluate the model during training.
        If X_val and y_val are not None, it reflects whether to evaluate the model on training data.
    tensorboard_dir : string
        path to log dir, if set, summary data will be stored to the tensorboard_dir/ directory for visualization with tensorboard. (default None)
    tensorboard_epoch_freq : int
        How many epochs between storing tensorboard checkpoint for visualization to log/ directory (default 5).
    tensorboard_weight_histograms : boolean
        If True updates tensorboard data in the logs/ directory for visualization
        of the weight histograms every tensorboard_epoch_freq epoch (default True).
    tensorboard_graph_vis : boolean
        If True stores the graph in the tensorboard summaries saved to log/ (default True).

    Examples
    --------
    See `tutorial_mnist_simple.py <https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_mnist_simple.py>`_

    >>> tl.utils.fit(network, train_op=tf.optimizers.Adam(learning_rate=0.0001),
    ...              cost=tl.cost.cross_entropy, X_train=X_train, y_train=y_train, acc=acc,
    ...              batch_size=64, n_epoch=20, _val=X_val, y_val=y_val, eval_train=True)
    >>> tl.utils.fit(network, train_op, cost, X_train, y_train,
    ...            acc=acc, batch_size=500, n_epoch=200, print_freq=5,
    ...            X_val=X_val, y_val=y_val, eval_train=False, tensorboard=True)